Reliable protein structure refinement using a physical energy function

Abstract

In the past decade, significant progress has been made in protein structure prediction. However, refining models to a level of resolution that is comparable with experimental results and can be used in studies like enzymatic activity still remains a major challenge. We have previously demonstrated that our modular protein-solvent energy function, uniquely involving a potential of mean force description for hydrophobic solvation, works well in protein globular structure prediction and loop modeling. In this work, we couple protein-solvent energy function with our global optimization method stochastic perturbation with soft constraints and use them to refine a collection of template models from submitted predictions to recent Critical Assessment of Techniques for Protein Structure Prediction blind prediction contests. A prediction protocol based on a selection of structures with the lowest energy is able to successfully refine all of the test proteins, and, more importantly, our energy function does not show degradation in prediction when sampling is exhausted.

FF12MC: A revised AMBER forcefield and new protein simulation protocol

“…Consistent with the need to use statistically‐derived knowledge‐based potentials for refining comparative models of protein structures, the accuracy of the physics‐based forcefield—such as those in the form of Eq. —has been suggested to be the primary factor limiting the simulation‐based comparative model refinement .…”

Protein Structure Refinement through Structure Selection and Averaging from Molecular Dynamics Ensembles

“…16,19–21 Although successful examples of MD-based refinement have been reported in the past, 2,11,19–26 consistent success appears to be hindered by a combination of insufficient sampling, 11,27,28 force field inaccuracies, 20,29 and an inability to reliably identify refined structures that may be generated during the course of an MD simulation. 11,23,29–32 To address these issues, statistical potentials 21,33–35 and optimized force fields 20,36,37 have been used as well as effective sampling techniques such as replica-exchange 19,24,25,33 and self-guided Langevin dynamics 38 simulations. In some studies it was possible to generate improved structures by as much as 0.5 Å in root-mean-square deviation (RMSD) in one out of five models, 25,33 but reliable identification of a single refined structure remained difficult.…”

“…11,23,29–32 To address these issues, statistical potentials 21,33–35 and optimized force fields 20,36,37 have been used as well as effective sampling techniques such as replica-exchange 19,24,25,33 and self-guided Langevin dynamics 38 simulations. In some studies it was possible to generate improved structures by as much as 0.5 Å in root-mean-square deviation (RMSD) in one out of five models, 25,33 but reliable identification of a single refined structure remained difficult. Recently, Fan et al 24 have shown that by mimicking the electrostatic effects with chaperone Hamiltonian replica-exchange MD simulation can generate refined structures for 10 out of 15 targets with improvements of more than 1 Å RMSD for the secondary structure elements, but again reliable selection of refined structures without knowledge of the native state remained challenging.…”

Protein Structure Refinement Algorithms

“…Recently, a flexible backbone modeling algorithm was applied [18][19][20] . All these methods have attained some degree of success; however, none has emerged as clearly superior 13,14,16 .…”

“…Several groups have worked on developing force fields [6][7][8][9][10][11][12][13][14][15] , and creating better sampling methods [16][17][18][19][20][21][22] .…”