Geometric Graph Learning to Predict Changes in Binding Free Energy and Protein Thermodynamic Stability upon Mutation

Md Masud Rana, Duc Duy Nguyen

Research output: Contribution to journalArticlepeer-review

Abstract

Accurate prediction of binding free energy changes upon mutations is vital for optimizing drugs, designing proteins, understanding genetic diseases, and cost-effective virtual screening. While machine learning methods show promise in this domain, achieving accuracy and generalization across diverse data sets remains a challenge. This study introduces Geometric Graph Learning for Protein-Protein Interactions (GGL-PPI), a novel approach integrating geometric graph representation and machine learning to forecast mutation-induced binding free energy changes. GGL-PPI leverages atom-level graph coloring and multiscale weighted colored geometric subgraphs to capture structural features of biomolecules, demonstrating superior performance on three standard data sets, namely, AB-Bind, SKEMPI 1.0, and SKEMPI 2.0 data sets. The model’s efficacy extends to predicting protein thermodynamic stability in a blind test set, providing unbiased predictions for both direct and reverse mutations and showcasing notable generalization. GGL-PPI’s precision in predicting changes in binding free energy and stability due to mutations enhances our comprehension of protein complexes, offering valuable insights for drug design endeavors.

Original languageEnglish
Pages (from-to)10870-10879
Number of pages10
JournalJournal of Physical Chemistry Letters
Volume14
Issue number49
DOIs
StatePublished - Dec 14 2023

Bibliographical note

Publisher Copyright:
© 2023 American Chemical Society.

ASJC Scopus subject areas

  • General Materials Science
  • Physical and Theoretical Chemistry

Fingerprint

Dive into the research topics of 'Geometric Graph Learning to Predict Changes in Binding Free Energy and Protein Thermodynamic Stability upon Mutation'. Together they form a unique fingerprint.

Cite this