Superior protein thermophilicity prediction with protein language model embeddings.

ABSTRACT: Protein thermostability is important in many areas of biotechnology, including enzyme engineering and protein-hybrid optoelectronics. Ever-growing protein databases and information on stability at different temperatures allow the training of machine learning models to predict whether proteins are thermophilic. In silico predictions could reduce costs and accelerate the development process by guiding researchers to more promising candidates. Existing models for predicting protein thermophilicity rely mainly on features derived from physicochemical properties. Recently, modern protein language models that directly use sequence information have demonstrated superior performance in several tasks. In this study, we evaluate the usefulness of protein language model embeddings for thermoph

SUBMITTER: Haselbeck F 

PROVIDER: S-EPMC10566323 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature