Self-supervised domain adaptation of protein language model based solely on positive enzyme-reaction pairs.

ABSTRACT: There is growing interest in developing predictive models of enzyme catalytic properties that leverage activity data spanning diverse enzyme families. A fundamental challenge lies in the inherent biases of public biochemical databases. These databases predominantly catalog valid enzyme activities, rarely include negative instances, and report quantitative catalytic parameters for only a relatively small subset of enzymes. Such limitations pose a major obstacle to supervised learning of enzyme catalytic properties. One existing approach for model training involves generating synthetic negative enzyme-activity pairs by recombining existing enzymes and their activity information, particularly substrates or chemical reactions, that were not originally associated within datasets. However, it re

SUBMITTER: Okuno T 

PROVIDER: S-EPMC12712682 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature