CDI-Type I: Bridging the Gap Between Next-Generation High Performance Hybrid Computers and Physics Based Computational Models for Quantitative Description of Molecular Recognition

University Of Delaware, Newark DE

Investigators

Abstract

TECHNICAL ABSTRACT This award is made on a proposal submitted to the Cyberenabled Discovery and Innovation initiative and in partnership with the Office of Experimental Program to Stimulate Competitive Research. This project projects on the From Data to Knowledge, and Virtual Organizations CDI themes. This award supports computational research and education to develop new polarizable force fields with an aim for application to the discovery of new drugs. The tools developed may also impact the discovery of new biomaterials. Molecular simulations play an integral role in the development of novel pharmaceuticals. Complementing experiment, computations strive to expedite the discovery process by screening for small-molecules with high binding affinity, specificity, and pharmacological properties. The fundamental quantity of interest is the binding affinity, and this is rigorously related to the free energy change associated with a binding reaction. Current methods employ empirical potential energy functions, or force fields, representing the physical interactions between the constituent atomic/molecular species. The electrostatic component of the interaction is treated in a mean field manner, with partial charges on given sites of the molecular construct taken to be fixed throughout the binding process; there is no explicit accounting for the differing nature of charge distribution for different ligand and protein conformations as physically dictated by quantum mechanics, whose inclusion has been demonstrated to be vital for accurate predictions. The PI will address this weakness in drug design by proposing transformative approaches to developing next-generation classical polarizable force fields to overcome current limitations. The challenge is the need for computing power to solve the non-linear problem of parameterizing a polarizable force field while simultaneously incorporating results of biologically relevant scale protein-ligand binding affinity simulations. With the emergence and integration of multicore architectures into commodity desktops, general-purpose graphics processing units (GPU), and special purpose field programmable gate arrays (FPGA), the concept of hybrid computing has entered the HPC arena. Cloud computing systems take advantage of these hybrid resources to generate larger data sets, without the users needing to have knowledge of, expertise in, or control over the technology infrastructure in the ?cloud.? Emerging cloud frameworks are far from transparently accommodating hybrid resources. The PIs will transform an open-source cloud computing framework such as Eucalyptus to transparently use hybrid resources for protein-ligand docking simulations, driven by intelligent scheduling policies based on game theory strategies. Knowledge deduction and use have to be built in and used among application tasks, each influencing the other?s next step. Feedback is also needed to adaptively drive new job generations from the application tasks. In the computational environment envisioned in this project, both knowledge and feedback require transforming human knowledge and discernment into cloud computing services that drive the parameter search. Intellectual merit: From the research point of view the work outlined in this proposal will provide automatic methods and tools for the parameterization of polarizable force field models for pharmaceutical molecules (Aim 1) and the protein-ligand binding affinity of large protein-ligand databases using polarizable force fields (Aim 2). A cross-campus cloud computing system that transparently and intelligently uses hybrid resources, i.e., multi-core and GPUs, in a unified, dynamically adaptable workspace, will support the simulations. This award supports educational and outreach activities to advance students? discovery and understanding of interdisciplinary research. Broader scientific and social impacts: There is great potential in terms of impact on the general scientific, and specifically the modeling communities. Research into more efficient and accurate approaches will significantly boost drug discovery and potentially the discovery of new biomaterials. NON-TECHNICAL SUMMARY This award is made on a proposal submitted to the Cyberenabled Discovery and Innovation initiative and in partnership with the Office of Experimental Program to Stimulate Competitive Research. This project projects on the From Data to Knowledge, and Virtual Organizations CDI themes. This award supports computational research and education to develop new computer simulation tools to enable the modeling of molecules and their interactions with potential application to the discovery of new drugs and the discovery of new biomaterials. The research involves intense computation to create models for the forces that exist between molecules and for their thermodynamic properties. The PIs will develop a method to harness and utilize different computing resources that are available through the internet using a technique called ?cloud computing.? This award supports educational and outreach activities to advance students? discovery and understanding of interdisciplinary research. Broader scientific and social impacts: There is great potential in terms of impact on the general scientific, and specifically the modeling communities. Research into more efficient and accurate approaches will significantly boost drug discovery and potentially the discovery of new biomaterials.