Topological Machine Learning for Protein-Nucleic Acid Binding Affinity Changes Upon Mutation

TL;DR

This paper introduces TopoML, a topological machine learning model that combines persistent Laplacian, physicochemical, topological, and sequence features to accurately predict how mutations affect protein-nucleic acid binding affinity.

Contribution

The paper presents a novel integrative topological machine learning framework that improves prediction accuracy of mutation effects on protein-nucleic acid interactions.

Findings

TopoML outperforms existing methods in binding affinity change prediction.

The model effectively captures complex topological and physicochemical features.

Validation on two mutation datasets demonstrates its robustness.

Abstract

Understanding how protein mutations affect protein-nucleic acid binding is critical for unraveling disease mechanisms and advancing therapies. Current experimental approaches are laborious, and computational methods remain limited in accuracy. To address this challenge, we propose a novel topological machine learning model (TopoML) combining persistent Laplacian (from topological data analysis) with multi-perspective features: physicochemical properties, topological structures, and protein Transformer-derived sequence embeddings. This integrative framework captures robust representations of protein-nucleic acid binding interactions. To validate the proposed method, we employ two datasets, a protein-DNA dataset with 596 single-point amino acid mutations, and a protein-RNA dataset with 710 single-point amino acid mutations. We show that the proposed TopoML model outperforms state-of-the-art methods in predicting mutation-induced binding affinity changes for protein-DNA and protein-RNA complexes.