Testing and improving the performance of protein thermostability predictors for the engineering of cellulases

Abstract

Thermostability of cellulases can be increased through amino acid substitutions and by protein engineering with predictors of protein thermostability. We have carried out a systematic analysis of the performance of 18 predictors for the engineering of cellulases. The predictors were PoPMuSiC, HoTMuSiC, I-Mutant 2.0, I-Mutant Suite, PremPS, Hotspot, Maestroweb, DynaMut, ENCoM ([Formula: see text] and [Formula: see text], mCSM, SDM, DUET, RosettaDesign, Cupsat (thermal and denaturant approaches), ConSurf, and Voronoia. The highest values of accuracy, F-measure, and MCC were obtained for DynaMut, SDM, RosettaDesign, and PremPS. A combination of the predictors provided an improvement in the performance. F-measure and MCC were improved by 14% and 28%, respectively. Accuracy and sensitivity were also improved by 9% and 20%, respectively, compared to the maximal values of single predictors. The reported values of the performance of the predictors and their combination may aid research in the engineering of thermostable cellulases as well as the further development of thermostability predictors.