Structure-Aware Antibody Design with Affinity-Optimized Inverse Folding
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908723311542272 |
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| author | Zhao, Xinyan Tang, Yi-Ching Monsia, Rivaaj Cantu, Victor J. Ramesh, Ashwin Kumar Liu, Xiaozhong An, Zhiqiang Jiang, Xiaoqian Kim, Yejin |
| author_facet | Zhao, Xinyan Tang, Yi-Ching Monsia, Rivaaj Cantu, Victor J. Ramesh, Ashwin Kumar Liu, Xiaozhong An, Zhiqiang Jiang, Xiaoqian Kim, Yejin |
| contents | Motivation: The clinical efficacy of antibody therapeutics critically depends on high-affinity target engagement, yet laboratory affinity-maturation campaigns are slow and costly. In computational settings, most protein language models (PLMs) are not trained to favor high-affinity antibodies, and existing preference optimization approaches introduce substantial computational overhead without clear affinity gains. Therefore, this work proposes SimBinder-IF, which converts the inverse folding model ESM-IF into an antibody sequence generator by freezing its structure encoder and training only its decoder to prefer experimentally stronger binders through preference optimization.
Results: On the 11-assay AbBiBench benchmark, SimBinder-IF achieves a 55 percent relative improvement in mean Spearman correlation between log-likelihood scores and experimentally measured binding affinity compared to vanilla ESM-IF (from 0.264 to 0.410). In zero-shot generalization across four unseen antigen-antibody complexes, the correlation improves by 156 percent (from 0.115 to 0.294). SimBinder-IF also outperforms baselines in top-10 precision for ten-fold or greater affinity improvements. A case study redesigning antibody F045-092 for A/California/04/2009 (pdmH1N1) shows that SimBinder-IF proposes variants with substantially lower predicted binding free energy changes than ESM-IF (mean Delta Delta G -75.16 vs -46.57). Notably, SimBinder-IF trains only about 18 percent of the parameters of the full ESM-IF model, highlighting its parameter efficiency for high-affinity antibody generation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_17815 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Structure-Aware Antibody Design with Affinity-Optimized Inverse Folding Zhao, Xinyan Tang, Yi-Ching Monsia, Rivaaj Cantu, Victor J. Ramesh, Ashwin Kumar Liu, Xiaozhong An, Zhiqiang Jiang, Xiaoqian Kim, Yejin Computational Engineering, Finance, and Science Biomolecules Motivation: The clinical efficacy of antibody therapeutics critically depends on high-affinity target engagement, yet laboratory affinity-maturation campaigns are slow and costly. In computational settings, most protein language models (PLMs) are not trained to favor high-affinity antibodies, and existing preference optimization approaches introduce substantial computational overhead without clear affinity gains. Therefore, this work proposes SimBinder-IF, which converts the inverse folding model ESM-IF into an antibody sequence generator by freezing its structure encoder and training only its decoder to prefer experimentally stronger binders through preference optimization. Results: On the 11-assay AbBiBench benchmark, SimBinder-IF achieves a 55 percent relative improvement in mean Spearman correlation between log-likelihood scores and experimentally measured binding affinity compared to vanilla ESM-IF (from 0.264 to 0.410). In zero-shot generalization across four unseen antigen-antibody complexes, the correlation improves by 156 percent (from 0.115 to 0.294). SimBinder-IF also outperforms baselines in top-10 precision for ten-fold or greater affinity improvements. A case study redesigning antibody F045-092 for A/California/04/2009 (pdmH1N1) shows that SimBinder-IF proposes variants with substantially lower predicted binding free energy changes than ESM-IF (mean Delta Delta G -75.16 vs -46.57). Notably, SimBinder-IF trains only about 18 percent of the parameters of the full ESM-IF model, highlighting its parameter efficiency for high-affinity antibody generation. |
| title | Structure-Aware Antibody Design with Affinity-Optimized Inverse Folding |
| topic | Computational Engineering, Finance, and Science Biomolecules |
| url | https://arxiv.org/abs/2512.17815 |