Abstract
Prediction of backbone torsion angles provides important constraints about the 3D structure of a protein and is receiving a growing interest in the structure prediction community. In this paper, we introduce a three-stage machine learning classifier to predict the 7-state torsion angles of a protein. The first two stages employ dynamic Bayesian and neural networks to produce an ab-initio prediction of torsion angle states starting from sequence profiles. The third stage is a committee classifier, which combines the ab-initio prediction with a structural frequency profile derived from templates obtained by HHsearch. We develop several structural profile models and obtain significant improvements over the Laplacian scoring technique through: (1) scaling templates by integer powers of sequence identity score, (2) incorporating other alignment scores as multiplicative factors (3) adjusting or optimizing parameters of the profile models with respect to the similarity interval of the target. We also demonstrate that the torsion angle prediction accuracy improves at all levels of target-template similarity even when templates are distant from the target. The improvement is at significantly higher rates as template structures gradually get closer to target.
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References
Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997)
Aydin, Z., Singh, A., Bilmes, J., Noble, W.S.: Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure. BMC Bioinform. 12, 154 (2011)
Aydin, Z., Thompson, J., Bilmes, J., Baker, D., Noble, W. S.: Protein torsion angle class prediction by a hybrid architecture of bayesian and neural networks. In: 13th International Conference on Bioinformatics and Computational Biology (2012)
Berjanskii, M.V., Neal, S., Wishart, D.S.: PREDITOR: a web server for predicting protein torsion angle restraints. Nucleic Acids Res. 34, W63–W69 (2006). (Web Server Issue)
Blum, B., Jordan, M., Kim, D., Das, R., Bradley, P., Baker, D.: Feature selection methods for improving protein structure prediction with Rosetta. In: Platt, J., Koller, D., Singer, Y., Roweis, S. (eds.) Advances in Neural Information Processing Systems 20, pp. 137–144. MIT Press, Cambridge (2008)
Cheng, J., Tegge, A.N., Baldi, P.: Machine learning methods for protein structure prediction. IEEE Rev. Biomed. Eng. 1, 41–49 (2008)
Cong, P., Li, D., Wang, Z., Tang, S., Li, T.: Spssm8: an accurate approach for predicting eight-state secondary structures of proteins. Biochimie 95(12), 2460–2464 (2013)
Faraggi, E., Zhang, T., Yang, Y., Kurgan, L., Zhou, Y.: SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles. PLoS One 7(2), e30361 (2012)
Henikoff, S., Henikoff, J.G.: Position-based sequence weights. J. Mol. Biol. 243, 574–578 (1994)
Hobohm, U., Sander, C.: Enlarged representative set of protein structures. Protein Sci. 3, 522–524 (1994)
Jones, D.T.: Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292, 195–202 (1999)
Li, D., Li, T., Cong, P., Xong, W., Sun, J.: A novel structural position-specific scoring matrix for the prediction of protein secondary structures. Bioinformatics 28(1), 32–39 (2012)
Mooney, C., Pollastri, G.: Beyond the twilight zone: automated prediction of structural properties of proteins by recursive neural networks and remote homology information. Proteins Struct. Funct. Bioinform. 77, 181–190 (2009)
Pollastri, G., Martin, A.J.M., Mooney, C., Vullo, A.: Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information. BMC Bioinform. 8, 201 (2007)
Rangwala, H., Karypis, G.: Introduction to Protein Structure Prediction: Methods and Algorithms. Wiley, Hoboken (2011)
Remmert, M., Biegert, A., Hauser, A., Soding, J.: Hhblits: lightning-fast iterative protein sequence searching by hmm-hmm alignment. Nat. Meth. 9(2), 173–175 (2011)
Shen, Y., Delaglio, F., Cornilescu, G., Bax, A.: TALOS+: a hybrid method for predicting protein backbone torsion angles from nmr chemical shifts. J. Biomol. NMR 44(4), 213–223 (2009)
Singh, H., Singh, S., Raghava, G.P.S.: Evaluation of protein dihedral angle prediction methods. PLoS One 9(8), e105667 (2014)
Soding, J.: Protein homology detection by HMM-HMM comparison. Bioinformatics 21, 951–960 (2005)
Soding, J.: Quick guide to HHsearch (2006). ftp://toolkit.genzentrum.lmu.de/pub/HHsearch/old/HHsearch/HHsearch1.5.1/HHsearch-guide.pdf
Soding, J., Remmert, M., Hauser, A.: HH-suite for sensitive sequence searching based on HMM-HMM alignment (2012). ftp://toolkit.genzentrum.lmu.de/pub/HH-suite/hhsuite-userguide.pdf
Song, J., Tan, H., Wang, M., Webb, G.I., Akutsu, T.: TANGLE: two-level support vector regression approach for protein backbone torsion angle prediction from primary sequences. PLoS One 7(2), e30361 (2012)
Sun, J., Tang, S., Xiong, W., Cong, P., Li, T.: Dsp: a protein shape string and its profile prediction server. Nucleic Acids Res. 40(W1), W298–W302 (2012)
Walsh, I., Bau, D., Martin, A.J.M., Mooney, C., Vullo, A., Pollastri, G.: Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks. BMC Struct. Biol. 9, 5 (2009)
Wang, G., Dunbrack Jr., R.L.: PISCES: a protein sequence culling server. Bioinformatics 19, 1589–1591 (2003). http://dunbrack.fccc.edu/PISCES.php
Wang, G., Dunbrack Jr., R.L.: PISCES: recent improvements to a pdb sequence culling server. Nucleic Acids Res. 33, W94–W98 (2005)
Wu, S., Zhang, Y.: ANGLOR: A composite machine-learning algorithm for protein backbone torsion angle prediction. PLoS One 3(10), e3400 (2008)
Wu, S., Zhang, Y.: MUSTER: improving protein sequence profile-profile alignments by using multiple sources of structure information. Proteins Struct. Funct. Bioinform. 72(2), 547–556 (2008)
Zemla, A., Venclovas, C., Fidelis, K., Rost, B.: A modified definition of Sov, a segment-based measure for protein secondary structure prediction assessment. Proteins 34, 220–223 (1999)
Zhou, Y., Duan, Y., Yang, Y., Faraggi, E., Lei, H.: Trends in template/fragment-free protein structure prediction. Theo. Chem. Acc. 128, 3–16 (2011)
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Aydin, Z., Baker, D., Noble, W.S. (2015). Template Scoring Methods for Protein Torsion Angle Prediction. In: Fred, A., Gamboa, H., Elias, D. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2015. Communications in Computer and Information Science, vol 574. Springer, Cham. https://doi.org/10.1007/978-3-319-27707-3_13
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