Lundegaard C, Lund O, Kesmir C, Brunak S, Nielsen M: Modeling the adaptive immune system: predictions and simulations. Bioinformatics 2007, 23(24):3265–3275. 10.1093/bioinformatics/btm471
Article
CAS
PubMed
Google Scholar
Rost B: PHD: predicting one-dimensional protein structure by profile-based neural networks. Methods Enzymol 1996, 266: 525–539. full_text
Article
CAS
PubMed
Google Scholar
Connolly M: Analytical molecular surface calculation. Journal of Applied Crystallography 1983, 16(5):548–558. 10.1107/S0021889883010985
Article
CAS
Google Scholar
Chothia C: The nature of the accessible and buried surfaces in proteins. J Mol Biol 1976, 105(1):1–12. 10.1016/0022-2836(76)90191-1
Article
CAS
PubMed
Google Scholar
Ahmad S, Gromiha MM, Sarai A: Real value prediction of solvent accessibility from amino acid sequence. Proteins 2003, 50(4):629–635. 10.1002/prot.10328
Article
CAS
PubMed
Google Scholar
Jones S, Thornton JM: Analysis of protein-protein interaction sites using surface patches. J Mol Biol 1997, 272(1):121–132. 10.1006/jmbi.1997.1234
Article
CAS
PubMed
Google Scholar
Jones S, Thornton JM: Prediction of protein-protein interaction sites using patch analysis. J Mol Biol 1997, 272(1):133–143. 10.1006/jmbi.1997.1233
Article
CAS
PubMed
Google Scholar
Haste Andersen P, Nielsen M, Lund O: Prediction of residues in discontinuous B-cell epitopes using protein 3D structures. Protein Sci 2006, 15(11):2558–2567. 10.1110/ps.062405906
Article
PubMed Central
PubMed
Google Scholar
Panchenko AR, Kondrashov F, Bryant S: Prediction of functional sites by analysis of sequence and structure conservation. Protein Sci 2004, 13(4):884–892. 10.1110/ps.03465504
Article
PubMed Central
CAS
PubMed
Google Scholar
Mooney S: Bioinformatics approaches and resources for single nucleotide polymorphism functional analysis. Brief Bioinform 2005, 6(1):44–56. 10.1093/bib/6.1.44
Article
CAS
PubMed
Google Scholar
Pollastri G, Baldi P, Fariselli P, Casadio R: Prediction of coordination number and relative solvent accessibility in proteins. Proteins 2002, 47(2):142–153. 10.1002/prot.10069
Article
CAS
PubMed
Google Scholar
Cheng J, Randall AZ, Sweredoski MJ, Baldi P: SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Research 2005, (33 Web Server):W72-W76. 10.1093/nar/gki396
Adamczak R, Porollo A, Meller J: Accurate prediction of solvent accessibility using neural networks-based regression. Proteins: Structure, Function, and Bioinformatics 2004, 56(4):753–767. 10.1002/prot.20176
Article
CAS
Google Scholar
Carugo O: Predicting residue solvent accessibility from protein sequence by considering the sequence environment. Protein Eng 2000, 13(9):607–609. 10.1093/protein/13.9.607
Article
CAS
PubMed
Google Scholar
Garg A, Kaur H, Raghava GPS: Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure. Proteins 2005, 61(2):318–324. 10.1002/prot.20630
Article
CAS
PubMed
Google Scholar
Pollastri G, Martin AJM, Mooney C, Vullo A: Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information. BMC Bioinformatics 2007, 8: 201. 10.1186/1471-2105-8-201
Article
PubMed Central
PubMed
Google Scholar
Wang J-Y, Lee H-M, Ahmad S: SVM-Cabins: prediction of solvent accessibility using accumulation cutoff set and support vector machine. Proteins 2007, 68(1):82–91. 10.1002/prot.21422
Article
CAS
PubMed
Google Scholar
Xu Z, Zhang C, Liu S, Zhou Y: QBES: Predicting real values of solvent accessibility from sequences by efficient, constrained energy optimization. Proteins: Structure, Function, and Bioinformatics 2006, 63(4):961–966. 10.1002/prot.20934
Article
CAS
Google Scholar
Yuan Z, Burrage K, Mattick JS: Prediction of protein solvent accessibility using support vector machines. Proteins 2002, 48(3):566–570. 10.1002/prot.10176
Article
CAS
PubMed
Google Scholar
Yuan Z, Huang B: Prediction of protein accessible surface areas by support vector regression. Proteins 2004, 57(3):558–564. 10.1002/prot.20234
Article
CAS
PubMed
Google Scholar
Dor O, Zhou Y: Achieving 80% ten-fold cross-validated accuracy for secondary structure prediction by large-scale training. Proteins 2007, 66(4):838–845. 10.1002/prot.21298
Article
CAS
PubMed
Google Scholar
Dor O, Zhou Y: Real-SPINE: an integrated system of neural networks for real-value prediction of protein structural properties. Proteins 2007, 68(1):76–81. 10.1002/prot.21408
Article
CAS
PubMed
Google Scholar
Faraggi E, Xue B, Zhou Y: Improving the prediction accuracy of residue solvent accessibility and real-value backbone torsion angles of proteins by guided-learning through a two-layer neural network. Proteins 2009, 74(4):847–856. 10.1002/prot.22193
Article
PubMed Central
CAS
PubMed
Google Scholar
Nguyen MN, Rajapakse JC: Two-stage support vector regression approach for predicting accessible surface areas of amino acids. Proteins 2006, 63(3):542–550. 10.1002/prot.20883
Article
CAS
PubMed
Google Scholar
Barton G: Jpred Distribution material.2007. [http://www.compbio.dundee.ac.uk/~www-jpred/data/]
Google Scholar
Cuff JA, Barton GJ: Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins 1999, 34(4):508–519. 10.1002/(SICI)1097-0134(19990301)34:4<508::AID-PROT10>3.0.CO;2-4
Article
CAS
PubMed
Google Scholar
Rost B, Sander C: Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol 1993, 232(2):584–599. 10.1006/jmbi.1993.1413
Article
CAS
PubMed
Google Scholar
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE: The Protein Data Bank. Nucl Acids Res 2000, 28(1):235–242. 10.1093/nar/28.1.235
Article
PubMed Central
CAS
PubMed
Google Scholar
Wang G, Dunbrack RLJ: PISCES: a protein sequence culling server. Bioinformatics 2003, 19(12):1589–1591. 10.1093/bioinformatics/btg224
Article
CAS
PubMed
Google Scholar
Kabsch W, Sander C: Dictionary of Protein Secondary Structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983, 22(12):2577–2637. 10.1002/bip.360221211
Article
CAS
PubMed
Google Scholar
Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997, 25(17):3389–3402. 10.1093/nar/25.17.3389
Article
PubMed Central
CAS
PubMed
Google Scholar
Li W, Jaroszewski L, Godzik A: Clustering of highly homologous sequences to reduce the size of large protein databases. Bioinformatics 2001, 17(3):282–283. 10.1093/bioinformatics/17.3.282
Article
CAS
PubMed
Google Scholar
Rumelhart D, Hinton G, Williams R: Learning internal representations by error propagation. In Parallel distributed processing. MIT Press Cambridge; 1986:318–363.
Google Scholar
Lund O, Nielsen M, Lundegaard C, KeSmir C, Brunak S: Immunological Bioinformatics. The MIT Press, Cambridge, Massachusetts, London, England; 2005.
Google Scholar
Spearman C: The proof and measurement of association between two things. J Psychol 1904, 15: 72–101. 10.2307/1412159
Article
Google Scholar
Petersen TN, Lundegaard C, Nielsen M, Bohr H, Bohr J, Brunak S, Gippert G, Lund O: Prediction of protein secondary structure at 80% accuracy. Proteins 2000, 41(1):17–20. 10.1002/1097-0134(20001001)41:1<17::AID-PROT40>3.0.CO;2-F
Article
CAS
PubMed
Google Scholar
Hobohm U, Scharf M, Schneider R, Sander C: Selection of representative protein data sets. Protein Sci 1992, 1: 409–417.
Article
PubMed Central
CAS
PubMed
Google Scholar