Assessment of protein disorder region predictions in CASP10

Proteins: Structure, Function and Bioinformatics

Volumn 82, Issue SUPPL.2, 2014, Pages 127-137

  Monastyrskyy, Bohdan ^a   Kryshtafovych, Andriy ^a   Moult, John ^b   Tramontano, Anna ^c   Fidelis, Krzysztof ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF MARYLAND BIOTECHNOLOGY INSTITUTE   (United States)

^c SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

Assessment of disorder prediction; CASP; Intrinsically disordered proteins; Prediction of disordered regions; Unstructured proteins

Indexed keywords

INTRINSICALLY DISORDERED PROTEIN;

ALGORITHM; ARTICLE; CLASSIFIER; CORRELATION COEFFICIENT; CRITICAL ASSESSMENT OF PROTEIN STRUCTURE PREDICTION; CRYSTAL STRUCTURE; DATA BASE; MATTHEWS CORRELATION COEFFICIENT; NUCLEAR MAGNETIC RESONANCE; PREDICTION; PRIORITY JOURNAL; PROBABILITY; PROTEIN DATABASE; PROTEIN STRUCTURE; RECEIVER OPERATING CHARACTERISTIC; SCORING SYSTEM; STRUCTURE ANALYSIS; X RAY CRYSTALLOGRAPHY;

ASSESSMENT OF DISORDER PREDICTION; CASP; INTRINSICALLY DISORDERED PROTEINS; PREDICTION OF DISORDERED REGIONS; UNSTRUCTURED PROTEINS;

COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; INTRINSICALLY DISORDERED PROTEINS; MODELS, MOLECULAR; MODELS, STATISTICAL; PROTEIN CONFORMATION;

EID: 84892954999     PISSN: 08873585     EISSN: 10970134     Source Type: Journal    
DOI: 10.1002/prot.24391     Document Type: Article

References (39)

1. Romero P, Obradovic Z, Kissinger C, Villafranca JE, Dunker AK. Identifying disorder regions in proteins from amino acid sequence. Proc IEEE Int Conf Neural Netw 1997;1:90-95.
2. Wright PE, Dyson HJ. Intrinsically unstructured proteins: re-assessing the protein structure-function paradigm. J Mol Biol 1999;293:321-331.
3. Dunker AK, Obradovic Z, Romero P, Garner EC, Brown CJ. Intrinsic protein disorder in complete genomes. Genome Inform Ser Workshop Genome Inform 2000;11:161-171.
4. Tompa P. Intrinsically unstructured proteins. Trends Biochem Sci 2002;27:527-533.
5. Uversky VN. A decade and a half of protein intrinsic disorder: biology still waits for physics. Protein Sci 2013;22:693-724.
6. Hsu WL, Oldfield CJ, Xue B, Meng J, Huang F, Romero P, Uversky VN, Dunker AK. Exploring the binding diversity of intrinsically disordered proteins involved in one-to-many binding. Protein Sci 2013;22:258-273.
7. Ota M, Koike R, Amemiya T, Tenno T, Romero PR, Hiroaki H, Dunker AK, Fukuchi S. An assignment of intrinsically disordered regions of proteins based on NMR structures. J Struct Biol 2013;181:29-36.
8. Cozzetto D, Jones DT. The contribution of intrinsic disorder prediction to the elucidation of protein function. Curr Opin Struct Biol 2013;23:467-472.
9. Oldfield CJ, Xue B, Van YY, Ulrich EL, Markley JL, Dunker AK, Uversky VN. Utilization of protein intrinsic disorder knowledge in structural proteomics. Biochim Biophys Acta 2013;1834:487-498.
10. Uversky VN, Dunker AK. The case for intrinsically disordered proteins playing contributory roles in molecular recognition without a stable 3D structure. F1000 Biol Rep 2013;5:1.
11. Janin J, Sternberg MJ. Protein flexibility, not disorder, is intrinsic to molecular recognition. F1000 Biol Rep 2013;5:2.
12. Sickmeier M, Hamilton JA, LeGall T, Vacic V, Cortese MS, Tantos A, Szabo B, Tompa P, Chen J, Uversky VN, Obradovic Z, Dunker AK. DisProt: the Database of Disordered Proteins. Nucleic Acids Res 2007;35(database issue):D786-793.
13. Fukuchi S, Sakamoto S, Nobe Y, Murakami SD, Amemiya T, Hosoda K, Koike R, Hiroaki H, Ota M. IDEAL: Intrinsically Disordered proteins with Extensive Annotations and Literature. Nucleic Acids Res 2012;40(database issue):D507-511.
14. He B, Wang K, Liu Y, Xue B, Uversky VN, Dunker AK. Predicting intrinsic disorder in proteins: an overview. Cell Res 2009;19:929-949.
15. Dosztanyi Z, Meszaros B, Simon I. Bioinformatical approaches to characterize intrinsically disordered/unstructured proteins. Brief Bioinform 2010;11:225-243.
16. Deng X, Eickholt J, Cheng J. A comprehensive overview of computational protein disorder prediction methods. Mol Biosyst 2012;8:114-121.
17. Peng ZL, Kurgan L. Comprehensive comparative assessment of in-silico predictors of disordered regions. Curr Protein Pept Sci 2012;13:6-18.
18. Monastyrskyy B, Fidelis K, Moult J, Tramontano A, Kryshtafovych A. Evaluation of disorder predictions in CASP9. Proteins 2011;79 Suppl 10:107-118.
19. Noivirt-Brik O, Prilusky J, Sussman JL. Assessment of disorder predictions in CASP8. Proteins 2009;77 Suppl 9:210-216.
20. Bordoli L, Kiefer F, Schwede T. Assessment of disorder predictions in CASP7. Proteins 2007;69 Suppl 8:129-136.
21. Jin Y, Dunbrack RL, Jr. Assessment of disorder predictions in CASP6. Proteins 2005;61 Suppl 7:167-175.
22. Melamud E, Moult J. Evaluation of disorder predictions in CASP5. Proteins 2003;53 Suppl 6:561-565.
23. 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.
24. Huber R. Conformational flexibility in protein molecules. Nature 1979;280:538-539.
25. Kryshtafovych A, Monastyrskyy B, Fidelis K. CASP Prediction Center infrastructure and evaluation measures in CASP10 and CASP ROLL. Proteins 2014;82(Suppl 2):7-13.
26. Taylor T, Tai C-H, Bai H, Kryshtafovych A, Montelione G, Lee B. Definition and classification of evaluation units for CASP10. Proteins 2014;82(Suppl 2):14-25.
27. Kryshtafovych A, Barbato A, Fidelis K, Monastyrskyy B, Schwede T, Tramontano A. Assessment of the assessment: evaluation of the model quality estimates in CASP10. Proteins 2014;82(Suppl 2):112-126.
28. Oates ME, Romero P, Ishida T, Ghalwash M, Mizianty MJ, Xue B, Dosztanyi Z, Uversky VN, Obradovic Z, Kurgan L, Dunker AK, Gough J. D(2)P(2): Database of Disordered Protein Predictions. Nucleic Acids Res 2013;41(database issue):D508-516.
29. Di Domenico T, Walsh I, Martin AJ, Tosatto SC. MobiDB: a comprehensive database of intrinsic protein disorder annotations. Bioinformatics 2012;28:2080-2081.
30. Ward JJ, Sodhi JS, McGuffin LJ, Buxton BF, Jones DT. Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. J Mol Biol 2004;337:635-645.
31. Davis J, Goadrich, M. The relationship between precision-recall and ROC curves. In Proceedings of the 23rd International Conference on Machine Learning. New York: ACM; 2006. pp 233-240.
32. Bunescu R, Ge R, Kate R, Marcotte E, Mooney R, Ramani A, Wong Y. Comparative experiments on learning information extractors for proteins and their interactions. J Artif Intell Med 2004:139-155.
33. Kok S, Domingos, P. Learning the structure of Markov logic networks. In 22nd International Conference on Machine Learning. New York: ACM Press; 2005. pp 441-448.
34. He HB, Garcia EA. Learning from imbalanced data. IEEE Trans Knowl Data Eng 2009;21:1263-1284.
35. Goadrich M, Oliphant L, Shavlik J. Learning ensembles of first-order clauses for recall-precision curves: a case study in biomedical information extraction. In 14th International Conference on Inductive Logic Programming (ILP); 2004. pp 421-456.
36. Fawcett T, Flach PA. A response to Webb and Ting's On the application of ROC analysis to predict classification performance under varying class distributions. Mach Learn 2005;58:33-38.
37. The R development Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2006.
38. http://ca.expasy.org/tools/pROC/. 2011
39. Magrane M, Consortium U. UniProt Knowledgebase: a hub of integrated protein data. Database (Oxford) 2011;2011:bar009.