Support vector training of protein alignment models.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
Chun-Nam John YuJaroslaw Pillardy

Abstract

Sequence to structure alignment is an important step in homology modeling of protein structures. Incorporation of features such as secondary structure, solvent accessibility, or evolutionary information improve sequence to structure alignment accuracy, but conventional generative estimation techniques for alignment models impose independence assumptions that make these features difficult to include in a principled way. In this paper, we overcome this problem using a Support Vector Machine (SVM) method that provides a well-founded way of estimating complex alignment models with hundred of thousands of parameters. Furthermore, we show that the method can be trained using a variety of loss functions. In a rigorous empirical evaluation, the SVM algorithm outperforms the generative alignment method SSALN, a highly accurate generative alignment model that incorporates structural information. The alignment model learned by the SVM aligns 50% of the residues correctly and aligns over 70% of the residues within a shift of four positions.

References

Nov 15, 1992·Proceedings of the National Academy of Sciences of the United States of America·S Henikoff, J G Henikoff
Mar 25, 1981·Journal of Molecular Biology·T F Smith, M S Waterman
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Nov 10, 2004·Proceedings of the National Academy of Sciences of the United States of America·Lior Pachter, Bernd Sturmfels
Apr 26, 2005·Nucleic Acids Research·Yang Zhang, Jeffrey Skolnick

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Citations

Apr 10, 2010·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·John KececiogluTravis Wheeler
Aug 17, 2011·The Journal of Chemical Physics·D V S Ravikant, Ron Elber
Dec 21, 2010·Journal of Chemical Information and Modeling·Fabian RathkeKlaus-Robert Müller
May 16, 2012·Journal of Chemical Information and Modeling·Alexandre Varnek, Igor Baskin

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