Assessment of Detection and Refinement Strategies for de novo Protein Structures Using Force Field and Statistical Potentials

Lee, Michael S.; Olson, Mark A.

doi:10.1021/ct600195f

Cited by 26 publications

(39 citation statements)

References 70 publications

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“…This opens up the possibility of applying SGLD-ReX to the protein structure refinement problem. 44 There are several caveats in this study. First and foremost, an implicit solvent model was used rather than explicit water molecules.…”

Section: Discussionmentioning

confidence: 88%

Protein Folding Simulations Combining Self-Guided Langevin Dynamics and Temperature-Based Replica Exchange

Lee

Olson

2010

J. Chem. Theory Comput.

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Abstract:Computer simulations are increasingly being used to predict thermodynamic observables for folding small proteins. Key to continued progress in this area is the development of algorithms that accelerate conformational sampling. Temperature-based replica exchange (ReX) is a commonly used protocol whereby simulations at several temperatures are simultaneously performed and temperatures are exchanged between simulations via a Metropolis criterion. Another method, selfguided Langevin dynamics (SGLD), expedites conformational sampling by accelerating lowfrequency, large-scale motions through the addition of an ad hoc momentum memory term. In this work, we combined these two complementary techniques and compared the results against conventional ReX formulations of molecular dynamics (MD) and Langevin dynamics (LD) simulations for the prediction of thermodynamic folding observables of the Trp-cage mini-protein. All simulations were performed with CHARMM using the PARAM22+CMAP force field and the generalized Born molecular volume implicit solvent model. While SGLD-ReX does not fold up the protein significantly faster than the two conventional ReX approaches, there is some evidence that the method improves sampling convergence by reducing topological folding barriers between energetically similar nearnative states. Unlike MD-ReX and LD-ReX, SGLD-ReX predicts melting temperatures, heat capacity curves, and folding free energies that are closer in agreement to the experimental observations. However, this favorable result may be due to distortions of the relative free energies of the folded and unfolded conformational basins caused by the ad hoc force term in the SGLD model.

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Section: Discussionmentioning

confidence: 88%

Protein Folding Simulations Combining Self-Guided Langevin Dynamics and Temperature-Based Replica Exchange

Lee

Olson

2010

J. Chem. Theory Comput.

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“…The histograms N obs in this work were obtained from previous analysis of a culled set of 1836 Protein Data Bank (PDB) structures which had better than 1.8-Å resolution and were less than 30% homologous to each other. [17] We deviated from the original DFIRE protocol by assigning Dr ¼ 0.5 Å at all distances and having r range from 0.25 to 14.75 Å , such that r cut ¼ 15 Å . The third scoring approach is application of the Rosetta energy function.…”

Section: Scoring Of Protein Structuresmentioning

confidence: 99%

“…This dataset contains eight-residue loops, which are relatively tractable refinement problem for a number of loop modeling algorithms, and longer 12-residue loops, which are almost universally challenging for structure prediction and refinement. [16] In addition to exploring the SGLD method, we also revisit the problem of detection of nativelike structures among decoys [17] by evaluating three scoring methods. The first is based on the force field and generalized Born implicit solvent model used to generate the loop conformations.…”

Section: Introductionmentioning

confidence: 99%

Comparison between self‐guided Langevin dynamics and molecular dynamics simulations for structure refinement of protein loop conformations

2011

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This article presents a comparative analysis of two replica-exchange simulation methods for the structure refinement of protein loop conformations, starting from low-resolution predictions. The methods are self-guided Langevin dynamics (SGLD) and molecular dynamics (MD) with a Nosé-Hoover thermostat. We investigated a small dataset of 8- and 12-residue loops, with the shorter loops placed initially from a coarse-grained lattice model and the longer loops from an enumeration assembly method (the Loopy program). The CHARMM22 + CMAP force field with a generalized Born implicit solvent model (molecular-surface parameterized GBSW2) was used to explore conformational space. We also assessed two empirical scoring methods to detect nativelike conformations from decoys: the all-atom distance-scaled ideal-gas reference state (DFIRE-AA) statistical potential and the Rosetta energy function. Among the eight-residue loop targets, SGLD out performed MD in all cases, with a median of 0.48 Å reduction in global root-mean-square deviation (RMSD) of the loop backbone coordinates from the native structure. Among the more challenging 12-residue loop targets, SGLD improved the prediction accuracy over MD by a median of 1.31 Å, representing a substantial improvement. The overall median RMSD for SGLD simulations of 12-residue loops was 0.91 Å, yielding refinement of a median 2.70 Å from initial loop placement. Results from DFIRE-AA and the Rosetta model applied to rescoring conformations failed to improve the overall detection calculated from the CHARMM force field. We illustrate the advantage of SGLD over the MD simulation model by presenting potential-energy landscapes for several loop predictions. Our results demonstrate that SGLD significantly outperforms traditional MD in the generation and populating of nativelike loop conformations and that the CHARMM force field performs comparably to other empirical force fields in identifying these conformations from the resulting ensembles.

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“…33,34 Nonetheless, current heuristic enumeration methods with a variety of scoring functions are much more robust than physics-based, all-atom sampling of loops and the application of the later to loops longer than 11 residues would take on challenges tantamount to the protein structure refinement problem. [38][39][40] …”

Section: Computational Cost and Overall Assessmentmentioning

confidence: 99%

Prediction of protein loop conformations using multiscale modeling methods with physical energy scoring functions

2007

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This article examines ab initio methods for the prediction of protein loops by a computational strategy of multiscale conformational sampling and physical energy scoring functions. Our approach consists of initial sampling of loop conformations from lattice-based low-resolution models followed by refinement using all-atom simulations. To allow enhanced conformational sampling, the replica exchange method was implemented. Physical energy functions based on CHARMM19 and CHARMM22 parameterizations with generalized Born (GB) solvent models were applied in scoring loop conformations extracted from the lattice simulations and, in the case of all-atom simulations, the ensemble of conformations were generated and scored with these models. Predictions are reported for 25 loop segments, each eight residues long and taken from a diverse set of 22 protein structures. We find that the simulations generally sampled conformations with low global root-mean-square-deviation (RMSD) for loop backbone coordinates from the known structures, whereas clustering conformations in RMSD space and scoring detected less favorable loop structures. Specifically, the lattice simulations sampled basins that exhibited an average global RMSD of 2.21 +/- 1.42 A, whereas clustering and scoring the loop conformations determined an RMSD of 3.72 +/- 1.91 A. Using CHARMM19/GB to refine the lattice conformations improved the sampling RMSD to 1.57 +/- 0.98 A and detection to 2.58 +/- 1.48 A. We found that further improvement could be gained from extending the upper temperature in the all-atom refinement from 400 to 800 K, where the results typically yield a reduction of approximately 1 A or greater in the RMSD of the detected loop. Overall, CHARMM19 with a simple pairwise GB solvent model is more efficient at sampling low-RMSD loop basins than CHARMM22 with a higher-resolution modified analytical GB model; however, the latter simulation method provides a more accurate description of the all-atom energy surface, yet demands a much greater computational cost.

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Assessment of Detection and Refinement Strategies for de novo Protein Structures Using Force Field and Statistical Potentials

Cited by 26 publications

References 70 publications

Protein Folding Simulations Combining Self-Guided Langevin Dynamics and Temperature-Based Replica Exchange

Protein Folding Simulations Combining Self-Guided Langevin Dynamics and Temperature-Based Replica Exchange

Comparison between self‐guided Langevin dynamics and molecular dynamics simulations for structure refinement of protein loop conformations

Prediction of protein loop conformations using multiscale modeling methods with physical energy scoring functions

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