Fallaize, CJ, Green, PJ, Mardia, KV orcid.org/0000-0003-0090-6235 et al. (1 more author) (2020) Bayesian protein sequence and structure alignment. Journal of the Royal Statistical Society: Series C (Applied Statistics), 69 (2). pp. 301-325. ISSN 0035-9254
Abstract
The structure of a protein is crucial in determining its functionality and is much more conserved than sequence during evolution. A key task in structural biology is to compare protein structures to determine evolutionary relationships, to estimate the function of newly discovered structures and to predict unknown structures. We propose a Bayesian method for protein structure alignment, with the prior on alignments based on functions which penalize ‘gaps’ in the aligned sequences. We show how a broad class of penalty functions fits into this framework, and how the resulting posterior distribution can be efficiently sampled. A commonly used gap penalty function is shown to be a special case, and we propose a new penalty function which alleviates an undesirable feature of the commonly used penalty. We illustrate our method on benchmark data sets and find that it competes well with popular tools from computational biology. Our method has the benefit of being able potentially to explore multiple competing alignments and to quantify their merits probabilistically. The framework naturally enables further information such as amino acid sequence to be included and could be adapted to other situations such as flexible proteins or domain swaps.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Royal Statistical Society. This is the peer reviewed version of the following article: Fallaize, C.J., Green, P.J., Mardia, K.V. and Barber, S. (2020), Bayesian protein sequence and structure alignment. J. R. Stat. Soc. C, 69: 301-325. doi:10.1111/rssc.12394, which has been published in final form at https://doi.org/10.1111/rssc.12394. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Gap penalty prior; Markov chain Monte Carlo sampling; Protein structure alignment; Structural bioinformatics; Unlabelled shape analysis |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Nov 2019 12:17 |
Last Modified: | 08 Jan 2021 01:38 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1111/rssc.12394 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:153794 |