GeoPep: A geometry-aware masked language model for protein-peptide binding site prediction

• URL: http://arxiv.org/abs/2510.27040v1
• Date: Thu, 30 Oct 2025 22:58:02 GMT
• Title: GeoPep: A geometry-aware masked language model for protein-peptide binding site prediction
• Authors: Dian Chen, Yunkai Chen, Tong Lin, Sijie Chen, Xiaolin Cheng,
• Abstract summary: We introduce GeoPep, a novel framework for peptide binding site prediction.<n>GeoPep fine-tunes ESM3's rich pre-learned representations from protein-protein binding to address the limited availability of protein-peptide binding data.
• Score: 4.517623379283838
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal approaches that integrate protein structure and sequence have achieved remarkable success in protein-protein interface prediction. However, extending these methods to protein-peptide interactions remains challenging due to the inherent conformational flexibility of peptides and the limited availability of structural data that hinder direct training of structure-aware models. To address these limitations, we introduce GeoPep, a novel framework for peptide binding site prediction that leverages transfer learning from ESM3, a multimodal protein foundation model. GeoPep fine-tunes ESM3's rich pre-learned representations from protein-protein binding to address the limited availability of protein-peptide binding data. The fine-tuned model is further integrated with a parameter-efficient neural network architecture capable of learning complex patterns from sparse data. Furthermore, the model is trained using distance-based loss functions that exploit 3D structural information to enhance binding site prediction. Comprehensive evaluations demonstrate that GeoPep significantly outperforms existing methods in protein-peptide binding site prediction by effectively capturing sparse and heterogeneous binding patterns.

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