The goal of the project is to develop new algorithms and computer codes based on the continuous fast multipole method that will dramatically decrease the computational complexity of large-scale modeling of redox processes and other bio- and photo-chemical reactions accompanying metabolic pathways of drug molecules. Redox processes are at the heart of various biological functions, including respiration, redox signaling, protection from oxidative stress. Redox-active enzymes serve as drug targets for antibacterial and antiviral therapy. Quantitative atomic-level description of redox processes in biomolecules paves the way to mechanistic understanding of their function and potentially to the development of novel therapeutic agents. Current state-of-the art in computational modeling of biochemical processes is to use hybrid quan- tum mechanics?molecular mechanics (QM/MM) methods that provide a balance between computational accuracy and ef?ciency. Furthermore, polarizable model potentials and polarizable QM/MM schemes become increasingly more important as they provide a more rigorous description of the classical environ- ment. In particular, polarizable models are essential for modeling redox processes as different oxidation states induce signi?cant changes in charge distribution in the surrounding environment. However, despite enormous computational speed-ups attained through describing the majority of the system classically, re- maining bottlenecks of the QM/MM models are due to the necessity of computing long-range electrostatic interactions in an extended system. The proposed algorithms aim to eliminate these bottlenecks and enable the users in academia and the industry to perform simulations of biological systems in a more ef?cient and robust way, using either classical point-charge or polarizable QM/MM models. New computer codes will be implemented within the Q-Chem quantum chemistry package developed by Q-Chem, Inc.
The proposed project aims to dramatically reduce the computational cost of accurate modeling of biochem- ical reactions in realistic environments. The resulting software will create new research opportunities in theoretical and applied biochemistry.
