AIUPred – Binding: Energy Embedding to Identify Disordered Binding Regions

Abstract

• AIUPred-binding uses innovative energy embeddings and AlphaMissense scores to accurately predict functional binding regions in IDRs. • Its transformer-based framework with energy embeddings enables effective transfer learning for disordered binding region prediction. • The tool achieves superior performance compared to existing methods, ranking among the top predictors in independent evaluations like CAID. • It is applicable to both human and non-human proteins, demonstrating versatility in identifying disordered binding regions. • AIUPred-binding is accessible via a user-friendly web server, API, and downloadable tools for seamless integration into research workflows. Intrinsically disordered regions (IDRs) play critical roles in various cellular processes, often mediating interactions through disordered binding regions that transition to ordered states. Experimental characterization of these functional regions is highly challenging, underscoring the need for fast and accurate computational tools. Despite their importance, predicting disordered binding regions remains a significant challenge due to limitations in existing datasets and methodologies. In this study, we introduce AIUPred-binding, a novel prediction tool leveraging a high dimensional mathematical representation of structural energies - we call energy embedding - and pathogenicity scores from AlphaMissense. By employing a transfer learning approach, AIUPred-binding demonstrates improved accuracy in identifying functional sites within IDRs. Our results highlight the tool’s ability to discern subtle features within disordered regions, addressing biases and other challenges associated with manually curated datasets. We present AIUPred-binding integrated into the AIUPred web framework as a versatile and efficient resource for understanding the functional roles of IDRs. AIUPred-binding is freely accessible at https://aiupred.elte.hu .

Topics & Concepts