Characterizing disease-associated human proteins without available protein structures or homologues

Abstract

AbstractMutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques such as RoseTTAFold and AlphaFold, we can predict the structure of these proteins even in the absence of structural homologues. We modeled and extracted the domains from 553 disease-associated human proteins. We noticed that the model quality was higher and the RMSD lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein-protein interfaces, conserved residues and destabilising effects caused by residue mutations in these predicted structures. We then explored whether the disease-associated mutations were in the proximity of these predicted functional sites or if they destabilized the protein structure based on ddG calculations. We could explain 80% of these disease-associated mutations based on proximity to functional sites or structural destabilization. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models.