Deep neural networks for predicting the affinity landscape of protein-protein interactions.

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 log₂ enrichment ratio (ER) values, which also correlated with the affinity of N-TIMP2

SUBMITTER: Meiri R 

PROVIDER: S-EPMC11416218 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature