Optimizing an Empirical Scoring Function for Transmembrane Protein Structure Determination

  • Authors:

  • Genetha Anne Gray;Tamara G. Kolda;Ken Sale;Malin M. Young

  • Affiliations:

  • Computational Sciences and Mathematics Research Department, Sandia National Laboratories, PO Box 969 MS 9159, Livermore, California 94551-0969, USA;Computational Sciences and Mathematics Research Department, Sandia National Laboratories, PO Box 969 MS 9159, Livermore, California 94551-0969, USA;Biosystems Research Department, Sandia National Laboratories, PO Box 969 MS 9951, Livermore, California 94551-0969, USA;Biosystems Research Department, Sandia National Laboratories, PO Box 969 MS 9951, Livermore, California 94551-0969, USA

  • Venue:
  • INFORMS Journal on Computing
  • Year:
  • 2004

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Abstract

We examine the problem of transmembrane protein structure determination. Like many questions that arise in biological research, this problem cannot be addressed generally by traditional laboratory experimentation alone. Instead, an approach that integrates experiment and computation is required. We formulate the transmembrane protein structure determination problem as a bound-constrained optimization problem using a special empirical scoring function, called Bundler, as the objective function. In this paper, we describe the optimization problem and its mathematical properties, and we examine results obtained using two different derivative-free optimization algorithms.