Skip to content
Log in
Open access

GeoPep: A Geometry-Aware Masked Language Model for Protein-Peptide Binding Site Prediction

Dian Chen Yunkai Chen Tong Lin Sijie Chen Levent Burak Kara Xiaolin Cheng
Jun 2026 · Journal of Chemical Information and Modeling · Vol 66, pp. 7377 - 7389 · 0 citations · 50 references
Medicine

TL;DR

GeoPep is introduced, a novel framework for peptide binding site prediction that leverages transfer learning from ESM3, a multimodal protein foundation model that significantly outperforms existing methods in protein–peptide binding site prediction.

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 hinders 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 prelearned representations from protein–protein binding to address the limited availability of protein–peptide binding data. The fine-tuned model is further integrated with a Kolmogorov–Arnold Network (KAN)-based architecture for complex nonlinear approximation. 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.

DOI PubMed Semantic Scholar
Read PDF

Similar papers

Open access Jun 2026

Hybrid Approach to Protein–Protein Complex Affinity Prediction Based on Language Models and Molecular Dynamics

HyBind-NN is developed, a multimodal graph neural network that integrates protein language models (PLMs) with 3D structural and dynamic datasets to predict protein–protein and protein–peptide affinity, and it is demonstrated that combining ESM-2 sequence embeddings with precise 3D Voronoi spatial geometry enables accurate affinity predictions across diverse structural datasets.

E. A. Bogdanova, A. Chernukhin, Alexey K. Shaytan · 0 citations
Jun 2026

RGTBind: RBF-gate graph transformer with spatially biased attention for protein–DNA binding-site prediction

RGTBind, a graph transformer that combines multi-scale radial basis function distance encoding with a learnable threshold-gating mechanism to model spatially informative residue interactions, achieved the best F1, AUC, and MCC among the compared methods.

Yi Qiu, Duo Zhao, Y. Ye et al. · 0 citations
Open access Jul 2026

ProteinDock: A physics-informed layer to improve protein-protein docking reliability

It is demonstrated that a truncated version of ProteinDock can be used to choose the optimal prediction among outputs from multiple deep learning-based tools, and shown that this strategy is a computationally efficient alternative to increasing the seed quantity for deep-learning predictions.

G. Rajagopal, Søren C. Spina, Joe Bailey et al. · 0 citations
Book Open access Jun 2026

PepEDiff: Out-of-Distribution Sampling Peptide Binder Design via Protein Embedding Diffusion

PepEDiff is presented, a novel peptide binder generator that designs binding sequences given a receptor protein and the target pocket residues that outperforms state-of-the-art approaches across benchmark tests and in the TIGIT case study, demonstrating its potential as a general, structure-free framework for zero-shot peptide binder design.

Po-Yu Liang, Tibo Duran, Jun Bai · 1 citation
Open access Aug 2026

PLMView: collaborative protein language model representations for fast and scalable specialized protein function inference

Applications to thioredoxins, visual opsins, and Tara Oceans environmental diatom cold-shock proteins show that PLMView can move from interpretable residue-level determinants in well-studied protein families to large-scale environmental functional discovery, linking molecular specialization to ecological distribution and transcriptional deployment across the global ocean.

Vinh-Son Pho, Alessandro Natale Bianchi, Mattéo Scarsini et al. · 0 citations