Complete Combinatorial Mutational Enumeration of a protein functional site enables sequence-landscape mapping and identifies highly-mutated variants that retain activity.

Autor:	Colom MS; Institute for Protein Innovation; Boston, Massachusetts, 02115, USA.; Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School; Boston, Massachusetts, 02115, USA.; current address: AI Proteins; Boston, Massachusetts, 02215, USA., Vucinic J; Université Fédérale de Toulouse; ANITI, IRIT-CNRS UMR 5505, Université Toulouse Capitole, 31000 Toulouse, France., Adolf-Bryfogle J; Institute for Protein Innovation; Boston, Massachusetts, 02115, USA.; Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School; Boston, Massachusetts, 02115, USA., Bowman JW; Institute for Protein Innovation; Boston, Massachusetts, 02115, USA.; Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School; Boston, Massachusetts, 02115, USA.; current address: AI Proteins; Boston, Massachusetts, 02215, USA., Verel S; Université Littoral Côte d'Opale; UR 4491, LISIC, F-62100 Calais, France., Moczygemba I; Institute for Protein Innovation; Boston, Massachusetts, 02115, USA.; Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School; Boston, Massachusetts, 02115, USA.; current address: AI Proteins; Boston, Massachusetts, 02215, USA., Schiex T; Université Fédérale de Toulouse; ANITI, INRAE-UR 875, 31000 Toulouse, France., Simoncini D; Université Fédérale de Toulouse; ANITI, IRIT-CNRS UMR 5505, Université Toulouse Capitole, 31000 Toulouse, France., Bahl CD; Institute for Protein Innovation; Boston, Massachusetts, 02115, USA.; Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School; Boston, Massachusetts, 02115, USA.; current address: AI Proteins; Boston, Massachusetts, 02215, USA.
Jazyk:	angličtina
Zdroj:	Research square [Res Sq] 2023 Sep 11. Date of Electronic Publication: 2023 Sep 11.
DOI:	10.21203/rs.3.rs-2248327/v2
Abstrakt:	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 towards achieving precise predictions of pathogen evolution, opening avenues for proactive mitigation. Competing Interests: Competing interests MSC, JTB, IM and CDB own stock in AI Proteins, Inc.
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu