WASP: a pipeline for functional annotation prediction based on AlphaFold structural models

Abstract

Abstract Protein function annotation is crucial for understanding biological processes and mechanisms. Traditionally, annotations rely on sequence homology, providing valuable insights but often leaving gaps even in well-characterised organisms. With AlphaFold enabling rapid generation of protein structural models, we can now infer function from three-dimensional shape. Here, we present WASP, a pipeline leveraging structural homology to enhance protein annotation prediction at scale, providing a more comprehensive understanding of protein functions across various organisms. WASP relies on network topology for better accuracy and more robust statistical power. We show that WASP achieves superior F1 scores compared to state-of-the-art sequence-based tools when recovering hidden annotations. On 20 industrially relevant organisms, WASP retrieves annotations for 20-30% of previously uncharacterised proteins. We further demonstrate utility in genome-scale metabolic model curation, identifying native candidates for 75-100% of orphan reactions. WASP highlights how structural homology can systematically discover annotations missed by sequence-based approaches.