Year: 2024
Type: article
Abstract: Studies determining protein-protein interactions (PPIs) by deep mutational scanning have focused mainly on a narrow range of affinities within complexes and thus include only partial coverage of the mutation space of given proteins. By inserting an affinity-reducing N-terminal alanine in the N-terminal domain of the tissue inhibitor of metalloproteinases-2 (N-TIMP2), we overcame the limitation of its narrow affinity range for matrix metalloproteinase 9 (MMP9 CAT ). We trained deep neural networks (DNNs) to quantitatively predict the binding affinity of unobserved wild-type variants and variants carrying an N-terminal alanine. Good correlation was obtained between predicted and observed log 2 enrichment ratio (ER) values, which also correlated with the affinity of N-TIMP2 variants to MMP9 CAT . Our ability to predict affinities of unobserved N-TIMP2 variants was confirmed on an independent dataset of experimentally validated N-TIMP2 proteins. This ability is of significant importance in the field of PPI prediction and for developing therapies targeting these interactions.
Source: iScience
Institutions: Ben-Gurion University of the Negev, Bar-Ilan University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,150
Funders: Israel Cancer Research Fund, World Cancer Research Fund International, Rosetrees Trust, United States-Israel Binational Science Foundation, Israel Cancer Association, Ben-Gurion University of the Negev, Council for Higher Education, National Institutes of Health, World Cancer Research Fund, National Institute of General Medical Sciences, Division of Chemistry
OpenAlex ID: W4401711307