NSF Org:	IIS Division of Information & Intelligent Systems
Recipient:	GEORGIA TECH RESEARCH CORP
Initial Amendment Date:	August 29, 2008
Latest Amendment Date:	August 29, 2008
Award Number:	0848389
Award Instrument:	Standard Grant
Program Manager:	Vasant G. Honavar IIS  Division of Information & Intelligent Systems CSE  Directorate for Computer and Information Science and Engineering
Start Date:	September 1, 2008
End Date:	August 31, 2011 (Estimated)
Total Intended Award Amount:	$200,000.00
Total Awarded Amount to Date:	$200,000.00
Funds Obligated to Date:	FY 2008 = $200,000.00
History of Investigator:	Alexander Gray (Principal Investigator) agray@cc.gatech.edu Charles Sherrill (Co-Principal Investigator) Jeffrey Skolnick (Co-Principal Investigator)
Recipient Sponsored Research Office:	Georgia Tech Research Corporation 926 DALNEY ST NW ATLANTA GA  US  30318-6395 (404)894-4819
Sponsor Congressional District:	05
Primary Place of Performance:	Georgia Institute of Technology 225 NORTH AVE NW ATLANTA GA  US  30332-0002
Primary Place of Performance Congressional District:	05
Unique Entity Identifier (UEI):	EMW9FC8J3HN4
Parent UEI:	EMW9FC8J3HN4
NSF Program(s):	Info Integration & Informatics
Primary Program Source:	01000809DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	7484, 9216, 9237, HPCC
Program Element Code(s):	736400
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

This work pursues the development of a new algorithmic framework which
allows for the first time efficient computation of higher-order
interactions in biomolecules. Algorithms are created to demonstrate
two important applications on much larger scales than were previously
tractable, each representing a new door to a larger class of further
possibilities: Axilrod-Teller (3-body) simulation, and Hartree-Fock
(4-index) quantum-level simulation. The multidisciplinary project
brings together experts in computer science, protein folding, and
quantum chemistry.

Biomolecular simulations usually break down complex chemical systems
into a balls-and-springs mechanical model augmented by torsional
terms, pair-wise point-charge electrostatic terms, and simple
pair-wise dispersion (van der Waals) interactions. However such models
often fail to capture important, complex non-additive interactions
found in real systems. Though the criticality of multi-body
potentials for more accurate and realistic molecular modeling has been
argued by various authors, their evaluation in systems beyond tiny
sizes has not been previously possible due to the unavailability of an
efficient way to realize the computation, which is cubic or higher.

The work augments a framework for computational problems called
Generalized N-Body Problems, which contains any such higher-order
physical potential. The framework was originally developed to
accelerate common bottleneck statistical computations based on
distances, utilizing multiple kd-trees and other space-partitioning
data structures to bring down computation times both asymptotically
and practically by multiple orders of magnitude. This work extends
the framework with higher-order hierarchical series approximation
techniques, demonstrating how to do a fast multipole-type method for
higher-order interactions for the first time, effectively creating a
Generalized Fast Multipole Method.

The algorithms are validated in biochemical systems chosen to clearly
illustrate many-body interactions: hydrogen bonds and three-body
dispersion interactions. Parameters for potential functions are
obtained using customized machine learning methods on dual data sets
generated by the co-PI's labs: high-quality quantum mechanical
benchmark data and experimental protein structures.

The goal is to demonstrate working many-body codes able to explore the
effect of modeling higher-order interactions on a larger scale and
more systematically than ever attempted previously. The intellectual
merit of the work is the elucidation of the first multi-tree multipole
method capable of accurately and scalably performing these fundamental
types of higher-order physics computations. The potential broader
impact is the ability to perform more accurate next-generation
molecular modeling, with implications for fundamental biology and drug
design.

For further information see the project web page at
http://www.cc.gatech.edu/~agray/gfmm.html

Please report errors in award information by writing to: awardsearch@nsf.gov.