MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning
Abstract Assessing changes in protein–protein binding affinity due to mutations helps understanding a wide range of crucial biological processes within cells. Despite significant efforts to create accurate computational models, predicting how mutations affect affinity remains challenging due to the...
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| Format: | Article |
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Wiley
2024-09-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202402918 |
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| _version_ | 1850262074795491328 |
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| author | Pengpai Li Zhi‐Ping Liu |
| author_facet | Pengpai Li Zhi‐Ping Liu |
| author_sort | Pengpai Li |
| collection | DOAJ |
| description | Abstract Assessing changes in protein–protein binding affinity due to mutations helps understanding a wide range of crucial biological processes within cells. Despite significant efforts to create accurate computational models, predicting how mutations affect affinity remains challenging due to the complexity of the biological mechanisms involved. In the present work, a geometric deep learning framework called MuToN is introduced for quantifying protein binding affinity change upon residue mutations. The method, designed with geometric attention networks, is mechanism‐aware. It captures changes in the protein binding interfaces of mutated complexes and assesses the allosteric effects of amino acids. Experimental results highlight MuToN's superiority compared to existing methods. Additionally, MuToN's flexibility and effectiveness are illustrated by its precise predictions of binding affinity changes between SARS‐CoV‐2 variants and the ACE2 complex. |
| format | Article |
| id | doaj-art-84e54daee5d249749b642b076d0d9aaf |
| institution | OA Journals |
| issn | 2198-3844 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-84e54daee5d249749b642b076d0d9aaf2025-08-20T01:55:16ZengWileyAdvanced Science2198-38442024-09-011135n/an/a10.1002/advs.202402918MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep LearningPengpai Li0Zhi‐Ping Liu1Department of Biomedical Engineering School of Control Science and Engineering Shandong University, Jinan Shandong 250061 ChinaDepartment of Biomedical Engineering School of Control Science and Engineering Shandong University, Jinan Shandong 250061 ChinaAbstract Assessing changes in protein–protein binding affinity due to mutations helps understanding a wide range of crucial biological processes within cells. Despite significant efforts to create accurate computational models, predicting how mutations affect affinity remains challenging due to the complexity of the biological mechanisms involved. In the present work, a geometric deep learning framework called MuToN is introduced for quantifying protein binding affinity change upon residue mutations. The method, designed with geometric attention networks, is mechanism‐aware. It captures changes in the protein binding interfaces of mutated complexes and assesses the allosteric effects of amino acids. Experimental results highlight MuToN's superiority compared to existing methods. Additionally, MuToN's flexibility and effectiveness are illustrated by its precise predictions of binding affinity changes between SARS‐CoV‐2 variants and the ACE2 complex.https://doi.org/10.1002/advs.202402918binding affinitygeometric deep learningmutation |
| spellingShingle | Pengpai Li Zhi‐Ping Liu MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning Advanced Science binding affinity geometric deep learning mutation |
| title | MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning |
| title_full | MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning |
| title_fullStr | MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning |
| title_full_unstemmed | MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning |
| title_short | MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning |
| title_sort | muton quantifies binding affinity changes upon protein mutations by geometric deep learning |
| topic | binding affinity geometric deep learning mutation |
| url | https://doi.org/10.1002/advs.202402918 |
| work_keys_str_mv | AT pengpaili mutonquantifiesbindingaffinitychangesuponproteinmutationsbygeometricdeeplearning AT zhipingliu mutonquantifiesbindingaffinitychangesuponproteinmutationsbygeometricdeeplearning |