Topological Machine Learning for Protein-Nucleic Acid Binding Affinity Changes Upon Mutation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910973216948224 |
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| author | Liu, Xiang Wee, Junjie Wei, Guo-Wei |
| author_facet | Liu, Xiang Wee, Junjie Wei, Guo-Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_22786 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Topological Machine Learning for Protein-Nucleic Acid Binding Affinity Changes Upon Mutation Liu, Xiang Wee, Junjie Wei, Guo-Wei Quantitative Methods 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. |
| title | Topological Machine Learning for Protein-Nucleic Acid Binding Affinity Changes Upon Mutation |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2505.22786 |