TemStaPro: protein thermostability prediction using sequence representations from protein language models.

ABSTRACT:

Motivation

Reliable prediction of protein thermostability from its sequence is valuable for both academic and industrial research. This prediction problem can be tackled using machine learning and by taking advantage of the recent blossoming of deep learning methods for sequence analysis. These methods can facilitate training on more data and, possibly, enable the development of more versatile thermostability predictors for multiple ranges of temperatures.

Results

We applied the principle of transfer learning to predict protein thermostability using embeddings generated by protein language models (pLMs) from an input protein sequence. We used large pLMs that were pre-trained on hundreds of millions of known sequences. The embeddings from such models allowed us to efficiently

SUBMITTER: Pudziuvelyte I 

PROVIDER: S-EPMC11001493 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature