EvoNB: A protein language model-based workflow for nanobody mutation prediction and optimization.
10.1016/j.jpha.2025.101260
- Author:
Danyang XIONG
1
;
Yongfan MING
2
;
Yuting LI
1
;
Shuhan LI
1
;
Kexin CHEN
2
;
Jinfeng LIU
1
;
Lili DUAN
3
;
Honglin LI
4
;
Min LI
2
;
Xiao HE
1
Author Information
1. 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.
2. School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
3. School of Physics and Electronics, Shandong Normal University, Jinan, 250014, China.
4. Innovation Center for Artificial Intelligence and Drug Discovery, East China Normal University, Shanghai, 200062, China.
- Publication Type:Journal Article
- Keywords:
AlphaFold 3;
ESM2 model;
Evolutionary-nanobody (EvoNB);
MD simulations;
Nanobody;
Protein language models (PLMs)
- From:
Journal of Pharmaceutical Analysis
2025;15(6):101260-101260
- CountryChina
- Language:English
-
Abstract:
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.
