Bayesian Protein Sequence and Structure Alignment. Issue 2 (8th January 2020)

• Record Type: Journal Article
• Authors: Fallaize, Christopher J.; Green, Peter J.; Mardia, Kanti V.; Barber, Stuart
• Is Part Of: Journal of the Royal Statistical Society. Volume 69:Issue 2(2020)
• Page Start: 301
• Page End: 325
• Publication Date: 2020-01-08
• Subjects: Gap penalty prior -- Markov chain Monte Carlo sampling -- Protein structure alignment -- Structural bioinformatics -- Unlabelled shape analysis; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12394
• Languages: English
• ISSNs: 0035-9254

Abstract

Summary: 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.