NDLI: Optimizing noisy funnel-like functions on the euclidean group with applications to protein docking

Content Provider	IEEE Xplore Digital Library
Author	Yang Shen Vakili, P. Vajda, S. Paschalidis, I.C.
Copyright Year	2007
Description	Author affiliation: Boston Univ., Brookline (Yang Shen; Vakili, P.)
Abstract	Formulated as an optimization problem, the final stages of protein docking can be viewed as optimizing a very noisy funnel-like function on the space of rigid body motions, the (special) Euclidean group SE(3). We have recently introduced a stochastic global optimization method, called semi-definite programming based underestimation (SDU) (Paschalidis et al., 2007), that constructs a convex quadratic under-estimator to the free energy funnel based on a sample of energy function evaluations and uses the quadratic under-estimator to guide future sampling. In this paper we show that the parameterization of SE(3) has a significant impact on the effectiveness of SDU and introduce a parameterization that dramatically reduces the number of very costly energy function evaluations. The resulting algorithm represents a significant gain (more than an order of magnitude) in computational efficiency compared to state-of-the-art Monte Carlo-based algorithms used for the same purpose.
Starting Page	4545
Ending Page	4550
File Size	244085
Page Count	6
File Format	PDF
ISBN	9781424414970
ISSN	01912216
DOI	10.1109/CDC.2007.4434379
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Optimization methods Biomedical engineering Manufacturing Orbital robotics Stochastic processes Functional programming Computational biology Systems engineering and theory Parallel robots
Content Type	Text
Resource Type	Article

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