Protein structure prediction methods (in alphabetical order)
1D protein predictions: datasetsKurgan LA, Miri Disfani F, 2011. Structural Protein Descriptors in 1-Dimension and Their Sequence-Based Predictions. Current Protein and Peptide Science, special issue on Machine Learning Models in Protein Bioinformatics, 12(6):470-489
ATPsite: datasets and supplementChen K, Mizianty MJ, Kurgan L, 2011. ATPsite: Sequence-based Prediction of ATP-binding Residues, Proteome Science, 9(Suppl. 1):S4
BEST: datasets and prediction modelGao J, Eshel E, Zhou Y, Jishou J, Kurgan L, 2012. BEST: improved prediction of B-cell epitopes from antigen sequences. PLoS ONE, 7(6): e40104
BETArPred: datasets and prediction modelKedarisetti KD, Mizianty M, Dick S, Kurgan LA, 2011. Improved sequence-based prediction of strand residues. Journal of Bioinformatics and Computational Biology, 9(1):67-89
BTcollocation: datasetsCampbell K, Kurgan LA, 2008. Sequence-only based prediction of beta-turn location and type using collocation of amino acid pairs. Open Bioinformatics Journal, 2:37-49
BTNpred: datasets and prediction modelZheng C, Kurgan LA, 2008. Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments, BMC Bioinformatics, 9:430
Consensus-based disorder prediction: datasetsPeng Z, Kurgan LA, 2011. On the complementarity of the consensus-based disorder prediction, Proceedings of the Pacific Symposium on Biocomputing (PSB 2012), 17:176-187
CRpred: datasets and prediction modelZhang T, Zhang H, Chen K, Shen S, Ruan J, Kurgan LA, 2008. Accurate sequence-based prediction of catalytic residues, Bioinformatics, 24(20):2329-2338
CRYSTALP2: datasets and prediction modelKurgan L, Razib A, Aghakhani S, Dick S, Mizianty M and Jahandideh S, 2009. CRYSTALP2: sequence-based protein crystallization propensity prediction, BMC Structural Biology, 9:50
DisCon: datasetsMizianty M, Zhang T, Xue B, Zhou Y, Dunker AK, Uversky VN, Kurgan LA, 2011. In-silico prediction of disorder content using hybrid sequence representation. BMC Bioinformatics, 12:245
FlexRP: datasetsChen K, Kurgan LA, Ruan J, 2007. Prediction of Flexible/Rigid Regions in Proteins from Sequences Using Collocated Amino Acid Pairs. BMC Structural Biology, 7:25
FOKIT: datasetsZhang H, Zhang T, Gao J, Ruan J, Shen S, Kurgan LA, 2012. Determination of Protein Folding Kinetic Types Using Sequence and Predicted Secondary Structure and Solvent Accessibility. Amino Acids, 42:271-283
MODAS: datasets and prediction modelMizianty M, Kurgan LA, 2009. Modular Prediction of Protein Structural Classes from Sequences of Twilight-Zone Identity with Predicting Sequences. BMC Bioinformatics, 10:414
MetaPPCP: datasetsMizianty M, Kurgan LA, 2009. Meta Prediction of Protein Crystallization Propensity. Biochemical and Biophysical Research Communications, 390(1):10-15
OMBBpred: datasets and prediction modelMizianty M, Kurgan LA, 2011. Improved Identification of Outer Membrane Beta Barrel Proteins Using Primary Sequence, Predicted Secondary Structure and Evolutionary Information. Proteins, 79(1):294-303
PFR-AF: datasetsGao J, Zhang T, Zhang H, Shen S, Ruan J, Kurgan LA, 2010. Accurate prediction of protein folding rates from sequence and sequence-derived residue flexibility and solvent accessibility. Proteins, 78(9):2114-2130
PFRES: datasetsChen K, Kurgan LA, 2007. PFRES: Protein fold classification by using evolutionary information and predicted secondary structure. Bioinformatics, 23(21):2843-2850
PPCpred: datasets and supplementMizianty M, Kurgan LA, 2011. Sequence-based Prediction of Protein Crystallization, Purification, and Production Propensity. Bioinformatics, 27(13):i24-i33
PPFR: datasetsJiang Y, Iglinski P, Kurgan LA, 2009. Prediction of Protein Folding Rates from Primary Sequences using Hybrid Sequence Representation. Journal of Computational Chemistry, 30(5):772-783
SCEC: datasetsChen K, Kurgan LA, Ruan J, 2008. Prediction of Protein Structural Class Using Novel Evolutionary Collocation Based Sequence Representation. Journal of Computational Chemistry, 29(10):1596-1604
SCPRED: datasets and prediction modelKurgan LA, Cios KJ, Chen K, 2008. SCPRED: Accurate Prediction of Protein Structural Class for Sequences of Twilight-zone Similarity with Predicting Sequences. BMC Bioinformatics, 9:226
Secondary structure benchmark datasetZhang H, Zhang T, Chen K, Kedarisetti KD, Mizianty MJ, Bao Q, Stach W, Kurgan LA, 2011. Critical Assessment of High-throughput Standalone Methods for Secondary Structure Prediction. Briefings in Bioinformatics, doi: 10.1093/bib/bbq088
Structure-based binding site prediction: datasets and supplementChen K, Mizianty MJ, Gao J, and Kurgan LA, 2011. A Critical Comparative Assessment of Predictions of Protein Binding Sites for Biologically Relevant Organic Compounds. Structure, 19(5):613-621
Other methods (in alphabetical order)
Discreitzation for Naive Bayes: supplementMizianty M, Kurgan LA, Ogiela M, 2010. Discretization as the Enabling Technique for the Naive Bayes and Semi-Naive Bayes Based Classification. Knowledge Engineering Review, 25(4):421-449