Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features.

ABSTRACT: Over the last three decades, the appeal for monoclonal antibodies (mAbs) as therapeutics has been steadily increasing as evident with FDA's recent landmark approval of the 100th mAb. Unlike mAbs that bind to single targets, multispecific biologics (msAbs) have garnered particular interest owing to the advantage of engaging distinct targets. One important modular component of msAbs is the single-chain variable fragment (scFv). Despite the exquisite specificity and affinity of these scFv modules, their relatively poor thermostability often hampers their development as a potential therapeutic drug. In recent years, engineering antibody sequences to enhance their stability by mutations has gained considerable momentum. As experimental methods for antibody engineering are time-intensive, labori

SUBMITTER: Harmalkar A 

PROVIDER: S-EPMC9872953 | biostudies-literature | 2023 Jan-Dec

REPOSITORIES: biostudies-literature