Sensitive inference of alignment-safe intervals from biodiverse protein sequence clusters using EMERALD
2023
Andreas Grigorjew | Artur Gynter | Fernando H. C. Dias | Benjamin Buchfink | Hajk-Georg Drost | Alexandru I. Tomescu
Abstract Sequence alignments are the foundations of life science research, but most innovation so far focuses on optimal alignments, while information derived from suboptimal solutions is ignored. We argue that one optimal alignment per pairwise sequence comparison is a reasonable approximation when dealing with very similar sequences but is insufficient when exploring the biodiversity of the protein universe at tree-of-life scale. To overcome this limitation, we introduce pairwise alignment-safety to uncover the amino acid positions robustly shared across all suboptimal solutions. We implement EMERALD, a software library for alignment-safety inference, and apply it to 400k sequences from the SwissProt database.
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