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[[Phyre / Phyre2|Phyre and Phyre2]] are amongst the top performing servers in the CASP international blind trials of structure prediction in homology modelling and remote fold recognition, and are designed with an emphasis on ease of use for non-experts.
[[Phyre / Phyre2|Phyre and Phyre2]] are amongst the top performing servers in the CASP international blind trials of structure prediction in homology modelling and remote fold recognition, and are designed with an emphasis on ease of use for non-experts.


[[RAPTOR (software)]] is a protein threading software that is based on integer programming. The basic algorithm for threading is described in<ref name="bowie1991"/> and is fairly straightforward to implement.
[http://raptorx.uchicago.edu RaptorX] is a protein threading software that is based on statistical learning.


[http://zhanglab.ccmb.med.umich.edu/QUARK QUARK] is an on-line server suitable for ''ab initio'' protein structure modeling.
[http://zhanglab.ccmb.med.umich.edu/QUARK QUARK] is an on-line server suitable for ''ab initio'' protein structure modeling.

Revision as of 21:32, 22 August 2011

Protein structure prediction is the prediction of the three-dimensional structure of a protein from its amino acid sequence — that is, the prediction of its secondary, tertiary, and quaternary structure from its primary structure. Structure prediction is fundamentally different from the inverse problem of protein design. Protein structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry; it is highly important in medicine (for example, in drug design) and biotechnology (for example, in the design of novel enzymes). Every two years, the performance of current methods is assessed in the CASP experiment (Critical Assessment of Techniques for Protein Structure Prediction).

Secondary structure

Secondary structure prediction is a set of techniques in bioinformatics that aim to predict the local secondary structures of proteins and RNA sequences based only on knowledge of their primary structure — amino acid or nucleotide sequence, respectively. For proteins, a prediction consists of assigning regions of the amino acid sequence as likely alpha helices, beta strands (often noted as "extended" conformations), or turns. The success of a prediction is determined by comparing it to the results of the DSSP algorithm applied to the crystal structure of the protein; for nucleic acids, it may be determined from the hydrogen bonding pattern. Specialized algorithms have been developed for the detection of specific well-defined patterns such as transmembrane helices and coiled coils in proteins, or canonical microRNA structures in RNA.[1]

The best modern methods of secondary structure prediction in proteins reach about 80% accuracy; this high accuracy allows the use of the predictions in fold recognition and ab initio protein structure prediction, classification of structural motifs, and refinement of sequence alignments. The accuracy of current protein secondary structure prediction methods is assessed in weekly benchmarks such as LiveBench and EVA.

Background

Early methods of secondary structure prediction, introduced in the 1960s and early 1970s,[2] focused on identifying likely alpha helices and were based mainly on helix-coil transition models.[3] Significantly more accurate predictions that included beta sheets were introduced in the 1970s and relied on statistical assessments based on probability parameters derived from known solved structures. These methods, applied to a single sequence, are typically at most about 60-65% accurate, and often underpredict beta sheets.[1] The evolutionary conservation of secondary structures can be exploited by simultaneously assessing many homologous sequences in a multiple sequence alignment, by calculating the net secondary structure propensity of an aligned column of amino acids. In concert with larger databases of known protein structures and modern machine learning methods such as neural nets and support vector machines, these methods can achieve up 80% overall accuracy in globular proteins.[4] The theoretical upper limit of accuracy is around 90%,[4] partly due to idiosyncrasies in DSSP assignment near the ends of secondary structures, where local conformations vary under native conditions but may be forced to assume a single conformation in crystals due to packing constraints. Limitations are also imposed by secondary structure prediction's inability to account for tertiary structure; for example, a sequence predicted as a likely helix may still be able to adopt a beta-strand conformation if it is located within a beta-sheet region of the protein and its side chains pack well with their neighbors. Dramatic conformational changes related to the protein's function or environment can also alter local secondary structure.

Chou-Fasman method

Main article: Chou-Fasman method

The Chou-Fasman method was among the first secondary structure prediction algorithms developed and relies predominantly on probability parameters determined from relative frequencies of each amino acid's appearance in each type of secondary structure.[5] The original Chou-Fasman parameters, determined from the small sample of structures solved in the mid-1970s, produce poor results compared to modern methods, though the parameterization has been updated since it was first published. The Chou-Fasman method is roughly 50-60% accurate in predicting secondary structures.[1]

GOR method

Main article: GOR method

The GOR method, named for the three scientists who developed it — Garnier, Osguthorpe, and Robson — is an information theory-based method developed not long after Chou-Fasman. It uses a more powerful probabilistic techniques of Bayesian inference.[6] The method is a specific optimized application of mathematics and algorithms developed in a series of papers by Robson and colleagues, eg.[7] and [8]). The GOR method is capable of continued extension by such principles, and has gone through several versions. The GOR method takes into account not only the probability of each amino acid having a particular secondary structure, but also the conditional probability of the amino acid assuming each structure given the contributions of its neighbors (it does not assume that the neighbors have that same structure). The approach is both more sensitive and more accurate than that of Chou and Fasman because amino acid structural propensities are only strong for a small number of amino acids such as proline and glycine. Weak contributions from each of many neighbors can add up to strong effect overall. The original GOR method was roughly 65% accurate and is dramatically more successful in predicting alpha helices than beta sheets, which it frequently mispredicted as loops or disorganized regions.[1] Later GOR methods considered also pairs of amino acids, significantly improving performance. The major difference from the following technique is perhaps that the weights in an implied network of contributing terms are assigned a priori, from statistical analysis of proteins of known structure, not by feedback to optimize agreement with a training set of such.

Machine learning

Neural network methods use training sets of solved structures to identify common sequence motifs associated with particular arrangements of secondary structures. These methods are over 70% accurate in their predictions, although beta strands are still often underpredicted due to the lack of three-dimensional structural information that would allow assessment of hydrogen bonding patterns that can promote formation of the extended conformation required for the presence of a complete beta sheet.[1]

Support vector machines have proven particularly useful for predicting the locations of turns, which are difficult to identify with statistical methods.[9] The requirement of relatively small training sets has also been cited as an advantage to avoid overfitting to existing structural data.[10]

Extensions of machine learning techniques attempt to predict more fine-grained local properties of proteins, such as backbone dihedral angles in unassigned regions. Both SVMs[11] and neural networks[12] have been applied to this problem.[9]

Other improvements

It is reported that in addition to the protein sequence, secondary structure formation depends on other factors. For example, it is reported that secondary structure tendencies depend also on local environment,[13] solvent accessibility of residues,[14] protein structural class,[15] and even the organism from which the proteins are obtained.[16] Based on such observations, some studies have shown that secondary structure prediction can be improved by addition of information about protein structural class,[17] residue accessible surface area[18][19] and also contact number information.[20]

Sequence covariation methods rely on the existence of a data set composed of multiple homologous RNA sequences with related but dissimilar sequences. These methods analyze the covariation of individual base sites in evolution; maintenance at two widely separated sites of a pair of base-pairing nucleotides indicates the presence of a structurally required hydrogen bond between those positions. The general problem of pseudoknot prediction has been shown to be NP-complete.[21]

Tertiary structure

The practical role of protein structure prediction is now more important than ever. Massive amounts of protein sequence data are produced by modern large-scale DNA sequencing efforts such as the Human Genome Project. Despite community-wide efforts in structural genomics, the output of experimentally determined protein structures—typically by time-consuming and relatively expensive X-ray crystallography or NMR spectroscopy—is lagging far behind the output of protein sequences.

The protein structure prediction remains an extremely difficult and unresolved undertaking. The two main problems are calculation of protein free energy and finding the global minimum of this energy. A protein structure prediction method must explore the space of possible protein structures which is astronomically large. These problems can be partially bypassed in "comparative" or homology modeling and fold recognition methods, in which the search space is pruned by the assumption that the protein in question adopts a structure that is close to the experimentally determined structure of another homologous protein. On the other hand, the de novo or ab initio protein structure prediction methods must explicitly resolve these problems.

Ab initio protein modelling

Main article: De novo protein structure prediction

Ab initio- or de novo- protein modelling methods seek to build three-dimensional protein models "from scratch", i.e., based on physical principles rather than (directly) on previously solved structures. There are many possible procedures that either attempt to mimic protein folding or apply some stochastic method to search possible solutions (i.e., global optimization of a suitable energy function). These procedures tend to require vast computational resources, and have thus only been carried out for tiny proteins. To predict protein structure de novo for larger proteins will require better algorithms and larger computational resources like those afforded by either powerful supercomputers (such as Blue Gene or MDGRAPE-3) or distributed computing (such as Folding@home, the Human Proteome Folding Project and Rosetta@Home). Although these computational barriers are vast, the potential benefits of structural genomics (by predicted or experimental methods) make ab initio structure prediction an active research field.[22]

As an intermediate step towards predicted protein structures, contact map predictions have been proposed.

Comparative protein modelling

Comparative protein modelling uses previously solved structures as starting points, or templates. This is effective because it appears that although the number of actual proteins is vast, there is a limited set of tertiary structural motifs to which most proteins belong. It has been suggested that there are only around 2,000 distinct protein folds in nature, though there are many millions of different proteins.

These methods may also be split into two groups [22]:

Homology modeling
is based on the reasonable assumption that two homologous proteins will share very similar structures. Because a protein's fold is more evolutionarily conserved than its amino acid sequence, a target sequence can be modeled with reasonable accuracy on a very distantly related template, provided that the relationship between target and template can be discerned through sequence alignment. It has been suggested that the primary bottleneck in comparative modelling arises from difficulties in alignment rather than from errors in structure prediction given a known-good alignment.[23] Unsurprisingly, homology modelling is most accurate when the target and template have similar sequences.
Protein threading[24]
scans the amino acid sequence of an unknown structure against a database of solved structures. In each case, a scoring function is used to assess the compatibility of the sequence to the structure, thus yielding possible three-dimensional models. This type of method is also known as 3D-1D fold recognition due to its compatibility analysis between three-dimensional structures and linear protein sequences. This method has also given rise to methods performing an inverse folding search by evaluating the compatibility of a given structure with a large database of sequences, thus predicting which sequences have the potential to produce a given fold.

Side chain geometry prediction

Accurate packing of the amino acid side chains represents a separate problem. Methods that specifically address the problem of predicting side chain geometry include dead-end elimination and the self-consistent mean field methods. The side chain conformations with low energy are usually determined on the rigid polypeptide backbone and using a set of discrete side chain conformations known as "rotamers" or a "conformational isomerism". The methods attempt to identify the set of rotamers that minimize the model's overall energy.

These methods use rotamer libraries, the collections of rotamers (favorable multi-angle conformations) for each residue type in proteins. Rotamer libraries may contain information about the conformation, its frequency, and the variance about mean dihedral angles, which can be used in sampling.[25] Rotamer libraries are derived from structural bioinformatics or other statistical analysis of side-chain conformations in known experimental structures of proteins, such as by clustering the observed conformations for tetrahedral carbons near the staggered (60°, 180°, -60°) values. Rotamer libraries can be backbone-independent, secondary-structure-dependent, or backbone-dependent. Backbone-independent rotamer libraries make no reference to backbone conformation, and are calculated from all available side chains of a certain type (for instance, the first example of a rotamer library, done by Ponder and Richards at Yale in 1987).[26] Secondary-structure-dependent libraries present different dihedral angles and/or rotamer frequencies for -helix, -sheet, or coil secondary structures.[27][28] Backbone-dependent rotamer libraries present conformations and/or frequencies dependent on the local backbone conformation as defined by the backbone dihedral angles and , regardless of secondary structure.[29] The modern versions of these "libraries" as used in most software are presented as multidimensional distributions of probability or frequency, where the peaks correspond to the dihedral-angle conformations considered as individual rotamers in the lists. Some versions are especially sensitive to the prohibited regions in that conformational space and are used primarily for structure validation,[30] while others emphasize relative frequencies in the favorable regions and are the form used primarily for structure prediction, such as the Dunbrack rotamer "libraries".

The side chain packing methods are most useful for analyzing the protein's hydrophobic core, where side chains are more closely packed; they have more difficulty addressing the looser constraints and higher flexibility of surface residues, which often occupy multiple rotamer conformations rather than just one.[31]

Prediction of structural classes

Statistical methods have been developed for predicting structural classes of proteins based on their amino acid composition,[32] pseudo amino acid composition[33][34][35][36] and functional domain composition.[37]

Quaternary structure

Main article: Protein–protein interaction prediction

In the case of complexes of two or more proteins, where the structures of the proteins are known or can be predicted with high accuracy, protein–protein docking methods can be used to predict the structure of the complex. Information of the effect of mutations at specific sites on the affinity of the complex helps to understand the complex structure and to guide docking methods.

Software

Main article: Protein structure prediction software

I-TASSER is the best server for protein structure prediction according to the 2006-2010 CASP experiments (CASP7, CASP8 and CASP9).

MODELLER is a popular software tool for producing homology models using methodology derived from NMR spectroscopy data processing. SwissModel provides an automated web server for basic homology modeling.

HHpred, bioinfo.pl and Robetta widely used servers for protein structure prediction. HHsearch is a free software package for protein threading and remote homology detection.

PEP-FOLD is a de novo approach aimed at predicting peptide structures from amino acid sequences, based on a HMM structural alphabet.[38][39]

Phyre and Phyre2 are amongst the top performing servers in the CASP international blind trials of structure prediction in homology modelling and remote fold recognition, and are designed with an emphasis on ease of use for non-experts.

RaptorX is a protein threading software that is based on statistical learning.

QUARK is an on-line server suitable for ab initio protein structure modeling.

Abalone is a Molecular Dynamics program for folding simulations with explicit or implicit water models.

TIP is a knowledgebase of STRUCTFAST[40] models and precomputed similarity relationships between sequences, structures, and binding sites. Several distributed computing projects concerning protein structure prediction have also been implemented, such as the Folding@home, Rosetta@home, Human Proteome Folding Project, Predictor@home, and TANPAKU.

The Foldit program seeks to investigate the pattern-recognition and puzzle-solving abilities inherent to the human mind in order to create more successful computer protein structure prediction software.

Computational approaches provide a fast alternative route to antibody structure prediction. Recently[when?] developed antibody FV region high resolution structure prediction algorithms, like RosettaAntibody, have been shown to generate high resolution homology models which have been used for successful docking.[41]

Reviews of software for structure prediction can be found at.[42] The progress and challenges in protein structure prediction has been reviewed in Zhang 2008.[22]

Automatic structure prediction servers

Main article: CASP

CASP, which stands for Critical Assessment of Techniques for Protein Structure Prediction, is a community-wide experiment for protein structure prediction taking place every two years since 1994. CASP provides users and research groups with an opportunity to assess the quality of available methods and automatic servers for protein structure prediction. Official results for automatic structure prediction servers in the CASP7 benchmark (2006) are discussed by Battey et al..[43] Official CASP8 results are available for automatic servers and for human and server predictors. Unofficial results for automatic servers of the 2008 CASP8 benchmark are summarized on several lab websites and ranked according to slightly varying criteria: Zhang lab, Grishin lab, McGuffin lab, Baker lab, and Cheng lab.

See also

References

  1. ^ a b c d e Mount DM (2004). Bioinformatics: Sequence and Genome Analysis. Vol. 2. Cold Spring Harbor Laboratory Press. ISBN 0879697121.
  2. ^ Guzzo, AV (1965). "Influence of Amino-Acid Sequence on Protein Structure". Biophys. J. 5 (6): 809–822. Bibcode:1965BpJ.....5..809G. doi:10.1016/S0006-3495(65)86753-4. PMC 1367904. PMID 5884309.
    Prothero, JW (1966). "Correlation between Distribution of Amino Acids and Alpha Helices". Biophys. J. 6 (3): 367–370. Bibcode:1966BpJ.....6..367P. doi:10.1016/S0006-3495(66)86662-6. PMC 1367951. PMID 5962284.
    Schiffer, M (1967). "Use of Helical Wheels to Represent Structures of Proteins and to Identify Segments with Helical Potential". Biophys. J. 7 (2): 121–35. Bibcode:1967BpJ.....7..121S. doi:10.1016/S0006-3495(67)86579-2. PMC 1368002. PMID 6048867. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
    Kotelchuck, D (1969). "The Influence of Short-Range Interactions on Protein Conformation, II. A Model for Predicting the α-Helical Regions of Proteins". Proc Natl Acad Sci USA. 62 (1): 14–21. doi:10.1073/pnas.62.1.14. PMC 285948. PMID 5253650. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
    Lewis, PN (1970). "Helix Probability Profiles of Denatured Proteins and Their Correlation with Native Structures". Proc Natl Acad Sci USA. 65 (4): 810–5. doi:10.1073/pnas.65.4.810. PMC 282987. PMID 5266152. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  3. ^ Froimowitz M, Fasman GD (1974). "Prediction of the secondary structure of proteins using the helix-coil transition theory". Macromolecules. 7 (5): 583–9. doi:10.1021/ma60041a009. PMID 4371089.
  4. ^ a b Dor O, Zhou Y (2006). "Achieving 80% tenfold cross-validated accuracy for secondary structure prediction by large-scale training". Proteins. 66 (4): 838–45. doi:10.1002/prot.21298. PMID 17177203.
  5. ^ Chou PY, Fasman GD (1974). "Prediction of protein conformation". Biochemistry. 13 (2): 222–245. doi:10.1021/bi00699a002. PMID 4358940.
  6. ^ Garnier J, Osguthorpe DJ, Robson B (1978). "Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins". J Mol Biol. 120 (1): 97–120. doi:10.1016/0022-2836(78)90297-8. PMID 642007.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  7. ^ Robson B, Pain RH (1971). "Analysis of the code relating sequence to conformation in proteins: possible implications for the mechanism of formation of helical regions". J. Mol. Biol. 58 (1): 237–59. doi:10.1016/0022-2836(71)90243-9. PMID 5088928. {{cite journal}}: Unknown parameter |month= ignored (help)
  8. ^ Robson B (1974). "Analysis of code relating sequences to conformation in globular proteins. Theory and application of expected information". Biochem. J. 141 (3): 853–67. PMC 1168191. PMID 4463965. {{cite journal}}: Unknown parameter |month= ignored (help)
  9. ^ a b Pham TH, Satou K, Ho TB (2005). "Support vector machines for prediction and analysis of beta and gamma-turns in proteins". J Bioinform Comput Biol. 3 (2): 343–358. doi:10.1142/S0219720005001089. PMID 15852509.{{cite journal}}: CS1 maint: multiple names: authors list (link) Cite error: The named reference "Pham" was defined multiple times with different content (see the help page).
  10. ^ Zhang Q, Yoon S, Welsh WJ (2005). "Improved method for predicting beta-turn using support vector machine". Bioinformatics. 21 (10): 2370–4. doi:10.1093/bioinformatics/bti358. PMID 15797917.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  11. ^ Zimmermann O, Hansmann UH (2006). "Support vector machines for prediction of dihedral angle regions". Bioinformatics. 22 (24): 3009–15. doi:10.1093/bioinformatics/btl489. PMID 17005536.
  12. ^ Kuang R, Leslie CS, Yang AS (2004). "Protein backbone angle prediction with machine learning approaches". Bioinformatics. 20 (10): 1612–21. doi:10.1093/bioinformatics/bth136. PMID 14988121.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  13. ^ Zhong L, Johnson WC Jr (1992). "Environment affects amino acid preference for secondary structure". Proc Natl Acad Sci USA. 89 (10): 4462–5. doi:10.1073/pnas.89.10.4462. PMC 49102. PMID 1584778. {{cite journal}}: Unknown parameter |unused_data= ignored (help)
  14. ^ Macdonald JR, Johnson WC Jr (2001). "Environmental features are important in determining protein secondary structure". Protein Sci. 10 (6): 1172–7. doi:10.1110/ps.420101. PMC 2374018. PMID 11369855.
  15. ^ Costantini S, Colonna G, Facchiano AM (2006). "Amino acid propensities for secondary structures are influenced by the protein structural class". Biochem Biophys Res Commun. 342 (2): 441–451. doi:10.1016/j.bbrc.2006.01.159. PMID 16487481.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  16. ^ Marashi SA; et al. (2007). "Adaptation of proteins to different environments: a comparison of proteome structural properties in Bacillus subtilis and Escherichia coli". J Theor Biol. 244 (1): 127–132. doi:10.1016/j.jtbi.2006.07.021. PMID 16945389. {{cite journal}}: Explicit use of et al. in: |author= (help)
  17. ^ Costantini S, Colonna G, Facchiano AM (2007). "PreSSAPro: a software for the prediction of secondary structure by amino acid properties". Comput Biol Chem. 31 (5–6): 389–392. doi:10.1016/j.compbiolchem.2007.08.010. PMID 17888742.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  18. ^ Momen-Roknabadi A; et al. (2008). "Impact of residue accessible surface area on the prediction of protein secondary structures". BMC Bioinformatics. 9: 357. doi:10.1186/1471-2105-9-357. PMC 2553345. PMID 18759992. {{cite journal}}: Explicit use of et al. in: |author= (help)CS1 maint: unflagged free DOI (link)
  19. ^ Adamczak R, Porollo A, Meller J (2005). "Combining prediction of secondary structure and solvent accessibility in proteins". Proteins. 59 (3): 467–475. doi:10.1002/prot.20441. PMID 15768403.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  20. ^ Lakizadeh A, Marashi SA (2009). "Addition of contact number information can improve protein secondary structure prediction by neural networks" (PDF). Excli J. 8: 66–73.
  21. ^ Lyngsø RB, Pedersen CN (2000). "RNA pseudoknot prediction in energy-based models". J Comput Biol. 7 (3–4): 409–427. doi:10.1089/106652700750050862. PMID 11108471.
  22. ^ a b c Zhang Y (2008). "Progress and challenges in protein structure prediction". Curr Opin Struct Biol. 18 (3): 342–8. doi:10.1016/j.sbi.2008.02.004. PMC 2680823. PMID 18436442.
  23. ^ Zhang Y and Skolnick J (2005). "The protein structure prediction problem could be solved using the current PDB library". Proc Natl Acad Sci USA. 102 (4): 1029–34. doi:10.1073/pnas.0407152101. PMC 545829. PMID 15653774.
  24. ^ Bowie JU, Luthy R, Eisenberg D (1991). "A method to identify protein sequences that fold into a known three-dimensional structure". Science. 253 (5016): 164–170. doi:10.1126/science.1853201. PMID 1853201.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  25. ^ Dunbrack, RL (2002). "Rotamer Libraries in the 21st Century". Curr. Opin. Struct. Biol. 12 (4): 431–440. doi:10.1016/S0959-440X(02)00344-5. PMID 12163064.
  26. ^ Ponder JW, Richards FM (1987). "Tertiary templates for proteins: use of packing criteria in the enumeration of allowed sequences for different structural classes". J. Mol. Biol. 193 (4): 775–791. doi:10.1016/0022-2836(87)90358-5. PMID 2441069.
  27. ^ Lovell SC, Word JM, Richardson JS, Richardson DC (2000). "The penultimate rotamer library". Proteins: Struc. Func. Genet. 40: 389–408. doi:10.1002/1097-0134(20000815)40:3<389::AID-PROT50>3.0.CO;2-2.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  28. ^ Richardson Rotamer Libraries
  29. ^ Dunbrack Rotamer Libraries
  30. ^ MolProbity
  31. ^ Voigt CA, Gordon DB, Mayo SL (2000). "Trading accuracy for speed: A quantitative comparison of search algorithms in protein sequence design". J Mol Biol. 299 (3): 789–803. doi:10.1006/jmbi.2000.3758. PMID 10835284.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  32. ^ Chou KC, Zhang CT (1995). "Prediction of protein structural classes". Crit. Rev. Biochem. Mol. Biol. 30 (4): 275–349. doi:10.3109/10409239509083488. PMID 7587280.
  33. ^ Chen C, Zhou X, Tian Y, Zou X, Cai P (2006). "Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network". Anal. Biochem. 357 (1): 116–21. doi:10.1016/j.ab.2006.07.022. PMID 16920060. {{cite journal}}: Unknown parameter |month= ignored (help)CS1 maint: multiple names: authors list (link)
  34. ^ Chen C, Tian YX, Zou XY, Cai PX, Mo JY (2006). "Using pseudo-amino acid composition and support vector machine to predict protein structural class". J. Theor. Biol. 243 (3): 444–8. doi:10.1016/j.jtbi.2006.06.025. PMID 16908032. {{cite journal}}: Unknown parameter |month= ignored (help)CS1 maint: multiple names: authors list (link)
  35. ^ Lin H, Li QZ (2007). "Using pseudo amino acid composition to predict protein structural class: approached by incorporating 400 dipeptide components". J Comput Chem. 28 (9): 1463–6. doi:10.1002/jcc.20554. PMID 17330882. {{cite journal}}: Unknown parameter |month= ignored (help)
  36. ^ Xiao X, Wang P, Chou KC (2008). "Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image". J. Theor. Biol. 254 (3): 691–6. doi:10.1016/j.jtbi.2008.06.016. PMID 18634802. {{cite journal}}: Unknown parameter |month= ignored (help)CS1 maint: multiple names: authors list (link)
  37. ^ Chou KC, Cai YD (2004). "Predicting protein structural class by functional domain composition". Biochem. Biophys. Res. Commun. 321 (4): 1007–9. doi:10.1016/j.bbrc.2004.07.059. PMID 15358128. {{cite journal}}: Unknown parameter |month= ignored (help)
  38. ^ Maupetit J, Derreumaux P, Tuffery P (2009). "A fast and accurate method for large-scale de novo peptide structure prediction". J Comput Chem.: In press.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  39. ^ Maupetit J, Derreumaux P, Tuffery P (2009). "PEP-FOLD: an online resource for de novo peptide structure prediction". Nucleic Acids Res. 37 (Web Server issue): W498–503. doi:10.1093/nar/gkp323. PMC 2703897. PMID 19433514.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  40. ^ Debe DA, Danzer JF, Goddard WA, Poleksic A (2006). "STRUCTFAST: Protein sequence remote homology detection and alignment using novel dynamic programming and profile-profile scoring". Proteins. 64 (4): 960–7. doi:10.1002/prot.21049. PMID 16786595.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  41. ^ Sivasubramanian A, Sircar A, Chaudhury S, Gray J J (2009). "Toward high-resolution homology modeling of antibody Fv regions and application to antibody–antigen docking". Proteins. 74 (2): 497–514. doi:10.1002/prot.22309. PMC 2909601. PMID 19062174.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  42. ^ Nayeem A, Sitkoff D, Krystek S Jr (2006). "A comparative study of available software for high-accuracy homology modeling: From sequence alignments to structural models". Protein Sci. 15 (4): 808–824. doi:10.1110/ps.051892906. PMC 2242473. PMID 16600967.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  43. ^ Battey JN, Kopp J, Bordoli L, Read RJ, Clarke ND, Schwede T (2007). "Automated server predictions in CASP7". Proteins. 69 (Suppl 8): 68–82. doi:10.1002/prot.21761. PMID 17894354.{{cite journal}}: CS1 maint: multiple names: authors list (link)

Samudrala R, Moult J (1998). "An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction". J. Mol. Biol. 275 (5): 895–916. doi:10.1006/jmbi.1997.1479. PMID 9480776. {{cite journal}}: Unknown parameter |month= ignored (help)

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