
%%%%%%%%% SUMMARY -- 1 page, third person

\title{\large \bf GPU-accelerated calculation of ligand binding affinities to biological macromolecules}
\date{}
\maketitle
\noindent {\small
\begin{tabular}{lll}
{\bf John D. Chodera} & Distinguished Postdoctoral Fellow, QB3, University of California, Berkeley & PI\\
{\bf Michael R. Shirts} & Assistant Professor, Department of Chemical Engineering, University of Virginia & co-PI\\
{\bf David L. Mobley} & Assistant Professor, Department of Chemistry, University of New Orleans & co-PI\\
{\bf Kim Branson} & Vertex Pharmaceuticals & collaborator\\
{\bf Vijay S. Pande} & Associate Professor of Chemistry, Stanford University, Simbios & collaborator
\end{tabular}
}

{\bf A detailed physical understanding of the determinants by which biological macromolecules bind their ligands (or inhibitors) with affinity and specificity would be of tremendous value to science and medicine}, making possible lead optimization of pharmaceuticals or tools for chemical biology, as well as providing insight to biophysics of binding, catalysis, and allosteric regulation.  
Alchemical free energy calculations have made great strides in this direction by quantitatively computing binding affinities through a rigorous but efficient treatment of the statistical mechanics, using molecular dynamics or Monte Carlo simulations.   
Up to now, the tremendous expense of these calculations (requiring cluster-weeks of computer simulation for even simple systems) has prohibited large-scale tests of the accuracy of current-generation force fields for this task, prevented extensive studies of the sensitivity of binding affinities to changes in protonation state, and kept these tools (even if proven accurate) out of reach of medicinal chemists and chemical biologists attempting to rationally design ligands for use as pharmacological agents or chemical tools.

{\bf The rise of general-purpose GPU computing is changing the landscape of high-performance computing} with floating point computing power (with 240 cores in NVIDIA Tesla C1060 GPUs, and 512 cores in next-generation Fermi architecture) up to three orders of magnitude beyond current multi-core CPUs, provided the application can be efficiently mapped onto this architecture, although obtaining such speedups requires arduously prepared specialized code.
Simbios, the NIH Center for Physical Simulation at Stanford University, has succeeded in producing an open library (OpenMM) for accelerating molecular dynamics calculations 50x to 1000x over single-CPU-core performance for standard simulation codes like AMBER for implicit and explicit solvent simulations using a single C1060 GPU. Through collaboration with the OpenMM developers, we have build a new freely-available code (YANK) for making use of the OpenMM toolkit to accelerate alchemical binding free energies, and to provide an open, extensible platform for large-scale testing of forcefield accuracy, experimenting with enhanced sampling algorithms, and aiding in the design of new ligands for biological macromolecules. 
{\bf The use of multiple GPUs allows us to reduce the wall clock time required for a single rigorous absolute alchemical binding free energy calculation in implicit solvent to a few hours.}
In preliminary calculations with a startup allocation on NCSA Lincoln, these implicit solvent binding simulations are not only possible, but in benchmark tests are 500 - 1000$\times$ faster than single CPU cores in the OpenMM implementation of multiple Generalized Born models.

{\bf We propose a number of experiments that will facilitate the development of methods for the quantitative computation of small-molecule binding affinities and the rational, physically-driven design of potent, selective ligands using alchemical free energy calculations in combination with GPU-accelerated computing.}
With this data, we will test and expand the capabilities of YANK, which will be made available to the modeling and commercial pharmaceutical community as free, open-source software.   
This data will also allow us to evaluate the accuracy and sensitivity of the current generation of implicit solvent models in computing binding affinities on an experimentally well-characterized set of protein systems for which high-quality calorimetric or binding data is available for a variety of ligands.   
Solvation free energies and binding affinities computed in explicit solvent are also available for many of these systems, providing valuable information guiding the development of a more accurate generation of solvent models. 

We will conduct additional experiments such sensitivity of computed binding affinities to various physical phenomena present in real biological systems that are critical for pharmaceutical design, such as allowing the protonation state to vary by holding the system at constant pH, modifying with post-translational modification events, such as phosphorylation events in kinases, an important target for anticancer therapeutics, as well as sampling over slow sidechain or nonlocal conformational changes.  
Finally, we will evaluate advanced sampling methods, including replica-exchange among alchemical states, expanded ensembles methods for sampling over chemical species, and mixtures of Monte Carlo and molecular dynamics moves in a Markov chain Monte Carlo framework for further enhancing sampling and reducing the computational expense of these simulations.

{\bf The collaboratory effort we have assembled for this project is uniquely suited to carry out these experiments, and will provide numerous benefits to the field of computational chemistry and biology both in the discoveries that will be made and the tools produced as a result of this study.}  
JDC, MRS, and DLM have extensive experience with alchemical free energy calculations and large-scale / high-performance computing, JDC and MRS with enhanced sampling algorithms and statistically efficient analysis of datasets, MRS with the design of efficient alchemical schemes, and KB with extensive experience in industrial computational drug design and access to medicinally relevant datasets prospective and retrospective manner. These datasets are being released into the public domain.  
VSP is a primary PI in the NIH Simbios Center developing OpenMM the GPU-enabled physics based simulation toolkit.  
Between the primary participants (JDC and the groups of MRS and DLM), we have sufficient manpower to exploit the resources provided by NCSA Lincoln for these aims.   
{\bf A Teragrid allocation on Lincoln is particularly important for this project because GPU computing resources of this magnitude are unavailable at a university level.}

% LocalWords:  HIV transcriptase transformative heteropolymers GROMACS org
% LocalWords:  Alchemistry enthalpies EPSCoR
