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Link to original content: http://pubmed.ncbi.nlm.nih.gov/38989563/
Complete combinatorial mutational enumeration of a protein functional site enables sequence-landscape mapping and identifies highly-mutated variants that retain activity - PubMed Skip to main page content
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. 2024 Aug;33(8):e5109.
doi: 10.1002/pro.5109.

Complete combinatorial mutational enumeration of a protein functional site enables sequence-landscape mapping and identifies highly-mutated variants that retain activity

Mireia Solà Colom  1   2 , Jelena Vučinić  3 , Jared Adolf-Bryfogle  1   2 , James W Bowman  1   2 , Sébastien Verel  4 , Isabelle Moczygemba  1   2 , Thomas Schiex  5 , David Simoncini  3 , Christopher D Bahl  1   2
Affiliations

Complete combinatorial mutational enumeration of a protein functional site enables sequence-landscape mapping and identifies highly-mutated variants that retain activity

Mireia Solà Colom et al. Protein Sci. 2024 Aug.

Abstract

Understanding how proteins evolve under selective pressure is a longstanding challenge. The immensity of the search space has limited efforts to systematically evaluate the impact of multiple simultaneous mutations, so mutations have typically been assessed individually. However, epistasis, or the way in which mutations interact, prevents accurate prediction of combinatorial mutations based on measurements of individual mutations. Here, we use artificial intelligence to define the entire functional sequence landscape of a protein binding site in silico, and we call this approach Complete Combinatorial Mutational Enumeration (CCME). By leveraging CCME, we are able to construct a comprehensive map of the evolutionary connectivity within this functional sequence landscape. As a proof of concept, we applied CCME to the ACE2 binding site of the SARS-CoV-2 spike protein receptor binding domain. We selected representative variants from across the functional sequence landscape for testing in the laboratory. We identified variants that retained functionality to bind ACE2 despite changing over 40% of evaluated residue positions, and the variants now escape binding and neutralization by monoclonal antibodies. This work represents a crucial initial stride toward achieving precise predictions of pathogen evolution, opening avenues for proactive mitigation.

Keywords: SARS‐CoV‐2; artificial intelligence; evolution; neutralizing antibody; protein binding; protein design; protein structure; structure prediction; virology.

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Conflict of interest statement

MSC, JTB, IM and CDB own stock in AI Proteins, Inc.

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