EvoNB: A protein language model-based workflow for nanobody mutation prediction and optimization
The identification and optimization of mutations in nanobodies are crucial for enhancing their therapeutic potential in disease prevention and control. However, this process is often complex and time-consuming, which limit its widespread application in practice. In this study, we developed a workflo...
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Elsevier
2025-06-01
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| Series: | Journal of Pharmaceutical Analysis |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095177925000772 |
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| author | Danyang Xiong Yongfan Ming Yuting Li Shuhan Li Kexin Chen Jinfeng Liu Lili Duan Honglin Li Min Li Xiao He |
| author_facet | Danyang Xiong Yongfan Ming Yuting Li Shuhan Li Kexin Chen Jinfeng Liu Lili Duan Honglin Li Min Li Xiao He |
| author_sort | Danyang Xiong |
| collection | DOAJ |
| description | The identification and optimization of mutations in nanobodies are crucial for enhancing their therapeutic potential in disease prevention and control. However, this process is often complex and time-consuming, which limit its widespread application in practice. In this study, we developed a workflow, named Evolutionary-Nanobody (EvoNB), to predict key mutation sites of nanobodies by combining protein language models (PLMs) and molecular dynamic (MD) simulations. By fine-tuning the ESM2 model on a large-scale nanobody dataset, the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced. The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies. Additionally, we selected four widely representative nanobody–antigen complexes to verify the predicted effects of mutations. MD simulations analyzed the energy changes caused by these mutations to predict their impact on binding affinity to the targets. The results showed that multiple mutations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target, further validating the potential of this workflow for designing and optimizing nanobody mutations. Additionally, sequence-based predictions are generally less dependent on structural absence, allowing them to be more easily integrated with tools for structural predictions, such as AlphaFold 3. Through mutation prediction and systematic analysis of key sites, we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes. The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field. |
| format | Article |
| id | doaj-art-d69eb9606c5e4eaa90a148d24a171cae |
| institution | Kabale University |
| issn | 2095-1779 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pharmaceutical Analysis |
| spelling | doaj-art-d69eb9606c5e4eaa90a148d24a171cae2025-08-20T03:50:50ZengElsevierJournal of Pharmaceutical Analysis2095-17792025-06-0115610126010.1016/j.jpha.2025.101260EvoNB: A protein language model-based workflow for nanobody mutation prediction and optimizationDanyang Xiong0Yongfan Ming1Yuting Li2Shuhan Li3Kexin Chen4Jinfeng Liu5Lili Duan6Honglin Li7Min Li8Xiao He9Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, ChinaSchool of Computer Science and Engineering, Central South University, Changsha, 410083, ChinaShanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, ChinaShanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, ChinaSchool of Computer Science and Engineering, Central South University, Changsha, 410083, ChinaShanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China; School of Science, Department of Basic Medicine and Clinical Pharmacy, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 210009, China; Corresponding author. School of Science, Department of Basic Medicine and Clinical Pharmacy, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 210009, China.School of Physics and Electronics, Shandong Normal University, Jinan, 250014, China; Corresponding author.Innovation Center for Artificial Intelligence and Drug Discovery, East China Normal University, Shanghai, 200062, China; Lingang Laboratory, Shanghai, 200062, ChinaSchool of Computer Science and Engineering, Central South University, Changsha, 410083, China; Corresponding author.Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China; Chongqing Key Laboratory of Precision Optics, Chongqing Institute of East China Normal University, Chongqing, 401120, China; New York University–East China Normal University Center for Computational Chemistry, New York University Shanghai, Shanghai, 200062, China; Corresponding author. Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China.The identification and optimization of mutations in nanobodies are crucial for enhancing their therapeutic potential in disease prevention and control. However, this process is often complex and time-consuming, which limit its widespread application in practice. In this study, we developed a workflow, named Evolutionary-Nanobody (EvoNB), to predict key mutation sites of nanobodies by combining protein language models (PLMs) and molecular dynamic (MD) simulations. By fine-tuning the ESM2 model on a large-scale nanobody dataset, the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced. The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies. Additionally, we selected four widely representative nanobody–antigen complexes to verify the predicted effects of mutations. MD simulations analyzed the energy changes caused by these mutations to predict their impact on binding affinity to the targets. The results showed that multiple mutations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target, further validating the potential of this workflow for designing and optimizing nanobody mutations. Additionally, sequence-based predictions are generally less dependent on structural absence, allowing them to be more easily integrated with tools for structural predictions, such as AlphaFold 3. Through mutation prediction and systematic analysis of key sites, we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes. The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.http://www.sciencedirect.com/science/article/pii/S2095177925000772NanobodyProtein language models (PLMs)ESM2 modelEvolutionary-nanobody (EvoNB)MD simulationsAlphaFold 3 |
| spellingShingle | Danyang Xiong Yongfan Ming Yuting Li Shuhan Li Kexin Chen Jinfeng Liu Lili Duan Honglin Li Min Li Xiao He EvoNB: A protein language model-based workflow for nanobody mutation prediction and optimization Journal of Pharmaceutical Analysis Nanobody Protein language models (PLMs) ESM2 model Evolutionary-nanobody (EvoNB) MD simulations AlphaFold 3 |
| title | EvoNB: A protein language model-based workflow for nanobody mutation prediction and optimization |
| title_full | EvoNB: A protein language model-based workflow for nanobody mutation prediction and optimization |
| title_fullStr | EvoNB: A protein language model-based workflow for nanobody mutation prediction and optimization |
| title_full_unstemmed | EvoNB: A protein language model-based workflow for nanobody mutation prediction and optimization |
| title_short | EvoNB: A protein language model-based workflow for nanobody mutation prediction and optimization |
| title_sort | evonb a protein language model based workflow for nanobody mutation prediction and optimization |
| topic | Nanobody Protein language models (PLMs) ESM2 model Evolutionary-nanobody (EvoNB) MD simulations AlphaFold 3 |
| url | http://www.sciencedirect.com/science/article/pii/S2095177925000772 |
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