Q-CHEM INC — Department of Health and Human Services SBIR Phase I: 400

Description

Project Summary 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 bioand photo chemical reactions accompanying metabolic pathways of drug moleculesRedox processes are at the heart of various biological functionsincluding respirationredox signalingprotection from oxidative stressRedox active enzymes serve as drug targets for antibacterial and antiviral therapyQuantitative 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 agentsCurrent state of the art in computational modeling of biochemical processes is to use hybrid quantum mechanics molecular mechanicsQM MMmethods that provide a balance between computational accuracy and ef ciencyFurthermorepolarizable model potentials and polarizable QM MM schemes become increasingly more important as they provide a more rigorous description of the classical environmentIn particularpolarizable models are essential for modeling redox processes as different oxidation states induce signi cant changes in charge distribution in the surrounding environmentHoweverdespite enormous computational speed ups attained through describing the majority of the system classicallyremaining bottlenecks of the QM MM models are due to the necessity of computing long range electrostatic interactions in an extended systemThe 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 wayusing either classical point charge or polarizable QM MM modelsNew computer codes will be implemented within the Q Chem quantum chemistry package developed by Q ChemInc Project Narrative The proposed project aims to dramatically reduce the computational cost of accurate modeling of biochemical reactions in realistic environmentsThe resulting software will create new research opportunities in theoretical and applied biochemistry