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Bayesian Multiple Protein Structure Alignment

  • Conference paper
Research in Computational Molecular Biology (RECOMB 2014)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 8394))

Abstract

Multiple protein structure alignment is an important tool in computational biology, with numerous algorithms published in the past two decades. However, recently literature highlights a growing recognition of the inconsistencies among alignments from different algorithms, and the instability of alignments obtained by individual algorithms under small fluctuations of the input structures. Here we present a probabilistic model-based approach to the problem of multiple structure alignment, using an explicit statistical model. The resulting algorithm produces a Bayesian posterior distribution over alignments which accounts for alignment uncertainty arising from evolutionary variability, experimental noise, and thermal fluctuation, as well as sensitivity to alignment algorithm parameters. We demonstrate the robustness of this approach on alignments identified previously in the literature as “difficult” for existing algorithms. We also show the potential for significant stabilization of tree reconstruction in structural phylogenetics.

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Author information

Authors and Affiliations

  1. Program in Computational Biology & Bioinformatics, Departments of Statistical Science and Computer Science, Duke University, NC, 27708, USA

    Rui Wang & Scott C. Schmidler

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  1. Rui Wang
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  2. Scott C. Schmidler
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Editors and Affiliations

  1. School of Computer Science, Tel Aviv University, 69978, Tel Aviv, Israel

    Roded Sharan

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© 2014 Springer International Publishing Switzerland

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Wang, R., Schmidler, S.C. (2014). Bayesian Multiple Protein Structure Alignment. In: Sharan, R. (eds) Research in Computational Molecular Biology. RECOMB 2014. Lecture Notes in Computer Science(), vol 8394. Springer, Cham. https://doi.org/10.1007/978-3-319-05269-4_27

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  • DOI: https://doi.org/10.1007/978-3-319-05269-4_27

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05268-7

  • Online ISBN: 978-3-319-05269-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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