Optimizing long intrinsic disorder predictors with protein evolutionary information

Journal of Bioinformatics and Computational Biology

Volumn 3, Issue 1, 2005, Pages 35-60

  Peng, Kang ^a   Vucetic, Slobodan ^a   Radivojac, Predrag ^b   Brown, Celeste J ^c   Dunker, A Keith ^d   Obradovic, Zoran ^a  

^a TEMPLE UNIVERSITY   (United States)

^b INDIANA UNIVERSITY   (United States)

^c UNIVERSITY OF IDAHO   (United States)

^d INDIANA UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

Evolutionary information; Intrinsic protein disorder; Neural networks; PONDR; Prediction; PSI BLAST

Indexed keywords

PROTEIN;

ACCURACY; AMINO ACID SEQUENCE; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER PROGRAM; INTERNET; MOLECULAR EVOLUTION; MOLECULAR MODEL; PREDICTION; PROCESS OPTIMIZATION; PROTEIN ANALYSIS; PROTEIN DATABASE; PROTEIN DOMAIN; PROTEIN FUNCTION; PROTEIN STRUCTURE; SEQUENCE HOMOLOGY; VALIDATION PROCESS;

ALGORITHMS; COMPUTER SIMULATION; CONSERVED SEQUENCE; EVOLUTION, MOLECULAR; MODELS, CHEMICAL; MODELS, GENETIC; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); PROTEINS; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, PROTEIN; SEQUENCE HOMOLOGY, AMINO ACID;

EID: 14544299774     PISSN: 02197200     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219720005000886     Document Type: Article

References (64)

1. Demchenko AP, Recognition between flexible protein molecules: Induced and assisted folding, J Mol Recognit 14(1):42-61, 2001.
2. Dunker AK, Obradovic Z, The protein trinity - linking function and disorder, Nat Biotechnol 19:805-806, 2001.
3. Dunker AK, Brown CJ, Lawson JD, Iakoucheva LM, Obradovic Z, Intrinsic disorder and protein function, Biochemistry 41(21 :6573-6582, 2002.
4. Uversky VN, Natively unfolded proteins: A point where biology waits for physics, Protein Sci 11(4):739-756, 2002.
5. Uversky VN, What does it mean to be natively unfolded, Eur J Biochem 269:2-12, 2002.
6. Wright PE, Dyson HJ, Intrinsically unstructured proteins: Re-assessing the protein structure-function paradigm, J Mol Biol 293 2):321-331, 1999.
7. Dunker AK, Lawson JD, Brown CJ, Romero P, Oh J, Oldfield CJ, Campen AM, Ratlif, Hipps KW, Ausio J, Nissen MS, Reeves R, Kang CH, Kissinger CR, Bailey RW, Griswold MD, Chiu W, Garner EC, Obradovic Z, Intrinsically disordered proteins, J Mol Graph Model 19:28-61, 2001.
8. Dunker AK, Brown CJ, Obradovic Z, Identification and functions of usefully disordered proteins, Adv Protein Chem 62:25-49, 2002.
9. Anfinsen CB, Principles that govern the folding of protein chains, Science 181:223-230, 1973.
10. Ptitsyn OB, Uversky VN, The molten globule is a third thermodynamical state of protein molecules, FEBS Lett 341:15-18, 1994.
11. Dolgikh DA, Gilmanshin RI, Brazhnikov EV, Bychkova VE, Semisotnov GV, Venyaminov SY, Ptitsyn OB, Alpha-Lactalbumin: Compact state with fluctuating tertiary structure? FEBS Lett 136(2):311-315, 1981.
12. Brown CJ, Takayama S, Campen A, Vise P, Marshall T, Oldfield CJ, Williams CJ, Dunker AK, Evolutionary rate heterogeneity in proteins with long disordered regions, J Mol Evol 55:102-107, 2002.
13. Radivojac P, Obradovic Z, Brown CJ, Dunker AK, Improving sequence alignments for intrinsically disordered proteins, in Proc of 7th Pacific Symposium on Biocomputing, Lihue, Hawaii, pp. 589-600, 2002.
14. Daughdrill GW, Chadsey MS, Karlinsey JE, Hughes KT, Dahlquist FW, The C-terminal half of the anti-sigma factor, FlgM, becomes structured when bound to its target, sigma 28, Nat Struct Biol 4 285-291, 1997.
15. Daughdrill GW, Hanely LJ, Dahlquist FW, The C-terminal half of the anti-sigma factor FlgM contains a dynamic equilibrium solution structure favoring helical conformations, Biochemistry 37 1076-1082, 1998.
16. Dedmon MM, Patel CN, Young GB, Pielak GJ, FlgM gains structure in living cells, in Proc Natl Acad Sci USA 99:12681-12684, 2002.
17. Romero P, Obradovic Z, Kissinger CR, Villafranca JE, Dunker AK, Identifying disordered regions in proteins from amino acid sequences, in Proc of IEEE International Conference on Neural Networks, Houston, Texas, pp. 90-95, 1997.
18. Romero P, Obradovic Z, Li X, Garner EC, Brown CJ, Dunker AK, Sequence complexity and disordered protein, Proteins 42:38-48, 2001.
19. Li X, Romero P, Rani M, Dunker AK, Obradovic Z, Predicting protein disorder for N-, C-, and internal regions, in Proc of Genome Informatics 10, Tokyo, Japan, pp. 30-40, 1999.
20. Vucetic S, Brown C, Dunker AK, Obradovic Z, Flavors of protein disorder, Proteins 52:573-584, 2003.
21. Williams RJ, The conformational mobility of proteins and its functional significance, Biochem Soc Trans 6:1123-1126, 1978.
22. Uversky VN, Gillespie JR, Fink AL, Why are 'natively unfolded' proteins unstructured under the physiological conditions? Proteins 42 3):415-427, 2000.
23. Wootton JC, Federhen S, Statistic of local complexity in amino acid sequences and sequence databases, Comput Chem 17:149-163, 1993.
24. Liu J, Rost B, NORSp: Predictions of long regions without regular secondary structure, Nucleic Acids Res 31(13):3833-3835, 2003.
25. Jones DT, Ward J, Prediction of disordered regions in proteins from position specific score matrices, Proteins, Special CASP5 Edition 53(S6):573-578, 2003.
26. Linding R, Jensen LJ, Diella F, Bork P, Gibson TJ, Russell RB, Protein disorder prediction: Implications for structural proteomics, Structure 11:1453-1459, 2003.
27. Linding R, Russell RB, Neduva V, Gibson TJ, GlobPlot: Exploring protein sequences for globularity and disorder, Nucleic Acids Res 31 13):3701-3708, 2003.
28. Radivojac P, Obradovic Z, Smith DK, Zhu G, Vucetic S, Brown CJ, David Lawson J, Dunker AK, Protein flexibility and intrinsic disorder, Protein Sci 13(1):71-80, 2004.
29. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE, The Protein Data Bank, Nucleic Acids Res 28:235-242, 2000.
30. Smith DK, Radivojac P, Obradovic Z, Dunker AK, Zhu G, Improved amino acid flexibility parameters, Protein Sci 12:1060-1072, 2003.
31. Vihinen M, Torkkila E, Riikonen P, Accuracy of protein flexibility predictions, Proteins 19:141-149, 1994.
32. Kyte J, Doolittle RF, A simple method for displaying the hydropathic character of a protein, J Mol Biol 157:105-132, 1982.
33. Galaktionov SG, Marshall GR, Prediction of protein structure in terms of intra-globular contacts: 1D to 2D to 3D, Technical Report, Center for Molecular Design, Washington University, St. Louis, 1996.
34. Romero P, Obradovic Z, Dunker AK, Intelligent data analysis for identifying protein disorder, Artif Int Rev 14:447-484, 2000.
35. Davidson R, Mackinnon J, Estimator and inference in econometrics, Estimator and Inference in Econometrics, Oxford University Press, New York, 1993.
36. Breiman L, Bagging predictors, Mach Learning 24(2):123-140, 1996.
37. Breiman L, Bias, variance, and arcing classifiers, Technical Report 460, University of California at Berkeley, 1996.
38. Chandonia JM, Karplus M, Neural networks for secondary structure and structural class prediction, Protein Sci 4:275-285, 1995.
39. Qian N, Sejnowski TJ, Predicting the secondary structure of globular proteins using neural network models, J Mol Biol 202:865-884, 1988.
40. King RD, Sternberg MJ, Identification and application of the concepts important for accurate and reliable protein secondary structure prediction, Protein Sci 5(11):2298-2310, 1996.
41. Jones DT, Protein secondary structure prediction based on position-specific scoring matrices, J Mol Biol 292:195-202, 1999.
42. Przybylski D, Rost B, Alignments grow, secondary structure prediction improves, Proteins 46:197-205, 2002.
43. Rost B, Sander C, Prediction of protein secondary structure at better than 70% accuracy, J Mol Biol 232(2), 584-599, 1993.
44. Altschul SF, Madden TL, Schiffer 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 25:3389-3402, 1997.
45. Efron B, Tibshirani RJ, An Introduction to the Bootstrap, Chapman & Hall, New York, 1993.
46. Riedmiller M, Braun H, A direct adaptive method for faster backpropagation learning: The RPROP algorithm, in Proc of the IEEE International Conference on Neural Networks, San Francisco, CA, 1993.
47. Henikoff S, Henikoff JG, Amino acid substitution matrices from protein blocks, in Proc Natl Acad Sci USA 89:10915-10919, 1992.
48. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ, Basic local alignment search tool, J Mol Biol 215:403-410, 1990.
49. Bishop CM, Neural Networks for Pattern Recognition, Oxford University Press, Oxford, UK, 1995.
50. Melamud E, Moult J, Evaluation of disorder predictions in CASP5, Proteins, Special CASP5 Edition 53(S6):561-565, 2003.
51. Obradovic Z, Peng K, Vucetic S, Radivojac P, Brown CJ, Dunker AK, Predicting intrinsic disorder from amino acid sequence, Proteins, Special CASP5 Edition 53(S6):566-572, 2003.
52. Garner E, Cannon P, Romero P, Obradovic Z, Dunker AK, Predicting disordered regions from amino sequence: Common theme despite differing structural characterization, Genome Inform Ser Workshop Genome Inform. 9:201-214, 1998.
53. Frishman D, Argos P, Incorporation of non-local interactions in protein secondary structure prediction from the amino acid sequence, Protein Eng 9:133-142, 1996.
54. Garner E, Romero P, Dunker AK, Brown CJ, Obradovic Z, Predicting binding regions within disordered proteins, Genome Inform. 10 41-50, 1999.
55. Bottger A, Bottger V, Garcia-Echeverria C, Chene P, Hochkeppel HK, Sampson W, Ang K, Howard SF, Picksley SM, Lane DP, Molecular characterization of the hdm2-p53 interaction, J Mol Biol 269 744-756, 1997.
56. Han B, Vucetic S, Obradovic Z, Reranking medline citations by relevance to a difficult biological query, in Proc IASTED Int'l Conf Neural Networks and Computational Intelligence, Cancun, Mexico, 1, pp. 38-43, 2003.
57. Peng K, Vucetic S, Han B, Xie H, Obradovic Z, Exploiting unlabeled data for improving accuracy of predictive data mining, in Proc Third IEEE Int'l Conf Data Mining, Melbourne, FL, pp. 267-274, 2003.
58. Vucetic S, Pokrajac D, Xie H, Obradovic Z, Detection of underrepresented biological sequences using class-conditional distribution models, in Proc Third SIAM Int'l Conf on Data Mining San Francisco, CA, pp. 279-283, 2003.
59. Rost B, Protein secondary structure prediction continues to rise, J Struct Biol 134:204-218, 2001.
60. Nevill-Manning CG, Witten IH, Protein is incompressible, in Proc of Data Compression Conference, Snowbird, Utah, pp. 257-266, 1999.
61. Kabsch W, Sander C, On the use of sequence homologies to predict protein structure: Identical pentapeptides can have completely different conformations, in Proc Nat Acad Sci USA 81, pp. 1075-1078, 1984.
62. Minor DL, Jr. Kim PS, Context-dependent secondary structure formation of a designed protein sequence, Nature 380:730-734, 1996.
63. Hamada D, Goto Y, The equilibrium intermediate of beta-lactoglobulin with non-native alpha-helical structure, J Mol Biol 269 479-487, 1997.
64. Sudarsanam S, Structural diversity of sequentially identical subsequences of proteins: Identical octapeptides can have different conformations, Proteins 30:228-231, 1998.