# Optimizing an Empirical Scoring Function for Transmembrane Protein Structure Determination

### Abstract

We examine the problem of transmembrane protein structure determination. Like many other questions that arise in biological research, this problem cannot be addressed by traditional laboratory experimentation alone. An approach that integrates experiment and computation is required. We investigate a procedure which states 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 some of its mathematical properties. We compare and contrast results obtained using two different derivative free optimization algorithms.

Type
Publication
INFORMS Journal on Computing
Date
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Citation
G. A. Gray, T. G. Kolda, K. L. Sale, M. M. Young. Optimizing an Empirical Scoring Function for Transmembrane Protein Structure Determination. INFORMS Journal on Computing (Special Issue on Computational Molecular Biology/Bioinformatics), Vol. 16, No. 4, pp. 406-418, 2004. https://doi.org/10.1287/ijoc.1040.0102

### Keywords

optimization, computational biology, nonlinear programming, parallel algorithm, protein structure, Bundler scoring function

### BibTeX

@article{GrKoSaYo04,
author = {Genetha Anne Gray and Tamara G. Kolda and Kenneth L. Sale and Malin M. Young},
title = {Optimizing an Empirical Scoring Function for Transmembrane Protein Structure Determination},
journal = {INFORMS Journal on Computing},
issuetitle = {Special Issue on Computational Molecular Biology/Bioinformatics},
volume = {16},
number = {4},
pages = {406--418},
year = {2004},
doi = {10.1287/ijoc.1040.0102},
}