Accelerating the prediction and discovery of peptide hydrogels with human-in-the-loop.

ABSTRACT: The amino acid sequences of peptides determine their self-assembling properties. Accurate prediction of peptidic hydrogel formation, however, remains a challenging task. This work describes an interactive approach involving the mutual information exchange between experiment and machine learning for robust prediction and design of (tetra)peptide hydrogels. We chemically synthesize more than 160 natural tetrapeptides and evaluate their hydrogel-forming ability, and then employ machine learning-experiment iterative loops to improve the accuracy of the gelation prediction. We construct a score function coupling the aggregation propensity, hydrophobicity, and gelation corrector C_g, and generate an 8,000-sequence library, within which the success rate of predicting hydrogel formation

SUBMITTER: Xu T 

PROVIDER: S-EPMC10313671 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature