Physics Based Protein Structure Refinement through Multiple Molecular Dynamics Trajectories and Structure Averaging

By V. Mirjalili, Keenan Noyes, Michael Feig. 2013

100 citations recorded by the source corpus.

We used molecular dynamics (MD) simulations for structure refinement of Critical Assessment of Techniques for Protein Structure Prediction 10 (CASP10) targets. Refinement was achieved by selecting structures from the MD‐based ensembles followed by structural averaging. The overall performance of this method in CASP10 is described, and specific aspects are analyzed in detail to provide insight into key components. In particular, the use of different restraint types, sampling from multiple short simulations versus a single long simulation, the success of a quality assessment criterion, the application of scoring versus averaging, and the impact of a final refinement step are discussed in detail. Proteins 2014; 82(Suppl 2):196–207. © 2013 Wiley Periodicals, Inc.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Local Error Estimates Dramatically Improve the Utility of Homology Models for Solving Crystal Structures by Molecular Replacement (2015, methodology): “Based on this measure, the best-performing predictors are TS049 (FEIG;Mirjalili et al., 2014), followed by TS197 (Mufold; Zhang et al.”
Driven to Near-Experimental Accuracy by Refinement via Molecular Dynamics Simulations (2019, methodology): “We already discussed that the new iterative protocol mostly out-performed the more conservative protocol employed in previous rounds of CASP, 14,19,20 with additional improvements when both protocols were combined.”
Assessment of the model refinement category in CASP12 (2017, methodology): “A few methods based on structure selection and averaging have been developed to this end.(11,46) GOAL, the top scoring approach according to the CASP12 score, uses a combination of trajectory averaging of atomistic explicit solvent MD simulations and energy minimization with an implicit solvent to a”
Protein homology model refinement by large-scale energy optimization (2018, background): “Structural averaging of molecular dynamics (MD) simulation trajectories (9, 10) and modeling with strong restraints to a high-resolution reference model (4, 5) have been shown to consistently improve model accuracy when starting models are close to the native structure.”
ReFOLD: a server for the refinement of 3D protein models guided by accurate quality estimates (2017, methodology): “The human refinement methods commonly use physics based approaches that select structures from Molecular Dynamics (MD)-based ensembles followed by structural averaging, which has led to a high-level of refinement success (9,10).”
Structure Refinement of Membrane Proteins via Molecular Dynamics Simulations (2018, methodology): “As in our previous protocol, we carried out extensive sampling via MD.23,25 Structures from the trajectories were then selected using different scoring functions.”
Assessment of Refinement of Template-Based Models in CASP11 (2016, methodology): “13 Here we analyze and discuss the performance of 53 groups participating in the CASP11 refinement experiment, consisting of 36 template-based modeling targets and one free-modeling target from the regular CASP experiment.”
Computational protein structure refinement: Almost there, yet still so far to go (2017, methodology): “61,87,107,118,119 In addition to physics-based force ﬁ elds, statistical potentials have also been used in MD-or minimization-based re ﬁ nement methods.”