GRAPE, a greedy accumulated strategy for computational protein engineering

Abstract

Nature harbors fascinating enzymatic catalysts with high efficiency, chemo-, regio- and stereoselectivity. However, the insufficient stability of the enzymes often prevents their widespread utilization for industrial processes. Not content with the finite repertoire of naturally occurring enzymes, protein engineering holds promises to extend the applications of the improved enzymes with desired physical and catalytic properties. Herein, we devised a computational strategy (greedy accumulated strategy for protein engineering, GRAPE) to enhance the thermostability of enzymes. Through scanning of all point mutations of the structural and evolutionary consensus analysis, a library containing fewer than 100 mutations was established for characterization. After preliminary experimental verification, effective mutations are clustered in a multidimensional physical property space and then accumulated via the greedy algorithm to produce the final designed enzyme. Using the recently reported IsPETase from Ideonella sakaiensis that decomposes PET under ambient temperatures as a starting point, we adopted the GRAPE strategy to come up with a DuraPETase (T_M=77°C, raised by 31°C) which showed drastically enhanced degradation performance (300-fold) on semicrystalline PET films at 40°C.

Keywords: Biocatalyst; Computational design; Enzyme design; Greedy algorithm; In silico screening; Protein engineering; Thermostability.