Title

Cluster computing for determining three-dimensional protein structure

Authors

Authors

P. Micikevicius;N. Deo

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

J. Supercomput.

Keywords

parallel algorithms; Beowulf cluster; molecular conformation; NUCLEAR-MAGNETIC-RESONANCE; DISTANCE GEOMETRY; SHORTEST-PATH; COMPUTATIONAL EXPERIENCE; ALGORITHMS; MACROMOLECULES; CONFORMATION; RESTRAINTS; MATRIX; Computer Science, Hardware & Architecture; Computer Science, Theory &; Methods; Engineering, Electrical & Electronic

Abstract

Determining the three-dimensional structure of proteins is crucial to efficient drug design and understanding biological processes. One successful method for computing the molecule's shape relies on inter-atomic distance bounds provided by Nuclear Magnetic Resonance spectroscopy. The accuracy of computed structures as well as the time required to obtain them are greatly improved if the gaps between the upper and lower distance-bounds are reduced. These gaps are reduced most effectively by applying the tetrangle inequality, derived from the Cayley-Menger determinant, to all atom-quadruples. However, tetrangle-inequality bound-smoothing is an extremely computation intensive task, requiring O(n(4)) time for an n-atom molecule. To reduce computation time, we propose a novel coarse-grained parallel algorithm intended for a Beowulf-type cluster of PCs. The algorithm employs p <= n/6 processors and requires O(n(4)/p) time and O(p(2)) communications, where n is the number of atoms in a molecule. The number of communications is at least an order of magnitude lower than in the earlier parallelizations. Our implementation utilized processors with at least 59% efficiency (including the communication overhead)-an impressive figure for a non-embarrassingly parallel problem on a cluster of workstations.

Journal Title

Journal of Supercomputing

Volume

34

Issue/Number

3

Publication Date

1-1-2005

Document Type

Article

Language

English

First Page

243

Last Page

271

WOS Identifier

WOS:000231761000002

ISSN

0920-8542

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