Deep learning-based prediction of enzyme optimal pH and design of point mutations to improve acid resistance

Abstract

An accurate deep learning predictor of enzyme optimal pH is essential to quantitatively describe how pH influences the enzyme catalytic activity. CatOpt, developed in this study, outperformed existing predictors of enzyme optimal pH (RMSE=0.833 and R2=0.479), and could provide good interpretability with informative residue attention weights. The classification of acidic and alkaline enzymes and prediction of enzyme optimal pH shifts caused by point mutations showcased the capability of CatOpt as an effective computational tool for identifying enzyme pH preferences. Furthermore, a single point mutation designed with the guidance of CatOpt successfully enhanced the activity of Pyrococcus horikoshii diacetylchitobiose deacetylase at low pH (pH=4.5/5.5) by approximately 7%, suggesting that CatOpt is a promising in-silico enzyme design tool for pH-dependent enzyme activities. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=166 HEIGHT=200 SRC="FIGDIR/small/623957v2_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@ff8b46org.highwire.dtl.DTLVardef@110c721org.highwire.dtl.DTLVardef@817014org.highwire.dtl.DTLVardef@1e55d6a_HPS_FORMAT_FIGEXP M_FIG C_FIG