Abstract
For the past decades, many efforts have been made in the fields of protein structure prediction. Among these, the protein backbone reconstruction problem (PBRP) has attracted much attention. The goal of PBRP is to reconstruct the 3D coordinates of all atoms along the protein backbone for given a target protein sequence and its C\(_{\alpha }\) coordinates. In order to improve the prediction accuracy, we attempt to refine the 3D coordinates of all backbone atoms by incorporating the state-of-the-art prediction softwares and support vector regression (SVR). We use the predicted coordinates of two excellent methods, PD2 and BBQ, as our feature candidates. Accordingly, we define more than 100 possible features. By means of the correlation analysis, we can identify several significant features deeply related to the prediction target. Then, a 5-fold cross validation is carried out to perform the experiments, in which the involved datasets range from CASP7 to CASP11. As the experimental results show, our method yields about 8 % improvement in RMSD over PD2, which is the most accurate predictor for the problem.
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References
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011)
Chang, H.Y., Yang, C.B., Ann, H.Y.: Refinement on O atom positions for protein backbone prediction. In: Proceedings of the 2nd WSEAS International Conference on Biomedical Electronics and Biomedical Informatics (BEBI 2009), Moscow, Russia, pp. 99–104 (2009)
Chen, K.Y., Yang, C.B., Huang, K.S.: Prediction of protein backbone structure by preference classification with SVM. In: Proceedings of the 9th International Conference on Information Systems and Technology Management, Sao Paulo, Brazil, pp. 1193–1206 (2012)
Gront, D., Kmiecik, S., Kolinski, A.: Backbone building from quadrilaterals: a fast and accurate algorithm for protein backbone reconstruction from alpha carbon coordinates. J. Comput. Chem. 28, 1593–1597 (2007)
Holm, L., Sander, C.: Database algorithm for generating protein backbone and side-chain coordinates from a C alpha trace application to model building and detection of coordinate errors. J. Mol. Biol. 21(1), 183–194 (1991)
Eisenberg, D., Bowie, J.U., Luthy, R.: A method to identify protein sequences that fold into a known three-dimensional structure. Science 253, 164–170 (1991)
Kabsch, W.: A solution for the best rotation to relate two sets of vectors. Acta Crystallogr. Sect. A 32, 922–923 (1976)
Kabsch, W.: A discussion of the solution for the best rotation to relate two sets of vectors. Acta Crystallogr. Sect. A 34, 827–828 (1978)
Kazmierkiewicz, R., Liwo, A., Scheraga, H.A.: Energy-based reconstruction of a protein backbone from its \(\alpha \)-carbon trace by a Monte-Carlo method. J. Comput. Chem. 23, 715–723 (2002)
Krasnogor, N., Hart, W.E., Smith, J., Pelta, D.A.: Protein structure prediction with evolutionary algorithms. In: Proceedings of the Genetic and Evolutionary Compution Conference, Orlando, USA, pp. 1596–1601 (1999)
Maupetit, J., Gautier, R., Tuffery, P.: SABBAC: online structural alphabet-based protein backbone reconstruction from alpha-carbon trace. Nucleic Acids Res. 34, W147–W151 (2006)
Moore, B.L., Kelley, L.A., Barber, J., Murray, J., MacDonald, J.T.: High-quality protein backbone reconstruction from alpha carbons using Gaussian mixture models. J. Comput. Chem. 34, 1881–1889 (2013)
Moult, J., Fidelis, K., Kryshtafovych, A., Rost, B., Tramontano, A.: Critical assessment of methods of protein structure prediction (CASP) x Round IX. Proteins 79, 1–5 (2011)
Ruczinski, I., Kooperberg, C., Bonneau, R., Baker, D.: Distribution of beta sheets in proteins with application to structure prediction. Proteins: Struct. Funct. Genet. 48, 85–97 (2008)
Smola, A.J., Scholkopf, B.: A tutorial on support vector regression. Stat. Comput. 14, 199–222 (2004)
Vapnik, V., Golowich, S.E., Smola, A.: Support vector method for function approximation, regression estimation, and signal processing. In: Advances in Neural Information Processing Systems 9, pp. 281–287. MIT Press (1996)
Wang, J.H., Yang, C.B., Tseng, C.T.: Reconstruction of protein backbone with the \(\alpha \)-carbon coordinates. J. Inf. Sci. Eng. 26(3), 1107–1119 (2010)
Wu, H.F., Yang, C.B., Hor, C.Y., Peng, Y.H., Tseng, K.T.: Protein backbone reconstruction with tool preference classification for standard and nonstandard proteins. In: Proceedings of the 12th Conference on Information Technology and Applications in Outlying Islands, Kinmen, Taiwan, pp. 175–182 (2013)
Yuan, H.C.: A survey of computational methods for protein structure prediction. Master’s thesis, National Sun Yat-sen University, Kaohsiung, Taiwan, July 2015
Acknowledgments
This research work was partially supported by the Ministry of Science and Technology of Taiwan under contract MOST 104-2221-E-110-018-MY3.
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Huang, DY., Hor, CY., Yang, CB. (2016). Coordinate Refinement on All Atoms of the Protein Backbone with Support Vector Regression. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2016. Lecture Notes in Computer Science(), vol 9728. Springer, Cham. https://doi.org/10.1007/978-3-319-41561-1_16
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DOI: https://doi.org/10.1007/978-3-319-41561-1_16
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