Improved prediction of stabilizing mutations in proteins by incorporation of mutational effects on ligand binding
Srivarshini Ganesan,
Nidhi Mittal,
Akash Bhat
et al.
Abstract:While many computational methods accurately predict destabilizing mutations, identifying stabilizing mutations has remained a challenge, due to their relative rarity. We tested DeltaDeltaG0 predictions from computational predictors such as Rosetta, ThermoMPNN, RaSP, and DeepDDG, using eighty-two mutants of the bacterial toxin CcdB as a test case. On this dataset, the best computational predictor is ThermoMPNN which identifies stabilizing mutations with a precision of 68%. However, the average increase in Tm fo… Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.