List of Protein subcellular localization prediction tools

From Wikipedia, the free encyclopedia
Jump to navigation Jump to search

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.


  • Descriptions sourced from the entry in the registry (used under CC-BY license) are indicated by link
Name Description References URL Year
AAIndexLoc Machine-learning-based algorithm that uses amino acid index to predict protein subcellular localization based on its sequence. ( entry) [1] 2008
APSLAP Prediction of apoptosis protein sub cellular Localization [2] 2013
AtSubP A highly accurate subcellular localization prediction tool for annotating the Arabidopsis thaliana proteome. ( entry) [3] 2010
BaCelLo BaCelLo is a predictor for the subcellular localization of proteins in eukaryotes. ( entry) [4] 2006
BAR+ BAR+ is a server for the structural and functional annotation of protein sequences ( entry) [5] 2011
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. ( entry) [6][5] 2017
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. ( entry) [7] 2005
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. ( entry) [8] 2004
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. ( entry) [9] 2003
Cell-PLoc A package of web-servers for predicting subcellular localization of proteins in various organisms. [10] 2008
CELLO CELLO uses a two-level Support Vector Machine system to assign localizations to both prokaryotic and eukaryotic proteins. [11][12] 2006
ClubSub-P ClubSub-P is a database of cluster-based subcellular localization (SCL) predictions for Archaea and Gram negative bacteria. [13] 2011
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. [14] 2010
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. ( entry) [15] 2013
cropPAL A data portal to access the compendium of data on crop protein subcellular locations. ( entry) [16] 2016
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. ( entry) [17] 2002
DeepLoc Prediction of eukaryotic protein subcellular localization using deep learning ( entry) [18] 2017
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. ( entry) [19][20] 2013
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. ( entry) [21] 2006
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. ( entry) [22] 2009
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. ( entry) [23] 2011
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. ( entry) [24] 2004
Euk-mPLoc 2.0 Predicting the subcellular localization of eukaryotic proteins with both single and multiple sites. [25] 2010
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. ( entry) [26] 2011
HMMTOP Prediction of transmembranes helices and topology of proteins. ( entry) [27][28] 2001
HSLpred Allows predicting the subcellular localization of human proteins. This is based on various type of residue composition of proteins using SVM technique. ( entry) [29] 2005
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. ( entry) [30] 2012
iLoc-Cell Predictor for subcellular locations of human proteins with multiple sites. ( entry) [31] 2012
KnowPredsite A knowledge-based approach to predict the localization site(s) of both single-localized and multi-localized proteins for all eukaryotes. [32] 2009
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. ( entry) [33] 2011
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. ( entry) [34][35][36] 2005
LOCATE LOCATE is a curated database that houses data describing the membrane organization and subcellular localization of mouse proteins. ( entry) [37] 2006
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. ( entry) [38] 2011
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. ( entry) [39] 2004
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 keywords. [35] 2005
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 ( entry) [40] 2012
LocTree3 Prediction of protein subcellular localization in 18 classes for eukaryota, 6 for bacteria and 3 for archaea ( entry) [40][41] 2014
MARSpred Prediction method  for discrimination between Mitochondrial-AARSs and Cytosolic-AARSs. ( entry) [42] 2012
MDLoc Dependency-Based Protein Subcellular Location Predictor. ( entry) [43] 2015
MemLoci Predictor for the subcellular localization of proteins associated or inserted in eukaryotes membranes. ( entry) [44] 2011
MemPype Prediction of topology and subcellular localization of Eukaryotic membrane proteins. ( entry) [45] 2011
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. [46] 2012
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. ( entry) [47] 2009
MitPred Web-server specifically trained to predict the proteins which are destined to localized in mitochondria in yeast and animals particularly. ( entry) [48] 2006
MultiLoc An SVM-based prediction engine for a wide range of subcellular locations. [49] 2006
Mycosub This web-server was used to predict the subcellular localizations of mycobacterial proteins based on optimal tripeptide compositions. ( entry) [50] 2015
NetNES Prediction of the leucine-rich nuclear export signals (NES) in eukaryotic proteins ( entry) [51] 2004
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. ( entry) [52] 2007
OBCOL Software we designed to perform organelle-based colocalisation analysis from multi-fluorophore microscopy 2D, 3D and 4D cell imaging. ( entry) [53] 2009
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. ( entry) [54][55] 2004
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. ( entry) [56] 2010
PlantLoc PlantLoc is a web server for predicting plant protein subcellular localization by substantiality motif. ( entry) [57] 2013
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. ( entry) [58][59] 2004
PredictNLS Prediction and analysis of nuclear localization signals ( entry) [60] 2000
PredictProtein Open Prediction of various aspects of protein structure and function. A user may submit a query to the server without registration. ( entry) [61][62][63][64] 2014
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. ( entry) [65][66] 2009
ProLoc-GO ProLoc-GO is an efficient sequence-based method by mining informative Gene Ontology terms for predicting protein subcellular localization. ( entry) [67] 2008
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. ( entry) [68] 2007
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. ( entry) [69] 2011
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. ( entry) [55] 2004
ProTox ProTox is a web server for the in silico prediction of oral toxicities of small molecules in rodents. ( entry) [70][71] 2018
PSLpred Method for subcellular localization proteins belongs to prokaryotic genomes. ( entry) [72] 2005
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. ( entry) [73] 2010
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). ( entry) [74][75] 2010
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. ( entry) [76] 2012
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. ( entry) [77][78] 2006
RegPhos RegPhos is a database for exploration of the phosphorylation network associated with an input of genes/proteins. Subcellular localization information is also included. ( entry) [79] 2011
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. ( entry) [80] 2011
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. ( entry) [81] 2011
S-PSorter A novel cell structure-driven classifier construction approach for predicting image-based protein subcellular location by employing the prior biological structural information. ( entry) [82] 2016
SChloro Prediction of protein sub-chloroplastinc localization. ( entry) [83] 2017
SCLAP An Adaptive Boosting Method for Predicting Subchloroplast Localization of Plant Proteins. [84] 2013
SCLPred SCLpred protein subcellular localization prediction by N-to-1 neural networks. [85] 2011
SecretomeP Predictions of non-classical (i.e. not signal peptide triggered) protein secretion ( entry) [86][87] 2005
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. ( entry) [88] 2015
SherLoc An SVM-based predictor combining MultiLoc with text-based features derived from PubMed abstracts. [89] 2007
SUBA3 A subcellular localisation database for Arabidopsis proteins, with online search interface. ( entry) [90][91] 2014
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. ( entry) [92] 2009
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. ( entry) [93] 2008
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. ( entry) [94] 2012
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. ( entry) [95][96] 2014
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. ( entry) [97] 2010
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. ( entry) [98] 2012
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. ( entry) [99] 2006
TargetRNA TargetRNA is a web based tool for identifying mRNA targets of small non-coding RNAs in bacterial species. ( entry) [100] 2008
TargetP Prediction of N-terminal sorting signals. [101] 2000
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. ( entry) [102] 2012
TetraMito Sequence-based predictor for identifying submitochondria location of proteins. ( entry) [103] 2013
TMBETA-NET Tool that predicts transmembrane beta strands in an outer membrane protein from its amino acid sequence. ( entry) [104][105] 2005
TMHMM Prediction of transmembrane helices to identify transmembrane proteins. [106] 2001
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 ( entry) [107] 1993
TPpred 1.0 Organelle targeting peptide prediction ( entry) [108] 2013
TPpred 2.0 Mitochondrial targeting peptide prediction ( entry) [109][108] 2015
TPpred 3.0 Organelle-targeting peptide detection and cleavage-site prediction ( entry) [110] 2015
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. ( entry) [111] 2010
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. ( entry) [112] 2008
WoLF PSORT WoLF PSORT is an extension of the PSORT II program for protein subcellular location prediction. ( entry) [113] 2007
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. ( entry) [114] 2010
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. ( entry) [115][116][117][118][119][120] 2006


  1. ^ Tantoso E, Li KB (August 2008). "AAIndexLoc: predicting subcellular localization of proteins based on a new representation of sequences using amino acid indices". Amino Acids. 35 (2): 345–53. doi:10.1007/s00726-007-0616-y. PMID 18163182.
  2. ^ Saravanan V, Lakshmi PT (December 2013). "APSLAP: an adaptive boosting technique for predicting subcellular localization of apoptosis protein". Acta Biotheoretica. 61 (4): 481–97. doi:10.1007/s10441-013-9197-1. PMID 23982307.
  3. ^ Kaundal R, Saini R, Zhao PX (September 2010). "Combining machine learning and homology-based approaches to accurately predict subcellular localization in Arabidopsis". Plant Physiology. 154 (1): 36–54. doi:10.1104/pp.110.156851. PMC 2938157. PMID 20647376.
  4. ^ Pierleoni A, Martelli PL, Fariselli P, Casadio R (July 2006). "BaCelLo: a balanced subcellular localization predictor". Bioinformatics. 22 (14): e408–16. doi:10.1093/bioinformatics/btl222. PMID 16873501.
  5. ^ a b Piovesan D, Martelli PL, Fariselli P, Zauli A, Rossi I, Casadio R (July 2011). "BAR-PLUS: the Bologna Annotation Resource Plus for functional and structural annotation of protein sequences". Nucleic Acids Research. 39 (Web Server issue): W197–202. doi:10.1093/nar/gkr292. PMC 3125743. PMID 21622657.
  6. ^ Profiti G, Martelli PL, Casadio R (July 2017). "The Bologna Annotation Resource (BAR 3.0): improving protein functional annotation". Nucleic Acids Research. 45 (W1): W285–W290. doi:10.1093/nar/gkx330. PMC 5570247. PMID 28453653.
  7. ^ Van Domselaar GH, Stothard P, Shrivastava S, Cruz JA, Guo A, Dong X, Lu P, Szafron D, Greiner R, Wishart DS (July 2005). "BASys: a web server for automated bacterial genome annotation". Nucleic Acids Research. 33 (Web Server issue): W455–9. doi:10.1093/nar/gki593. PMC 1160269. PMID 15980511.
  8. ^ Berven FS, Flikka K, Jensen HB, Eidhammer I (July 2004). "BOMP: a program to predict integral beta-barrel outer membrane proteins encoded within genomes of Gram-negative bacteria". Nucleic Acids Research. 32 (Web Server issue): W394–9. doi:10.1093/nar/gkh351. PMC 441489. PMID 15215418.
  9. ^ Taylor PD, Attwood TK, Flower DR (July 2003). "BPROMPT: A consensus server for membrane protein prediction". Nucleic Acids Research. 31 (13): 3698–700. doi:10.1093/nar/gkg554. PMC 168961. PMID 12824397.
  10. ^ Chou KC, Shen HB (2008-01-01). "Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms". Nature Protocols. 3 (2): 153–62. doi:10.1038/nprot.2007.494. PMID 18274516.
  11. ^ Yu CS, Lin CJ, Hwang JK (May 2004). "Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions". Protein Science. 13 (5): 1402–6. doi:10.1110/ps.03479604. PMC 2286765. PMID 15096640.
  12. ^ Yu CS, Chen YC, Lu CH, Hwang JK (August 2006). "Prediction of protein subcellular localization". Proteins. 64 (3): 643–51. doi:10.1002/prot.21018. PMID 16752418.
  13. ^ Paramasivam N, Linke D (2011). "ClubSub-P: Cluster-Based Subcellular Localization Prediction for Gram-Negative Bacteria and Archaea". Frontiers in Microbiology. 2: 218. doi:10.3389/fmicb.2011.00218. PMC 3210502. PMID 22073040.
  14. ^ Goudenège D, Avner S, Lucchetti-Miganeh C, Barloy-Hubler F (March 2010). "CoBaltDB: Complete bacterial and archaeal orfeomes subcellular localization database and associated resources". BMC Microbiology. 10: 88. doi:10.1186/1471-2180-10-88. PMC 2850352. PMID 20331850.
  15. ^ Coronnello C, Benos PV (July 2013). "ComiR: Combinatorial microRNA target prediction tool". Nucleic Acids Research. 41 (Web Server issue): W159–64. doi:10.1093/nar/gkt379. PMC 3692082. PMID 23703208.
  16. ^ Hooper CM, Castleden IR, Aryamanesh N, Jacoby RP, Millar AH (January 2016). "Finding the Subcellular Location of Barley, Wheat, Rice and Maize Proteins: The Compendium of Crop Proteins with Annotated Locations (cropPAL)". Plant & Cell Physiology. 57 (1): e9. doi:10.1093/pcp/pcv170. PMID 26556651.
  17. ^ Cserzö, Miklos; Eisenhaber, Frank; Eisenhaber, Birgit; Simon, Istvan (Sep 2002). "On filtering false positive transmembrane protein predictions". Protein Engineering, Design and Selection. 15 (9): 745–752. doi:10.1093/protein/15.9.745. ISSN 1741-0134.
  18. ^ Almagro Armenteros JJ, Sønderby CK, Sønderby SK, Nielsen H, Winther O (November 2017). "DeepLoc: prediction of protein subcellular localization using deep learning". Bioinformatics. 33 (21): 3387–3395. doi:10.1093/bioinformatics/btx431. PMID 29036616.
  19. ^ Maragkakis M, Reczko M, Simossis VA, Alexiou P, Papadopoulos GL, Dalamagas T, Giannopoulos G, Goumas G, Koukis E, Kourtis K, Vergoulis T, Koziris N, Sellis T, Tsanakas P, Hatzigeorgiou AG (July 2009). "DIANA-microT web server: elucidating microRNA functions through target prediction". Nucleic Acids Research. 37 (Web Server issue): W273–6. doi:10.1093/nar/gkp292. PMC 2703977. PMID 19406924.
  20. ^ Paraskevopoulou MD, Georgakilas G, Kostoulas N, Vlachos IS, Vergoulis T, Reczko M, Filippidis C, Dalamagas T, Hatzigeorgiou AG (July 2013). "DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows". Nucleic Acids Research. 41 (Web Server issue): W169–73. doi:10.1093/nar/gkt393. PMC 3692048. PMID 23680784.
  21. ^ Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J (January 2006). "DrugBank: a comprehensive resource for in silico drug discovery and exploration". Nucleic Acids Research. 34 (Database issue): D668–72. doi:10.1093/nar/gkj067. PMC 1347430. PMID 16381955.
  22. ^ Horler RS, Butcher A, Papangelopoulos N, Ashton PD, Thomas GH (January 2009). "EchoLOCATION: an in silico analysis of the subcellular locations of Escherichia coli proteins and comparison with experimentally derived locations". Bioinformatics. 25 (2): 163–6. doi:10.1093/bioinformatics/btn596. PMID 19015139.
  23. ^ Fucile G, Di Biase D, Nahal H, La G, Khodabandeh S, Chen Y, Easley K, Christendat D, Kelley L, Provart NJ (January 2011). "ePlant and the 3D data display initiative: integrative systems biology on the world wide web". PLOS One. 6 (1): e15237. Bibcode:2011PLoSO...615237F. doi:10.1371/journal.pone.0015237. PMC 3018417. PMID 21249219.
  24. ^ Bhasin M, Raghava GP (July 2004). "ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST". Nucleic Acids Research. 32 (Web Server issue): W414–9. doi:10.1093/nar/gkh350. PMC 441488. PMID 15215421.
  25. ^ Chou KC, Shen HB (April 2010). "A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0". PLOS One. 5 (4): e9931. Bibcode:2010PLoSO...5.9931C. doi:10.1371/journal.pone.0009931. PMC 2848569. PMID 20368981.
  26. ^ Ye H, Ye L, Kang H, Zhang D, Tao L, Tang K, Liu X, Zhu R, Liu Q, Chen YZ, Li Y, Cao Z (January 2011). "HIT: linking herbal active ingredients to targets". Nucleic Acids Research. 39 (Database issue): D1055–9. doi:10.1093/nar/gkq1165. PMC 3013727. PMID 21097881.
  27. ^ Tusnády, Gábor E.; Simon, István (Oct 1998). "Principles governing amino acid composition of integral membrane proteins: application to topology prediction 1 1Edited by J. Thornton". Journal of Molecular Biology. 283 (2): 489–506. doi:10.1006/jmbi.1998.2107. ISSN 0022-2836. PMID 9769220.
  28. ^ Tusnady, G. E.; Simon, I. (2001-09-01). "The HMMTOP transmembrane topology prediction server". Bioinformatics. 17 (9): 849–850. doi:10.1093/bioinformatics/17.9.849. ISSN 1367-4803.
  29. ^ Garg A, Bhasin M, Raghava GP (April 2005). "Support vector machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search". The Journal of Biological Chemistry. 280 (15): 14427–32. doi:10.1074/jbc.M411789200. PMID 15647269.
  30. ^ Wang JC, Chu PY, Chen CM, Lin JH (July 2012). "idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach". Nucleic Acids Research. 40 (Web Server issue): W393–9. doi:10.1093/nar/gks496. PMC 3394295. PMID 22649057.
  31. ^ Chou KC, Wu ZC, Xiao X (February 2012). "iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites". Molecular BioSystems. 8 (2): 629–41. doi:10.1039/c1mb05420a. PMID 22134333.
  32. ^ Lin HN, Chen CT, Sung TY, Ho SY, Hsu WL (December 2009). "Protein subcellular localization prediction of eukaryotes using a knowledge-based approach". BMC Bioinformatics. 10 Suppl 15: S8. doi:10.1186/1471-2105-10-S15-S8. PMC 2788359. PMID 19958518.
  33. ^ Amaral PP, Clark MB, Gascoigne DK, Dinger ME, Mattick JS (January 2011). "lncRNAdb: a reference database for long noncoding RNAs". Nucleic Acids Research. 39 (Database issue): D146–51. doi:10.1093/nar/gkq1138. PMC 3013714. PMID 21112873.
  34. ^ Nair R, Rost B (July 2003). "LOC3D: annotate sub-cellular localization for protein structures". Nucleic Acids Research. 31 (13): 3337–40. doi:10.1093/nar/gkg514. PMC 168921. PMID 12824321.
  35. ^ a b Nair R, Rost B (April 2005). "Mimicking cellular sorting improves prediction of subcellular localization". Journal of Molecular Biology. 348 (1): 85–100. doi:10.1016/j.jmb.2005.02.025. PMID 15808855.
  36. ^ Nair R, Rost B (December 2003). "Better prediction of sub-cellular localization by combining evolutionary and structural information". Proteins. 53 (4): 917–30. CiteSeerX doi:10.1002/prot.10507. PMID 14635133.
  37. ^ Fink JL, Aturaliya RN, Davis MJ, Zhang F, Hanson K, Teasdale MS, Kai C, Kawai J, Carninci P, Hayashizaki Y, Teasdale RD (January 2006). "LOCATE: a mouse protein subcellular localization database". Nucleic Acids Research. 34 (Database issue): D213–7. doi:10.1093/nar/gkj069. PMC 1347432. PMID 16381849.
  38. ^ Rastogi S, Rost B (January 2011). "LocDB: experimental annotations of localization for Homo sapiens and Arabidopsis thaliana". Nucleic Acids Research. 39 (Database issue): D230–4. doi:10.1093/nar/gkq927. PMC 3013784. PMID 21071420.
  39. ^ Nair R, Rost B (July 2004). "LOCnet and LOCtarget: sub-cellular localization for structural genomics targets". Nucleic Acids Research. 32 (Web Server issue): W517–21. doi:10.1093/nar/gkh441. PMC 441579. PMID 15215440.
  40. ^ a b Goldberg T, Hamp T, Rost B (September 2012). "LocTree2 predicts localization for all domains of life". Bioinformatics. 28 (18): i458–i465. doi:10.1093/bioinformatics/bts390. PMC 3436817. PMID 22962467.
  41. ^ Goldberg T, Hecht M, Hamp T, Karl T, Yachdav G, Ahmed N, Altermann U, Angerer P, Ansorge S, Balasz K, Bernhofer M, Betz A, Cizmadija L, Do KT, Gerke J, Greil R, Joerdens V, Hastreiter M, Hembach K, Herzog M, Kalemanov M, Kluge M, Meier A, Nasir H, Neumaier U, Prade V, Reeb J, Sorokoumov A, Troshani I, Vorberg S, Waldraff S, Zierer J, Nielsen H, Rost B (July 2014). "LocTree3 prediction of localization". Nucleic Acids Research. 42 (Web Server issue): W350–5. doi:10.1093/nar/gku396. PMC 4086075. PMID 24848019.
  42. ^ Panwar B, Raghava GP (May 2012). "Predicting sub-cellular localization of tRNA synthetases from their primary structures". Amino Acids. 42 (5): 1703–13. doi:10.1007/s00726-011-0872-8. PMID 21400228.
  43. ^ Simha R, Briesemeister S, Kohlbacher O, Shatkay H (June 2015). "Protein (multi-)location prediction: utilizing interdependencies via a generative model". Bioinformatics. 31 (12): i365–74. doi:10.1093/bioinformatics/btv264. PMC 4765880. PMID 26072505.
  44. ^ Pierleoni A, Martelli PL, Casadio R (May 2011). "MemLoci: predicting subcellular localization of membrane proteins in eukaryotes". Bioinformatics. 27 (9): 1224–30. doi:10.1093/bioinformatics/btr108. PMID 21367869.
  45. ^ Pierleoni A, Indio V, Savojardo C, Fariselli P, Martelli PL, Casadio R (July 2011). "MemPype: a pipeline for the annotation of eukaryotic membrane proteins". Nucleic Acids Research. 39 (Web Server issue): W375–80. doi:10.1093/nar/gkr282. PMC 3125734. PMID 21543452.
  46. ^ Magnus M, Pawlowski M, Bujnicki JM (December 2012). "MetaLocGramN: A meta-predictor of protein subcellular localization for Gram-negative bacteria". Biochimica et Biophysica Acta. 1824 (12): 1425–33. doi:10.1016/j.bbapap.2012.05.018. PMID 22705560.
  47. ^ Hausser J, Berninger P, Rodak C, Jantscher Y, Wirth S, Zavolan M (July 2009). "MirZ: an integrated microRNA expression atlas and target prediction resource". Nucleic Acids Research. 37 (Web Server issue): W266–72. doi:10.1093/nar/gkp412. PMC 2703880. PMID 19468042.
  48. ^ Kumar M, Verma R, Raghava GP (March 2006). "Prediction of mitochondrial proteins using support vector machine and hidden Markov model". The Journal of Biological Chemistry. 281 (9): 5357–63. doi:10.1074/jbc.M511061200. PMID 16339140.
  49. ^ Höglund A, Dönnes P, Blum T, Adolph HW, Kohlbacher O (May 2006). "MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition". Bioinformatics. 22 (10): 1158–65. doi:10.1093/bioinformatics/btl002. PMID 16428265.
  50. ^ Zhu PP, Li WC, Zhong ZJ, Deng EZ, Ding H, Chen W, Lin H (February 2015). "Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition". Molecular BioSystems. 11 (2): 558–63. doi:10.1039/c4mb00645c. PMID 25437899.
  51. ^ la Cour T, Kiemer L, Mølgaard A, Gupta R, Skriver K, Brunak S (June 2004). "Analysis and prediction of leucine-rich nuclear export signals". Protein Engineering, Design & Selection. 17 (6): 527–36. doi:10.1093/protein/gzh062. PMID 15314210.
  52. ^ King, Brian R; Guda, Chittibabu (2007). "ngLOC: an n-gram-based Bayesian method for estimating the subcellular proteomes of eukaryotes". Genome Biology. 8 (5): R68. doi:10.1186/gb-2007-8-5-r68. ISSN 1465-6906. PMC 1929137. PMID 17472741.
  53. ^ Woodcroft BJ, Hammond L, Stow JL, Hamilton NA (November 2009). "Automated organelle-based colocalization in whole-cell imaging". Cytometry. Part A. 75 (11): 941–50. doi:10.1002/cyto.a.20786. PMID 19746416.
  54. ^ Lu Z, Szafron D, Greiner R, Lu P, Wishart DS, Poulin B, Anvik J, Macdonell C, Eisner R (March 2004). "Predicting subcellular localization of proteins using machine-learned classifiers". Bioinformatics. 20 (4): 547–56. CiteSeerX doi:10.1093/bioinformatics/btg447. PMID 14990451.
  55. ^ a b Szafron D, Lu P, Greiner R, Wishart DS, Poulin B, Eisner R, Lu Z, Anvik J, Macdonell C, Fyshe A, Meeuwis D (July 2004). "Proteome Analyst: custom predictions with explanations in a web-based tool for high-throughput proteome annotations". Nucleic Acids Research. 32 (Web Server issue): W365–71. doi:10.1093/nar/gkh485. PMC 441623. PMID 15215412.
  56. ^ Liu X, Ouyang S, Yu B, Liu Y, Huang K, Gong J, Zheng S, Li Z, Li H, Jiang H (July 2010). "PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach". Nucleic Acids Research. 38 (Web Server issue): W609–14. doi:10.1093/nar/gkq300. PMC 2896160. PMID 20430828.
  57. ^ Tang S, Li T, Cong P, Xiong W, Wang Z, Sun J (July 2013). "PlantLoc: an accurate web server for predicting plant protein subcellular localization by substantiality motif". Nucleic Acids Research. 41 (Web Server issue): W441–7. doi:10.1093/nar/gkt428. PMC 3692052. PMID 23729470.
  58. ^ Bagos PG, Liakopoulos TD, Spyropoulos IC, Hamodrakas SJ (July 2004). "PRED-TMBB: a web server for predicting the topology of beta-barrel outer membrane proteins". Nucleic Acids Research. 32 (Web Server issue): W400–4. doi:10.1093/nar/gkh417. PMC 441555. PMID 15215419.
  59. ^ Bagos PG, Liakopoulos TD, Spyropoulos IC, Hamodrakas SJ (March 2004). "A Hidden Markov Model method, capable of predicting and discriminating beta-barrel outer membrane proteins". BMC Bioinformatics. 5: 29. doi:10.1186/1471-2105-5-29. PMC 385222. PMID 15070403.
  60. ^ Cokol M, Nair R, Rost B (November 2000). "Finding nuclear localization signals". EMBO Reports. 1 (5): 411–5. doi:10.1093/embo-reports/kvd092. PMC 1083765. PMID 11258480.
  61. ^ Yachdav G, Kloppmann E, Kajan L, Hecht M, Goldberg T, Hamp T, Hönigschmid P, Schafferhans A, Roos M, Bernhofer M, Richter L, Ashkenazy H, Punta M, Schlessinger A, Bromberg Y, Schneider R, Vriend G, Sander C, Ben-Tal N, Rost B (July 2014). "PredictProtein--an open resource for online prediction of protein structural and functional features". Nucleic Acids Research. 42 (Web Server issue): W337–43. doi:10.1093/nar/gku366. PMC 4086098. PMID 24799431.
  62. ^ Kaján L, Yachdav G, Vicedo E, Steinegger M, Mirdita M, Angermüller C, Böhm A, Domke S, Ertl J, Mertes C, Reisinger E, Staniewski C, Rost B (2013). "Cloud prediction of protein structure and function with PredictProtein for Debian". BioMed Research International. 2013: 1–6. doi:10.1155/2013/398968. PMC 3732596. PMID 23971032.
  63. ^ Rost B, Liu J (July 2003). "The PredictProtein server". Nucleic Acids Research. 31 (13): 3300–4. doi:10.1093/nar/gkg508. PMC 168915. PMID 12824312.
  64. ^ Rost B, Yachdav G, Liu J (July 2004). "The PredictProtein server". Nucleic Acids Research. 32 (Web Server issue): W321–6. doi:10.1093/nar/gkh377. PMC 441515. PMID 15215403.
  65. ^ Mower JP (July 2009). "The PREP suite: predictive RNA editors for plant mitochondrial genes, chloroplast genes and user-defined alignments". Nucleic Acids Research. 37 (Web Server issue): W253–9. doi:10.1093/nar/gkp337. PMC 2703948. PMID 19433507.
  66. ^ Mower JP (April 2005). "PREP-Mt: predictive RNA editor for plant mitochondrial genes". BMC Bioinformatics. 6: 96. doi:10.1186/1471-2105-6-96. PMC 1087475. PMID 15826309.
  67. ^ Huang WL, Tung CW, Ho SW, Hwang SF, Ho SY (February 2008). "ProLoc-GO: utilizing informative Gene Ontology terms for sequence-based prediction of protein subcellular localization". BMC Bioinformatics. 9: 80. doi:10.1186/1471-2105-9-80. PMC 2262056. PMID 18241343.
  68. ^ Huang WL, Tung CW, Huang HL, Hwang SF, Ho SY (2007). "ProLoc: prediction of protein subnuclear localization using SVM with automatic selection from physicochemical composition features". Bio Systems. 90 (2): 573–81. doi:10.1016/j.biosystems.2007.01.001. PMID 17291684.
  69. ^ Yang B, Sayers S, Xiang Z, He Y (January 2011). "Protegen: a web-based protective antigen database and analysis system". Nucleic Acids Research. 39 (Database issue): D1073–8. doi:10.1093/nar/gkq944. PMC 3013795. PMID 20959289.
  70. ^ Drwal, Malgorzata N.; Banerjee, Priyanka; Dunkel, Mathias; Wettig, Martin R.; Preissner, Robert (2014-05-16). "ProTox: a web server for the in silico prediction of rodent oral toxicity". Nucleic Acids Research. 42 (W1): W53–W58. doi:10.1093/nar/gku401. ISSN 1362-4962. PMC 4086068. PMID 24838562.
  71. ^ Banerjee, Priyanka; Eckert, Andreas O; Schrey, Anna K; Preissner, Robert (2018-04-30). "ProTox-II: a webserver for the prediction of toxicity of chemicals". Nucleic Acids Research. 46 (W1): W257–W263. doi:10.1093/nar/gky318. ISSN 0305-1048. PMC 6031011. PMID 29718510.
  72. ^ Bhasin M, Garg A, Raghava GP (May 2005). "PSLpred: prediction of subcellular localization of bacterial proteins". Bioinformatics. 21 (10): 2522–4. doi:10.1093/bioinformatics/bti309. PMID 15699023.
  73. ^ Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, Dao P, Sahinalp SC, Ester M, Foster LJ, Brinkman FS (July 2010). "PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes". Bioinformatics. 26 (13): 1608–15. doi:10.1093/bioinformatics/btq249. PMC 2887053. PMID 20472543.
  74. ^ Yu NY, Laird MR, Spencer C, Brinkman FS (January 2011). "PSORTdb--an expanded, auto-updated, user-friendly protein subcellular localization database for Bacteria and Archaea". Nucleic Acids Research. 39 (Database issue): D241–4. doi:10.1093/nar/gkq1093. PMC 3013690. PMID 21071402.
  75. ^ Rey S, Acab M, Gardy JL, Laird MR, deFays K, Lambert C, Brinkman FS (January 2005). "PSORTdb: a protein subcellular localization database for bacteria". Nucleic Acids Research. 33 (Database issue): D164–8. doi:10.1093/nar/gki027. PMC 539981. PMID 15608169.
  76. ^ Wu HJ, Ma YK, Chen T, Wang M, Wang XJ (July 2012). "PsRobot: a web-based plant small RNA meta-analysis toolbox". Nucleic Acids Research. 40 (Web Server issue): W22–8. doi:10.1093/nar/gks554. PMC 3394341. PMID 22693224.
  77. ^ Guda C, Subramaniam S (November 2005). "pTARGET [corrected] a new method for predicting protein subcellular localization in eukaryotes". Bioinformatics. 21 (21): 3963–9. doi:10.1093/bioinformatics/bti650. PMID 16144808.
  78. ^ Guda C (July 2006). "pTARGET: a web server for predicting protein subcellular localization". Nucleic Acids Research. 34 (Web Server issue): W210–3. doi:10.1093/nar/gkl093. PMC 1538910. PMID 16844995.
  79. ^ Lee TY, Bo-Kai Hsu J, Chang WC, Huang HD (January 2011). "RegPhos: a system to explore the protein kinase-substrate phosphorylation network in humans". Nucleic Acids Research. 39 (Database issue): D777–87. doi:10.1093/nar/gkq970. PMC 3013804. PMID 21037261.
  80. ^ Elefant N, Berger A, Shein H, Hofree M, Margalit H, Altuvia Y (January 2011). "RepTar: a database of predicted cellular targets of host and viral miRNAs". Nucleic Acids Research. 39 (Database issue): D188–94. doi:10.1093/nar/gkq1233. PMC 3013742. PMID 21149264.
  81. ^ Eggenhofer F, Tafer H, Stadler PF, Hofacker IL (July 2011). "RNApredator: fast accessibility-based prediction of sRNA targets". Nucleic Acids Research. 39 (Web Server issue): W149–54. doi:10.1093/nar/gkr467. PMC 3125805. PMID 21672960.
  82. ^ Shao W, Liu M, Zhang D (January 2016). "Human cell structure-driven model construction for predicting protein subcellular location from biological images". Bioinformatics. 32 (1): 114–21. doi:10.1093/bioinformatics/btv521. PMID 26363175.
  83. ^ Savojardo C, Martelli PL, Fariselli P, Casadio R (February 2017). "SChloro: directing Viridiplantae proteins to six chloroplastic sub-compartments". Bioinformatics. 33 (3): 347–353. doi:10.1093/bioinformatics/btw656. PMC 5408801. PMID 28172591.
  84. ^ Saravanan V, Lakshmi PT (February 2013). "SCLAP: an adaptive boosting method for predicting subchloroplast localization of plant proteins". OMICS. 17 (2): 106–15. doi:10.1089/omi.2012.0070. PMID 23289782.
  85. ^ Mooney C, Wang YH, Pollastri G (October 2011). "SCLpred: protein subcellular localization prediction by N-to-1 neural networks". Bioinformatics. 27 (20): 2812–9. doi:10.1093/bioinformatics/btr494. PMID 21873639.
  86. ^ Bendtsen JD, Jensen LJ, Blom N, Von Heijne G, Brunak S (April 2004). "Feature-based prediction of non-classical and leaderless protein secretion". Protein Engineering, Design & Selection. 17 (4): 349–56. doi:10.1093/protein/gzh037. PMID 15115854.
  87. ^ Bendtsen JD, Kiemer L, Fausbøll A, Brunak S (October 2005). "Non-classical protein secretion in bacteria". BMC Microbiology. 5: 58. doi:10.1186/1471-2180-5-58. PMC 1266369. PMID 16212653.
  88. ^ Xu YY, Yang F, Zhang Y, Shen HB (April 2015). "Bioimaging-based detection of mislocalized proteins in human cancers by semi-supervised learning". Bioinformatics. 31 (7): 1111–9. doi:10.1093/bioinformatics/btu772. PMC 4382902. PMID 25414362.
  89. ^ Shatkay H, Höglund A, Brady S, Blum T, Dönnes P, Kohlbacher O (June 2007). "SherLoc: high-accuracy prediction of protein subcellular localization by integrating text and protein sequence data". Bioinformatics. 23 (11): 1410–7. doi:10.1093/bioinformatics/btm115. PMID 17392328.
  90. ^ Tanz SK, Castleden I, Hooper CM, Vacher M, Small I, Millar HA (January 2013). "SUBA3: a database for integrating experimentation and prediction to define the SUBcellular location of proteins in Arabidopsis". Nucleic Acids Research. 41 (Database issue): D1185–91. doi:10.1093/nar/gks1151. PMC 3531127. PMID 23180787.
  91. ^ Hooper CM, Tanz SK, Castleden IR, Vacher MA, Small ID, Millar AH (December 2014). "SUBAcon: a consensus algorithm for unifying the subcellular localization data of the Arabidopsis proteome". Bioinformatics. 30 (23): 3356–64. doi:10.1093/bioinformatics/btu550. PMID 25150248.
  92. ^ Du P, Cao S, Li Y (November 2009). "SubChlo: predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm". Journal of Theoretical Biology. 261 (2): 330–5. doi:10.1016/j.jtbi.2009.08.004. PMID 19679138.
  93. ^ Dunkel M, Günther S, Ahmed J, Wittig B, Preissner R (July 2008). "SuperPred: drug classification and target prediction". Nucleic Acids Research. 36 (Web Server issue): W55–9. doi:10.1093/nar/gkn307. PMC 2447784. PMID 18499712.
  94. ^ Hecker N, Ahmed J, von Eichborn J, Dunkel M, Macha K, Eckert A, Gilson MK, Bourne PE, Preissner R (January 2012). "SuperTarget goes quantitative: update on drug-target interactions". Nucleic Acids Research. 40 (Database issue): D1113–7. doi:10.1093/nar/gkr912. PMC 3245174. PMID 22067455.
  95. ^ Gfeller, David; Michielin, Olivier; Zoete, Vincent (2013-09-17). "Shaping the interaction landscape of bioactive molecules". Bioinformatics. 29 (23): 3073–3079. doi:10.1093/bioinformatics/btt540. ISSN 1460-2059. PMID 24048355.
  96. ^ Gfeller, David; Grosdidier, Aurélien; Wirth, Matthias; Daina, Antoine; Michielin, Olivier; Zoete, Vincent (2014-05-03). "SwissTargetPrediction: a web server for target prediction of bioactive small molecules". Nucleic Acids Research. 42 (W1): W32–W38. doi:10.1093/nar/gku293. ISSN 1362-4962. PMC 4086140. PMID 24792161.
  97. ^ Lim E, Pon A, Djoumbou Y, Knox C, Shrivastava S, Guo AC, Neveu V, Wishart DS (January 2010). "T3DB: a comprehensively annotated database of common toxins and their targets". Nucleic Acids Research. 38 (Database issue): D781–6. doi:10.1093/nar/gkp934. PMC 2808899. PMID 19897546.
  98. ^ Doyle EL, Booher NJ, Standage DS, Voytas DF, Brendel VP, Vandyk JK, Bogdanove AJ (July 2012). "TAL Effector-Nucleotide Targeter (TALE-NT) 2.0: tools for TAL effector design and target prediction". Nucleic Acids Research. 40 (Web Server issue): W117–22. doi:10.1093/nar/gks608. PMC 3394250. PMID 22693217.
  99. ^ Li H, Gao Z, Kang L, Zhang H, Yang K, Yu K, Luo X, Zhu W, Chen K, Shen J, Wang X, Jiang H (July 2006). "TarFisDock: a web server for identifying drug targets with docking approach". Nucleic Acids Research. 34 (Web Server issue): W219–24. doi:10.1093/nar/gkl114. PMC 1538869. PMID 16844997.
  100. ^ Tjaden B (July 2008). "TargetRNA: a tool for predicting targets of small RNA action in bacteria". Nucleic Acids Research. 36 (Web Server issue): W109–13. doi:10.1093/nar/gkn264. PMC 2447797. PMID 18477632.
  101. ^ Emanuelsson O, Nielsen H, Brunak S, von Heijne G (July 2000). "Predicting subcellular localization of proteins based on their N-terminal amino acid sequence". Journal of Molecular Biology. 300 (4): 1005–16. doi:10.1006/jmbi.2000.3903. PMID 10891285.
  102. ^ Magariños MP, Carmona SJ, Crowther GJ, Ralph SA, Roos DS, Shanmugam D, Van Voorhis WC, Agüero F (January 2012). "TDR Targets: a chemogenomics resource for neglected diseases". Nucleic Acids Research. 40 (Database issue): D1118–27. doi:10.1093/nar/gkr1053. PMC 3245062. PMID 22116064.
  103. ^ Lin H, Chen W, Yuan LF, Li ZQ, Ding H (June 2013). "Using over-represented tetrapeptides to predict protein submitochondria locations". Acta Biotheoretica. 61 (2): 259–68. doi:10.1007/s10441-013-9181-9. PMID 23475502.
  104. ^ Gromiha MM, Ahmad S, Suwa M (April 2004). "Neural network-based prediction of transmembrane beta-strand segments in outer membrane proteins". Journal of Computational Chemistry. 25 (5): 762–7. Bibcode:1984JCoCh...5..500B. doi:10.1002/jcc.10386. PMID 14978719.
  105. ^ Gromiha MM, Ahmad S, Suwa M (July 2005). "TMBETA-NET: discrimination and prediction of membrane spanning beta-strands in outer membrane proteins". Nucleic Acids Research. 33 (Web Server issue): W164–7. doi:10.1093/nar/gki367. PMC 1160128. PMID 15980447.
  106. ^ Krogh, Anders; Larsson, Björn; von Heijne, Gunnar; Sonnhammer, Erik L.L (Jan 2001). "Predicting transmembrane protein topology with a hidden markov model: application to complete genomes". Journal of Molecular Biology. 305 (3): 567–580. doi:10.1006/jmbi.2000.4315. ISSN 0022-2836. PMID 11152613.
  107. ^ Hofmann, K; Stoffel, W (1993). "TMbase—A database of membrane spanning proteins segments" (PDF). Biol Chem Hoppe-Seyler. 374: 166.
  108. ^ a b Indio V, Martelli PL, Savojardo C, Fariselli P, Casadio R (April 2013). "The prediction of organelle-targeting peptides in eukaryotic proteins with Grammatical-Restrained Hidden Conditional Random Fields". Bioinformatics. 29 (8): 981–8. doi:10.1093/bioinformatics/btt089. PMID 23428638.
  109. ^ Savojardo C, Martelli PL, Fariselli P, Casadio R (October 2015). "TPpred3 detects and discriminates mitochondrial and chloroplastic targeting peptides in eukaryotic proteins". Bioinformatics. 31 (20): 3269–75. doi:10.1093/bioinformatics/btv367. PMID 26079349.
  110. ^ Savojardo C, Martelli PL, Fariselli P, Casadio R (October 2015). "TPpred3 detects and discriminates mitochondrial and chloroplastic targeting peptides in eukaryotic proteins". Bioinformatics. 31 (20): 3269–75. doi:10.1093/bioinformatics/btv367. PMID 26079349.
  111. ^ Zhu F, Han B, Kumar P, Liu X, Ma X, Wei X, Huang L, Guo Y, Han L, Zheng C, Chen Y (January 2010). "Update of TTD: Therapeutic Target Database". Nucleic Acids Research. 38 (Database issue): D787–91. doi:10.1093/nar/gkp1014. PMC 2808971. PMID 19933260.
  112. ^ Ellis LB, Gao J, Fenner K, Wackett LP (July 2008). "The University of Minnesota pathway prediction system: predicting metabolic logic". Nucleic Acids Research. 36 (Web Server issue): W427–32. doi:10.1093/nar/gkn315. PMC 2447765. PMID 18524801.
  113. ^ Horton P, Park KJ, Obayashi T, Fujita N, Harada H, Adams-Collier CJ, Nakai K (July 2007). "WoLF PSORT: protein localization predictor". Nucleic Acids Research. 35 (Web Server issue): W585–7. doi:10.1093/nar/gkm259. PMC 1933216. PMID 17517783.
  114. ^ Briesemeister S, Rahnenführer J, Kohlbacher O (July 2010). "YLoc--an interpretable web server for predicting subcellular localization". Nucleic Acids Research. 38 (Web Server issue): W497–502. doi:10.1093/nar/gkq477. PMC 2896088. PMID 20507917.
  115. ^ Blancafort P, Magnenat L, Barbas CF (March 2003). "Scanning the human genome with combinatorial transcription factor libraries". Nature Biotechnology. 21 (3): 269–74. doi:10.1038/nbt794. PMID 12592412.
  116. ^ Dreier B, Fuller RP, Segal DJ, Lund CV, Blancafort P, Huber A, Koksch B, Barbas CF (October 2005). "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". The Journal of Biological Chemistry. 280 (42): 35588–97. doi:10.1074/jbc.M506654200. PMID 16107335.
  117. ^ Dreier B, Segal DJ, Barbas CF (November 2000). "Insights into the molecular recognition of the 5'-GNN-3' family of DNA sequences by zinc finger domains". Journal of Molecular Biology. 303 (4): 489–502. doi:10.1006/jmbi.2000.4133. PMID 11054286.
  118. ^ Mandell JG, Barbas CF (July 2006). "Zinc Finger Tools: custom DNA-binding domains for transcription factors and nucleases". Nucleic Acids Research. 34 (Web Server issue): W516–23. doi:10.1093/nar/gkl209. PMC 1538883. PMID 16845061.
  119. ^ Dreier B, Beerli RR, Segal DJ, Flippin JD, Barbas CF (August 2001). "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". The Journal of Biological Chemistry. 276 (31): 29466–78. doi:10.1074/jbc.M102604200. PMID 11340073.
  120. ^ Segal DJ, Dreier B, Beerli RR, Barbas CF (March 1999). "Toward controlling gene expression at will: selection and design of zinc finger domains recognizing each of the 5'-GNN-3' DNA target sequences". Proceedings of the National Academy of Sciences of the United States of America. 96 (6): 2758–63. Bibcode:1999PNAS...96.2758S. doi:10.1073/pnas.96.6.2758. PMC 15842. PMID 10077584.