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Epistasis meets pleiotropy in shaping biophysical protein subspaces associated with antimicrobial resistance.
Ogbunugafor, C Brandon; Guerrero, Rafael F; Shakhnovich, Eugene I; Shoulders, Matthew D.
Afiliação
  • Ogbunugafor CB; Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT.
  • Guerrero RF; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA.
  • Shakhnovich EI; Santa Fe Institute, Santa Fe, NM.
  • Shoulders MD; Department of Biological Sciences, North Carolina State University, Raleigh, NC.
bioRxiv ; 2023 Apr 09.
Article em En | MEDLINE | ID: mdl-37066177
ABSTRACT
Protein space is a rich analogy for genotype-phenotype maps, where amino acid sequence is organized into a high-dimensional space that highlights the connectivity between protein variants. It is a useful abstraction for understanding the process of evolution, and for efforts to engineer proteins towards desirable phenotypes. Few framings of protein space consider how higher-level protein phenotypes can be described in terms of their biophysical dimensions, nor do they rigorously interrogate how forces like epistasis-describing the nonlinear interaction between mutations and their phenotypic consequences-manifest across these dimensions. In this study, we deconstruct a low-dimensional protein space of a bacterial enzyme (dihydrofolate reductase; DHFR) into "subspaces" corresponding to a set of kinetic and thermodynamic traits [(kcat, KM, Ki, and Tm (melting temperature)]. We then examine how three mutations (eight alleles in total) display pleiotropy in their interactions across these subspaces. We extend this approach to examine protein spaces across three orthologous DHFR enzymes (Escherichia coli, Listeria grayi, and Chlamydia muridarum), adding a genotypic context dimension through which epistasis occurs across subspaces. In doing so, we reveal that protein space is a deceptively complex notion, and that the process of protein evolution and engineering should consider how interactions between amino acid substitutions manifest across different phenotypic subspaces.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Risk_factors_studies Idioma: En Revista: BioRxiv Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Risk_factors_studies Idioma: En Revista: BioRxiv Ano de publicação: 2023 Tipo de documento: Article