Sequence based residue depth prediction using evolutionary information and predicted secondary structure

BMC Bioinformatics

Volumn 9, Issue , 2008, Pages

  Zhang, Hua ^a,b   Zhang, Tuo ^a,b   Chen, Ke ^b   Shen, Shiyi ^a,c   Ruan, Jishou ^a,c   Kurgan, Lukasz ^b  

^a NANKAI UNIVERSITY   (China)

^b UNIVERSITY OF ALBERTA   (Canada)

^c NANKAI UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

EVOLUTIONARY INFORMATION; FOLDING PREDICTIONS; MEAN ABSOLUTE ERROR; MEAN RELATIVE ERROR; PREDICTION PERFORMANCE; SECONDARY STRUCTURES; SOLVENT ACCESSIBILITY; UNIFORM DISTRIBUTION;

AMINO ACIDS; ERRORS; HYDROPHILICITY; HYDROPHOBICITY; INFORMATION USE; PROTEINS; SOLVENTS;

FORECASTING;

AMINO ACID; ARGININE; ASPARTIC ACID; GLUTAMIC ACID; LIGAND; LYSINE; PROTEIN;

ACCURACY; AMINO ACID SEQUENCE; ARTICLE; CALCULATION; MOLECULAR EVOLUTION; PREDICTION; PROTEIN FOLDING; PROTEIN SECONDARY STRUCTURE; SCORING SYSTEM; SIMULATION; VALIDATION PROCESS; ALGORITHM; ARTIFICIAL INTELLIGENCE; CHEMICAL MODEL; CHEMICAL STRUCTURE; CHEMISTRY; COMPUTER SIMULATION; GENETICS; METHODOLOGY; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS; ULTRASTRUCTURE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; EVOLUTION, MOLECULAR; MODELS, CHEMICAL; MODELS, MOLECULAR; PROTEIN STRUCTURE, SECONDARY; PROTEINS; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, PROTEIN;

EID: 54049091165     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-9-388     Document Type: Article

References (63)

1. Anfinsen CB Principles that govern the folding of protein chains Science 1973, 181:223-230. 10.1126/science.181.4096.223 4124164
2. Bradley P Chivian D Meiler J Misura K Rohl C Schief W Wedemeyer W Schueler-Furman O Murphy P Schonbrun J Strauss C Baker D Rosetta predictions in CASP5: Successes, failures, and prospects for complete automation Proteins 2003, 53(Suppl 6):457-468. 10.1002/prot.10552 14579334
3. Moult J Fidelis K Kryshtafovych A Rost B Hubbard T Tramontano A Critical assessment of methods of protein structure prediction - Round VII Proteins 2007, 69(Suppl 8):3-9. 10.1002/prot.21767 17918729
4. Lee B Richards F The interpretation of protein structures: Estimation of static accessibility J Mol Biol 1971, 55:379-400. 10.1016/ 0022-2836(71)90324-X 5551392
5. Connoly ML Solvent accessibility surfaces of protein and nucleic acids Science 1983, 221:709-713. 10.1126/science.6879170 6879170
6. Eisenberg D McLachlan AD Solvation energy in protein folding and binding Nature 1986, 319:199-203. 10.1038/319199a0 3945310
7. Gromiha MM Oobatake M Kono H Uedaira H Sarai A Role of structural and sequence information in the prediction of protein stability changes, comparison between buried and partially buried mutations Protein Engineering 1999, 12:549-555. 10.1093/protein/12.7.549 10436080
8. Cheng J Baldi P A machine learning information retrieval approach to protein fold recognition Bioinformatics 2006, 22(12):1456-63. 10.1093/ bioinformatics/btl102 16547073
9. Liu S Zhang C Liang S Zhou Y Fold recognition by concurrent use of solvent accessibility and residue depth Proteins 2007, 68:636-645. 10.1002/prot.21459 17510969
10. Rost B Sander C Conservation and prediction of solvent accessibility in protein families Proteins 1994, 20:216-226. 10.1002/prot.340200303 7892171
11. Ahmad S Gromiha MM Sarai A Real value prediction of solvent accessibility from amino acid sequence Proteins 2003, 50:629-635. 10.1002/prot.10328 12577269
12. Yuan Z Huang B Prediction of protein accessible surface areas by support vector regression Proteins 2004, 57:558-564. 10.1002/prot.20234 15382233
13. Garg A Kaur H Raghava GP Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure Proteins 2005, 61(2):318-24. 10.1002/prot.20630 16106377
14. Wang JY Lee HM Ahmad S Prediction and evolutionary information analysis of protein solvent accessibility using multiple linear regression Proteins 2005, 61:481-491. 10.1002/prot.20620 16170780
15. Nguyen MN Rajapakse JC Two-stage support vector regression approach for predicting accessible surface areas of amino acids Proteins 2006, 63:542-550. 10.1002/prot.20883 16456847
16. Yuan Z Zhang F Davis MJ Boden M Teasdale RD Predicting the solvent accessibility of transmembrane residues from protein sequence J Proteome Res 2006, 5:1063-1070. 10.1021/pr050397b 16674095
17. Wang JY Lee HM Ahmad S SVM-Cabins: Prediction of Solvent Accessibility Using Accumulation Cutoff Set and Support Vector Machine Proteins 2007, 68:82-91. 10.1002/prot.21422 17436325
18. Atilgan AR Akan P Baysal C Small-World Communication of Residues and Significance for Protein Dynamics Biophys J 2004, 86:85-91. 1303839 14695252
19. Chan HS Dill KA Origins of structure in globular proteins Proc Natl Acad Sci USA 1990, 87:6388-6392. 54539 2385597 10.1073/pnas.87.16.6388
20. Bartlett GJ Porter CT Borkakoti N Thornton JM Analysis of Catalytic Residues in Enzyme Active Sites J Mol Bio 2002, 324:105-121. 10.1016/ S0022-2836(02)01036-7
21. Pedersen TG Sigurskjold BW Andersen KV Kjaer M Poulsen FM Dobson CM Redfield C A nuclear-magnetic-resonance study of the hydrogen-exchange behavior of lysozyme in crystals and solution J Mol Biol 1991, 218:413-426. 10.1016/0022-2836(91)90722-I 2010918
22. Chakravarty S Varadarajan R Residue depth: A novel parameter for the analysis of protein structure and stability Structure 1999, 7:723-732. 10.1016/S0969-2126(99)80097-5 10425675
23. Pintar A Carugo O Pongor S Atom depth as a descriptor of the protein interior Biophys J 2003, 84:2553-2561. 1302821 12668463
24. Pintar A Carugo O Pongor S DPX, for the analysis of the protein core Bioinformatics 2003, 19:313-314. 10.1093/bioinformatics/19.2.313 12538266
25. Varrazzo D Bernini A Spiga O Ciutti A Chiellini SV Bracci L Niccolai N Three-dimensional computation of atom depth in complex molecular structures Bioinformatics 2005, 21(12):2856-2860. 10.1093/bioinformatics/ bti444 15827080
26. Gutteridge A Bartlett GJ Thornton JM Using a neural network and spatial clustering to predict the location of active sites in enzymes J Mol Biol 2003, 330:719-734. 10.1016/S0022-2836(03)00515-1 12850142
27. Kitchen J Saunders RE Warwicker J Charge environments around phosphorylation sites in proteins BMC Struct Biol 2008, 8:19. 2291461 18366741 10.1186/1472-6807-8-19
28. Zhou H Zhou Y Single-body residue-level knowledge-based energy score combined with sequence-profile and secondary structure information for fold recognition Proteins 2004, 55:1005-1013. 10.1002/prot.20007 15146497
29. Pintar A Pongor S The "first in-last out" hypothesis on protein folding revisited Proteins 2005, 60:584-590. 10.1002/prot.20529 16021637
30. Yuan Z Wang ZX Quantifying the relationship of protein burying depth and sequence Proteins 2008, 70:509-516. 10.1002/prot.21545 17705271
31. Smola AJ Schölkopf B A tutorial on support vector regression Statistics and Computing 2004, 14:199-222. 10.1023/ B:STCO.0000035301.49549.88
32. Yuan Z Better prediction of protein contact number using a support vector regression analysis of amino acid sequence BMC Bioinformatics 2005, 6:248. 1277819 16221309 10.1186/1471-2105-6-248
33. Raghava GP Han JH Correlation and prediction of gene expression level from amino acid and dipeptide composition of its protein BMC Bioinformatics 2005, 6:59. 1083413 15773999 10.1186/1471-2105-6-59
34. Song J Burrage K Predicting residue-wise contact orders in proteins by support vector regression BMC Bioinformatics 2006, 7:425. 1618864 17014735 10.1186/1471-2105-7-425
35. Liu W Meng X Xu Q Flower DR Li T Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models BMC Bioinformatics 2006, 7:182. 1513606 16579851 10.1186/ 1471-2105-7-182
36. 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, 25:3389-3402. 146917 9254694 10.1093/nar/25.17.3389
37. Jones DT Protein secondary structure prediction based on position-specific scoring matrices J Mol Biol 1999, 292:195-202. 10.1006/ jmbi.1999.3091 10493868
38. Bryson K McGuffin LJ Marsden RL Ward JJ Sodhi JS Jones DT Protein structure prediction servers at University College London Nucl Acids Res 2005, 33(Web Server):W36-38. 1160171 15980489 10.1093/nar/gki410
39. Noguchi T Akiyama Y PDB-REPRDB: A database of representative protein chains from the Protein Data Bank (PDB) in 2003 Nucleic Acids Res 2003, 31:492-493. 165469 12520060 10.1093/nar/gkg022
40. Berman HM Westbrook J Feng Z Gilliland G Bhat TN Weissig H Shindyalov IN Bourne PE The Protein Data Bank Nucleic Acids Res 2000, 28:235-242. 102472 10592235 10.1093/nar/28.1.235
41. Li W Godzik A Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences Bioinformatics 2006, 22:1658-9. 10.1093/bioinformatics/btl158 16731699
42. Needleman SB Wunsch CD A general method applicable to the search for similarities in the amino acid sequence of two proteins J Mol Biol 1970, 48:443-453. 10.1016/0022-2836(70)90057-4 5420325
43. Sanner MF Olson AJ Spehner JC Reduced surface: An efficient way to compute molecular surfaces Biopolymers 1996, 38:305-320. 10.1002/ (SICI)1097-0282(199603)38:3<305::AID-BIP4>3.0.CO;2-Y 8906967
44. Hamelryck T An amino acid has two sides: A new 2D measure provides a different view of solvent exposure Proteins 2005, 59:38-48. 10.1002/ prot.20379 15688434
45. Hubbard SJ Thornton JM NACCESS Department of Biochemistry and Molecular Biology, University College, London 1993
46. Karypis G YASSPP: Better Kernels and Coding Schemes Lead to Improvements in Protein Secondary Structure Prediction Proteins 2006, 64:575-586. 10.1002/prot.21036 16763996
47. Birzele F Kramer S A new representation for protein secondary structure prediction based on frequent patterns Bioinformatics 2006, 22:2628-34. 10.1093/bioinformatics/btl453 16940325
48. Chen K Kurgan L PFRES: Protein Fold Classification by Using Evolutionary Information and Predicted Secondary Structure Bioinformatics 2007, 23:2843-2850. 10.1093/bioinformatics/btm475 17942446
49. Ivankov DN Finkelstein AV Prediction of protein folding rates from the amino acid sequence-predicted secondary structure Proc Nat Acad Sci USA 2004, 101:8942-4. 428451 15184682 10.1073/pnas.0402659101
50. Fuchs PF Alix AJ High accuracy prediction of beta-turns and their types using propensities and multiple alignments Proteins 2005, 59:828-839. 10.1002/prot.20461 15822097
51. Wang Y Xue Z Xu J Better prediction of the location of alpha-turns in proteins with support vector machine Proteins 2006, 65:49-54. 10.1002/ prot.21062 16894602
52. Andersen CAF Palmer AG Brunak S Rost B Continuum Secondary Structure Captures Protein Flexibility Structure 2002, 10:175-184. 10.1016/ S0969-2126(02)00700-1 11839303
53. Vapnik V Statistical learning theory New York: Wiley 1998.
54. Yuan Z Bailey TL Teasdale RD Prediction of protein B-factor profiles Proteins 2005, 58:905-912. 10.1002/prot.20375 15645415
55. Altman: DG Bland JM Quartiles, quintiles, centiles, and other quantiles BMJ 1994, 309:996. 2541298 7950724
56. Yu L Liu H Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution Proceedings of the 10th International Conference on Machine Learning 2003, 856-863.
57. Hsu CW Lin CJ A comparison on methods for multi-class support vector machines IEEE Trans Neural Networks 2002, 13:415-425. 10.1109/72.991427
58. Kabsch W Sander C Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features Biopolymers 1983, 22:2577-2637. 10.1002/bip.360221211 6667333
59. Kawashima S Kanehisa M AAindex: Amino acid index database Nucleic Acids Res 2000, 28:374. 102411 10592278 10.1093/nar/28.1.374
60. Sweet RM Eisenberg D Correlation of sequence hydrophobicities measures similarity in three dimensional protein structure J Mol Biol 1983, 171:479-488. 10.1016/0022-2836(83)90041-4 6663622
61. Vihinen M Torkkila E Riikonen P Accuracy of protein flexibility predictions Proteins 1994, 19:141-149. 10.1002/prot.340190207 8090708
62. Chen K Kurgan LA Ruan J Optimization of the Sliding Window Size for Protein Structure Prediction Proceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Toronto, Ontario, Canada 2006, 366-372.
63. Sonego P Kocsor A Pongor S ROC analysis: Applications to the classification of biological sequences and 3D structures Briefings in Bioinformatics 2008, 9(3):198-209. 10.1093/bib/bbm064 18192302