Cluster Computing for Determining Three-Dimensional Protein Structure

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(n4) 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(n4/p) time and O(p2) 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.