THPLM: a sequence-based deep learning framework for protein stability changes prediction upon point variations using pretrained protein language model.

ABSTRACT:

Motivation

Quantitative determination of protein thermodynamic stability is a critical step in protein and drug design. Reliable prediction of protein stability changes caused by point variations contributes to developing-related fields. Over the past decades, dozens of structure-based and sequence-based methods have been proposed, showing good prediction performance. Despite the impressive progress, it is necessary to explore wild-type and variant protein representations to address the problem of how to represent the protein stability change in view of global sequence. With the development of structure prediction using learning-based methods, protein language models (PLMs) have shown accurate and high-quality predictions of protein structure. Because PLM captures the atomic-level

SUBMITTER: Gong J 

PROVIDER: S-EPMC10627365 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature