Hirose S, Yokota K, Kuroda Y, Wako H, Endo S, Kanai S, et al. Prediction of protein motions from amino acid sequence and its application to protein-protein interaction. BMC Struct Biol. 2010;10:20. [
Link] [
DOI:10.1186/1472-6807-10-20]
Chen K, Kurgan LA, Ruan J. Prediction of flexible/rigid regions from protein sequences using k-spaced amino acid pairs. BMC Struct Biol. 2007;7:25. [
Link] [
DOI:10.1186/1472-6807-7-25]
Karplus PA, Schulz GE. Prediction of chain flexibility in proteins. Naturwissenschaften. 1985;72(4):212-3. [
Link] [
DOI:10.1007/BF01195768]
Vihinen M, Torkkila E, Riikonen P. Accuracy of protein flexibility predictions. Proteins. 1994;19(2):141-9. [
Link] [
DOI:10.1002/prot.340190207]
Smith DK, Radivojac P, Obradovic Z, Dunker AK, Zhu G. Improved amino acid flexibility parameters. Protein Sci. 2003;12(5):1060-72. [
Link] [
DOI:10.1110/ps.0236203]
Schlessinger A, Rost B. Protein flexibility and rigidity predicted from sequence. Proteins. 2005;61(1):115-26. [
Link] [
DOI:10.1002/prot.20587]
Schlessinger A, Yachdav G, Rost B. PROFbval: Predict flexible and rigid residues in proteins. Bioinformatics. 2006;22(7):891-3. [
Link] [
DOI:10.1093/bioinformatics/btl032]
Pan XY, Shen HB. Robust prediction of B-factor profile from sequence using two-stage SVR based on random forest feature selection. Protein Pept Lett. 2009;16(12):1447-54. [
Link] [
DOI:10.2174/092986609789839250]
De Brevern AG, Bornot A, Craveur P, Etchebest C, Gelly JC. PredyFlexy: Flexibility and local structure prediction from sequence. Nucleic Acids Res. 2012;40(Web Server issue):W317-22. [
Link]
Bornot A, Etchebest C, De Brevern AG. Predicting protein flexibility through the prediction of local structures. Proteins. 2011;79(3):839-52. [
Link] [
DOI:10.1002/prot.22922]
Trott O, Siggers K, Rost B, Palmer AG. Protein conformational flexibility prediction using machine learning. J Magn Reson. 2008;192(1):37-47. [
Link] [
DOI:10.1016/j.jmr.2008.01.011]
Zhang T, Faraggi E, Zhou Y. Fluctuations of backbone torsion angles obtained from NMR-determined structures and their prediction. Proteins. 2010;78(16):3353-62. [
Link] [
DOI:10.1002/prot.22842]
Arab SS, Sadeghi M, ESlahchi C, Pezeshk H. Prediction of flexibility in proteins based on sequence of amino acids. Biotechnol Tarbiat Modares Univ. 2010;0(1):1-10. [Persian] [
Link]
Chen XW, Chen J. Protein flexibility modeling using kernel based methods. International Joint Conference on Neural Networks. Montreal: IEEE; 2005. [
Link]
Yuan Z, Bailey TL, Teasdale RD. Prediction of protein flexibility profile [Internet]. St Lucia: The University of Queensland; 2014 [cited 2015 May]. Available from: https://www.researchgate.net/publication/228899091_Prediction_of_Protein_Flexibility_Profile [
Link]
Kuznetsov IB, McDuffie M. FlexPred: A web-server for predicting residue positions involved in conformational switches in proteins. Bioinformation. 2008.3(3):134-6. [
Link]
Hinsen K. Structural flexibility in proteins: Impact of the crystal environment. Bioinformatics. 2008;24(4):521-8. [
Link] [
DOI:10.1093/bioinformatics/btm625]
Jamroz M, Kolinski A, Kmiecik S. CABS-flex predictions of protein flexibility compared with NMR ensembles. Bioinformatics. 2014;30(15):2150-4. [
Link] [
DOI:10.1093/bioinformatics/btu184]
Markvoort AJ. Coarse‐grained molecular dynamics. In: Van Santen RA, Sautet P, editors. Computational methods in catalysis and materials science: An introduction for scientists and engineers. New Jersey: John Wiley & Sons; 2009. pp. 151-66. [
Link] [
DOI:10.1002/9783527625482.ch8]
Jamroz M, Orozco M, Kolinski A, Kmiecik S. Consistent view of protein fluctuations from all-atom molecular dynamics and coarse-grained dynamics with knowledge-based force-field. J Chem Theory Comput. 2013;9(1):119-25. [
Link] [
DOI:10.1021/ct300854w]
Jamroz M, Kolinski A, Kmiecik S. CABS-flex: Server for fast simulation of protein structure fluctuations. Nucleic Acids Res. 2013;41(Web Server issue):W427-31. [
Link]
Noble WS. What is a support vector machine?. Nat Biotechnol. 2006;24(12):1565-7. [
Link] [
DOI:10.1038/nbt1206-1565]
Meyer D. Support vector machines, the interface to libsvm in package e1071 [Internet]. Wien: FH Technikum Wien; 2014 [cited 2015 May]. Available from: https://cran.r-project.org/web/packages/e1071/vignettes/svmdoc.pdf [
Link]
Bank PD. Protein data bank. Nat New Biol. 1971;233:223. [
Link] [
DOI:10.1038/newbio233223b0]
Huang Y, Niu B, Gao Y, Fu L, Li W. CD-HIT Suite: A web server for clustering and comparing biological sequences. Bioinformatics. 2010;26(5):680-2. [
Link] [
DOI:10.1093/bioinformatics/btq003]
Kotsiantis S, Kanellopoulos D, Pintelas P. Handling imbalanced datasets: A review. GESTS Int Trans Comput Sci Eng. 2006;30. 29- Garnier J, Robson B. The GOR method for predicting secondary structures in proteins. In: Fasman GD, editor. Prediction of protein structure and the principles of protein conformation. Berlin: Springer Science & Business Media; 1989. pp. 417-65. [
Link]
Zahiri J, Yaghoubi O, Mohammad Noori M, Ebrahimpour R, Masoudi Nejad A. PPIevo: Protein-protein interaction prediction from PSSM based evolutionary information. Genomics. 2013;102(4):237-42. [
Link] [
DOI:10.1016/j.ygeno.2013.05.006]
Pollastri G, Baldi P, Fariselli P, Casadio R. Prediction of coordination number and relative solvent accessibility in proteins. Proteins. 2002;47(2):142-53. [
Link] [
DOI:10.1002/prot.10069]
29- Garnier J, Robson B. The GOR method for predicting secondary structures in proteins. In: Fasman GD, editor. Prediction of protein structure and the principles of protein conformation. Berlin: Springer Science & Business Media; 1989. pp. 417-65. [
Link] [
DOI:10.1007/978-1-4613-1571-1_10]
Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389-402. [
Link] [
DOI:10.1093/nar/25.17.3389]
Meiler J, Müller M, Zeidler A, Schmäschke F. Generation and evaluation of dimension-reduced amino acid parameter representations by artificial neural networks. Mol Model Annu. 2001;7(9):360-9. [
Link] [
DOI:10.1007/s008940100038]
Magnan CN, Baldi P. SSpro/ACCpro 5: Almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity. Bioinformatics. 2014;30(18):2592-7. [
Link] [
DOI:10.1093/bioinformatics/btu352]
