Implementation, Validation and Profiling of a Genetic Algorithm for Molecular Conformational Optimization

Prediction of the lowest energy conformation of a protein chain is a challenging optimization problem in computational chemistry and biology. Simple lattice-based protein models have been shown to be effective representations of the characteristics of proteins important in protein folding. An effective genetic algorithm for conformational optimization of proteins represented by the hydrophobic-hydrophillic lattice model was recently published. In this work, we create a publically available implementation of this genetic optimization algorithm. Tests of our implementation show equivalent performance to that reported for the original, in terms of both optimal conformation and number of function evaluations. In addition, we test our implementation across a range of data set sizes to characterize the performance of the algorithm as chain length increases: benchmarking that is necessary for future optimization and parallelization of the algorithm

The algorithmics of folding proteins on lattices

It should be possible to predict the fold of a protein into its native conformation, once we are given the sequence of the constituent amino acids. This is known as the protein structure prediction problem and is sometimes referred to as the problem of deciphering the second half of the genetic code. While large proteins fold in nature in seconds, computational chemists and biologists have found that folding proteins to their minimum energy conformations is a challenging unsolved optimization problem. Computational complexity theory has been useful in explaining, at least partially, this (Levinthal’s) paradox. The pedagogic cross-disciplinary survey by Ngo, Marks and Karplus (Computational Complexity, Protein Structure Prediction and the Levinthal Paradox, Birkhauser, Basel, 1994) provides an excellent starting point for non-biologists to take a plunge into the problem of folding proteins. Since then, there has been remarkable progress in the algorithmics of folding proteins on discrete lattice models, an account of which is presented herein