A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides.

ABSTRACT: Owing to their ability to maintain a thermodynamically stable fold at extremely high temperatures, thermophilic proteins (TTPs) play a critical role in basic research and a variety of applications in the food industry. As a result, the development of computation models for rapidly and accurately identifying novel TTPs from a large number of uncharacterized protein sequences is desirable. In spite of existing computational models that have already been developed for characterizing thermophilic proteins, their performance and interpretability remain unsatisfactory. We present a novel sequence-based thermophilic protein predictor, termed SCMTPP, for improving model predictability and interpretability. First, an up-to-date and high-quality dataset consisting of 1853 TPPs and 3233 non-TPPs was

SUBMITTER: Charoenkwan P 

PROVIDER: S-EPMC8664844 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature