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Probabilistic Search and Energy Guidance for Biased Decoy Sampling in Ab Initio Protein Structure Prediction
Sept.-Oct. 2013 (vol. 10 no. 5)
pp. 1162-1175
Kevin Molloy, George Mason University, Fairfax
Sameh Saleh, George Mason University, Fairfax
Amarda Shehu, George Mason University, Fairfax
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.
Index Terms:
Proteins,Trajectory,Probability distribution,Energy resolution,Energy states,energy bias,Protein structure prediction,probabilistic conformational search,near-native conformations
Kevin Molloy, Sameh Saleh, Amarda Shehu, "Probabilistic Search and Energy Guidance for Biased Decoy Sampling in Ab Initio Protein Structure Prediction," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 5, pp. 1162-1175, Sept.-Oct. 2013, doi:10.1109/TCBB.2013.29
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