Singular value decomposition of protein sequences as a method to visualize sequence and residue space.

ABSTRACT: Singular value decomposition (SVD) of multiple sequence alignments (MSAs) is an important and rigorous method to identify subgroups of sequences within the MSA, and to extract consensus and covariance sequence features that define the alignment and distinguish the subgroups. This information can be correlated to structure, function, stability, and taxonomy. However, the mathematics of SVD is unfamiliar to many in the field of protein science. Here, we attempt to present an intuitive yet comprehensive description of SVD analysis of MSAs. We begin by describing the underlying mathematics of SVD in a way that is both rigorous and accessible. Next, we use SVD to analyze sequences generated with a simplified model in which the extent of sequence conservation and covariance between different pos

SUBMITTER: Baxter-Koenigs AR 

PROVIDER: S-EPMC9514065 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature