Probabilistic Search and Energy Guidance for Biased Decoy Sampling in Ab Initio Protein Structure Prediction
Issue No. 05 - Sept.-Oct. (2013 vol. 10)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2013.29
Kevin Molloy , Dept. of Comput. Sci., George Mason Univ., Fairfax, VA, USA
Sameh Saleh , Dept. of Comput. Sci., George Mason Univ., Fairfax, VA, USA
Amarda Shehu , Dept. of Comput. Sci., George Mason Univ., Fairfax, VA, USA
Adequate sampling of the conformational space is a central challenge in ab initio protein structure prediction. In the absence of a template structure, a conformational search procedure guided by an energy function explores the conformational space, gathering an ensemble of low-energy decoy conformations. If the sampling is inadequate, the native structure may be missed altogether. Even if reproduced, a subsequent stage that selects a subset of decoys for further structural detail and energetic refinement may discard near-native decoys if they are high energy or insufficiently represented in the ensemble. Sampling should produce a decoy ensemble that facilitates the subsequent selection of near-native decoys. In this paper, we investigate a robotics-inspired framework that allows directly measuring the role of energy in guiding sampling. Testing demonstrates that a soft energy bias steers sampling toward a diverse decoy ensemble less prone to exploiting energetic artifacts and thus more likely to facilitate retainment of near-native conformations by selection techniques. We employ two different energy functions, the associative memory Hamiltonian with water and Rosetta. Results show that enhanced sampling provides a rigorous testing of energy functions and exposes different deficiencies in them, thus promising to guide development of more accurate representations and energy functions.
Proteins, Trajectory, Probability distribution, Energy resolution, Energy states
K. Molloy, S. Saleh and A. Shehu, "Probabilistic Search and Energy Guidance for Biased Decoy Sampling in Ab Initio Protein Structure Prediction," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 5, pp. 1162-1175, 2014.