Data-driven modeling

Contemporary structural biology is advancing through sophisticated data-driven methodologies that effectively address historically challenging topics including protein-protein interactions and intrinsically disordered proteins.

Our lab has developed iSPOT (Advanced Materials 2014 and JSB 2016), an innovative computational framework that transforms diverse experimental measurements into cohesive structural models. This data-centric approach leverages multiple information streams—particularly SAXS and NMR—to resolve complex biomolecular architectures with unprecedented accuracy. The resulting computational toolkit enables robust analysis of protein-protein interactions and structural disorder, providing critical insights that bridge fundamental mechanistic understanding.