Sanbo Qin

eLife Assessment This important study presents a sequence-based method for predicting drug-interacting residues in intrinsically disordered proteins (IDPs), addressing a significant challenge in understanding small-molecule:IDP interactions. The findings have solid support through examples underscoring the role of aromatic interactions. While predicted binding sites remain coarse, validation was done on a total of 10 IDPs at varying depths.

Abstract Intrinsically disordered proteins (IDPs) are now well-recognized as drug targets. Identifying drug-interacting residues (DIRs) is valuable for both optimizing compounds and elucidating the mechanism of action. Currently, NMR chemical shift perturbation and all-atom molecular dynamics (MD) simulations are the primary tools for this purpose. Here we present DIRseq, a fast method for predicting DIRs from the amino-acid sequence.

eLife assessment In this useful study, a solid machine learning approach based on a broad set of systems to predict the R2 relaxation rates of residues in intrinsically disordered proteins (IDPs) is described.