Developing improved MD codes for understanding processive cellulases

Abstract

The mechanism of action of cellulose-degrading enzymes is illuminated through a multidisciplinary collaboration that uses molecular dynamics (MD) simulations and expands the capabilities of MD codes to allow simulations of enzymes and substrates on petascale computational facilities. There is a class of glycoside hydrolase enzymes called cellulases that are thought to decrystallize and processively depolymerize cellulose using biochemical processes that are largely not understood. Understanding the mechanisms involved and improving the efficiency of this hydrolysis process through computational models and protein engineering presents a compelling grand challenge. A detailed understanding of cellulose structure, dynamics and enzyme function at the molecular level is required to direct protein engineers to the right modifications or to understand if natural thermodynamic or kinetic limits are in play. Much can be learned about processivity by conducting carefully designed molecular dynamics (MD) simulations of the binding and catalytic domains of cellulases with various substrate configurations, solvation models and thermodynamic protocols. Most of these numerical experiments, however, will require significant modification of existing code and algorithms in order to efficiently use current (terascale) and future (petascale) hardware to the degree of parallelism necessary to simulate a system of the size proposed here. This work will develop MD codes that can efficiently use terascale and petascale systems, not just for simple classical MD simulations, but also for more advanced methods, including umbrella sampling with complex restraints and reaction coordinates, transition path sampling, steered molecular dynamics, and quantum mechanical/molecular mechanical simulations of systems the size of cellulose degrading enzymes acting on cellulose.