Protein structure by semidefinite facial reduction

Authors:
Babak Alipanahi;Nathan Krislock;Ali Ghodsi;Henry Wolkowicz;Logan Donaldson;Ming Li
Affiliations:
David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada;INRIA Grenoble Rhône-Alpes, France;Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada;Department of Combinatorics and Optimization, University of Waterloo, Waterloo, Ontario, Canada;Department of Biology, York University, Toronto, Ontario, Canada;David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada
Venue:
RECOMB'12 Proceedings of the 16th Annual international conference on Research in Computational Molecular Biology
Year:
2012

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Abstract

All practical contemporary protein NMR structure determination methods use molecular dynamics coupled with a simulated annealing schedule. The objective of these methods is to minimize the error of deviating from the NOE distance constraints. However, this objective function is highly nonconvex and, consequently, difficult to optimize. Euclidean distance geometry methods based on semidefinite programming (SDP) provide a natural formulation for this problem. However, complexity of SDP solvers and ambiguous distance constraints are major challenges to this approach. The contribution of this paper is to provide a new SDP formulation of this problem that overcomes these two issues for the first time. We model the protein as a set of intersecting two- and three-dimensional cliques, then we adapt and extend a technique called semidefinite facial reduction to reduce the SDP problem size to approximately one quarter of the size of the original problem. The reduced SDP problem can not only be solved approximately 100 times faster, but is also resistant to numerical problems from having erroneous and inexact distance bounds.