TemBERTure: advancing protein thermostability prediction with deep learning and attention mechanisms.

ABSTRACT:

Motivation

Understanding protein thermostability is essential for numerous biotechnological applications, but traditional experimental methods are time-consuming, expensive, and error-prone. Recently, deep learning (DL) techniques from natural language processing (NLP) was extended to the field of biology, since the primary sequence of proteins can be viewed as a string of amino acids that follow a physicochemical grammar.

Results

In this study, we developed TemBERTure, a DL framework that predicts thermostability class and melting temperature from protein sequences. Our findings emphasize the importance of data diversity for training robust models, especially by including sequences from a wider range of organisms. Additionally, we suggest using attention scores from Deep Le

SUBMITTER: Rodella C 

PROVIDER: S-EPMC11262459 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature