TemStaPro: protein thermostability prediction using sequence representations from protein language models.
Journal:
Bioinformatics (Oxford, England)
Published Date:
Mar 29, 2024
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.