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Low Rank Approximation Methods for Identifying Impactful Pairwise Protein Mutations

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Algorithms and Methods in Structural Bioinformatics

Abstract

Assessing how an amino acid mutation impacts protein structure and stability provides insights that advance drug design efforts for combating a variety of debilitating diseases. Performing point mutations via mutagenesis experiments on physical proteins to infer the effects of the mutations is time consuming even for a single amino acid substitution, let alone for all possible mutations. Computational approaches are available for generating mutant decoys and assessing them en masse to infer which mutations are impactful. However due to the combinatorial increase in the count of possible decoys with two mutations, even computational approaches have their limitations. In this work we have generated in silico for several proteins the sets of exhaustive mutants with two amino acid substitutions. We explore how to reconstruct from the exhaustive sets the information about which pairwise mutations are impactful, with the added goal that we sample as few decoys as possible. Previously, we sampled a subset of the ground truth and attempted a reconstruction of the data using singular value decomposition. Here we introduce a new tailored approach that decomposes the result into two components, the low-rank and sparse matrix. We demonstrate that this approach outperforms previous attempts at low sampling percentages with a variety of biologically inspired and random sampling strategies.

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Correspondence to Filip Jagodzinski .

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Daw, C., Cruz, B.B., Majeske, N., Jagodzinski, F., Islam, T., Hutchinson, B. (2022). Low Rank Approximation Methods for Identifying Impactful Pairwise Protein Mutations. In: Haspel, N., Jagodzinski, F., Molloy, K. (eds) Algorithms and Methods in Structural Bioinformatics. Computational Biology. Springer, Cham. https://doi.org/10.1007/978-3-031-05914-8_4

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  • DOI: https://doi.org/10.1007/978-3-031-05914-8_4

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