PepAnno: A structure-aware deep learning framework for bioactive peptide prediction, structural visualization, and physicochemical profiling

Peptides are gaining prominence as therapeutic candidates due to their diverse physiological functions and structural simplicity. Although multiple computational tools exist for bioactive peptide prediction, many suffer from limitations such as non-intuitive interfaces, sequence-only representations, insufficient structural awareness, restricted interpretability, or fragmented analysis workflows, leading to reduced research efficiency and higher costs. To address these challenges, we present PepAnno (https://bis.zju.edu.cn/pepanno/), a comprehensive and user-friendly web server for multi-functional peptide annotation. PepAnno is powered by a novel structure-aware, multi-view geometric deep learning framework that integrates pre-trained sequence embeddings with predicted 3D structural graphs through a dual-stream architecture combining a Transformer and a GATv2 network. A cross-modal attention mechanism is employed to effectively fuse semantic and geometric representations, enabling accurate multi-task prediction across 7 key bioactivities, including antimicrobial and anticancer properties. Comprehensive evaluation on seven curated bioactivity datasets demonstrates that PepAnno achieves robust and competitive predictive performance across tasks, consistently outperforming or matching existing methods in terms of discrimination and stability. Beyond functional prediction, PepAnno provides automated calculation of physicochemical properties, structure visualization, and access to an integrated repository of peptide-related databases and tools. By enabling one-click peptide annotation, PepAnno offers an efficient and interpretable solution for large-scale peptide analysis and facilitates downstream experimental design and peptide-based drug discovery.Author summary: PepAnno is an integrated web server developed to advance the study of bioactive peptides—small yet versatile molecules with significant therapeutic and diagnostic potential. Although several computational tools have been developed to identify peptide activities, researchers often need to rely on multiple independent platforms to obtain functional, structural, and physicochemical information, resulting in fragmented and inefficient workflows. More importantly, most existing predictors operate as black boxes, offering limited mechanistic insight into how specific spatial motifs govern biological functions. To bridge this gap, we developed PepAnno, a comprehensive and user-friendly web server. PepAnno is powered by a novel structure-aware, multi-view deep learning framework that synergizes sequence semantics with 3D structural geometry. By leveraging a strict hierarchical transfer learning strategy, it achieves highly accurate predictions across seven major functional categories, effectively overcoming the challenge of data scarcity. Crucially, PepAnno breaks the barrier by providing native biological interpretability. It dynamically maps the model’s cross-attention weights onto 3D structures, empowering researchers to visually pinpoint key functional residues. Along with automated physicochemical profiling and a curated knowledge base of peptide resources, PepAnno unifies robust prediction, structural interpretability, and centralized data access. This integrated design significantly streamlines research workflows, helping scientists formulate mechanistically meaningful hypotheses and accelerating peptide-based drug discovery.