Applications of deep learning in protein structure prediction : from complexes to intrinsically disordered proteins

Abstract

[EMBARGOED UNTIL 05/01/2025] Proteins are essential biomolecules that play crucial roles in various biological processes within organisms. Their complex interactions and structures are fundamental to understanding cellular mechanisms and developing therapeutic strategies. However, traditional experimental methods like X-ray crystallography and NMR spectroscopy, despite their accuracy, are costly and time-consuming. This has prompted the exploration of computational models, particularly deep learning techniques, to predict protein structures, especially of complexes, more efficiently. This dissertation presents the development of DNCON2_Inter, DeepComplex, and Disformer. DNCON2_Inter predicts inter-chain contacts in homooligomers using deep convolutional neural networks, leveraging monomeric multiple sequence alignments (MSAs) and co-evolutionary features to enhance prediction accuracy. Using the predicted inter-chain contacts as distant restraints, quaternary structures can be produced. High precision of the inter-chain contacts leads to better quality models of the complexes. DeepComplex, a web server, extends this approach to predict inter-chain contacts and reconstruct quaternary structures of both homodimers and heterodimers. Finally, Disformer, was proposed in the prediction of intrinsically disordered proteins (IDP) which only gain structural and functional importance upon interaction with other molecules. Disformer employs a transformer-based dual graph approach combining Graph Attention Networks (GAT) and Graph Convolutional Neural Networks (GCN). It excels in predicting intrinsically disordered regions (IDRs) by leveraging both sequence-based and structure-based features for a comprehensive graph-based binary node classification. These contributions collectively enhance the predictive capabilities of protein structural analysis, providing new insights into protein interactions and disorder. This research not only advances our understanding of protein dynamics but also paves the way for future developments in the prediction and analysis of protein complexes and IDPs.Includes bibliographical references