Improving protein solubility and activity by introducing small peptide tags designed with machine learning models.

ABSTRACT: Improving catalytic ability of enzymes is critical to the success of many metabolic engineering projects, but the search space of possible protein mutants is too large to explore exhaustively through experiments. To some extent, highly soluble enzymes tend to exhibit high activity due to their better folding quality. Here, we demonstrate that an optimization algorithm based on a regression model can effectively design short peptide tags to improve solubility of a few model enzymes. Based on the protein sequence information, a support vector regression model we recently developed was used to evaluate protein solubility after small peptide tags were introduced to a target protein. The optimization algorithm guided the sequences of the tags to evolve towards variants that had higher solubilit

SUBMITTER: Han X 

PROVIDER: S-EPMC7334598 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature