DeepSTABp: A Deep Learning Approach for the Prediction of Thermal Protein Stability.

ABSTRACT: Proteins are essential macromolecules that carry out a plethora of biological functions. The thermal stability of proteins is an important property that affects their function and determines their suitability for various applications. However, current experimental approaches, primarily thermal proteome profiling, are expensive, labor-intensive, and have limited proteome and species coverage. To close the gap between available experimental data and sequence information, a novel protein thermal stability predictor called DeepSTABp has been developed. DeepSTABp uses a transformer-based protein language model for sequence embedding and state-of-the-art feature extraction in combination with other deep learning techniques for end-to-end protein melting temperature prediction. DeepSTABp can pred

SUBMITTER: Jung F 

PROVIDER: S-EPMC10138888 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature