List of protein subcellular localization prediction tools: Difference between revisions
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==Tools== |
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*'' Descriptions sourced from the https://bio.tools/ registry under CC-BY license'' |
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| AAIndexLoc|| Machine-learning-based algorithm that uses amino acid index to predict protein subcellular localization based on its sequence. ([https://bio.toolss/AAIndexLoc bio.tools entry]) ||<ref> {{cite journal|pmid=18163182|year=2008|author1=Tantoso|first1=E|title=AAIndex ''Loc'': Predicting subcellular localization of proteins based on a new representation of sequences using amino acid indices|journal=Amino Acids|volume=35|issue=2|pages=345–53|last2=Li|first2=K. B|doi=10.1007/s00726-007-0616-y}} </ref>|| http://aaindexloc.bii.a-star.edu.sg/|| |
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| APSLAP|| Prediction of apoptosis protein sub cellular Localization |
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| <ref name="pmid23982307">{{cite journal|first=|date=Dec 2013|year=|title=APSLAP: an adaptive boosting technique for predicting subcellular localization of apoptosis protein|url=|journal=Acta Biotheor|volume=61|issue=4|pages=481–97|doi=10.1007/s10441-013-9197-1|issn=|pmid=23982307|via=|author=Saravanan V, Lakshmi PTV}}</ref> |
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| AtSubP|| A highly accurate subcellular localization prediction tool for annotating the Arabidopsis thaliana proteome. ([https://bio.toolss/AtSubP bio.tools entry]) ||<ref> {{cite journal|pmid=20647376|pmc=2938157|year=2010|author1=Kaundal|first1=R|title=Combining machine learning and homology-based approaches to accurately predict subcellular localization in Arabidopsis|journal=Plant Physiology|volume=154|issue=1|pages=36–54|last2=Saini|first2=R|last3=Zhao|first3=P. X|doi=10.1104/pp.110.156851}} </ref>|| http://bioinfo3.noble.org/AtSubP/|| |
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| Cell-PLoc|| A package of web-servers for predicting subcellular localization of proteins in various organisms.|| <ref>{{Cite journal|last=Chou|first=Kuo-Chen|last2=Shen|first2=Hong-Bin|date=2008-01-01|title=Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms|url=http://www.nature.com/nprot/journal/v3/n2/abs/nprot.2007.494.html|journal=Nature Protocols|language=en|volume=3|issue=2|pages=153–162|doi=10.1038/nprot.2007.494|issn=1754-2189}}</ref> |
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| BaCelLo|| BaCelLo is a predictor for the subcellular localization of proteins in eukaryotes. ([https://bio.toolss/bacello bio.tools entry]) ||<ref> {{cite journal|doi=10.1093/bioinformatics/btl222|pmid=16873501|title=Ba ''Cel'' ''Lo'': A balanced subcellular localization predictor|journal=Bioinformatics|volume=22|issue=14|pages=e408|year=2006|last1=Pierleoni|first1=A|last2=Martelli|first2=P. L|last3=Fariselli|first3=P|last4=Casadio|first4=R}} </ref>|| http://gpcr.biocomp.unibo.it/bacello/index.htm|| |
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| BAR+|| BAR+ is a server for the structural and functional annotation of protein sequences ([https://bio.toolss/bar bio.tools entry]) ||<ref> {{cite journal|doi=10.1093/nar/gkr292|pmid=21622657|pmc=3125743|title=BAR-PLUS: The Bologna Annotation Resource Plus for functional and structural annotation of protein sequences|journal=Nucleic Acids Research|volume=39|issue=Web Server issue|pages=W197–202|year=2011|last1=Piovesan|first1=D|last2=Luigi Martelli|first2=P|last3=Fariselli|first3=P|last4=Zauli|first4=A|last5=Rossi|first5=I|last6=Casadio|first6=R}}</ref>|| http://bar.biocomp.unibo.it/bar2.0/|| |
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| CELLO|| CELLO uses a two-level Support Vector Machine system to assign localizations to both prokaryotic and eukaryotic proteins.|| <ref name="pmid15096640">{{cite journal | author = Yu CS, Lin CJ, Hwang JK | title = Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions | journal = Protein Sci. | volume = 13 | issue = 5 | pages = 1402–6 |date=May 2004 | pmid = 15096640 | pmc = 2286765 | doi = 10.1110/ps.03479604 | url = | issn = }}</ref><ref name="pmid16752418">{{cite journal | author = Yu CS, Chen YC, Lu CH, Hwang JK | title = Prediction of protein subcellular localization | journal = Proteins | volume = 64 | issue = 3 | pages = 643–51 |date=August 2006 | pmid = 16752418 | doi = 10.1002/prot.21018 | url = | issn = }}</ref> |
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| BAR|| BAR 3.0 is a server for the annotation of protein sequences relying on a comparative large-scale analysis on the entire UniProt. With BAR 3.0 and a sequence you can annotate when possible: function (Gene Ontology), structure (Protein Data Bank), protein domains (Pfam). Also if your sequence falls into a cluster with a structural/some structural template/s we provide an alignment towards the template/templates based on the Cluster-HMM (HMM profile) that allows you to directly compute your 3D model. Cluster HMMs are available for downloading. ([https://bio.toolss/BAR8984 bio.tools entry]) ||<ref> {{cite journal|doi=10.1093/nar/gkx330|title=The Bologna Annotation Resource (BAR 3.0): Improving protein functional annotation|journal=Nucleic Acids Research|volume=45|pages=W285|year=2017|last1=Profiti|first1=Giuseppe|last2=Martelli|first2=Pier Luigi|last3=Casadio|first3=Rita}} </ref><ref> {{cite journal|doi=10.1093/nar/gkr292|pmid=21622657|pmc=3125743|title=BAR-PLUS: The Bologna Annotation Resource Plus for functional and structural annotation of protein sequences|journal=Nucleic Acids Research|volume=39|issue=Web Server issue|pages=W197–202|year=2011|last1=Piovesan|first1=D|last2=Luigi Martelli|first2=P|last3=Fariselli|first3=P|last4=Zauli|first4=A|last5=Rossi|first5=I|last6=Casadio|first6=R}} </ref>|| https://bar.biocomp.unibo.it/bar3/|| |
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| ClubSub-P|| ClubSub-P is a database of cluster-based subcellular localization (SCL) predictions for Archaea and Gram negative bacteria.|| <ref name="pmid22073040">{{cite journal | author = Nagarajan Paramasivam, Dirk Linke | title = ClubSub-P is a database of cluster-based subcellular localization (SCL) predictions for Archaea and Gram negative bacteria | journal = Frontiers in Microbiology | volume = 2| year = 2011 | pmid = 22073040 | doi = 10.3389/Ffmicb.2011.00218 | pmc=3210502 | pages=218}}</ref> |
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| BASys|| BASys (Bacterial Annotation System) is a tool for automated annotation of bacterial genomic (chromosomal and plasmid) sequences including gene/protein names, GO functions, COG functions, possible paralogues and orthologues, molecular weights, isoelectric points, operon structures, subcellular localization, signal peptides, transmembrane regions, secondary structures, 3-D structures, reactions, and pathways. ([https://bio.toolss/basys bio.tools entry]) ||<ref> {{cite journal|pmid=15980511|pmc=1160269|year=2005|author1=Van Domselaar|first1=G. H|title=BASys: A web server for automated bacterial genome annotation|journal=Nucleic Acids Research|volume=33|issue=Web Server issue|pages=W455–9|last2=Stothard|first2=P|last3=Shrivastava|first3=S|last4=Cruz|first4=J. A|last5=Guo|first5=A|last6=Dong|first6=X|last7=Lu|first7=P|last8=Szafron|first8=D|last9=Greiner|first9=R|last10=Wishart|first10=D. S|doi=10.1093/nar/gki593}} </ref>|| http://basys.ca|| |
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| Euk-mPLoc 2.0|| Predicting the subcellular localization of eukaryotic proteins with both single and multiple sites.|| <ref name="chou2">{{cite journal|first=|year=2010|title=A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites Euk-mPLoc 2.0|url=|journal=PLoS ONE|volume=5|issue=4|pages=e9931|doi=10.1371/journal.pone.0009931|issn=|pmc=2848569|pmid=20368981|via=|author=Chou KC, Shen HB}}</ref> |
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| BOMP|| The beta-barrel Outer Membrane protein Predictor (BOMP) takes one or more fasta-formatted polypeptide sequences from Gram-negative bacteria as input and predicts whether or not they are beta-barrel integral outer membrane proteins. ([https://bio.toolss/bomp bio.tools entry]) ||<ref> {{cite journal|pmid=15215418|pmc=441489|year=2004|author1=Berven|first1=F. S|title=BOMP: A program to predict integral beta-barrel outer membrane proteins encoded within genomes of Gram-negative bacteria|journal=Nucleic Acids Research|volume=32|issue=Web Server issue|pages=W394–9|last2=Flikka|first2=K|last3=Jensen|first3=H. B|last4=Eidhammer|first4=I|doi=10.1093/nar/gkh351}} </ref>|| http://www.bioinfo.no/tools/bomp|| |
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| CoBaltDB|| CoBaltDB is a novel powerful platform that provides easy access to the results of multiple localization tools and support for predicting prokaryotic protein localizations.|| <ref name="pmid20331850">{{cite journal|first=|year=2010|title=CoBaltDB: Complete bacterial and archaeal orfeomes subcellular localization database and associated resources|url=|journal=BMC Microbiol.|volume=10|issue=|pages=88|doi=10.1186/1471-2180-10-88|issn=|pmc=2850352|pmid=20331850|via=|author=Goudenège D, Avner S, Lucchetti-Miganeh C, Barloy-Hubler F}}</ref> |
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| BPROMPT|| Bayesian PRediction Of Membrane Protein Topology (BPROMPT) uses a Bayesian Belief Network to combine the results of other membrane protein prediction methods for a protein sequence. ([https://bio.toolss/bprompt bio.tools entry]) ||<ref> {{cite journal|pmid=12824397|pmc=168961|year=2003|author1=Taylor|first1=P. D|title=BPROMPT: A consensus server for membrane protein prediction|journal=Nucleic acids research|volume=31|issue=13|pages=3698–700|last2=Attwood|first2=T. K|last3=Flower|first3=D. R}} </ref>|| http://www.ddg-pharmfac.net/bprompt/BPROMPT/BPROMPT.html|| |
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| HSLpred|| This method allow to predict subcellular localization of human proteins. This method combines power of composition based SVM models and similarity search techniques PSI-BLAST.|| <ref name="pmid15647269">{{cite journal | author = Garg A, Bhasin M, Raghava GP | title = Support vector machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search | journal = J. Biol. Chem. | volume = 280 | issue = 15 | pages = 14427–32 |date=April 2005 | pmid = 15647269 | doi = 10.1074/jbc.M411789200 | url = | issn = }}</ref> |
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| ComiR|| ComiR is a web tool for combinatorial microRNA (miRNA) target prediction. Given an messenger RNA (mRNA) in human, mouse, fly or worm genomes, ComiR predicts whether a given mRNA is targeted by a set of miRNAs. ([https://bio.toolss/comir bio.tools entry]) ||<ref> {{cite journal|pmid=23703208|pmc=3692082|year=2013|author1=Coronnello|first1=C|title=ComiR: Combinatorial microRNA target prediction tool|journal=Nucleic Acids Research|volume=41|issue=Web Server issue|pages=W159–64|last2=Benos|first2=P. V|doi=10.1093/nar/gkt379}} </ref>|| http://www.benoslab.pitt.edu/comir/|| |
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| KnowPredsite|| A knowledge-based approach to predict the localization site(s) of both single-localized and multi-localized proteins for all eukaryotes.|| <ref name="pmid19958518">{{cite journal|first=|date=December 2009|year=|title=Protein subcellular localization prediction of eukaryotes using a knowledge-based approach|url=http://www.biomedcentral.com/1471-2105/10/S15/S8|journal=BMC Bioinformatics|volume=10|issue=|pages=S8|doi=10.1186/1471-2105-10-S15-S8|issn=|pmc=2788359|pmid=19958518|via=|author=Lin HN, Chen CT, Sung TY, Ho SY, and Hsu WL.}}</ref> |
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| cropPAL|| A data portal to access the compendium of data on crop protein subcellular locations. ([https://bio.toolss/croppal bio.tools entry]) ||<ref> {{cite journal|doi=10.1093/pcp/pcv170|pmid=26556651|title=Finding the Subcellular Location of Barley, Wheat, Rice and Maize Proteins: The Compendium of Crop Proteins with Annotated Locations (cropPAL)|journal=Plant and Cell Physiology|volume=57|issue=1|pages=e9|year=2016|last1=Hooper|first1=Cornelia M|last2=Castleden|first2=Ian R|last3=Aryamanesh|first3=Nader|last4=Jacoby|first4=Richard P|last5=Millar|first5=A. Harvey}} </ref>|| http://crop-pal.org/|| |
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| LOCtree|| Prediction based on mimicking the cellular sorting mechanism using a hierarchical implementation of [[support vector machines]]. LOCtree is a comprehensive predictor incorporating predictions based on [[PROSITE]]/[[PFAM]] signatures as well as [[SwissProt]] [[Keyword (computer programming)|keywords]].|| <ref name="pmid15808855">{{cite journal | author = Nair R, Rost B | title = Mimicking cellular sorting improves prediction of subcellular localization | journal = J. Mol. Biol. | volume = 348 | issue = 1 | pages = 85–100 |date=April 2005 | pmid = 15808855 | doi = 10.1016/j.jmb.2005.02.025 | url = | issn = }}</ref> |
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| DAS-TMfilter|| DAS (Dense Alignment Surface) is based on low-stringency dot-plots of the query sequence against a set of library sequences - non-homologous membrane proteins - using a previously derived, special scoring matrix. The method provides a high precision hyrdophobicity profile for the query from which the location of the potential transmembrane segments can be obtained. The novelty of the DAS-TMfilter algorithm is a second prediction cycle to predict TM segments in the sequences of the TM-library. ([https://bio.toolss/DAS-TMfilter bio.tools entry]) |||| http://mendel.imp.ac.at/sat/DAS/DAS.html|| |
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| LocTree2/3|| Subcellular localization prediction for all proteins in all domains of life. LocTree2/3 predicts 3 classes for Archaea, 6 for Bacteria and 18 for Eukaryota || <ref name="pmid22962467">{{cite journal | author = Goldberg T, Hamp T, Rost B | title = LocTree2 predicts localization for all domains of life | journal = Bioinformatics | volume = 28 | pages = i458-i465 | year = 2012 | pmid = 22962467 | doi = 10.1093/bioinformatics/bts390 | url = | issn = | issue=18 | pmc=3436817}}</ref><ref name="pmid24848019">{{cite journal | author = Goldberg T, Hecht M, Hamp T, Karl T, Yachdav G, Nielsen H, Rost B ''et al.'' | title = LocTree3 prediction of localization | journal = Nucleic Acids Research | year = 2014 | pmid = 24848019 | doi = 10.1093/nar/gku396 | url = | issn = | volume=42 | issue=Web Server issue | pages=W350–5}}</ref> |
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| DeepLoc|| Prediction of eukaryotic protein subcellular localization using deep learning ([https://bio.toolss/DeepLoc bio.tools entry]) ||<ref> {{cite journal|doi=10.1093/bioinformatics/btx431|pmid=29036616|title=Deep ''Loc'': Prediction of protein subcellular localization using deep learning|journal=Bioinformatics|volume=33|issue=21|pages=3387|year=2017|last1=Almagro Armenteros|first1=José Juan|last2=Sønderby|first2=Casper Kaae|last3=Sønderby|first3=Søren Kaae|last4=Nielsen|first4=Henrik|last5=Winther|first5=Ole}} </ref>|| http://www.cbs.dtu.dk/services/DeepLoc/|| |
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| DIANA-microT v5.0|| Web server which predicts targets for miRNAs and provides functional information on the predicted miRNA:target gene interaction from various online biological resources. Updates enable the association of miRNAs to diseases through bibliographic analysis and connection to the UCSC genome browser. Updates include sophisticated workflows. ([https://bio.toolss/diana-microt bio.tools entry]) ||<ref> {{cite journal|pmid=19406924|pmc=2703977|year=2009|author1=Maragkakis|first1=M|title=DIANA-microT web server: Elucidating microRNA functions through target prediction|journal=Nucleic Acids Research|volume=37|issue=Web Server issue|pages=W273–6|last2=Reczko|first2=M|last3=Simossis|first3=V. A|last4=Alexiou|first4=P|last5=Papadopoulos|first5=G. L|last6=Dalamagas|first6=T|last7=Giannopoulos|first7=G|last8=Goumas|first8=G|last9=Koukis|first9=E|last10=Kourtis|first10=K|last11=Vergoulis|first11=T|last12=Koziris|first12=N|last13=Sellis|first13=T|last14=Tsanakas|first14=P|last15=Hatzigeorgiou|first15=A. G|doi=10.1093/nar/gkp292}} </ref><ref> {{cite journal|pmid=23680784|pmc=3692048|year=2013|author1=Paraskevopoulou|first1=M. D|title=DIANA-microT web server v5.0: Service integration into miRNA functional analysis workflows|journal=Nucleic Acids Research|volume=41|issue=Web Server issue|pages=W169–73|last2=Georgakilas|first2=G|last3=Kostoulas|first3=N|last4=Vlachos|first4=I. S|last5=Vergoulis|first5=T|last6=Reczko|first6=M|last7=Filippidis|first7=C|last8=Dalamagas|first8=T|last9=Hatzigeorgiou|first9=A. G|doi=10.1093/nar/gkt393}} </ref>|| http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=MicroT_CDS/index|| |
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| PSORT|| The first widely used method for protein subcellular localization prediction, developed under the leadership of [[PSORT|Kenta Nakai]]. Now researchers are also encouraged to use other PSORT programs such as WoLF PSORT and PSORTb for making predictions for certain types of organisms (see below). [[PSORT]] prediction performances are lower than those of recently developed predictors.||<ref name="pmid1946347">{{cite journal | author = Nakai K, Kanehisa M | title = Expert system for predicting protein localization sites in gram-negative bacteria | journal = Proteins | volume = 11 | issue = 2 | pages = 95–110 | year = 1991 | pmid = 1946347 | doi = 10.1002/prot.340110203 | url = | issn = }}</ref> |
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| DrugBank|| DrugBank is a unique bioinformatics/cheminformatics resource that combines detailed drug (i.e. chemical) data with comprehensive drug target (i.e. protein) information. The database contains >4100 drug entries including >800 FDA approved small molecule and biotech drugs as well as >3200 experimental drugs. Additionally, >14,000 protein or drug target sequences are linked to these drug entries. ([https://bio.toolss/drugbank bio.tools entry]) ||<ref> {{cite journal|pmid=16381955|pmc=1347430|year=2006|author1=Wishart|first1=D. S|title=Drug ''Bank'': A comprehensive resource for in silico drug discovery and exploration|journal=Nucleic Acids Research|volume=34|issue=Database issue|pages=D668–72|last2=Knox|first2=C|last3=Guo|first3=A. C|last4=Shrivastava|first4=S|last5=Hassanali|first5=M|last6=Stothard|first6=P|last7=Chang|first7=Z|last8=Woolsey|first8=J|doi=10.1093/nar/gkj067}} </ref>|| http://redpoll.pharmacy.ualberta.ca/drugbank/index.html|| |
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| PSORTb|| Prediction of bacterial protein localization.|| <ref name="pmid12824378">{{cite journal|first=|date=July 2003|year=|title=PSORT-B: Improving protein subcellular localization prediction for Gram-negative bacteria|url=|journal=Nucleic Acids Res.|volume=31|issue=13|pages=3613–7|doi=10.1093/nar/gkg602|issn=|pmc=169008|pmid=12824378|via=|author=Gardy JL, Spencer C, Wang K, Ester M, Tusnády GE, Simon I, Hua S, deFays K, Lambert C, Nakai K, Brinkman FS}}</ref><ref name="pmid15501914">{{cite journal|first=|date=March 2005|year=|title=PSORTb v.2.0: expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis|url=|journal=Bioinformatics|volume=21|issue=5|pages=617–23|doi=10.1093/bioinformatics/bti057|issn=|pmid=15501914|via=|||author=Gardy JL, Laird MR, Chen F, Rey S, Walsh CJ, Ester M, Brinkman FS}}</ref><ref>{{Cite journal|last=Yu|first=Nancy Y.|last2=Wagner|first2=James R.|last3=Laird|first3=Matthew R.|last4=Melli|first4=Gabor|last5=Rey|first5=Sébastien|last6=Lo|first6=Raymond|last7=Dao|first7=Phuong|last8=Sahinalp|first8=S. Cenk|last9=Ester|first9=Martin|date=2010-07-01|title=PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes|journal=Bioinformatics|volume=26|issue=13|pages=1608–1615|doi=10.1093/bioinformatics/btq249|issn=1367-4811|pmc=2887053|pmid=20472543}}</ref> |
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| E.Coli Index|| Comprehensive guide of information relating to E. coli; home of Echobase: a database of E. coli genes characterized since the completion of the genome. ([https://bio.toolss/E.Coli_Index bio.tools entry]) ||<ref> {{cite journal|pmid=19015139|year=2009|author1=Horler|first1=R. S|title=EchoLOCATION: An in silico analysis of the subcellular locations of Escherichia coli proteins and comparison with experimentally derived locations|journal=Bioinformatics|volume=25|issue=2|pages=163–6|last2=Butcher|first2=A|last3=Papangelopoulos|first3=N|last4=Ashton|first4=P. D|last5=Thomas|first5=G. H|doi=10.1093/bioinformatics/btn596}} </ref>|| http://www.york.ac.uk/res/thomas/|| |
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| MetaLocGramN|| Meta subcellular localization predictor of Gram-negative protein. MetaLocGramN is a gateway to a number of primary prediction methods (various types: signal peptide, beta-barrel, transmembrane helices and subcellular localization predictors). In author's benchmark, MetaLocGramN performed better in comparison to other SCL predictive methods, since the average Matthews correlation coefficient reached 0.806 that enhanced the predictive capability by 12% (compared to PSORTb3). MetaLocGramN can be run via [[SOAP]].|| <ref name="pmid22705560">{{cite journal|first=|date=December 2012|year=|title=MetaLocGramN: a meta-predictor of protein subcellular localization for Gram-negative bacteria|url=http://www.sciencedirect.com/science/article/pii/S1570963912001185|journal=BBA − Proteins and Proteomics|volume=1824|issue=12|pages=1425–33|doi=10.1016/j.bbapap.2012.05.018|issn=|pmc=|pmid=22705560|via=|author=Magnus M, Pawlowski M, Bujnicki JM}}</ref> |
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| ePlant|| A suite of open-source world wide web-based tools for the visualization of large-scale data sets from the model organism Arabidopsis thaliana. It can be applied to any model organism. Currently has 3 modules: a sequence conservation explorer that includes homology relationships and single nucleotide polymorphism data, a protein structure model explorer, a molecular interaction network explorer, a gene product subcellular localization explorer, and a gene expression pattern explorer. ([https://bio.toolss/eplant bio.tools entry]) ||<ref> {{cite journal|pmid=21249219|pmc=3018417|year=2011|author1=Fucile|first1=G|title=E ''Plant'' and the 3D data display initiative: Integrative systems biology on the world wide web|journal=PLoS ONE|volume=6|issue=1|pages=e15237|last2=Di Biase|first2=D|last3=Nahal|first3=H|last4=La|first4=G|last5=Khodabandeh|first5=S|last6=Chen|first6=Y|last7=Easley|first7=K|last8=Christendat|first8=D|last9=Kelley|first9=L|last10=Provart|first10=N. J|doi=10.1371/journal.pone.0015237|bibcode=2011PLoSO...615237F}} </ref>|| http://bar.utoronto.ca/eplant/|| |
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| PredictNLS|| Prediction of [[nuclear localization signal]]s.|| <ref name="pmid12520032">{{cite journal|first=|date=January 2003|year=|title=NLSdb: database of nuclear localization signals|url=|journal=Nucleic Acids Res.|volume=31|issue=1|pages=397–9|doi=10.1093/nar/gkg001|issn=|pmc=165448|pmid=12520032|via=|author=Nair R, Carter P, Rost B}}</ref><ref>{{Cite journal|last=Cokol|first=M.|last2=Nair|first2=R.|last3=Rost|first3=B.|date=2000-11-01|title=Finding nuclear localization signals|journal=EMBO Reports|volume=1|issue=5|pages=411–415|doi=10.1093/embo-reports/kvd092|issn=1469-221X|pmc=1083765|pmid=11258480}}</ref> |
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| ESLpred|| ESLpred is a tool for predicting subcellular localization of proteins using support vector machines. The predictions are based on dipeptide and amino acid composition, and physico-chemical properties. ([https://bio.toolss/eslpred bio.tools entry]) ||<ref> {{cite journal|pmid=15215421|pmc=441488|year=2004|author1=Bhasin|first1=M|title=ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST|journal=Nucleic Acids Research|volume=32|issue=Web Server issue|pages=W414–9|last2=Raghava|first2=G. P|doi=10.1093/nar/gkh350}} </ref>|| http://www.imtech.res.in/raghava/eslpred/|| |
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| HIT|| A comprehensive and fully curated database for Herb Ingredients?? Targets (HIT). Those herbal ingredients with protein target information were carefully curated. The molecular target information involves those proteins being directly/indirectly activated/inhibited, protein binders and enzymes whose substrates or products are those compounds. Those up/down regulated genes are also included under the treatment of individual ingredients. In addition, the experimental condition, observed bioactivity and various references are provided as well for user??s reference. The database can be queried via keyword search or similarity search. Crosslinks have been made to TTD, DrugBank, KEGG, PDB, Uniprot, Pfam, NCBI, TCM-ID and other databases. ([https://bio.toolss/hit bio.tools entry]) ||<ref> {{cite journal|pmid=21097881|pmc=3013727|year=2011|author1=Ye|first1=H|title=HIT: Linking herbal active ingredients to targets|journal=Nucleic Acids Research|volume=39|issue=Database issue|pages=D1055–9|last2=Ye|first2=L|last3=Kang|first3=H|last4=Zhang|first4=D|last5=Tao|first5=L|last6=Tang|first6=K|last7=Liu|first7=X|last8=Zhu|first8=R|last9=Liu|first9=Q|last10=Chen|first10=Y. Z|last11=Li|first11=Y|last12=Cao|first12=Z|doi=10.1093/nar/gkq1165}} </ref>|| http://lifecenter.sgst.cn/hit/|| |
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| SCLPred|| SCLpred protein subcellular localization prediction by N-to-1 neural networks.|| <ref name="pmid21873639">{{cite journal|first=|date=October 2011|year=|title=SCLpred: protein subcellular localization prediction by N-to-1 neural networks.|url=|journal=Bioinformatics|volume=27|issue=20|pages=2812–9|doi=10.1093/bioinformatics/btr494|issn=|pmid=21873639|via=|||author=Mooney C, Wang YH, Pollastri G.}}</ref> |
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| HMMTOP|| Prediction of transmembranes helices and topology of proteins. ([https://bio.toolss/HMMTOP bio.tools entry]) |||| http://www.enzim.hu/hmmtop/|| |
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| SecretomeP|| Prediction of eukaryotic proteins that are secreted via a non-traditional secretory mechanism.|| <ref name="pmid15115854">{{cite journal | author = Bendtsen JD, Jensen LJ, Blom N, Von Heijne G, Brunak S | title = Feature-based prediction of non-classical and leaderless protein secretion | journal = Protein Eng. Des. Sel. | volume = 17 | issue = 4 | pages = 349–56 |date=April 2004 | pmid = 15115854 | doi = 10.1093/protein/gzh037 | url = | issn = }}</ref> |
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| HSLpred|| Allows predicting the subcellular localization of human proteins. This is based on various type of residue composition of proteins using SVM technique. ([https://bio.toolss/HSLpred bio.tools entry]) ||<ref> {{cite journal|pmid=15647269|year=2005|author1=Garg|first1=A|title=Support vector machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search|journal=Journal of Biological Chemistry|volume=280|issue=15|pages=14427–32|last2=Bhasin|first2=M|last3=Raghava|first3=G. P|doi=10.1074/jbc.M411789200}} </ref>|| http://www.imtech.res.in/raghava/hslpred/|| |
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| SherLoc|| An SVM-based predictor combining MultiLoc with text-based features derived from PubMed abstracts.|| <ref name="pmid17392328">{{cite journal|first=|date=June 2007|year=|title=SherLoc: high-accuracy prediction of protein subcellular localization by integrating text and protein sequence data|url=|journal=Bioinformatics|volume=23|issue=11|pages=1410–7|doi=10.1093/bioinformatics/btm115|issn=|pmid=17392328|via=|||author=Shatkay H, Höglund A, Brady S, Blum T, Dönnes P, Kohlbacher O}}</ref> |
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| idTarget|| idTarget is a web server for identifying biomolecular targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach. idTarget screens against protein structures in PDB. ([https://bio.toolss/idtarget bio.tools entry]) ||<ref> {{cite journal|pmid=22649057|pmc=3394295|year=2012|author1=Wang|first1=J. C|title=Id ''Target'': A web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach|journal=Nucleic Acids Research|volume=40|issue=Web Server issue|pages=W393–9|last2=Chu|first2=P. Y|last3=Chen|first3=C. M|last4=Lin|first4=J. H|doi=10.1093/nar/gks496}} </ref>|| http://idtarget.rcas.sinica.edu.tw|| |
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| SCLAP|| An Adaptive Boosting Method for Predicting Subchloroplast Localization of Plant Proteins.|| <ref name="pmid23289782">{{cite journal|first=|date=Jan 2013|year=|title=SCLAP: An Adaptive Boosting Method for Predicting Subchloroplast Localization of Plant Proteins|url=|journal=OMICS|volume=17|issue=2|pages=106–15|doi=10.1089/omi.2012.0070|issn=|pmid=23289782|via=|||author=Saravanan V, Lakshmi PTV}}</ref> |
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| iLoc-Cell|| Predictor for subcellular locations of human proteins with multiple sites. ([https://bio.toolss/iLoc-Cell bio.tools entry]) ||<ref> {{cite journal|pmid=22134333|year=2012|author1=Chou|first1=K. C|title=I ''Loc''-Hum: Using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites|journal=Mol. Bio ''Syst''|volume=8|issue=2|pages=629–41|last2=Wu|first2=Z. C|last3=Xiao|first3=X|doi=10.1039/c1mb05420a}} </ref>|| http://www.jci-bioinfo.cn/iLoc-Hum|| |
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| lncRNAdb|| lncRNAdb database contains a comprehensive list of long noncoding RNAs (lncRNAs) that have been shown to have, or to be associated with, biological functions in eukaryotes, as well as messenger RNAs that have regulatory roles. Each entry contains referenced information about the RNA, including sequences, structural information, genomic context, expression, subcellular localization, conservation, functional evidence and other relevant information. lncRNAdb can be searched by querying published RNA names and aliases, sequences, species and associated protein-coding genes, as well as terms contained in the annotations, such as the tissues in which the transcripts are expressed and associated diseases. In addition, lncRNAdb is linked to the UCSC Genome Browser for visualization and Noncoding RNA Expression Database (NRED) for expression information from a variety of sources. ([https://bio.toolss/lncrnadb bio.tools entry]) ||<ref> {{cite journal|pmid=21112873|pmc=3013714|year=2011|author1=Amaral|first1=P. P|title=LncRNAdb: A reference database for long noncoding RNAs|journal=Nucleic Acids Research|volume=39|issue=Database issue|pages=D146–51|last2=Clark|first2=M. B|last3=Gascoigne|first3=D. K|last4=Dinger|first4=M. E|last5=Mattick|first5=J. S|doi=10.1093/nar/gkq1138}} </ref>|| http://www.lncrnadb.org/|| |
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| TPpred2/3 || Prediction of N-terminal organelle [[target peptide]].|| <ref name="pmid24974200">{{cite journal | author = Savojardo C, Martelli PL, Fariselli P, Casadio R | title = TPpred2: improving the prediction of mitochondrial targeting peptide cleavage sites by exploiting sequence motifs | journal = Bioinformatics | volume = 30 | issue = 20 | pages = 2973–2974 |date= June 2014 | pmid = 24974200 | doi = 10.1093/bioinformatics/btu411 | url = | issn = }}</ref><ref name="pmid26079349">{{cite journal | author = Savojardo C, Martelli PL, Fariselli P, Casadio R | title = TPpred3 detects and discriminates mitochondrial and chloroplastic targeting peptides in eukaryotic proteins | journal = Bioinformatics | volume = 31 | issue = 20 | pages = 3269–75 |date= October 2015 | pmid = 26079349 | doi = 10.1093/bioinformatics/btv367 | url = | issn = }}</ref> |
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| Loc3D|| LOC3D is a database of predicted subcellular localization for eukaryotic proteins of known three-dimensional (3D) structure and includes tools to predict the subcellular localization for submitted protein sequences. ([https://bio.toolss/loc3d bio.tools entry]) ||<ref> {{cite journal|pmid=12824321|pmc=168921|year=2003|author1=Nair|first1=R|title=LOC3D: Annotate sub-cellular localization for protein structures|journal=Nucleic acids research|volume=31|issue=13|pages=3337–40|last2=Rost|first2=B}} </ref><ref> {{cite journal|pmid=15808855|year=2005|author1=Nair|first1=R|title=Mimicking cellular sorting improves prediction of subcellular localization|journal=Journal of Molecular Biology|volume=348|issue=1|pages=85–100|last2=Rost|first2=B|doi=10.1016/j.jmb.2005.02.025}} </ref><ref> {{cite journal|pmid=14635133|year=2003|author1=Nair|first1=R|title=Better prediction of sub-cellular localization by combining evolutionary and structural information|journal=Proteins: Structure, Function, and Bioinformatics|volume=53|issue=4|pages=917–30|last2=Rost|first2=B|doi=10.1002/prot.10507}} </ref>|| http://cubic.bioc.columbia.edu/db/LOC3d/|| |
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| TMHMM|| Prediction of transmembrane helices to identify [[transmembrane protein]]s. || |
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| LOCATE|| LOCATE is a curated database that houses data describing the membrane organization and subcellular localization of mouse proteins. ([https://bio.toolss/locate bio.tools entry]) ||<ref> {{cite journal|pmid=16381849|pmc=1347432|year=2006|author1=Fink|first1=J. L|title=LOCATE: A mouse protein subcellular localization database|journal=Nucleic Acids Research|volume=34|issue=Database issue|pages=D213–7|last2=Aturaliya|first2=R. N|last3=Davis|first3=M. J|last4=Zhang|first4=F|last5=Hanson|first5=K|last6=Teasdale|first6=M. S|last7=Kai|first7=C|last8=Kawai|first8=J|last9=Carninci|first9=P|last10=Hayashizaki|first10=Y|last11=Teasdale|first11=R. D|doi=10.1093/nar/gkj069}} </ref>|| http://locate.imb.uq.edu.au/|| |
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| WoLF PSORT|| An updated version of PSORT/PSORT II for the prediction of eukaryotic sequences.|| <ref name="pmid17517783">{{cite journal|first=|date=July 2007|year=|title=WoLF PSORT: protein localization predictor|url=|journal=Nucleic Acids Res.|volume=35|issue=Web Server issue|pages=W585–7|doi=10.1093/nar/gkm259|issn=|pmc=1933216|pmid=17517783|via=|author=Horton P, Park KJ, Obayashi T, Fujita N, Harada H, Adams-Collier CJ, Nakai K}}</ref> |
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| LocDB|| LocDB is a manually curated database with experimental annotations for the subcellular localizations of proteins in Homo sapiens (HS, human) and Arabidopsis thaliana (AT, thale cress). Each database entry contains the experimentally derived localization in Gene Ontology (GO) terminology, the experimental annotation of localization, localization predictions by state-of-the-art methods and, where available, the type of experimental information. LocDB is searchable by keyword, protein name and subcellular compartment, as well as by identifiers from UniProt, Ensembl and TAIR resources. ([https://bio.toolss/locdb bio.tools entry]) ||<ref> {{cite journal|pmid=21071420|pmc=3013784|year=2011|author1=Rastogi|first1=S|title=LocDB: Experimental annotations of localization for Homo sapiens and Arabidopsis thaliana|journal=Nucleic Acids Research|volume=39|issue=Database issue|pages=D230–4|last2=Rost|first2=B|doi=10.1093/nar/gkq927}} </ref>|| http://www.rostlab.org/services/locDB/|| |
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| LOCtarget|| LOCtarget is a tool for predicting, and a database of pre-computed predictions for, sub-cellular localization of eukaryotic and prokaryotic proteins. Several methods are employed to make the predictions, including text analysis of SWISS-PROT keywords, nuclear localization signals, and the use of neural networks. ([https://bio.toolss/loctarget bio.tools entry]) ||<ref> {{cite journal|pmid=15215440|pmc=441579|year=2004|author1=Nair|first1=R|title=LOCnet and LOCtarget: Sub-cellular localization for structural genomics targets|journal=Nucleic Acids Research|volume=32|issue=Web Server issue|pages=W517–21|last2=Rost|first2=B|doi=10.1093/nar/gkh441}} </ref>|| http://www.rostlab.org/services/LOCtarget/|| |
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| LocTree2|| Framework to predict localization in life's three domains, including globular and membrane proteins (3 classes for archaea; 6 for bacteria and 18 for eukaryota). The resulting method, LocTree2, works well even for protein fragments. It uses a hierarchical system of support vector machines that imitates the cascading mechanism of cellular sorting. The method reaches high levels of sustained performance (eukaryota: Q18=65%, bacteria: Q6=84%). LocTree2 also accurately distinguishes membrane and non-membrane proteins. In our hands, it compared favorably with top methods when tested on new data ([https://bio.toolss/loctree2 bio.tools entry]) ||<ref> {{cite journal|doi=doi:10.1093/bioinformatics/bts390}}</ref>|| https://rostlab.org/owiki/index.php/Loctree2|| |
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| LocTree3|| Prediction of protein subcellular localization in 18 classes for eukaryota, 6 for bacteria and 3 for archaea ([https://bio.toolss/loctree3 bio.tools entry]) ||<ref> {{cite journal|doi=10.1093/nar/gku396|pmid=24848019|pmc=4086075|title=LocTree3 prediction of localization|journal=Nucleic Acids Research|volume=42|issue=Web Server issue|pages=W350–5|year=2014|last1=Goldberg|first1=Tatyana|last2=Hecht|first2=Maximilian|last3=Hamp|first3=Tobias|last4=Karl|first4=Timothy|last5=Yachdav|first5=Guy|last6=Ahmed|first6=Nadeem|last7=Altermann|first7=Uwe|last8=Angerer|first8=Philipp|last9=Ansorge|first9=Sonja|last10=Balasz|first10=Kinga|last11=Bernhofer|first11=Michael|last12=Betz|first12=Alexander|last13=Cizmadija|first13=Laura|last14=Do|first14=Kieu Trinh|last15=Gerke|first15=Julia|last16=Greil|first16=Robert|last17=Joerdens|first17=Vadim|last18=Hastreiter|first18=Maximilian|last19=Hembach|first19=Katharina|last20=Herzog|first20=Max|last21=Kalemanov|first21=Maria|last22=Kluge|first22=Michael|last23=Meier|first23=Alice|last24=Nasir|first24=Hassan|last25=Neumaier|first25=Ulrich|last26=Prade|first26=Verena|last27=Reeb|first27=Jonas|last28=Sorokoumov|first28=Aleksandr|last29=Troshani|first29=Ilira|last30=Vorberg|first30=Susann|display-authors=29}} </ref><ref> {{cite journal|doi=10.1093/bioinformatics/bts390|pmid=22962467|title=LocTree2 predicts localization for all domains of life|journal=Bioinformatics|volume=28|issue=18|pages=i458|year=2012|last1=Goldberg|first1=Tatyana|last2=Hamp|first2=Tobias|last3=Rost|first3=Burkhard}} </ref>|| https://rostlab.org/services/loctree3/|| |
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| MARSpred|| Prediction method  for discrimination between Mitochondrial-AARSs and Cytosolic-AARSs. ([https://bio.toolss/MARSpred bio.tools entry]) ||<ref> {{cite journal|pmid=21400228|year=2012|author1=Panwar|first1=B|title=Predicting sub-cellular localization of tRNA synthetases from their primary structures|journal=Amino Acids|volume=42|issue=5|pages=1703–13|last2=Raghava|first2=G. P|doi=10.1007/s00726-011-0872-8}} </ref>|| http://www.imtech.res.in/raghava/marspred/|| |
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| MDLoc|| Dependency-Based Protein Subcellular Location Predictor. ([https://bio.toolss/MDLoc bio.tools entry]) ||<ref> {{cite journal|pmid=26072505|pmc=4765880|year=2015|author1=Simha|first1=R|title=Protein (multi-)location prediction: Utilizing interdependencies via a generative model|journal=Bioinformatics|volume=31|issue=12|pages=i365–74|last2=Briesemeister|first2=S|last3=Kohlbacher|first3=O|last4=Shatkay|first4=H|doi=10.1093/bioinformatics/btv264}} </ref>|| http://128.4.31.235/|| |
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| MemLoci|| Predictor for the subcellular localization of proteins associated or inserted in eukaryotes membranes. ([https://bio.toolss/memloci bio.tools entry]) ||<ref> {{cite journal|doi=10.1093/bioinformatics/btr108|pmid=21367869|title=Mem ''Loci'': Predicting subcellular localization of membrane proteins in eukaryotes|journal=Bioinformatics|volume=27|issue=9|pages=1224|year=2011|last1=Pierleoni|first1=Andrea|last2=Martelli|first2=Pier Luigi|last3=Casadio|first3=Rita}} </ref>|| https://mu2py.biocomp.unibo.it/memloci|| |
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| MemPype|| Prediction of topology and subcellular localization of Eukaryotic membrane proteins. ([https://bio.toolss/mempype bio.tools entry]) ||<ref> {{cite journal|doi=10.1093/nar/gkr282|pmid=21543452|pmc=3125734|title=Mem ''Pype'': A pipeline for the annotation of eukaryotic membrane proteins|journal=Nucleic Acids Research|volume=39|issue=Web Server issue|pages=W375–80|year=2011|last1=Pierleoni|first1=A|last2=Indio|first2=V|last3=Savojardo|first3=C|last4=Fariselli|first4=P|last5=Martelli|first5=P. L|last6=Casadio|first6=R}} </ref>|| https://mu2py.biocomp.unibo.it/mempype|| |
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| MirZ|| MirZ is a web server that for evaluation and analysis of miRNA. It integrates two miRNA resources: the smiRNAdb miRNA expression atlas and the E1MMo miRNA target prediction algorithm. ([https://bio.toolss/mirz bio.tools entry]) ||<ref> {{cite journal|pmid=19468042|pmc=2703880|year=2009|author1=Hausser|first1=J|title=MirZ: An integrated microRNA expression atlas and target prediction resource|journal=Nucleic Acids Research|volume=37|issue=Web Server issue|pages=W266–72|last2=Berninger|first2=P|last3=Rodak|first3=C|last4=Jantscher|first4=Y|last5=Wirth|first5=S|last6=Zavolan|first6=M|doi=10.1093/nar/gkp412}} </ref>|| http://www.mirz.unibas.ch|| |
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| MitPred|| Web-server specifically trained to predict the proteins which are destined to localized in mitochondria in yeast and animals particularly. ([https://bio.toolss/MitPred bio.tools entry]) ||<ref> {{cite journal|pmid=16339140|year=2006|author1=Kumar|first1=M|title=Prediction of mitochondrial proteins using support vector machine and hidden Markov model|journal=Journal of Biological Chemistry|volume=281|issue=9|pages=5357–63|last2=Verma|first2=R|last3=Raghava|first3=G. P|doi=10.1074/jbc.M511061200}} </ref>|| http://www.imtech.res.in/raghava/mitpred/|| |
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| Mycosub|| This web-server was used to predict the subcellular localizations of mycobacterial proteins based on optimal tripeptide compositions. ([https://bio.toolss/Mycosub bio.tools entry]) ||<ref> {{cite journal|pmid=25437899|year=2015|author1=Zhu|first1=P. P|title=Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition|journal=Molecular Bio ''Systems''|volume=11|issue=2|pages=558–63|last2=Li|first2=W. C|last3=Zhong|first3=Z. J|last4=Deng|first4=E. Z|last5=Ding|first5=H|last6=Chen|first6=W|last7=Lin|first7=H|doi=10.1039/c4mb00645c}} </ref>|| http://lin.uestc.edu.cn/server/Mycosub|| |
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| NetNES|| Prediction of the leucine-rich nuclear export signals (NES) in eukaryotic proteins ([https://bio.toolss/netnes bio.tools entry]) ||<ref> {{cite journal|pmid=15314210|year=2004|author1=La Cour|first1=T|title=Analysis and prediction of leucine-rich nuclear export signals|journal=Protein Engineering, Design and Selection|volume=17|issue=6|pages=527–36|last2=Kiemer|first2=L|last3=Mølgaard|first3=A|last4=Gupta|first4=R|last5=Skriver|first5=K|last6=Brunak|first6=S|doi=10.1093/protein/gzh062}} </ref>|| http://cbs.dtu.dk/services/NetNES/|| |
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| ngLOC|| ngLOC is an n-gram-based Bayesian classifier that predicts subcellular localization of proteins both in prokaryotes and eukaryotes. The overall prediction accuracy varies from 85.3% to 91.4% across species. ([https://bio.toolss/ngLOC bio.tools entry]) |||| http://genome.unmc.edu/ngLOC/index.html|| |
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| OBCOL|| Software we designed to perform organelle-based colocalisation analysis from multi-fluorophore microscopy 2D, 3D and 4D cell imaging. ([https://bio.toolss/OBCOL bio.tools entry]) ||<ref> {{cite journal|pmid=19746416|year=2009|author1=Woodcroft|first1=B. J|title=Automated organelle-based colocalization in whole-cell imaging|journal=Cytometry Part A|volume=75|issue=11|pages=941–50|last2=Hammond|first2=L|last3=Stow|first3=J. L|last4=Hamilton|first4=N. A|doi=10.1002/cyto.a.20786}} </ref>|| http://obcol.imb.uq.edu.au/|| |
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| PA-SUB|| PA-SUB (Proteome Analyst Specialized Subcellular Localization Server) can be used to predict the subcellular localization of proteins using established machine learning techniques. ([https://bio.toolss/PA-SUB bio.tools entry]) ||<ref> {{cite journal|pmid=14990451|year=2004|author1=Lu|first1=Z|title=Predicting subcellular localization of proteins using machine-learned classifiers|journal=Bioinformatics|volume=20|issue=4|pages=547–56|last2=Szafron|first2=D|last3=Greiner|first3=R|last4=Lu|first4=P|last5=Wishart|first5=D. S|last6=Poulin|first6=B|last7=Anvik|first7=J|last8=MacDonell|first8=C|last9=Eisner|first9=R|doi=10.1093/bioinformatics/bth026}} </ref><ref> {{cite journal|pmid=15215412|pmc=441623|year=2004|author1=Szafron|first1=D|title=Proteome Analyst: Custom predictions with explanations in a web-based tool for high-throughput proteome annotations|journal=Nucleic Acids Research|volume=32|issue=Web Server issue|pages=W365–71|last2=Lu|first2=P|last3=Greiner|first3=R|last4=Wishart|first4=D. S|last5=Poulin|first5=B|last6=Eisner|first6=R|last7=Lu|first7=Z|last8=Anvik|first8=J|last9=MacDonell|first9=C|last10=Fyshe|first10=A|last11=Meeuwis|first11=D|doi=10.1093/nar/gkh485}} </ref>|| http://www.cs.ualberta.ca/~bioinfo/PA/Sub/|| |
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| PharmMapper|| PharmMapper is a web server that identifies potential drug targets from its PharmTargetDB for a given input molecule. Potential targets are identified from a prediction of the spatial arrangement of features essential for a given molecule to interact with a target. ([https://bio.toolss/pharmmapper bio.tools entry]) ||<ref> {{cite journal|pmid=20430828|pmc=2896160|year=2010|author1=Liu|first1=X|title=Pharm ''Mapper'' server: A web server for potential drug target identification using pharmacophore mapping approach|journal=Nucleic Acids Research|volume=38|issue=Web Server issue|pages=W609–14|last2=Ouyang|first2=S|last3=Yu|first3=B|last4=Liu|first4=Y|last5=Huang|first5=K|last6=Gong|first6=J|last7=Zheng|first7=S|last8=Li|first8=Z|last9=Li|first9=H|last10=Jiang|first10=H|doi=10.1093/nar/gkq300}} </ref>|| http://59.78.96.61/pharmmapper|| |
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| PlantLoc|| PlantLoc is a web server for predicting plant protein subcellular localization by substantiality motif. ([https://bio.toolss/plantloc bio.tools entry]) ||<ref> {{cite journal|pmid=23729470|pmc=3692052|year=2013|author1=Tang|first1=S|title=Plant ''Loc'': An accurate web server for predicting plant protein subcellular localization by substantiality motif|journal=Nucleic Acids Research|volume=41|issue=Web Server issue|pages=W441–7|last2=Li|first2=T|last3=Cong|first3=P|last4=Xiong|first4=W|last5=Wang|first5=Z|last6=Sun|first6=J|doi=10.1093/nar/gkt428}} </ref>|| http://cal.tongji.edu.cn/PlantLoc/|| |
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| PRED-TMBB|| PRED-TMBB is a tool that takes a Gram-negative bacteria protein sequence as input and predicts the transmembrane strands and the probability of it being an outer membrane beta-barrel protein. The user has a choice of three different decoding methods. ([https://bio.toolss/pred-tmbb bio.tools entry]) ||<ref> {{cite journal|pmid=15215419|pmc=441555|year=2004|author1=Bagos|first1=P. G|title=PRED-TMBB: A web server for predicting the topology of beta-barrel outer membrane proteins|journal=Nucleic Acids Research|volume=32|issue=Web Server issue|pages=W400–4|last2=Liakopoulos|first2=T. D|last3=Spyropoulos|first3=I. C|last4=Hamodrakas|first4=S. J|doi=10.1093/nar/gkh417}} </ref><ref> {{cite journal|pmid=15070403|pmc=385222|year=2004|author1=Bagos|first1=P. G|title=A Hidden Markov Model method, capable of predicting and discriminating beta-barrel outer membrane proteins|journal=BMC Bioinformatics|volume=5|pages=29|last2=Liakopoulos|first2=T. D|last3=Spyropoulos|first3=I. C|last4=Hamodrakas|first4=S. J|doi=10.1186/1471-2105-5-29}} </ref>|| http://bioinformatics.biol.uoa.gr/PRED-TMBB/|| |
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| PredictNLS|| Prediction and analysis of nuclear localization signals ([https://bio.toolss/predictnls bio.tools entry]) ||<ref> {{cite journal|pmid=11258480|pmc=1083765|year=2000|author1=Cokol|first1=M|title=Finding nuclear localization signals|journal=EMBO reports|volume=1|issue=5|pages=411–5|last2=Nair|first2=R|last3=Rost|first3=B|doi=10.1093/embo-reports/kvd092}} </ref>|| https://www.rostlab.org/owiki/index.php/PredictNLS|| |
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| PredictProtein Open|| Prediction of various aspects of protein structure and function. A user may submit a query to the server without registration. ([https://bio.toolss/predictprotein_open bio.tools entry]) ||<ref> {{cite journal|doi=10.1093/nar/gku366|pmid=24799431|pmc=4086098|title=Predict ''Protein''—an open resource for online prediction of protein structural and functional features|journal=Nucleic Acids Research|volume=42|issue=Web Server issue|pages=W337–43|year=2014|last1=Yachdav|first1=Guy|last2=Kloppmann|first2=Edda|last3=Kajan|first3=Laszlo|last4=Hecht|first4=Maximilian|last5=Goldberg|first5=Tatyana|last6=Hamp|first6=Tobias|last7=Hönigschmid|first7=Peter|last8=Schafferhans|first8=Andrea|last9=Roos|first9=Manfred|last10=Bernhofer|first10=Michael|last11=Richter|first11=Lothar|last12=Ashkenazy|first12=Haim|last13=Punta|first13=Marco|last14=Schlessinger|first14=Avner|last15=Bromberg|first15=Yana|last16=Schneider|first16=Reinhard|last17=Vriend|first17=Gerrit|last18=Sander|first18=Chris|last19=Ben-Tal|first19=Nir|last20=Rost|first20=Burkhard}} </ref><ref> {{cite journal|doi=10.1155/2013/398968|title=Cloud Prediction of Protein Structure and Function with Predict ''Protein'' for Debian|journal=Bio ''Med'' Research International|volume=2013|pages=1|year=2013|last1=Kaján|first1=László|last2=Yachdav|first2=Guy|last3=Vicedo|first3=Esmeralda|last4=Steinegger|first4=Martin|last5=Mirdita|first5=Milot|last6=Angermüller|first6=Christof|last7=Böhm|first7=Ariane|last8=Domke|first8=Simon|last9=Ertl|first9=Julia|last10=Mertes|first10=Christian|last11=Reisinger|first11=Eva|last12=Staniewski|first12=Cedric|last13=Rost|first13=Burkhard}} </ref><ref> {{cite journal|doi=10.1093/nar/gkg508|pmid=12824312|pmc=168915|title=The Predict ''Protein'' server|journal=Nucleic Acids Research|volume=31|issue=13|pages=3300–4|year=2003|last1=Rost|first1=B|last2=Liu|first2=J}} </ref><ref> {{cite journal|doi=10.1093/nar/gkh377|pmid=15215403|pmc=441515|title=The Predict ''Protein'' server|journal=Nucleic Acids Research|volume=32|issue=Web Server issue|pages=W321–6|year=2004|last1=Rost|first1=B|last2=Yachdav|first2=G|last3=Liu|first3=J}} </ref>|| http://ppopen.informatik.tu-muenchen.de/|| |
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| PREP Suite|| The PREP (Predictive RNA Editors for Plants) suite predicts sites of RNA editing based on the principle that editing in plant organelles increases the conservation of proteins across species. Predictors for mitochondrial genes, chloroplast genes, and alignments input by the user are included. ([https://bio.toolss/prep_suite bio.tools entry]) ||<ref> {{cite journal|pmid=19433507|pmc=2703948|year=2009|author1=Mower|first1=J. P|title=The PREP suite: Predictive RNA editors for plant mitochondrial genes, chloroplast genes and user-defined alignments|journal=Nucleic Acids Research|volume=37|issue=Web Server issue|pages=W253–9|doi=10.1093/nar/gkp337}} </ref><ref> {{cite journal|pmid=15826309|pmc=1087475|year=2005|author1=Mower|first1=J. P|title=PREP-Mt: Predictive RNA editor for plant mitochondrial genes|journal=BMC Bioinformatics|volume=6|pages=96|doi=10.1186/1471-2105-6-96}} </ref>|| http://prep.unl.edu/|| |
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| ProLoc-GO|| ProLoc-GO is an efficient sequence-based method by mining informative Gene Ontology terms for predicting protein subcellular localization. ([https://bio.toolss/ProLoc-GO bio.tools entry]) ||<ref> {{cite journal|doi=10.1186/1471-2105-9-80|pmid=18241343|pmc=2262056|title=Pro ''Loc''-GO: Utilizing informative Gene Ontology terms for sequence-based prediction of protein subcellular localization|journal=BMC Bioinformatics|volume=9|pages=80|year=2008|last1=Huang|first1=Wen-Lin|last2=Tung|first2=Chun-Wei|last3=Ho|first3=Shih-Wen|last4=Hwang|first4=Shiow-Fen|last5=Ho|first5=Shinn-Ying}} </ref>|| http://140.113.239.45/prolocgo/|| |
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| ProLoc|| Evolutionary support vector machine (ESVM) based classifier with automatic selection from a large set of physicochemical composition (PCC) features to design an accurate system for predicting protein subnuclear localization. ([https://bio.toolss/ProLoc6694 bio.tools entry]) ||<ref> {{cite journal|pmid=17291684|year=2007|author1=Huang|first1=W. L|title=Pro ''Loc'': Prediction of protein subnuclear localization using SVM with automatic selection from physicochemical composition features|journal=Biosystems|volume=90|issue=2|pages=573–81|last2=Tung|first2=C. W|last3=Huang|first3=H. L|last4=Hwang|first4=S. F|last5=Ho|first5=S. Y|doi=10.1016/j.biosystems.2007.01.001}} </ref>|| http://140.113.239.45/proloc/|| |
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| Protegen|| Protegen is a web-based database and analysis system that curates, stores and analyzes protective antigens. Protegen includes basic antigen information and experimental evidence curated from peer-reviewed articles. It also includes detailed gene/protein information (e.g. DNA and protein sequences, and COG classification). Different antigen features, such as protein weight and pI, and subcellular localizations of bacterial proteins are precomputed. ([https://bio.toolss/protegen bio.tools entry]) ||<ref> {{cite journal|pmid=20959289|pmc=3013795|year=2011|author1=Yang|first1=B|title=Protegen: A web-based protective antigen database and analysis system|journal=Nucleic Acids Research|volume=39|issue=Database issue|pages=D1073–8|last2=Sayers|first2=S|last3=Xiang|first3=Z|last4=He|first4=Y|doi=10.1093/nar/gkq944}} </ref>|| http://www.violinet.org/protegen|| |
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| Proteome Analyst|| Proteome Analyst is a high-throughput tool for predicting properties for each protein in a proteome. The user provides a proteome in fasta format, and the system employs Psi-blast, Psipred and Modeller to predict protein function and subcellular localization. Proteome Analyst uses machine-learned classifiers to predict things such as GO molecular function. User-supplied training data can also be used to create custom classifiers. ([https://bio.toolss/proteome_analyst bio.tools entry]) ||<ref> {{cite journal|pmid=15215412|pmc=441623|year=2004|author1=Szafron|first1=D|title=Proteome Analyst: Custom predictions with explanations in a web-based tool for high-throughput proteome annotations|journal=Nucleic Acids Research|volume=32|issue=Web Server issue|pages=W365–71|last2=Lu|first2=P|last3=Greiner|first3=R|last4=Wishart|first4=D. S|last5=Poulin|first5=B|last6=Eisner|first6=R|last7=Lu|first7=Z|last8=Anvik|first8=J|last9=MacDonell|first9=C|last10=Fyshe|first10=A|last11=Meeuwis|first11=D|doi=10.1093/nar/gkh485}} </ref>|| http://www.cs.ualberta.ca/~bioinfo/PA/|| |
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| ProTox|| ProTox is a web server for the in silico prediction of oral toxicities of small molecules in rodents. ([https://bio.toolss/ProTox bio.tools entry]) |||| http://tox.charite.de/tox|| |
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| PSLpred|| Method for subcellular localization proteins belongs to prokaryotic genomes. ([https://bio.toolss/PSLpred bio.tools entry]) ||<ref> {{cite journal|pmid=15699023|year=2005|author1=Bhasin|first1=M|title=PSLpred: Prediction of subcellular localization of bacterial proteins|journal=Bioinformatics|volume=21|issue=10|pages=2522–4|last2=Garg|first2=A|last3=Raghava|first3=G. P|doi=10.1093/bioinformatics/bti309}} </ref>|| http://www.imtech.res.in/raghava/pslpred/|| |
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| PSORTb|| PSORTb (for “bacterial” PSORT) is a high-precision localization prediction method for bacterial proteins.PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. PSORTb version improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies. ([https://bio.toolss/PSORTb bio.tools entry]) ||<ref> {{cite journal|doi=10.1093/bioinformatics/btq249|pmid=20472543|title=PSORTb 3.0: Improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes|journal=Bioinformatics|volume=26|issue=13|pages=1608|year=2010|last1=Yu|first1=Nancy Y|last2=Wagner|first2=James R|last3=Laird|first3=Matthew R|last4=Melli|first4=Gabor|last5=Rey|first5=Sébastien|last6=Lo|first6=Raymond|last7=Dao|first7=Phuong|last8=Sahinalp|first8=S. Cenk|last9=Ester|first9=Martin|last10=Foster|first10=Leonard J|last11=Brinkman|first11=Fiona S. L}} </ref>|| http://www.psort.org/psortb/|| |
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| PSORTdb|| PSORTdb (part of the PSORT family) is a database of protein subcellular localizations for bacteria and archaea that contains both information determined through laboratory experimentation (ePSORTdb dataset) and computational predictions (cPSORTdb dataset). ([https://bio.toolss/psortdb bio.tools entry]) ||<ref> {{cite journal|pmid=21071402|pmc=3013690|year=2011|author1=Yu|first1=N. Y|title=PSORTdb--an expanded, auto-updated, user-friendly protein subcellular localization database for Bacteria and Archaea|journal=Nucleic Acids Research|volume=39|issue=Database issue|pages=D241–4|last2=Laird|first2=M. R|last3=Spencer|first3=C|last4=Brinkman|first4=F. S|doi=10.1093/nar/gkq1093}} </ref><ref> {{cite journal|pmid=15608169|pmc=539981|year=2005|author1=Rey|first1=S|title=PSORTdb: A protein subcellular localization database for bacteria|journal=Nucleic Acids Research|volume=33|issue=Database issue|pages=D164–8|last2=Acab|first2=M|last3=Gardy|first3=J. L|last4=Laird|first4=M. R|last5=Defays|first5=K|last6=Lambert|first6=C|last7=Brinkman|first7=F. S|doi=10.1093/nar/gki027}} </ref>|| http://db.psort.org|| |
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| psRobot|| psRobot is a web-based tool for plant small RNA meta-analysis. psRobot computes stem-loop small RNA prediction, which aligns user uploaded sequences to the selected genome, extracts their predicted precursors, and predicts whether the precursors can fold into stem-loop shaped secondary structure. psRobot also computes small RNA target prediction, which predict the possible targets of user provided small RNA sequences from the selected transcript library. ([https://bio.toolss/psrobot bio.tools entry]) ||<ref> {{cite journal|pmid=22693224|pmc=3394341|year=2012|author1=Wu|first1=H. J|title=Ps ''Robot'': A web-based plant small RNA meta-analysis toolbox|journal=Nucleic Acids Research|volume=40|issue=Web Server issue|pages=W22–8|last2=Ma|first2=Y. K|last3=Chen|first3=T|last4=Wang|first4=M|last5=Wang|first5=X. J|doi=10.1093/nar/gks554}} </ref>|| http://omicslab.genetics.ac.cn/psRobot/|| |
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| pTARGET|| pTARGET predicts the subcellular localization of eukaryotic proteins based on the occurrence patterns of location-specific protein functional domains and the amino acid compositional differences in proteins from nine distinct subcellular locations. ([https://bio.toolss/ptarget bio.tools entry]) ||<ref> {{cite journal|pmid=16144808|year=2005|author1=Guda|first1=C|title=PTARGET corrected a new method for predicting protein subcellular localization in eukaryotes|journal=Bioinformatics|volume=21|issue=21|pages=3963–9|last2=Subramaniam|first2=S|doi=10.1093/bioinformatics/bti650}} </ref><ref> {{cite journal|pmid=16844995|pmc=1538910|year=2006|author1=Guda|first1=C|title=PTARGET: A web server for predicting protein subcellular localization|journal=Nucleic Acids Research|volume=34|issue=Web Server issue|pages=W210–3|doi=10.1093/nar/gkl093}} </ref>|| http://bioinformatics.albany.edu/~ptarget|| |
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| RegPhos|| RegPhos is a database for exploration of the phosphorylation network associated with an input of genes/proteins. Subcellular localization information is also included. ([https://bio.toolss/regphos bio.tools entry]) ||<ref> {{cite journal|pmid=21037261|pmc=3013804|year=2011|author1=Lee|first1=T. Y|title=Reg ''Phos'': A system to explore the protein kinase-substrate phosphorylation network in humans|journal=Nucleic Acids Research|volume=39|issue=Database issue|pages=D777–87|last2=Bo-Kai Hsu|first2=J|last3=Chang|first3=W. C|last4=Huang|first4=H. D|doi=10.1093/nar/gkq970}} </ref>|| http://regphos.mbc.nctu.edu.tw/|| |
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| RepTar|| RepTar is a database of miRNA target predictions, based on the RepTar algorithm that is independent of evolutionary conservation considerations and is not limited to seed pairing sites. ([https://bio.toolss/reptar bio.tools entry]) ||<ref> {{cite journal|pmid=21149264|pmc=3013742|year=2011|author1=Elefant|first1=N|title=Rep ''Tar'': A database of predicted cellular targets of host and viral miRNAs|journal=Nucleic Acids Research|volume=39|issue=Database issue|pages=D188–94|last2=Berger|first2=A|last3=Shein|first3=H|last4=Hofree|first4=M|last5=Margalit|first5=H|last6=Altuvia|first6=Y|doi=10.1093/nar/gkq1233}} </ref>|| http://reptar.ekmd.huji.ac.il|| |
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| RNApredator|| RNApredator is a web server for the prediction of bacterial sRNA targets. The user can choose from a large selection of genomes. Accessibility of the target to the sRNA is considered. ([https://bio.toolss/rnapredator bio.tools entry]) ||<ref> {{cite journal|pmid=21672960|pmc=3125805|year=2011|author1=Eggenhofer|first1=F|title=RNApredator: Fast accessibility-based prediction of sRNA targets|journal=Nucleic Acids Research|volume=39|issue=Web Server issue|pages=W149–54|last2=Tafer|first2=H|last3=Stadler|first3=P. F|last4=Hofacker|first4=I. L|doi=10.1093/nar/gkr467}} </ref>|| http://rna.tbi.univie.ac.at/RNApredator|| |
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| S-PSorter|| A novel cell structure-driven classifier construction approach for predicting image-based protein subcellular location by employing the prior biological structural information. ([https://bio.toolss/S-PSorter bio.tools entry]) ||<ref> {{cite journal|pmid=26363175|year=2016|author1=Shao|first1=W|title=Human cell structure-driven model construction for predicting protein subcellular location from biological images|journal=Bioinformatics|volume=32|issue=1|pages=114–21|last2=Liu|first2=M|last3=Zhang|first3=D|doi=10.1093/bioinformatics/btv521}} </ref>|| https://github.com/shaoweinuaa/S-PSorter|| |
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| SChloro|| Prediction of protein sub-chloroplastinc localization. ([https://bio.toolss/schloro bio.tools entry]) ||<ref> {{cite journal|doi=10.1093/bioinformatics/btw656|title=SChloro: Directing ''Viridiplantaeproteins'' to six chloroplastic sub-compartments|journal=Bioinformatics|pages=btw656|year=2016|last1=Savojardo|first1=Castrense|last2=Martelli|first2=Pier Luigi|last3=Fariselli|first3=Piero|last4=Casadio|first4=Rita}} </ref>|| http://schloro.biocomp.unibo.it|| |
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| SecretomeP|| Predictions of non-classical (i.e. not signal peptide triggered) protein secretion ([https://bio.toolss/secretomep bio.tools entry]) ||<ref> {{cite journal|pmid=15115854|year=2004|author1=Bendtsen|first1=J. D|title=Feature-based prediction of non-classical and leaderless protein secretion|journal=Protein Engineering Design and Selection|volume=17|issue=4|pages=349–56|last2=Jensen|first2=L. J|last3=Blom|first3=N|last4=von Heijne|first4=G|last5=Brunak|first5=S|doi=10.1093/protein/gzh037}} </ref><ref> {{cite journal|pmid=16212653|pmc=1266369|year=2005|author1=Bendtsen|first1=J. D|title=Non-classical protein secretion in bacteria|journal=BMC Microbiology|volume=5|pages=58|last2=Kiemer|first2=L|last3=Fausbøll|first3=A|last4=Brunak|first4=S|doi=10.1186/1471-2180-5-58}} </ref>|| http://cbs.dtu.dk/services/SecretomeP/|| |
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| SemiBiomarker|| New semi-supervised protocol that can use unlabeled cancer protein data in model construction by an iterative and incremental training strategy.It can result in improved accuracy and sensitivity of subcellular location difference detection. ([https://bio.toolss/SemiBiomarker bio.tools entry]) ||<ref> {{cite journal|pmid=25414362|pmc=4382902|year=2015|author1=Xu|first1=Y. Y|title=Bioimaging-based detection of mislocalized proteins in human cancers by semi-supervised learning|journal=Bioinformatics|volume=31|issue=7|pages=1111–9|last2=Yang|first2=F|last3=Zhang|first3=Y|last4=Shen|first4=H. B|doi=10.1093/bioinformatics/btu772}} </ref>|| http://www.csbio.sjtu.edu.cn/bioinf/SemiBiomarker/|| |
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| SUBA3|| A subcellular localisation database for Arabidopsis proteins, with online search interface. ([https://bio.toolss/suba3 bio.tools entry]) ||<ref> {{cite journal|doi=10.1093/nar/gks1151|pmid=23180787|pmc=3531127|title=SUBA3: A database for integrating experimentation and prediction to define the SUBcellular location of proteins in Arabidopsis|journal=Nucleic Acids Research|volume=41|issue=Database issue|pages=D1185–91|year=2012|last1=Tanz|first1=Sandra K|last2=Castleden|first2=Ian|last3=Hooper|first3=Cornelia M|last4=Vacher|first4=Michael|last5=Small|first5=Ian|last6=Millar|first6=Harvey A}} </ref><ref> {{cite journal|doi=10.1093/bioinformatics/btu550|pmid=25150248|title=SUBAcon: A consensus algorithm for unifying the subcellular localization data of the Arabidopsis proteome|journal=Bioinformatics|volume=30|issue=23|pages=3356|year=2014|last1=Hooper|first1=Cornelia M|last2=Tanz|first2=Sandra K|last3=Castleden|first3=Ian R|last4=Vacher|first4=Michael A|last5=Small|first5=Ian D|last6=Millar|first6=A. Harvey}} </ref>|| http://suba3.plantenergy.uwa.edu.au/|| |
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| SubChlo|| Computational system for predicting protein subchloroplast locations from its primary sequence. It can locate the protein whose subcellular location is chloroplast in one of the four parts: envelope (which consists of outer membrane and inner membrane), thylakoid lumen, stroma and thylakoid membrane. ([https://bio.toolss/SubChlo bio.tools entry]) ||<ref> {{cite journal|pmid=19679138|year=2009|author1=Du|first1=P|title=Sub ''Chlo'': Predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm|journal=Journal of Theoretical Biology|volume=261|issue=2|pages=330–5|last2=Cao|first2=S|last3=Li|first3=Y|doi=10.1016/j.jtbi.2009.08.004}} </ref>|| http://bioinfo.au.tsinghua.edu.cn/software/subchlo/|| |
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| SuperPred|| The SuperPred web server compares the structural fingerprint of an input molecule to a database of drugs connected to their drug targets and affected pathways. As the biological effect is well predictable, if the structural similarity is sufficient, the web-server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets. Such information can be useful in drug classification and target prediction. ([https://bio.toolss/superpred bio.tools entry]) ||<ref> {{cite journal|pmid=18499712|pmc=2447784|year=2008|author1=Dunkel|first1=M|title=Super ''Pred'': Drug classification and target prediction|journal=Nucleic Acids Research|volume=36|issue=Web Server issue|pages=W55–9|last2=Günther|first2=S|last3=Ahmed|first3=J|last4=Wittig|first4=B|last5=Preissner|first5=R|doi=10.1093/nar/gkn307}} </ref>|| http://bioinformatics.charite.de/superpred|| |
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| SuperTarget|| Web resource for analyzing drug-target interactions. Integrates drug-related info associated with medical indications, adverse drug effects, drug metabolism, pathways and Gene Ontology (GO) terms for target proteins. ([https://bio.toolss/supertarget bio.tools entry]) ||<ref> {{cite journal|pmid=22067455|pmc=3245174|year=2012|author1=Hecker|first1=N|title=Super ''Target'' goes quantitative: Update on drug-target interactions|journal=Nucleic Acids Research|volume=40|issue=Database issue|pages=D1113–7|last2=Ahmed|first2=J|last3=von Eichborn|first3=J|last4=Dunkel|first4=M|last5=Macha|first5=K|last6=Eckert|first6=A|last7=Gilson|first7=M. K|last8=Bourne|first8=P. E|last9=Preissner|first9=R|doi=10.1093/nar/gkr912}} </ref>|| http://bioinformatics.charite.de/supertarget/|| |
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| SwissTargetPrediction|| SwissTargetPrediction is a web server for target prediction of bioactive small molecules. This website allows you to predict the targets of a small molecule. Using a combination of 2D and 3D similarity measures, it compares the query molecule to a library of 280 000 compounds active on more than 2000 targets of 5 different organisms. ([https://bio.toolss/SwissTargetPrediction bio.tools entry]) |||| http://www.swisstargetprediction.ch|| |
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| T3DB|| The Toxin and Toxin-Target Database (T3DB) is a unique bioinformatics resource that compiles comprehensive information about common or ubiquitous toxins and their toxin-targets. Each T3DB record (ToxCard) contains over 80 data fields providing detailed information on chemical properties and descriptors, toxicity values, protein and gene sequences (for both targets and toxins), molecular and cellular interaction data, toxicological data, mechanistic information and references. This information has been manually extracted and manually verified from numerous sources, including other electronic databases, government documents, textbooks and scientific journals. A key focus of the T3DB is on providing ??depth?? over ??breadth?? with detailed descriptions, mechanisms of action, and information on toxins and toxin-targets. Potential applications of the T3DB include clinical metabolomics, toxin target prediction, toxicity prediction and toxicology education. ([https://bio.toolss/t3db bio.tools entry]) ||<ref> {{cite journal|pmid=19897546|pmc=2808899|year=2010|author1=Lim|first1=E|title=T3DB: A comprehensively annotated database of common toxins and their targets|journal=Nucleic Acids Research|volume=38|issue=Database issue|pages=D781–6|last2=Pon|first2=A|last3=Djoumbou|first3=Y|last4=Knox|first4=C|last5=Shrivastava|first5=S|last6=Guo|first6=A. C|last7=Neveu|first7=V|last8=Wishart|first8=D. S|doi=10.1093/nar/gkp934}} </ref>|| http://www.t3db.org|| |
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| TALE-NT|| Transcription activator-like (TAL) Effector-Nucleotide Targeter 2.0 (TALE-NT) is a suite of web-based tools that allows for custom design of TAL effector repeat arrays for desired targets and prediction of TAL effector binding sites. ([https://bio.toolss/TALE-NT bio.tools entry]) ||<ref> {{cite journal|pmid=22693217|pmc=3394250|year=2012|author1=Doyle|first1=E. L|title=TAL Effector-Nucleotide Targeter (TALE-NT) 2.0: Tools for TAL effector design and target prediction|journal=Nucleic Acids Research|volume=40|issue=Web Server issue|pages=W117–22|last2=Booher|first2=N. J|last3=Standage|first3=D. S|last4=Voytas|first4=D. F|last5=Brendel|first5=V. P|last6=Vandyk|first6=J. K|last7=Bogdanove|first7=A. J|doi=10.1093/nar/gks608}} </ref>|| https://boglab.plp.iastate.edu/|| |
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| TarFisDock|| Target Fishing Dock (TarFisDock) is a web server that docks small molecules with protein structures in the Potential Drug Target Database (PDTD) in an effort to discover new drug targets. ([https://bio.toolss/tarfisdock bio.tools entry]) ||<ref> {{cite journal|pmid=16844997|pmc=1538869|year=2006|author1=Li|first1=H|title=Tar ''Fis'' ''Dock'': A web server for identifying drug targets with docking approach|journal=Nucleic Acids Research|volume=34|issue=Web Server issue|pages=W219–24|last2=Gao|first2=Z|last3=Kang|first3=L|last4=Zhang|first4=H|last5=Yang|first5=K|last6=Yu|first6=K|last7=Luo|first7=X|last8=Zhu|first8=W|last9=Chen|first9=K|last10=Shen|first10=J|last11=Wang|first11=X|last12=Jiang|first12=H|doi=10.1093/nar/gkl114}} </ref>|| http://www.dddc.ac.cn/tarfisdock/|| |
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| TargetRNA|| TargetRNA is a web based tool for identifying mRNA targets of small non-coding RNAs in bacterial species. ([https://bio.toolss/targetrna bio.tools entry]) ||<ref> {{cite journal|pmid=18477632|pmc=2447797|year=2008|author1=Tjaden|first1=B|title=TargetRNA: A tool for predicting targets of small RNA action in bacteria|journal=Nucleic Acids Research|volume=36|issue=Web Server issue|pages=W109–13|doi=10.1093/nar/gkn264}} </ref>|| http://cs.wellesley.edu/~btjaden/TargetRNA2/|| |
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| TDR Targets|| Tropical Disease Research (TDR) Database: Designed and developed to facilitate the rapid identification and prioritization of molecular targets for drug development, focusing on pathogens responsible for neglected human diseases. The database integrates pathogen specific genomic information with functional data for genes collected from various sources, including literature curation. Information can be browsed and queried. ([https://bio.toolss/tdr_targets bio.tools entry]) ||<ref> {{cite journal|pmid=22116064|pmc=3245062|year=2012|author1=Magariños|first1=M. P|title=TDR Targets: A chemogenomics resource for neglected diseases|journal=Nucleic Acids Research|volume=40|issue=Database issue|pages=D1118–27|last2=Carmona|first2=S. J|last3=Crowther|first3=G. J|last4=Ralph|first4=S. A|last5=Roos|first5=D. S|last6=Shanmugam|first6=D|last7=Van Voorhis|first7=W. C|last8=Agüero|first8=F|doi=10.1093/nar/gkr1053}} </ref>|| http://tdrtargets.org/|| |
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| TetraMito|| Sequence-based predictor for identifying submitochondria location of proteins. ([https://bio.toolss/TetraMito bio.tools entry]) ||<ref> {{cite journal|pmid=23475502|year=2013|author1=Lin|first1=H|title=Using over-represented tetrapeptides to predict protein submitochondria locations|journal=Acta Biotheoretica|volume=61|issue=2|pages=259–68|last2=Chen|first2=W|last3=Yuan|first3=L. F|last4=Li|first4=Z. Q|last5=Ding|first5=H|doi=10.1007/s10441-013-9181-9}} </ref>|| http://lin.uestc.edu.cn/server/TetraMito|| |
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| TMBETA-NET|| Tool that predicts transmembrane beta strands in an outer membrane protein from its amino acid sequence. ([https://bio.toolss/tmbeta-net bio.tools entry]) ||<ref> {{cite journal|pmid=14978719|year=2004|author1=Gromiha|first1=M. M|title=Neural network-based prediction of transmembrane beta-strand segments in outer membrane proteins|journal=Journal of Computational Chemistry|volume=25|issue=5|pages=762–7|last2=Ahmad|first2=S|last3=Suwa|first3=M|doi=10.1002/jcc.10386}} </ref><ref> {{cite journal|pmid=15980447|pmc=1160128|year=2005|author1=Gromiha|first1=M. M|title=TMBETA-NET: Discrimination and prediction of membrane spanning beta-strands in outer membrane proteins|journal=Nucleic Acids Research|volume=33|issue=Web Server issue|pages=W164–7|last2=Ahmad|first2=S|last3=Suwa|first3=M|doi=10.1093/nar/gki367}} </ref>|| http://psfs.cbrc.jp/tmbeta-net/|| |
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| TMPred|| The TMpred program makes a prediction of membrane-spanning regions and their orientation. The algorithm is based on the statistical analysis of TMbase, a database of naturally occurring transmembrane proteins ([https://bio.toolss/TMPred bio.tools entry]) |||| http://embnet.vital-it.ch/software/TMPRED_form.html|| |
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| TPpred 1.0|| Organelle targeting peptide prediction ([https://bio.toolss/tppred_1.0 bio.tools entry]) ||<ref> {{cite journal|doi=10.1093/bioinformatics/btt089|pmid=23428638|title=The prediction of organelle-targeting peptides in eukaryotic proteins with Grammatical-Restrained Hidden Conditional Random Fields|journal=Bioinformatics|volume=29|issue=8|pages=981|year=2013|last1=Indio|first1=Valentina|last2=Martelli|first2=Pier Luigi|last3=Savojardo|first3=Castrense|last4=Fariselli|first4=Piero|last5=Casadio|first5=Rita}} </ref>|| http://tppred.biocomp.unibo.it/tppred/default/index|| |
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| TPpred 2.0|| Mitochondrial targeting peptide prediction ([https://bio.toolss/tppred_2.0 bio.tools entry]) ||<ref> {{cite journal|doi=doi:10.1093/bioinformatics/btv367}} </ref><ref> {{cite journal|doi=doi:10.1093/bioinformatics/btt089}} </ref>|| https://tppred3.biocomp.unibo.it|| |
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| TPpred 3.0|| Organelle-targeting peptide detection and cleavage-site prediction ([https://bio.toolss/tppred_3.0 bio.tools entry]) ||<ref> {{cite journal|doi=10.1093/bioinformatics/btv367|pmid=26079349|title=TPpred3 detects and discriminates mitochondrial and chloroplastic targeting peptides in eukaryotic proteins|journal=Bioinformatics|volume=31|issue=20|pages=3269|year=2015|last1=Savojardo|first1=Castrense|last2=Martelli|first2=Pier Luigi|last3=Fariselli|first3=Piero|last4=Casadio|first4=Rita}} </ref>|| http://tppred3.biocomp.unibo.it/tppred3|| |
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| TTD|| Therapeutic Target Database (TTD) has been developed to provide information about therapeutic targets and corresponding drugs. TTD includes information about successful, clinical trial and research targets, approved, clinical trial and experimental drugs linked to their primary targets, new ways to access data by drug mode of action, recursive search of related targets or drugs, similarity target and drug searching, customized and whole data download, and standardized target ID. ([https://bio.toolss/ttd bio.tools entry]) ||<ref> {{cite journal|pmid=19933260|pmc=2808971|year=2010|author1=Zhu|first1=F|title=Update of TTD: Therapeutic Target Database|journal=Nucleic Acids Research|volume=38|issue=Database issue|pages=D787–91|last2=Han|first2=B|last3=Kumar|first3=P|last4=Liu|first4=X|last5=Ma|first5=X|last6=Wei|first6=X|last7=Huang|first7=L|last8=Guo|first8=Y|last9=Han|first9=L|last10=Zheng|first10=C|last11=Chen|first11=Y|doi=10.1093/nar/gkp1014}} </ref>|| http://bidd.nus.edu.sg/group/cjttd/|| |
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| UM-PPS|| The University of Minnesota Pathway Prediction System (UM-PPS) is a web tool that recognizes functional groups in organic compounds that are potential targets of microbial catabolic reactions and predicts transformations of these groups based on biotransformation rules. Multi-level predictions are made. ([https://bio.toolss/um-pps bio.tools entry]) ||<ref> {{cite journal|pmid=18524801|pmc=2447765|year=2008|author1=Ellis|first1=L. B|title=The University of Minnesota pathway prediction system: Predicting metabolic logic|journal=Nucleic Acids Research|volume=36|issue=Web Server issue|pages=W427–32|last2=Gao|first2=J|last3=Fenner|first3=K|last4=Wackett|first4=L. P|doi=10.1093/nar/gkn315}} </ref>|| http://eawag-bbd.ethz.ch/predict/aboutPPS.html|| |
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| WoLF PSORT|| WoLF PSORT is an extension of the PSORT II program for protein subcellular location prediction. ([https://bio.toolss/wolf_psort bio.tools entry]) ||<ref> {{cite journal|pmid=17517783|pmc=1933216|year=2007|author1=Horton|first1=P|title=WoLF PSORT: Protein localization predictor|journal=Nucleic Acids Research|volume=35|issue=Web Server issue|pages=W585–7|last2=Park|first2=K. J|last3=Obayashi|first3=T|last4=Fujita|first4=N|last5=Harada|first5=H|last6=Adams-Collier|first6=C. J|last7=Nakai|first7=K|doi=10.1093/nar/gkm259}} </ref>|| https://wolfpsort.hgc.jp/|| |
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| YLoc|| YLoc is a web server for the prediction of subcellular localization. Predictions are explained and biological properties used for the prediction highlighted. In addition, a confidence estimates rates the reliability of individual predictions. ([https://bio.toolss/yloc bio.tools entry]) ||<ref> {{cite journal|pmid=20507917|pmc=2896088|year=2010|author1=Briesemeister|first1=S|title=YLoc--an interpretable web server for predicting subcellular localization|journal=Nucleic Acids Research|volume=38|issue=Web Server issue|pages=W497–502|last2=Rahnenführer|first2=J|last3=Kohlbacher|first3=O|doi=10.1093/nar/gkq477}} </ref>|| http://www.multiloc.org/YLoc|| |
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| Zinc Finger Tools|| Zinc Finger Tools provides several tools for selecting zinc finger protein target sites and for designing the proteins that will target them. ([https://bio.toolss/zinc_finger_tools bio.tools entry]) ||<ref> {{cite journal|pmid=12592412|year=2003|author1=Blancafort|first1=P|title=Scanning the human genome with combinatorial transcription factor libraries|journal=Nature Biotechnology|volume=21|issue=3|pages=269–74|last2=Magnenat|first2=L|last3=Barbas Cf|first3=3rd|doi=10.1038/nbt794}} </ref><ref> {{cite journal|pmid=16107335|year=2005|author1=Dreier|first1=B|title=Development of zinc finger domains for recognition of the 5'-CNN-3' family DNA sequences and their use in the construction of artificial transcription factors|journal=Journal of Biological Chemistry|volume=280|issue=42|pages=35588–97|last2=Fuller|first2=R. P|last3=Segal|first3=D. J|last4=Lund|first4=C. V|last5=Blancafort|first5=P|last6=Huber|first6=A|last7=Koksch|first7=B|last8=Barbas Cf|first8=3rd|doi=10.1074/jbc.M506654200}} </ref><ref> {{cite journal|pmid=11054286|year=2000|author1=Dreier|first1=B|title=Insights into the molecular recognition of the 5'-GNN-3' family of DNA sequences by zinc finger domains|journal=Journal of Molecular Biology|volume=303|issue=4|pages=489–502|last2=Segal|first2=D. J|last3=Barbas Cf|first3=3rd|doi=10.1006/jmbi.2000.4133}} </ref><ref> {{cite journal|pmid=16845061|pmc=1538883|year=2006|author1=Mandell|first1=J. G|title=Zinc Finger Tools: Custom DNA-binding domains for transcription factors and nucleases|journal=Nucleic Acids Research|volume=34|issue=Web Server issue|pages=W516–23|last2=Barbas Cf|first2=3rd|doi=10.1093/nar/gkl209}} </ref><ref> {{cite journal|pmid=11340073|year=2001|author1=Dreier|first1=B|title=Development of zinc finger domains for recognition of the 5'-ANN-3' family of DNA sequences and their use in the construction of artificial transcription factors|journal=Journal of Biological Chemistry|volume=276|issue=31|pages=29466–78|last2=Beerli|first2=R. R|last3=Segal|first3=D. J|last4=Flippin|first4=J. D|last5=Barbas Cf|first5=3rd|doi=10.1074/jbc.M102604200}} </ref><ref> {{cite journal|pmid=10077584|pmc=15842|year=1999|author1=Segal|first1=D. J|title=Toward controlling gene expression at will: Selection and design of zinc finger domains recognizing each of the 5'-GNN-3' DNA target sequences|journal=Proceedings of the National Academy of Sciences of the United States of America|volume=96|issue=6|pages=2758–63|last2=Dreier|first2=B|last3=Beerli|first3=R. R|last4=Barbas Cf|first4=3rd}} </ref>|| http://www.scripps.edu/mb/barbas/zfdesign/zfdesignhome.php|| |
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Revision as of 12:58, 31 December 2017
This list of protein subcellular localisation prediction tools includes software, databases, and web services that are used for protein subcellular localization prediction.
Some tools are included that are commonly used to infer location through predicted structural properties, such as signal peptide or transmembrane helices, and these tools output predictions of these features rather than specific locations. These software related to protein structure prediction may also appear in lists of protein structure prediction software.
Tools
- Descriptions sourced from the https://bio.tools/ registry under CC-BY license
| Name | Description | References | URL | |
|---|---|---|---|---|
| AAIndexLoc | Machine-learning-based algorithm that uses amino acid index to predict protein subcellular localization based on its sequence. (bio.tools entry) | [1] | http://aaindexloc.bii.a-star.edu.sg/ | |
| AtSubP | A highly accurate subcellular localization prediction tool for annotating the Arabidopsis thaliana proteome. (bio.tools entry) | [2] | http://bioinfo3.noble.org/AtSubP/ | |
| BaCelLo | BaCelLo is a predictor for the subcellular localization of proteins in eukaryotes. (bio.tools entry) | [3] | http://gpcr.biocomp.unibo.it/bacello/index.htm | |
| BAR+ | BAR+ is a server for the structural and functional annotation of protein sequences (bio.tools entry) | [4] | http://bar.biocomp.unibo.it/bar2.0/ | |
| BAR | BAR 3.0 is a server for the annotation of protein sequences relying on a comparative large-scale analysis on the entire UniProt. With BAR 3.0 and a sequence you can annotate when possible: function (Gene Ontology), structure (Protein Data Bank), protein domains (Pfam). Also if your sequence falls into a cluster with a structural/some structural template/s we provide an alignment towards the template/templates based on the Cluster-HMM (HMM profile) that allows you to directly compute your 3D model. Cluster HMMs are available for downloading. (bio.tools entry) | [5][6] | https://bar.biocomp.unibo.it/bar3/ | |
| BASys | BASys (Bacterial Annotation System) is a tool for automated annotation of bacterial genomic (chromosomal and plasmid) sequences including gene/protein names, GO functions, COG functions, possible paralogues and orthologues, molecular weights, isoelectric points, operon structures, subcellular localization, signal peptides, transmembrane regions, secondary structures, 3-D structures, reactions, and pathways. (bio.tools entry) | [7] | http://basys.ca | |
| BOMP | The beta-barrel Outer Membrane protein Predictor (BOMP) takes one or more fasta-formatted polypeptide sequences from Gram-negative bacteria as input and predicts whether or not they are beta-barrel integral outer membrane proteins. (bio.tools entry) | [8] | http://www.bioinfo.no/tools/bomp | |
| BPROMPT | Bayesian PRediction Of Membrane Protein Topology (BPROMPT) uses a Bayesian Belief Network to combine the results of other membrane protein prediction methods for a protein sequence. (bio.tools entry) | [9] | http://www.ddg-pharmfac.net/bprompt/BPROMPT/BPROMPT.html | |
| ComiR | ComiR is a web tool for combinatorial microRNA (miRNA) target prediction. Given an messenger RNA (mRNA) in human, mouse, fly or worm genomes, ComiR predicts whether a given mRNA is targeted by a set of miRNAs. (bio.tools entry) | [10] | http://www.benoslab.pitt.edu/comir/ | |
| cropPAL | A data portal to access the compendium of data on crop protein subcellular locations. (bio.tools entry) | [11] | http://crop-pal.org/ | |
| DAS-TMfilter | DAS (Dense Alignment Surface) is based on low-stringency dot-plots of the query sequence against a set of library sequences - non-homologous membrane proteins - using a previously derived, special scoring matrix. The method provides a high precision hyrdophobicity profile for the query from which the location of the potential transmembrane segments can be obtained. The novelty of the DAS-TMfilter algorithm is a second prediction cycle to predict TM segments in the sequences of the TM-library. (bio.tools entry) | http://mendel.imp.ac.at/sat/DAS/DAS.html | ||
| DeepLoc | Prediction of eukaryotic protein subcellular localization using deep learning (bio.tools entry) | [12] | http://www.cbs.dtu.dk/services/DeepLoc/ | |
| DIANA-microT v5.0 | Web server which predicts targets for miRNAs and provides functional information on the predicted miRNA:target gene interaction from various online biological resources. Updates enable the association of miRNAs to diseases through bibliographic analysis and connection to the UCSC genome browser. Updates include sophisticated workflows. (bio.tools entry) | [13][14] | http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=MicroT_CDS/index | |
| DrugBank | DrugBank is a unique bioinformatics/cheminformatics resource that combines detailed drug (i.e. chemical) data with comprehensive drug target (i.e. protein) information. The database contains >4100 drug entries including >800 FDA approved small molecule and biotech drugs as well as >3200 experimental drugs. Additionally, >14,000 protein or drug target sequences are linked to these drug entries. (bio.tools entry) | [15] | http://redpoll.pharmacy.ualberta.ca/drugbank/index.html | |
| E.Coli Index | Comprehensive guide of information relating to E. coli; home of Echobase: a database of E. coli genes characterized since the completion of the genome. (bio.tools entry) | [16] | http://www.york.ac.uk/res/thomas/ | |
| ePlant | A suite of open-source world wide web-based tools for the visualization of large-scale data sets from the model organism Arabidopsis thaliana. It can be applied to any model organism. Currently has 3 modules: a sequence conservation explorer that includes homology relationships and single nucleotide polymorphism data, a protein structure model explorer, a molecular interaction network explorer, a gene product subcellular localization explorer, and a gene expression pattern explorer. (bio.tools entry) | [17] | http://bar.utoronto.ca/eplant/ | |
| ESLpred | ESLpred is a tool for predicting subcellular localization of proteins using support vector machines. The predictions are based on dipeptide and amino acid composition, and physico-chemical properties. (bio.tools entry) | [18] | http://www.imtech.res.in/raghava/eslpred/ | |
| HIT | A comprehensive and fully curated database for Herb Ingredients?? Targets (HIT). Those herbal ingredients with protein target information were carefully curated. The molecular target information involves those proteins being directly/indirectly activated/inhibited, protein binders and enzymes whose substrates or products are those compounds. Those up/down regulated genes are also included under the treatment of individual ingredients. In addition, the experimental condition, observed bioactivity and various references are provided as well for user??s reference. The database can be queried via keyword search or similarity search. Crosslinks have been made to TTD, DrugBank, KEGG, PDB, Uniprot, Pfam, NCBI, TCM-ID and other databases. (bio.tools entry) | [19] | http://lifecenter.sgst.cn/hit/ | |
| HMMTOP | Prediction of transmembranes helices and topology of proteins. (bio.tools entry) | http://www.enzim.hu/hmmtop/ | ||
| HSLpred | Allows predicting the subcellular localization of human proteins. This is based on various type of residue composition of proteins using SVM technique. (bio.tools entry) | [20] | http://www.imtech.res.in/raghava/hslpred/ | |
| idTarget | idTarget is a web server for identifying biomolecular targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach. idTarget screens against protein structures in PDB. (bio.tools entry) | [21] | http://idtarget.rcas.sinica.edu.tw | |
| iLoc-Cell | Predictor for subcellular locations of human proteins with multiple sites. (bio.tools entry) | [22] | http://www.jci-bioinfo.cn/iLoc-Hum | |
| lncRNAdb | lncRNAdb database contains a comprehensive list of long noncoding RNAs (lncRNAs) that have been shown to have, or to be associated with, biological functions in eukaryotes, as well as messenger RNAs that have regulatory roles. Each entry contains referenced information about the RNA, including sequences, structural information, genomic context, expression, subcellular localization, conservation, functional evidence and other relevant information. lncRNAdb can be searched by querying published RNA names and aliases, sequences, species and associated protein-coding genes, as well as terms contained in the annotations, such as the tissues in which the transcripts are expressed and associated diseases. In addition, lncRNAdb is linked to the UCSC Genome Browser for visualization and Noncoding RNA Expression Database (NRED) for expression information from a variety of sources. (bio.tools entry) | [23] | http://www.lncrnadb.org/ | |
| Loc3D | LOC3D is a database of predicted subcellular localization for eukaryotic proteins of known three-dimensional (3D) structure and includes tools to predict the subcellular localization for submitted protein sequences. (bio.tools entry) | [24][25][26] | http://cubic.bioc.columbia.edu/db/LOC3d/ | |
| LOCATE | LOCATE is a curated database that houses data describing the membrane organization and subcellular localization of mouse proteins. (bio.tools entry) | [27] | http://locate.imb.uq.edu.au/ | |
| LocDB | LocDB is a manually curated database with experimental annotations for the subcellular localizations of proteins in Homo sapiens (HS, human) and Arabidopsis thaliana (AT, thale cress). Each database entry contains the experimentally derived localization in Gene Ontology (GO) terminology, the experimental annotation of localization, localization predictions by state-of-the-art methods and, where available, the type of experimental information. LocDB is searchable by keyword, protein name and subcellular compartment, as well as by identifiers from UniProt, Ensembl and TAIR resources. (bio.tools entry) | [28] | http://www.rostlab.org/services/locDB/ | |
| LOCtarget | LOCtarget is a tool for predicting, and a database of pre-computed predictions for, sub-cellular localization of eukaryotic and prokaryotic proteins. Several methods are employed to make the predictions, including text analysis of SWISS-PROT keywords, nuclear localization signals, and the use of neural networks. (bio.tools entry) | [29] | http://www.rostlab.org/services/LOCtarget/ | |
| LocTree2 | Framework to predict localization in life's three domains, including globular and membrane proteins (3 classes for archaea; 6 for bacteria and 18 for eukaryota). The resulting method, LocTree2, works well even for protein fragments. It uses a hierarchical system of support vector machines that imitates the cascading mechanism of cellular sorting. The method reaches high levels of sustained performance (eukaryota: Q18=65%, bacteria: Q6=84%). LocTree2 also accurately distinguishes membrane and non-membrane proteins. In our hands, it compared favorably with top methods when tested on new data (bio.tools entry) | [30] | https://rostlab.org/owiki/index.php/Loctree2 | |
| LocTree3 | Prediction of protein subcellular localization in 18 classes for eukaryota, 6 for bacteria and 3 for archaea (bio.tools entry) | [31][32] | https://rostlab.org/services/loctree3/ | |
| MARSpred | Prediction method  for discrimination between Mitochondrial-AARSs and Cytosolic-AARSs. (bio.tools entry) | [33] | http://www.imtech.res.in/raghava/marspred/ | |
| MDLoc | Dependency-Based Protein Subcellular Location Predictor. (bio.tools entry) | [34] | http://128.4.31.235/ | |
| MemLoci | Predictor for the subcellular localization of proteins associated or inserted in eukaryotes membranes. (bio.tools entry) | [35] | https://mu2py.biocomp.unibo.it/memloci | |
| MemPype | Prediction of topology and subcellular localization of Eukaryotic membrane proteins. (bio.tools entry) | [36] | https://mu2py.biocomp.unibo.it/mempype | |
| MirZ | MirZ is a web server that for evaluation and analysis of miRNA. It integrates two miRNA resources: the smiRNAdb miRNA expression atlas and the E1MMo miRNA target prediction algorithm. (bio.tools entry) | [37] | http://www.mirz.unibas.ch | |
| MitPred | Web-server specifically trained to predict the proteins which are destined to localized in mitochondria in yeast and animals particularly. (bio.tools entry) | [38] | http://www.imtech.res.in/raghava/mitpred/ | |
| Mycosub | This web-server was used to predict the subcellular localizations of mycobacterial proteins based on optimal tripeptide compositions. (bio.tools entry) | [39] | http://lin.uestc.edu.cn/server/Mycosub | |
| NetNES | Prediction of the leucine-rich nuclear export signals (NES) in eukaryotic proteins (bio.tools entry) | [40] | http://cbs.dtu.dk/services/NetNES/ | |
| ngLOC | ngLOC is an n-gram-based Bayesian classifier that predicts subcellular localization of proteins both in prokaryotes and eukaryotes. The overall prediction accuracy varies from 85.3% to 91.4% across species. (bio.tools entry) | http://genome.unmc.edu/ngLOC/index.html | ||
| OBCOL | Software we designed to perform organelle-based colocalisation analysis from multi-fluorophore microscopy 2D, 3D and 4D cell imaging. (bio.tools entry) | [41] | http://obcol.imb.uq.edu.au/ | |
| PA-SUB | PA-SUB (Proteome Analyst Specialized Subcellular Localization Server) can be used to predict the subcellular localization of proteins using established machine learning techniques. (bio.tools entry) | [42][43] | http://www.cs.ualberta.ca/~bioinfo/PA/Sub/ | |
| PharmMapper | PharmMapper is a web server that identifies potential drug targets from its PharmTargetDB for a given input molecule. Potential targets are identified from a prediction of the spatial arrangement of features essential for a given molecule to interact with a target. (bio.tools entry) | [44] | http://59.78.96.61/pharmmapper | |
| PlantLoc | PlantLoc is a web server for predicting plant protein subcellular localization by substantiality motif. (bio.tools entry) | [45] | http://cal.tongji.edu.cn/PlantLoc/ | |
| PRED-TMBB | PRED-TMBB is a tool that takes a Gram-negative bacteria protein sequence as input and predicts the transmembrane strands and the probability of it being an outer membrane beta-barrel protein. The user has a choice of three different decoding methods. (bio.tools entry) | [46][47] | http://bioinformatics.biol.uoa.gr/PRED-TMBB/ | |
| PredictNLS | Prediction and analysis of nuclear localization signals (bio.tools entry) | [48] | https://www.rostlab.org/owiki/index.php/PredictNLS | |
| PredictProtein Open | Prediction of various aspects of protein structure and function. A user may submit a query to the server without registration. (bio.tools entry) | [49][50][51][52] | http://ppopen.informatik.tu-muenchen.de/ | |
| PREP Suite | The PREP (Predictive RNA Editors for Plants) suite predicts sites of RNA editing based on the principle that editing in plant organelles increases the conservation of proteins across species. Predictors for mitochondrial genes, chloroplast genes, and alignments input by the user are included. (bio.tools entry) | [53][54] | http://prep.unl.edu/ | |
| ProLoc-GO | ProLoc-GO is an efficient sequence-based method by mining informative Gene Ontology terms for predicting protein subcellular localization. (bio.tools entry) | [55] | http://140.113.239.45/prolocgo/ | |
| ProLoc | Evolutionary support vector machine (ESVM) based classifier with automatic selection from a large set of physicochemical composition (PCC) features to design an accurate system for predicting protein subnuclear localization. (bio.tools entry) | [56] | http://140.113.239.45/proloc/ | |
| Protegen | Protegen is a web-based database and analysis system that curates, stores and analyzes protective antigens. Protegen includes basic antigen information and experimental evidence curated from peer-reviewed articles. It also includes detailed gene/protein information (e.g. DNA and protein sequences, and COG classification). Different antigen features, such as protein weight and pI, and subcellular localizations of bacterial proteins are precomputed. (bio.tools entry) | [57] | http://www.violinet.org/protegen | |
| Proteome Analyst | Proteome Analyst is a high-throughput tool for predicting properties for each protein in a proteome. The user provides a proteome in fasta format, and the system employs Psi-blast, Psipred and Modeller to predict protein function and subcellular localization. Proteome Analyst uses machine-learned classifiers to predict things such as GO molecular function. User-supplied training data can also be used to create custom classifiers. (bio.tools entry) | [58] | http://www.cs.ualberta.ca/~bioinfo/PA/ | |
| ProTox | ProTox is a web server for the in silico prediction of oral toxicities of small molecules in rodents. (bio.tools entry) | http://tox.charite.de/tox | ||
| PSLpred | Method for subcellular localization proteins belongs to prokaryotic genomes. (bio.tools entry) | [59] | http://www.imtech.res.in/raghava/pslpred/ | |
| PSORTb | PSORTb (for “bacterial” PSORT) is a high-precision localization prediction method for bacterial proteins.PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. PSORTb version improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies. (bio.tools entry) | [60] | http://www.psort.org/psortb/ | |
| PSORTdb | PSORTdb (part of the PSORT family) is a database of protein subcellular localizations for bacteria and archaea that contains both information determined through laboratory experimentation (ePSORTdb dataset) and computational predictions (cPSORTdb dataset). (bio.tools entry) | [61][62] | http://db.psort.org | |
| psRobot | psRobot is a web-based tool for plant small RNA meta-analysis. psRobot computes stem-loop small RNA prediction, which aligns user uploaded sequences to the selected genome, extracts their predicted precursors, and predicts whether the precursors can fold into stem-loop shaped secondary structure. psRobot also computes small RNA target prediction, which predict the possible targets of user provided small RNA sequences from the selected transcript library. (bio.tools entry) | [63] | http://omicslab.genetics.ac.cn/psRobot/ | |
| pTARGET | pTARGET predicts the subcellular localization of eukaryotic proteins based on the occurrence patterns of location-specific protein functional domains and the amino acid compositional differences in proteins from nine distinct subcellular locations. (bio.tools entry) | [64][65] | http://bioinformatics.albany.edu/~ptarget | |
| RegPhos | RegPhos is a database for exploration of the phosphorylation network associated with an input of genes/proteins. Subcellular localization information is also included. (bio.tools entry) | [66] | http://regphos.mbc.nctu.edu.tw/ | |
| RepTar | RepTar is a database of miRNA target predictions, based on the RepTar algorithm that is independent of evolutionary conservation considerations and is not limited to seed pairing sites. (bio.tools entry) | [67] | http://reptar.ekmd.huji.ac.il | |
| RNApredator | RNApredator is a web server for the prediction of bacterial sRNA targets. The user can choose from a large selection of genomes. Accessibility of the target to the sRNA is considered. (bio.tools entry) | [68] | http://rna.tbi.univie.ac.at/RNApredator | |
| S-PSorter | A novel cell structure-driven classifier construction approach for predicting image-based protein subcellular location by employing the prior biological structural information. (bio.tools entry) | [69] | https://github.com/shaoweinuaa/S-PSorter | |
| SChloro | Prediction of protein sub-chloroplastinc localization. (bio.tools entry) | [70] | http://schloro.biocomp.unibo.it | |
| SecretomeP | Predictions of non-classical (i.e. not signal peptide triggered) protein secretion (bio.tools entry) | [71][72] | http://cbs.dtu.dk/services/SecretomeP/ | |
| SemiBiomarker | New semi-supervised protocol that can use unlabeled cancer protein data in model construction by an iterative and incremental training strategy.It can result in improved accuracy and sensitivity of subcellular location difference detection. (bio.tools entry) | [73] | http://www.csbio.sjtu.edu.cn/bioinf/SemiBiomarker/ | |
| SUBA3 | A subcellular localisation database for Arabidopsis proteins, with online search interface. (bio.tools entry) | [74][75] | http://suba3.plantenergy.uwa.edu.au/ | |
| SubChlo | Computational system for predicting protein subchloroplast locations from its primary sequence. It can locate the protein whose subcellular location is chloroplast in one of the four parts: envelope (which consists of outer membrane and inner membrane), thylakoid lumen, stroma and thylakoid membrane. (bio.tools entry) | [76] | http://bioinfo.au.tsinghua.edu.cn/software/subchlo/ | |
| SuperPred | The SuperPred web server compares the structural fingerprint of an input molecule to a database of drugs connected to their drug targets and affected pathways. As the biological effect is well predictable, if the structural similarity is sufficient, the web-server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets. Such information can be useful in drug classification and target prediction. (bio.tools entry) | [77] | http://bioinformatics.charite.de/superpred | |
| SuperTarget | Web resource for analyzing drug-target interactions. Integrates drug-related info associated with medical indications, adverse drug effects, drug metabolism, pathways and Gene Ontology (GO) terms for target proteins. (bio.tools entry) | [78] | http://bioinformatics.charite.de/supertarget/ | |
| SwissTargetPrediction | SwissTargetPrediction is a web server for target prediction of bioactive small molecules. This website allows you to predict the targets of a small molecule. Using a combination of 2D and 3D similarity measures, it compares the query molecule to a library of 280 000 compounds active on more than 2000 targets of 5 different organisms. (bio.tools entry) | http://www.swisstargetprediction.ch | ||
| T3DB | The Toxin and Toxin-Target Database (T3DB) is a unique bioinformatics resource that compiles comprehensive information about common or ubiquitous toxins and their toxin-targets. Each T3DB record (ToxCard) contains over 80 data fields providing detailed information on chemical properties and descriptors, toxicity values, protein and gene sequences (for both targets and toxins), molecular and cellular interaction data, toxicological data, mechanistic information and references. This information has been manually extracted and manually verified from numerous sources, including other electronic databases, government documents, textbooks and scientific journals. A key focus of the T3DB is on providing ??depth?? over ??breadth?? with detailed descriptions, mechanisms of action, and information on toxins and toxin-targets. Potential applications of the T3DB include clinical metabolomics, toxin target prediction, toxicity prediction and toxicology education. (bio.tools entry) | [79] | http://www.t3db.org | |
| TALE-NT | Transcription activator-like (TAL) Effector-Nucleotide Targeter 2.0 (TALE-NT) is a suite of web-based tools that allows for custom design of TAL effector repeat arrays for desired targets and prediction of TAL effector binding sites. (bio.tools entry) | [80] | https://boglab.plp.iastate.edu/ | |
| TarFisDock | Target Fishing Dock (TarFisDock) is a web server that docks small molecules with protein structures in the Potential Drug Target Database (PDTD) in an effort to discover new drug targets. (bio.tools entry) | [81] | http://www.dddc.ac.cn/tarfisdock/ | |
| TargetRNA | TargetRNA is a web based tool for identifying mRNA targets of small non-coding RNAs in bacterial species. (bio.tools entry) | [82] | http://cs.wellesley.edu/~btjaden/TargetRNA2/ | |
| TDR Targets | Tropical Disease Research (TDR) Database: Designed and developed to facilitate the rapid identification and prioritization of molecular targets for drug development, focusing on pathogens responsible for neglected human diseases. The database integrates pathogen specific genomic information with functional data for genes collected from various sources, including literature curation. Information can be browsed and queried. (bio.tools entry) | [83] | http://tdrtargets.org/ | |
| TetraMito | Sequence-based predictor for identifying submitochondria location of proteins. (bio.tools entry) | [84] | http://lin.uestc.edu.cn/server/TetraMito | |
| TMBETA-NET | Tool that predicts transmembrane beta strands in an outer membrane protein from its amino acid sequence. (bio.tools entry) | [85][86] | http://psfs.cbrc.jp/tmbeta-net/ | |
| TMPred | The TMpred program makes a prediction of membrane-spanning regions and their orientation. The algorithm is based on the statistical analysis of TMbase, a database of naturally occurring transmembrane proteins (bio.tools entry) | http://embnet.vital-it.ch/software/TMPRED_form.html | ||
| TPpred 1.0 | Organelle targeting peptide prediction (bio.tools entry) | [87] | http://tppred.biocomp.unibo.it/tppred/default/index | |
| TPpred 2.0 | Mitochondrial targeting peptide prediction (bio.tools entry) | [88][89] | https://tppred3.biocomp.unibo.it | |
| TPpred 3.0 | Organelle-targeting peptide detection and cleavage-site prediction (bio.tools entry) | [90] | http://tppred3.biocomp.unibo.it/tppred3 | |
| TTD | Therapeutic Target Database (TTD) has been developed to provide information about therapeutic targets and corresponding drugs. TTD includes information about successful, clinical trial and research targets, approved, clinical trial and experimental drugs linked to their primary targets, new ways to access data by drug mode of action, recursive search of related targets or drugs, similarity target and drug searching, customized and whole data download, and standardized target ID. (bio.tools entry) | [91] | http://bidd.nus.edu.sg/group/cjttd/ | |
| UM-PPS | The University of Minnesota Pathway Prediction System (UM-PPS) is a web tool that recognizes functional groups in organic compounds that are potential targets of microbial catabolic reactions and predicts transformations of these groups based on biotransformation rules. Multi-level predictions are made. (bio.tools entry) | [92] | http://eawag-bbd.ethz.ch/predict/aboutPPS.html | |
| WoLF PSORT | WoLF PSORT is an extension of the PSORT II program for protein subcellular location prediction. (bio.tools entry) | [93] | https://wolfpsort.hgc.jp/ | |
| YLoc | YLoc is a web server for the prediction of subcellular localization. Predictions are explained and biological properties used for the prediction highlighted. In addition, a confidence estimates rates the reliability of individual predictions. (bio.tools entry) | [94] | http://www.multiloc.org/YLoc | |
| Zinc Finger Tools | Zinc Finger Tools provides several tools for selecting zinc finger protein target sites and for designing the proteins that will target them. (bio.tools entry) | [95][96][97][98][99][100] | http://www.scripps.edu/mb/barbas/zfdesign/zfdesignhome.php |
Some other tools and related references can also be found at the following site.
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