Prodepth: Predict residue depth by support vector regression approach from protein sequences only

PLoS ONE

Volumn 4, Issue 9, 2009, Pages

  Song, Jiangning ^a,b   Tan, Hao ^a   Mahmood, Khalid ^a   Law, Ruby H P ^a   Buckle, Ashley M ^a   Webb, Geoffrey I ^a   Akutsu, Tatsuya ^b   Whisstock, James C ^a  

^a MONASH UNIVERSITY   (Australia)

^b KYOTO UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ACCESS TO INFORMATION; ACCURACY; AMINO ACID SEQUENCE; ANALYTICAL ERROR; ARTICLE; CORRELATION COEFFICIENT; DATA ANALYSIS SOFTWARE; ESCHERICHIA COLI; INTERMETHOD COMPARISON; PREDICTION; PROTEIN DATABASE; PROTEIN SECONDARY STRUCTURE; QUANTITATIVE ANALYSIS; RECEIVER OPERATING CHARACTERISTIC; RELIABILITY; RESIDUE ANALYSIS; SENSITIVITY AND SPECIFICITY; SEQUENCE ANALYSIS; SEQUENCE HOMOLOGY; SUPPORT VECTOR MACHINE; VALIDATION PROCESS; WEB BROWSER; ANIMAL; BIOLOGY; CHEMISTRY; COMPUTER PROGRAM; ENZYMOLOGY; HUMAN; INSTRUMENTATION; METHODOLOGY; PROTEOMICS; REGRESSION ANALYSIS; REPRODUCIBILITY; SEQUENCE ALIGNMENT;

AMINOACYL TRANSFER RIBONUCLEIC ACID HYDROLASE; AMINOACYL-TRNA HYDROLASE; CARBOXYLESTERASE; PROTEIN; SOLVENT;

ANIMALS; CARBOXYLIC ESTER HYDROLASES; COMPUTATIONAL BIOLOGY; ESCHERICHIA COLI; HUMANS; PROTEIN STRUCTURE, SECONDARY; PROTEINS; PROTEOMICS; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; ROC CURVE; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE; SOLVENTS;

EID: 70349392341     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0007072     Document Type: Article

References (82)

1. Bajaj K, Madhusudhan MS, Adkar BV, Chakrabarti P, Ramakrishnan C, et al. (2005) Mutagenesis-based definitions and probes of residue burial in proteins. Proc Natl Acad Sci USA 102: 16221-16226.
2. Lee B, Richards FM (1971) The interpretation of protein structures: Estimation of static accessibility. J Mol Biol 55: 379-400.
3. Chothia C (1974) Hydrophobic bonding and accessible surface area in proteins. Nature 248: 338-339.
4. Connolly M (1983) Solvent-accessible surfaces of proteins and nucleic acids. Science 221: 709-713.
5. Miller S, Lesk AM, Janin J, Chothia C (1987) The accessible surface area and stability of oligomeric proteins. Nature 328: 834-836.
6. Rost B, Sander C (1994) Conservation and prediction of solvent accessibility in protein families. Proteins 20: 216-226.
7. Pintar A, Carugo O, Pongor S (2003) Atom depth as a descriptor of the protein interior. Biophys J 84: 2553-2561.
8. Pintar A, Carugo O, Pongor S (2003) Atom depth in protein structure and function. Trends Biochem Sci 28: 593-597.
9. Chakravarty S, Varadarajan R (1999) Residue depth: a novel parameter for the analysis of protein structure and stability. Structure 7: 723-732.
10. Varrazzo D, Bernini A, Spiga O, Ciutti A, Chiellini S, et al. (2005) Three-dimensional computation of atom depth in complex molecular structures. Bioinformatics 21: 2856-2860.
11. Rost B, Yachdav G, Liu J (2004) The PredictProtein server. Nucleic Acids Res 32: W321-326.
12. Blundell TL, Jhoti H, Abell C (2002) High-throughput crystallography for lead discovery in drug design. Nat Rev Drug Discov 1: 45-54.
13. Schlessinger A, Yachdav G, Rost B (2006) PROFbval: predict flexible and rigid residues in proteins. Bioinformatics 22: 891-893.
14. Schlessinger A, Punta M, Rost B (2007) Natively unstructured regions in proteins identified from contact predictions. Bioinformatics 23: 2376-2384.
15. Schlessinger A, Liu J, Rost B (2007) Natively unstructured loops differ from other loops. PLoS Comput Biol 3: e140.
16. Ofran Y, Mysore V, Rost B (2007) Prediction of DNA-binding residues from sequence. Bioinformatics 23: i347-353.
17. Ofran Y, Rost B (2007) Protein-protein interaction hotspots carved into sequences. PLoS Comput Biol 3: e119.
18. Shrake A, Rupley JA (1973) Environment and exposure to solvent of protein atoms. Lysozyme and insulin. J Mol Biol 79: 351-371.
19. Pintar A, Carugo O, Pongor S (2003) DPX: for the analysis of the protein core. Bioinformatics 19: 313-314.
20. Vlahovicek K, Pintar A, Parthasarathi L, Carugo O, Pongor S (2005) CX, DPX and PRIDE: WWW servers for the analysis and comparison of protein 3D structures. Nucleic Acids Res 33: W252-254.
21. Kalidas Y, Chandra N (2008) PocketDepth: a new depth based algorithm for identification of ligand binding sites in proteins. J Struct Biol 161: 31-42.
22. Coleman RG, Sharp KA (2006) Travel depth, a new shape descriptor for macromolecules: application to ligand binding. J Mol Biol 362: 441-458.
23. Zhou H, Zhou Y (2005) Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments. Proteins 58: 321-328.
24. Liu S, Zhang C, Liang S, Zhou Y (2007) Fold recognition by concurrent use of solvent accessibility and residue depth. Proteins 68: 636-645.
25. Zhang W, Liu S, Zhou Y (2008) SP5: improving protein fold recognition by using torsion angle profiles and profile-based gap penalty model. PLoS ONE 3: e2325.
26. Hamelryck T (2005) An amino acid has two sides: a new 2D measure provides a different view of solvent exposure. Proteins 59: 38-48.
27. Song J, Tan H, Takemoto K, Akutsu T (2008) HSEpred: predict half-sphere exposure from protein sequences. Bioinformatics 24: 1489-1497.
28. Stout M, Bacardit J, Hirst JD, Krasnogor N (2008) Prediction of recursive convex hull class assignments for protein residues. Bioinformatics 24: 916-923.
29. Pollastri G, Baldi P, Fariselli P, Casadio R (2001) Improved prediction of the number of residue contacts in proteins by recurrent neural networks. Bioinformatics 17: S234-242.
30. Kinjo AR, Horimoto K, Nishikawa K (2005) Predicting absolute contact numbers of native protein structure from amino acid sequence. Proteins 58: 158-165.
31. Baker D, Sali A (2001) Protein structure prediction and structural genomics. Science 94: 93-96.
32. Schueler-Furman O, Wang C, Bradley P, Misura K, Baker D (2005) Progress in modeling of protein structures and interactions. Science 310: 638-642.
33. Chen H, Zhou HX (2005) Prediction of solvent accessibility and sites of deleterious mutations from protein sequence. Nucleic Acids Res 33: 3193-3199.
34. Smith CK, Regan L (1995) Guidelines for protein design: the energetics of beta sheet side chain interactions. Science 270: 980-982.
35. Bowie JU, Lüthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253: 164-170.
36. Yuan Z, Wang ZX (2008) Quantifying the relationship of protein burying depth and sequence. Proteins 70: 509-516.
37. Zhang H, Zhang T, Chen K, Shen S, Ruan J, et al. (2008) Sequence based residue depth prediction using evolutionary information and predicted secondary structure. BMC Bioinformatics 9: 388.
38. Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22: 2577-2637.
39. Chandonia JM, Karplus M (1995) Neural networks for secondary structure and structural class predictions. Protein Sci 4: 275-285.
40. Yuan Z (2005) Better prediction of protein contact number using a support vector regression analysis of amino acid sequence. BMC Bioinformatics 6: 248.
41. Song J, Burrage K (2006) Predicting residue-wise contact orders in proteins by support vector regression. BMC Bioinformatics 7: 425.
42. Song J, Yuan Z, Tan H, Huber T, Burrage K (2007) Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure. Bioinformatics 23: 3147-3154.
43. Jones DT (2007) Improving the accuracy of transmembrane protein topology prediction using evolutionary information. Bioinformatics 23: 538-544.
44. Ishida T, Kinoshita K (2007) PrDOS: prediction of disordered protein regions from amino acid sequence. Nucleic Acids Res 35: W460-464.
45. Cheng J, Baldi P (2007) Improved residue contact prediction using support vector machines and a large feature set. BMC Bioinformatics 8: 113.
46. Ferre F, Clote P (2005) Disulfide connectivity prediction using secondary structure information and diresidue frequencies. Bioinformatics 21: 2336-2346.
47. Chen K, Kurgan L (2007) PFRES: protein fold classification by using evolutionary information and predicted secondary structure. Bioinformatics 23: 2843-2850.
48. Kurgan L, Cios K, Chen K (2008) SCPRED: accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences. BMC Bioinformatics 9: 226.
49. Kurgan LA, Zhang T, Zhang H, Shen S, Ruan J (2008) Secondary structure-based assignment of the protein structural classes. Amino Acids 35: 551-564.
50. Cheng J, Randall AZ, Sweredoski MJ, Baldi P (2005) SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Res 33: W72-76.
51. Schmitt E, Mechulam Y, Fromant M, Plateau P, Blanquet S (1997) Crystal structure at 1.2 A resolution and active site mapping of Escherichia coli peptidyl-tRNA hydrolase. EMBO J 16: 4760-4769.
52. Marcotte EM, Monzingo AF, Ernst SR, Brzezinski R, Robertus JD (1996) X-ray structure of an anti-fungal chitosanase from streptomyces N174. Nat Struct Biol 3: 155-162.
53. Lacombe-Harvey ME, Fukamizo T, Gagnon J, Ghinet MG, Dennhart N, et al. (2009) Accessory active site residues of Streptomyces sp. N174 chitosanase: variations on a common theme in the lysozyme superfamily. FEBS J 276: 857-869.
54. Whisstock JC, Lesk AM (2003) Prediction of protein function from protein sequence and structure. Q Rev Biophys 36: 307-340.
55. Noguchi T, Akiyama Y (2003) PDB-REPRDB: a database of representative protein chains from the Protein Data Bank (PDB) in 2003. Nucleic Acids Res 31: 492-493.
56. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, et al. (2000) The Protein Data Bank. Nucleic Acids Res 28: 235-242.
57. Murzin AG, Brenner SE, Hubbard T, Chothia C (1995) SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 247: 536-540.
58. Wang ZX, Yuan Z (2000) How good is prediction of protein structural class by the component-coupled method? Proteins 38: 165-175.
59. Vapnik V (1998) Statistical learning theory. New York, NY: Wiley.
60. Vapnik V (2000) The nature of statistical learning theory. New York, NY: Springer.
61. Raghava GP, Han JH (2005) Correlation and prediction of gene expression level from amino acid and dipeptide composition of its protein. BMC Bioinformatics 6: 59.
62. Nguyen MN, Rajapakse JC (2006) Two-stage support vector regression approach for predicting accessible surface areas of amino acids. Proteins 63: 542-550.
63. Wang X, Li A, Jiang Z, Feng H (2006) Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding scheme. BMC Bioinformatics 7: 32.
64. Wan J, Liu W, Xu Q, Ren Y, Flower DR, et al. (2006) SVRMHC prediction server for MHC-binding peptides. BMC Bioinformatics 7: 463.
65. Liu W, Meng X, Xu Q, Flower DR, Li T (2006) Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models. BMC Bioinformatics 7: 182.
66. Lee M, Jeong CS, Kim D (2007) Predicting and improving the protein sequence alignment quality by support vector regression. BMC Bioinformatics 8: 471.
67. Qiu J, Sheffler W, Baker D, Noble WS (2008) Ranking predicted protein structures with support vector regression. Proteins 71: 1175-1182.
68. Joachims T (1999) Making Large-Scale SVM Learning Practical. In: Scholkopf B, Burges C, Smola A, eds. Advances in Kernel Methods - Support Vector Learning. Cambridge, Massachusetts: MIT Press.
69. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, et al. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25: 3389-3402.
70. Bromberg Y, Rost B (2007) SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Res 35: 3823-3835.
71. Kinjo AR, Nakamura H (2008) Nature of protein family signatures: insights from singular value analysis of position-specific scoring matrices. PLoS ONE 3: e1963.
72. Rost B, Sander C (1993) Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol 232: 584-599.
73. Ahmad S, Sarai A (2005) PSSM-based prediction of DNA binding sites in proteins. BMC Bioinformatics 6: 33.
74. Xie D, Li A, Wang M, Fan Z, Feng H (2005) LOCSVMPSI: a web server for subcellular localization of eukaryotic proteins using SVM and profile of PSI-BLAST. Nucleic Acids Res 33: W105-110.
75. Song J, Burrage K, Yuan Z, Huber T (2006) Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information. BMC Bioinformatics 7: 124.
76. Kumar M, Gromiha MM, Raghava GP (2007) Identification of DNA-binding proteins using support vector machines and evolutionary profiles. BMC Bioinformatics 8: 463.
77. Kalita MK, Nandal UK, Pattnaik A, Sivalingam A, Ramasamy G, et al. (2008) CyclinPred: a SVM-based method for predicting cyclin protein sequences. PLoS ONE 3: e2605.
78. Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292: 195-202.
79. Ward JJ, Sodhi JS, McGuffin LJ, Buxton BF, Jones DT (2004) Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. J Mol Biol 337: 635-645.
80. Radivojac P, Iakoucheva LM, Oldfield CJ, Obradovic Z, Uversky VN, et al. (2007) Intrinsic disorder and functional proteomics. Biophys J 92: 1439-1456.
81. Schlessinger A, Punta M, Yachdav G, Kajan L, Rost B (2009) Improved disorder prediction by combination of orthogonal approaches. PLoS One 4: e4433.
82. DeLano WL (2002) The PyMOL User's Manual. DeLano Scientific, San Carlos, California, USA.