Prediction of mutation-induced protein stability changes based on the geometric representations learned by a self-supervised method.

ABSTRACT:

Background

Thermostability is a fundamental property of proteins to maintain their biological functions. Predicting protein stability changes upon mutation is important for our understanding protein structure-function relationship, and is also of great interest in protein engineering and pharmaceutical design.

Results

Here we present mutDDG-SSM, a deep learning-based framework that uses the geometric representations encoded in protein structure to predict the mutation-induced protein stability changes. mutDDG-SSM consists of two parts: a graph attention network-based protein structural feature extractor that is trained with a self-supervised learning scheme using large-scale high-resolution protein structures, and an eXtreme Gradient Boosting model-based stability change pre

SUBMITTER: Li SS 

PROVIDER: S-EPMC11360314 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature