Robust Bio-active Peptide Prediction Using Multi-objective Optimization
BIOSCIENCESWORLD '10 Proceedings of the 2010 International Conference on Biosciences
On discrete models and immunological algorithms for protein structure prediction
Natural Computing: an international journal
Populating Local Minima in the Protein Conformational Space
BIBM '11 Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine
An evolutionary model based on hill-climbing search operators for protein structure prediction
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Biased decoy sampling to aid the selection of near-native protein conformations
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Off-lattice protein structure prediction with homologous crossover
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Efficient basin hopping in the protein energy surface
BIBM '12 Proceedings of the 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Advances in Artificial Intelligence - Special issue on Artificial Intelligence Applications in Biomedicine
Informatics-driven Protein-protein Docking
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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We present an evolutionary stochastic search algorithm to obtain a discrete representation of the protein energy surface in terms of an ensemble of conformations representing local minima. This objective is of primary importance in protein structure modeling, whether the goal is to obtain a broad view of potentially different structural states thermodynamically available to a protein system or to predict a single representative structure of a unique functional native state. In this paper, we focus on the latter setting, and show how approaches from evolutionary computation for effective stochastic search and multi-objective analysis can be combined to result in protein conformational search algorithms with high exploration capability. From a broad computational perspective, the contributions of this paper are on how to balance global and local search of some high-dimensional search space and how to guide the search in the presence of a noisy, inaccurate scoring function. From an application point of view, the contributions are demonstrated in the domain of template-free protein structure prediction on the primary subtask of sampling diverse low-energy decoy conformations of an amino-acid sequence. Comparison with the approach used for decoy sampling in the popular Rosetta protocol on 20 diverse protein sequences shows that the evolutionary algorithm proposed in this paper is able to access lower-energy regions with similar or better proximity to the known native structure.