Accurate Identification of Protein Binding Sites for All Drug Modalities Using ALLSites
Minjie Mou, Mingxi Lu, Zhimeng Zhou, Yanlin Ren, Xinyuan Yu, Ziqi Pan, Yuan Zhou, Hao Yang, Lingyan Zheng, GU Shukai, Yang Zhang, Wei Hu, F B Li, Haibin Dai, Feng Zhu · Advanced Science · 2025
Proteins interact with diverse molecular modalities, yet the incomplete identification of their binding sites has left the proteome-wide druggability largely underexplored. Although various computational methods have been developed for the prediction of protein binding sites, existing approaches are limited by their specificity to a single drug modality, dependence on high-quality structural data, or insufficient predictive accuracy. Here, a unified sequence-based framework, ALLSites, is constructed to identify proteome-wide binding sites across all drug modalities. Leveraging ESM-2 embeddings, ALLSites integrates a gated convolutional network with a transformer architecture to capture both global and local sequence features, effectively modeling residue interactions directly from sequence. This design bridges the gap between sequence-based and structure-based approaches, enabling ALLSites to achieve superior predictive performance across diverse drug modalities, including proteins, peptides, small molecules, carbohydrates, DNA, and RNA. It achieves state-of-the-art performance among sequence-based methods and matches the accuracy of the best structure-based tools. By enabling accurate and structure-free binding site prediction across all drug modalities, ALLSites is expected to expand the druggable proteome and provide a powerful resource for drug discovery.