Accurate prediction of protein structural classes using functional domains and predicted secondary structure sequences

References

• Attwood , T.K. , Bradley , P. , Flower , D.R. , Gaulton , A. , Maudling , N. , Mitchell , A.L. , … and Zygouri , C. 2003 . Prints and its automatic supplement, preprints . Nucleic Acids Research , 31 ( 2 ) : 400 – 402 .
   PubMedGoogle Scholar
• Berman , H.M. , Westbrook , J. , Feng , Z. , Gilliland , G. , Bhat , T.N. , Weissig , H. , … and Bourne , P.E. 2000 . The protein data bank . Nucleic Acids Research , 28 : 235 – 242 .
   PubMed Web of Science ®Google Scholar
• Bru , C. , Courcelle , E. , Carr‘ere , S. , Beausse , Y. , Dalmar , S. and Kahn , D. 2005 . The prodom database of protein domain families: More emphasis on 3D . Nucleic Acids Research , 33 : D212 – D215 .
   PubMed Web of Science ®Google Scholar
• Chen , K. , Kurgan , L.A. and Ruan , J. 2008 . Prediction of protein structural class using novel evolutionary collocation-based sequence representation . Journal of Computational Chemistry , 29 : 1596 – 1604 .
   PubMedGoogle Scholar
• Chou , K.C. 1995 . Computer-Aided Drug Discovery . Proteins: Structure, Function, and Genetics , 21 : 319 – 344 .
   PubMed Web of Science ®Google Scholar
• Chou , K.C. 2006 . Structural bioinformatics and its impact to biomedical science and drug discovery . Frontiers in Medicinal Chemistry , 3 : 455 – 502 .
   Google Scholar
• Chou , K.C. and Cai , Y.D. 2002 . Using functional domain composition and support vectormachines for prediction of protein subcellular location . Journal of Biological Chemistry , 277 : 45765 – 45769 .
   PubMed Web of Science ®Google Scholar
• Chou , K.C. and Cai , Y.D. 2004 . Predicting protein structural class by functional domain composition . Biochemical and Biophysical Research Communications , 321 : 1007 – 1009 .
   PubMed Web of Science ®Google Scholar
• Chou , K.C. and Zhang , C.T. 1995 . Prediction of protein structural classes . Critical Reviews in Biochemistry and Molecular Biology , 30 ( 4 ) : 275 – 349 .
   PubMed Web of Science ®Google Scholar
• Daughdrill , G.W. , Pielak , G.J. , Uversky , V.N. , Cortese , M.S. and Dunker , A.K. 2005 . “ Natively disordered proteins ” . In Handbook of protein folding , Edited by: Buchner , J. and Kiefhaber , T. 271 – 353 . Weinheim : Wiley-VCH, Verlag GmbH & Co. KGaA .
   Google Scholar
• Ding , Y.S. , Zhang , T.L. and Chou , K.C. 2007 . Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network . Protein and Peptide Letters , 14 : 811 – 815 .
   PubMedGoogle Scholar
• Dosztanyi , Z. and Tompa , P. 2008 . Prediction of protein disorder . Methods in Molecular Biology , 426 : 103 – 115 .
   PubMedGoogle Scholar
• Dunbrack, R.L. (2010). xml2pdb. http://dunbrack.fccc.edu/xml2pdb.php
   Google Scholar
• Dunker , A.K. , Cortese , M.S. , Romero , P. , Iakoucheva , L.M. and Uversky , V.N. 2005 . Flexible nets: The roles of intrinsic disorder in protein interaction networks . FEBS Journal , 272 : 5129 – 5148 .
   PubMed Web of Science ®Google Scholar
• Dunker , A.K. , Garner , E. , Guilliot , S. , Romero , P. , Albrecht , K. , Hart , J. , … and Villafranca , J.E. 1998 . Proceedings of the Pacific Symposium on Biocomputing , 7 : 473 – 484 .
   Google Scholar
• Dunker , A.K. , Lawson , J.D. , Brown , C.J. , Williams , R.M. , Romero , P. , Oh , J.S. , … and Obradovic , Z. 2001 . Intrinsically disordered protein . Journal of Molecular Graphics and Modelling , 19 : 26 – 59 .
   PubMed Web of Science ®Google Scholar
• Dunker , A.K. , Obradovic , Z. , Romero , P. , Garner , E.C. and Brown , C.J. 2000 . Intrinsic protein disorder in complete genomes . Proceedings of Genome Informatics. Workshop on Genome Informatics , 11 : 161 – 171 .
   PubMedGoogle Scholar
• Efron, B. (1982). The Jackknife, the Bootstrap, and other resampling plans, Society of Industrial and Applied Mathematics CBMS-NSF, Monographs.
   Google Scholar
• Fayyad, U.M., & Irani, K.B. (1993). Multi-interval discretisation of continuous valued attributes for classification learning. In Thirteenth International Joint Conference on Artificial Intelligence, (pp. 1022–1027). Morgan Kaufmann.
   Google Scholar
• Ferron , F. , Longhi , S. , Canard , B. and Karlin , D. 2006 . A practical overview of protein disorder prediction methods . Proteins , 65 ( 1 ) : 1 – 14 .
   PubMed Web of Science ®Google Scholar
• Finn , R.D. , Tate , J. , Mistry , J. , Coggill , P.C. , Sammut , J.S. , Hotz , H.R. , … and Sonnhammer , E.L. 2008 . The Pfam protein families database . Nucleic Acids Research , 36 : D281 – D288 .
   PubMed Web of Science ®Google Scholar
• Haft , D.H. , Selengut , J.D. and White , O. 2003 . The TIGRFAMs database of protein families . Nucleic Acids Research , 31 ( 1 ) : 371 – 373 .
   PubMed Web of Science ®Google Scholar
• Hall, M.A. (1999). Correlation-based feature selection for machine learning (PhD Thesis). The University of Waikato).
   Google Scholar
• Hastie , T. and Tibshirani , R. 1998 . Classification by pairwise coupling . Annals of Statistics , 26 ( 2 ) : 451 – 471 .
   Web of Science ®Google Scholar
• He , B. , Wang , K. , Liu , Y. , Xue , B. , Uversky , V.N. and Dunker , A.K. 2009 . Predicting intrinsic disorder in proteins: An overview . Cell Research , 19 : 929 – 949 .
   PubMed Web of Science ®Google Scholar
• Hulo , N. , Bairoch , A. , Bulliard , V. , Cerutti , L. , De Castro , E. , Langendijk-Genevaux , P.S. , … and Sigrist , C.J.A. 2006 . The PROSITE database . Nucleic Acids Research , 34 : D227 – D230 .
   PubMed Web of Science ®Google Scholar
• Hunter , S. , Apweiler , R. , Attwood , T.K. , Bairoch , A. , Bateman , A. , Binns , D. , … and Yeats , C. 2009 . InterPro: The integrative protein signature database . Nucleic Acids Research , 37 : D211 – D215 .
   PubMed Web of Science ®Google Scholar
• Iakoucheva , L.M. , Brown , C.J. , Lawson , J.D. , Obradovic , Z. and Dunker , A.K. 2002 . Intrinsic disorder in cell-signaling and cancer-associated proteins . Journal of Molecular Biology , 323 : 573 – 584 .
   PubMed Web of Science ®Google Scholar
• 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, 226–240.
   Google Scholar
• Kurgan , L.A. and Homaeian , L. 2006 . Prediction of structural classes for protein sequences and domains: Impact of prediction algorithms, sequence representation and homology, and test procedures on accuracy . Pattern Recognition , 39 : 2323 – 2343 .
   Google Scholar
• Letunic , I. , Copley , R.R. , Pils , B. , Pinkert , S. , Schultz , J. and Bork , P. 2006 . SMART 5: Domains in the context of genomes and networks . Nucleic Acids Research , 34 : D257 – D260 .
   PubMed Web of Science ®Google Scholar
• Luo , R.Y. , Feng , Z.P. and Liu , J.K. 2002 . Prediction of protein structural class by amino acid and polypeptide composition . European Journal of Biochemistry , 269 : 4219 – 4225 .
   PubMedGoogle Scholar
• Marwan , N. , Romano , M.C. , Thiel , M. and Kurths , J. 2007 . Recurrence plots for the analysis of complex systems . Physics Reports , 438 : 237 – 329 .
   Web of Science ®Google Scholar
• Mcguffin , L.J. , Bryson , K. and Jones , D.T. 2000 . The PSIPRED protein structure prediction server . Bioinformatics , 16 : 404 – 405 .
   PubMed Web of Science ®Google Scholar
• Mi , H. , Guo , N. , Kejariwal , A. and Thomas , P.D. 2007 . PANTHER version 6: Protein sequence and function evolution data with expanded representation of biological pathways . Nucleic Acids Research , 35 : D247 – D252 .
   Web of Science ®Google Scholar
• Mizianty , M.J. and Kurgan , L. 2009 . Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences . BMC Bioinformatics , 10 : 414 – 438 .
   PubMedGoogle Scholar
• Murzin , A.G. , Brenner , S.E. , Hubbard , T. and Chot , C. 1995 . SCOP: A structural classification of proteins database for the investigation of sequences and structures . Journal of Molecular Biology , 247 : 536 – 540 .
   PubMed Web of Science ®Google Scholar
• National Center for Biotechnology Information (NCBI). http://www.ncbi.nlm.nih.gov/protein/.
   Google Scholar
• Nikolskaya , A.N. , Arighi , C.N. , Huang , H. , Barker , W.C. and Wu , C.H. 2006 . PIRSF family classification system for protein functional and evolutionary analysis . Evolutionary Bioinformatics , 2 : 197 – 209 .
   Google Scholar
• Pearl, J. (1984). Heuristics: Intelligent search strategies for computer problem solving. Boston, MA: Addison-Wesley.
   Google Scholar
• Platt , J. 2000 . “ Probabilistic outputs for support vector machines and comparison to regularized likelihood methods ” . In Advances in large margin classifiers , Edited by: Smola , A.J. , Bartlett , P. , Scholkopf , B. and Schuurmans , D. 1 – 74 . Cambridge , MA : MIT Press .
   Google Scholar
• Radivojac , P. , Iakoucheva , L.M. , Oldfield , C.J. , Obradovic , Z. , Uversky , V.N. and Dunker , A.K. 2007 . Intrinsic disorder and functional proteomics . Biophysical Journal , 92 : 1439 – 1456 .
   PubMed Web of Science ®Google Scholar
• Sun , X.D. and Huang , R.B. 2006 . Prediction of protein structural classes using support vector machines . Amino Acids , 30 : 469 – 475 .
   PubMed Web of Science ®Google Scholar
• Tompa , P. 2002 . Trends in Biochemical Sciences , 27 : 527 – 533 .
   PubMed Web of Science ®Google Scholar
• Uversky , V.N. 2002 . Natively unfolded proteins: A point where biology waits for physics . Protein Science , 11 : 739 – 756 .
   PubMed Web of Science ®Google Scholar
• Uversky, V.N. (2010). The mysterious unfoldome: Structureless, underappreciated, yet vital part of any given proteome. J Biomed Biotechnol, 568068.
   Google Scholar
• Uversky , V.N. 2011 . Intrinsically disordered proteins from A to Z . International Journal of Biochemistry & Cell Biology , 43 ( 8 ) : 1090 – 1103 .
   PubMedGoogle Scholar
• Uversky , V.N. and Dunker , A.K. 2010 . Understanding protein non-folding . Biochimica et Biophysica Acta , 1804 : 1231 – 1264 .
   PubMed Web of Science ®Google Scholar
• Uversky , V.N. , Gillespie , J.R. and Fink , A.L. 2000 . Why are ‘natively unfolded’ proteins unstructured under physiologic conditions? . Proteins , 41 : 415 – 427 .
   PubMed Web of Science ®Google Scholar
• Uversky , V.N. , Oldfield , C.J. and Dunker , A.K. 2005 . Showing your ID: Intrinsic disorder as an ID for recognition, regulation and cell signaling . Journal of Molecular Recognition , 18 : 343 – 384 .
   PubMed Web of Science ®Google Scholar
• Uversky , V.N. , Oldfield , C.J. and Dunker , A.K. 2008 . Intrinsically disordered proteins in human diseases: Introducing the D2 concept . Annual Review of Biophysics , 37 : 215 – 246 .
   PubMed Web of Science ®Google Scholar
• Vucetic , S. , Xie , H. , Iakoucheva , L.M. , Oldfield , C.J. , Dunker , A.K. , Obradovic , Z. and Uversky , V.N. 2007 . Functional anthology of intrinsic disorder 2. Cellular components, domains, technical terms, developmental processes, and coding sequence diversities correlated with long disordered regions . Journal of Proteome Research , 6 : 1899 – 1916 .
   PubMed Web of Science ®Google Scholar
• Ward , J.J. , Sodhi , J.S. , McGuffin , L.J. , Buxton , B.F. and Jones , D.T. 2004 . Prediction and functional analysis of native disorder in proteins from the three kingdoms of life . Journal of Molecular Biology , 337 : 635 – 645 .
   PubMed Web of Science ®Google Scholar
• Wilson , D. , Madera , M. , Vogel , C. , Chothia , C. and Gough , J. 2007 . The SUPERFAMILY database in 2007: Families and functions . Nucleic Acids Research , 35 : D308 – D313 .
   PubMed Web of Science ®Google Scholar
• Witten, I.H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques. Waltham, MA: Morgan Kaufmann.
   Google Scholar
• Wright , P.E. and Dyson , H.J. 1999 . Intrinsically unstructured proteins: Reassessing the protein structure-function paradigm . Journal of Molecular Biology , 293 : 321 – 331 .
   PubMed Web of Science ®Google Scholar
• Xie , H. , Vucetic , S. , Iakoucheva , L.M. , Oldfield , C.J. , Dunker , A.K. , Obradovic , Z. and Uversky , V.N. 2007 . Functional anthology of intrinsic disorder 3. Ligands, post-translational modifications, and diseases associated with intrinsically disordered proteins . Journal of Proteome Research , 6 : 1917 – 1932 .
   PubMed Web of Science ®Google Scholar
• Xie , H. , Vucetic , S. , Iakoucheva , L.M. , Oldfield , C.J. , Dunker , A.K. , Uversky , V.N. and Obradovic , Z. 2007 . Functional anthology of intrinsic disorder 1. Biological processes and functions of proteins with long disordered regions . Journal of Proteome Research , 6 : 1882 – 1898 .
   PubMed Web of Science ®Google Scholar
• Xue , B. , Dunbrack , R.L. , Williams , R.W. , Dunker , A.K. and Uversky , V.N. 2010 . PONDR-FIT: A meta-predictor of intrinsically disordered amino acids . Biochimica et Biophysica Acta , 1804 ( 4 ) : 996 – 1010 .
   PubMed Web of Science ®Google Scholar
• Xue , B. , Oldfield , C.J. , Dunker , A.K. and Uversky , V.N. 2009 . CDF it all: Consensus prediction of intrinsically disordered proteins based on various cumulative distribution functions . FEBS Letters , 583 ( 9 ) : 1469 – 1474 .
   PubMed Web of Science ®Google Scholar
• Yang, J.Y., Peng, Z.L., & Chen, X. (2010). Prediction of protein structural classes for low-homology sequences based on predicted secondary structure. BMC Bioinformatics S9(11).
   Google Scholar
• Yang , J.Y. , Peng , Z.L. , Yu , Z.G. , Zhang , R.J. , Anh , V. and Wang , D. 2009 . Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation . Journal of Theoretical Biology , 257 : 618 – 626 .
   PubMed Web of Science ®Google Scholar
• Yeats , C. , Lees , J. , Reid , A. , Kellam , P. , Martin , N. , Liu , X. and Orengo , C. 2008 . Gene3D: Comprehensive structural and functional annotation of genomes . Nucleic Acids Research , 36 : D414 – D418 .
   Google Scholar
• Zdobnov , E.M. and Apweiler , R. 2001 . InterProScan–an integration platform for the signature-recognition methods in InterPro . Bioinformatics , 17 ( 9 ) : 847 – 848 .
   PubMed Web of Science ®Google Scholar