ApexGen: Simultaneous design of peptide binder sequence and structure for target proteins
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arXiv
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| Natura: | Preprint |
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2025
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| author | Xia, Xiaoqiong de la Fuente-Nunez, Cesar |
| author_facet | Xia, Xiaoqiong de la Fuente-Nunez, Cesar |
| contents | Peptide-based drugs can bind to protein interaction sites that small molecules often cannot, and are easier to produce than large protein drugs. However, designing effective peptide binders is difficult. A typical peptide has an enormous number of possible sequences, and only a few of these will fold into the right 3D shape to match a given protein target. Existing computational methods either generate many candidate sequences without considering how they will fold, or build peptide backbones and then find suitable sequences afterward. Here we introduce ApexGen, a new AI-based framework that simultaneously designs a peptide's amino-acid sequence and its three-dimensional structure to fit a given protein target. For each target, ApexGen produces a full all-atom peptide model in a small number of deterministic integration steps. In tests on hundreds of protein targets, the peptides designed by ApexGen fit tightly onto their target surfaces and cover nearly the entire binding site. These peptides have shapes similar to those found in natural protein-peptide complexes, and they show strong predicted binding affinity in computational experiments. Because ApexGen couples sequence and structure design at every step of Euler integration within a flow-matching sampler, it is much faster and more efficient than prior approaches. This unified method could greatly accelerate the discovery of new peptide-based therapeutics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14663 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ApexGen: Simultaneous design of peptide binder sequence and structure for target proteins Xia, Xiaoqiong de la Fuente-Nunez, Cesar Biomolecules Peptide-based drugs can bind to protein interaction sites that small molecules often cannot, and are easier to produce than large protein drugs. However, designing effective peptide binders is difficult. A typical peptide has an enormous number of possible sequences, and only a few of these will fold into the right 3D shape to match a given protein target. Existing computational methods either generate many candidate sequences without considering how they will fold, or build peptide backbones and then find suitable sequences afterward. Here we introduce ApexGen, a new AI-based framework that simultaneously designs a peptide's amino-acid sequence and its three-dimensional structure to fit a given protein target. For each target, ApexGen produces a full all-atom peptide model in a small number of deterministic integration steps. In tests on hundreds of protein targets, the peptides designed by ApexGen fit tightly onto their target surfaces and cover nearly the entire binding site. These peptides have shapes similar to those found in natural protein-peptide complexes, and they show strong predicted binding affinity in computational experiments. Because ApexGen couples sequence and structure design at every step of Euler integration within a flow-matching sampler, it is much faster and more efficient than prior approaches. This unified method could greatly accelerate the discovery of new peptide-based therapeutics. |
| title | ApexGen: Simultaneous design of peptide binder sequence and structure for target proteins |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2511.14663 |