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Annual International Conference on Research in Computational Molecular Biology

RECOMB 2009: Research in Computational Molecular Biology pp 31–45Cite as

Boosting Protein Threading Accuracy

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 5541))

Abstract

Protein threading is one of the most successful protein structure prediction methods. Most protein threading methods use a scoring function linearly combining sequence and structure features to measure the quality of a sequence-template alignment so that a dynamic programming algorithm can be used to optimize the scoring function. However, a linear scoring function cannot fully exploit interdependency among features and thus, limits alignment accuracy.

This paper presents a nonlinear scoring function for protein threading, which not only can model interactions among different protein features, but also can be efficiently optimized using a dynamic programming algorithm. We achieve this by modeling the threading problem using a probabilistic graphical model Conditional Random Fields (CRF) and training the model using the gradient tree boosting algorithm. The resultant model is a nonlinear scoring function consisting of a collection of regression trees. Each regression tree models a type of nonlinear relationship among sequence and structure features. Experimental results indicate that this new threading model can effectively leverage weak biological signals and improve both alignment accuracy and fold recognition rate greatly.

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Author information

Authors and Affiliations

  1. Toyota Technological Institute at Chicago, Chicago, IL, USA, 60637

    Jian Peng & Jinbo Xu

Authors
  1. Jian Peng
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  2. Jinbo Xu
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Editors and Affiliations

  1. Computer Science Department, James H. Clark Center, 318 Campus Drive, RM S266, CA 94305-5428,, Stanford, USA

    Serafim Batzoglou

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© 2009 Springer-Verlag Berlin Heidelberg

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Peng, J., Xu, J. (2009). Boosting Protein Threading Accuracy. In: Batzoglou, S. (eds) Research in Computational Molecular Biology. RECOMB 2009. Lecture Notes in Computer Science(), vol 5541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02008-7_3

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  • DOI: https://doi.org/10.1007/978-3-642-02008-7_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02007-0

  • Online ISBN: 978-3-642-02008-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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