Abstract
Intrinsically disordered regions in proteins are relatively frequent and important for our understanding of molecular recognition and assembly, and protein structure and function. From an algorithmic standpoint, flagging large disordered regions is also important for ab initio protein structure prediction methods. Here we first extract a curated, non-redundant, data set of protein disordered regions from the Protein Data Bank and compute relevant statistics on the length and location of these regions. We then develop an ab initio predictor of disordered regions called DISpro which uses evolutionary information in the form of profiles, predicted secondary structure and relative solvent accessibility, and ensembles of 1D-recursive neural networks. DISpro is trained and cross validated using the curated data set. The experimental results show that DISpro achieves an accuracy of 92.8% with a false positive rate of 5%. DISpro is a member of the SCRATCH suite of protein data mining tools available through http://www.igb.uci.edu/servers/psss.html.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Altschul, S., Madden, T., Schaffer, A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D. 1997. Gapped blast and psi-blast: A new generation of protein database search programs. Nucleic Acids Res., 25(17):3389–3402.
Baldi, P. and Pollastri, G. 2003. The principled design of large-scale recursive neural network architectures–DAG-RNNs and the protein structure prediction problem. Journal of Machine Learning Research, 4:575–602.
Bengio, Y. and Frasconi, P. 1996. Input-output HMM's for sequence processing. IEEE Transactions on Neural Networks, 7(5):1231–1249.
Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., and Bourne, P. 2000. The protein data bank. Nucleic Acids Research, 28:235–242.
Dunker, A.K., Brown, C.J., Lawson, J.D., Iakoucheva, L.M. and Obradovic, Z. 2002. Intrinsic disorder and protein function. Biochemistry, 41(21):6573–6582.
Frasconi, P., Passerini, A., and Vullo, A. 2002. A two-stage svm architecture for predicting the disulfide bonding state of cysteines. In Proc. IEEE Workshop on Neural Networks for Signal Processing, pp. 25–34.
Jones, D.T. 1999. Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol., 292:195–202.
Kabsch, W. and Sander, C. 1983. Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, 22:2577–2637.
Li, X., Romero, P., Rani, M., Dunker, A., and Obradovic, Z. 1999. Predicting protein disorder for n-, c-, and internal regions. Genome Inform., 42:38–48.
Linding, R., Jensen, L.J., Diella, F., Bork, P., Gibson, T.J., and Russell, R.B. 2003. Protein disorder prediction: Implications for structural proteomics. Structure, 11(11):1453–1459.
Mika, S. and Rost, B. 2003. Uniqueprot: Creating representative protein-sequence sets. Nucleic Acids Res., 31(13):3789–3791.
Pollastri, G., Baldi, P., Fariselli, P. and Casadio, R. 2001a. Prediction of coordination number and relative solvent accessibility in proteins. Proteins, 47:142–153.
Pollastri, G., Przybylski, D., Rost, B., and Baldi, P. 2001b. Improving the prediction of protein secondary strucure in three and eight classes using recurrent neural networks and profiles. Proteins, 47:228–235.
Przybylski, D. and Rost, B. 2002. Alignments grow, secondary structure prediction improves. Proteins, 46:195–205.
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(3):635–645.
Wootton, J. 1994. Non-globular domains in protein sequences: Automated segmentation using complexity measures. Computational Chemistry, 18:269–285.
Wright, P.E. and Dyson, H.J. 1999. Intrinsically unstructured proteins: Re-assessing the protein structure-function paradigm. Journal of Molecular Biology, 293(2):321–331.
Acknowledgements
The authors wish to thank anonymous reviewers for helpful comments. Work supported by the Institute for Genomics and Bioinformatics at UCI and a Laurel Wilkening Faculty Innovation award, an NIH Biomedical Informatics Training grant (LM-07443-01), an NSF MRI grant (EIA-0321390), a Sun Microsystems award, a grant from the University of California Systemwide Biotechnology Research and Education Program (UC BREP) to PB.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Cheng, J., Sweredoski, M.J. & Baldi, P. Accurate Prediction of Protein Disordered Regions by Mining Protein Structure Data. Data Min Knowl Disc 11, 213–222 (2005). https://doi.org/10.1007/s10618-005-0001-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10618-005-0001-y