AIUPred: combining energy estimation with deep learning for the enhanced prediction of protein disorder

Abstract

Abstract Intrinsically disordered proteins and protein regions (IDPs/IDRs) carry out important biological functions without relying on a single well-defined conformation. As these proteins are a challenge to study experimentally, computational methods play important roles in their characterization. One of the commonly used tools is the IUPred web server which provides prediction of disordered regions and their binding sites. IUPred is rooted in a simple biophysical model and uses a limited number of parameters largely derived on globular protein structures only. This enabled an incredibly fast and robust prediction method, however, its limitations have also become apparent in light of recent breakthrough methods using deep learning techniques. Here, we present AIUPred, a novel version of IUPred which incorporates deep learning techniques into the energy estimation framework. It achieves improved performance while keeping the robustness of the original method. Based on the evaluation of recent benchmark datasets, AIUPred scored amongst the top three single sequence based methods. With a new web server we offer fast and reliable visual analysis for users as well as options to analyze whole genomes in mere seconds with the downloadable package. AIUPred is available at https://aiupred.elte.hu.