Prediction of Protein Loop Conformations Using the AGBNP Implicit Solvent Model and Torsion Angle Sampling

Felts, Anthony K.; Gallicchio, Emilio; Chekmarev, Dmitriy S.; Paris, Kristina A.; Friesner, Richard A.; Levy, Ronald M.

doi:10.1021/ct800051k

Cited by 62 publications

(75 citation statements)

References 73 publications

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“…[41] A summary of the results in Table 1 [30,41] It should be noted that the PLOP method has undergone recent improvements in conformational sampling and detection accuracy applied to different loop target datasets. [41][42][43] Results for DFIRE-AA are the mean/ The outcome from these two loops provides a range of results probably to be observed from modeling a much larger dataset of targets. For 1bkf, the profile computed by MD simulation shows at 2.5-2.8 Å RMSD a large conformational free-energy surface that encompasses the starting structure predicted by Loopy.…”

Section: A Olson S Chaudhury and M S Leementioning

confidence: 98%

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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Section: A Olson S Chaudhury and M S Leementioning

confidence: 98%

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

2011

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“…Ten and twenty five μs MD simulations were run in AGBNP2 39–41 implicit solvent and in vacuum, respectively, to obtain converged free energy estimates in implicit solvent and vacuum environments.…”

Section: Methodsmentioning

confidence: 99%

Connecting Free Energy Surfaces in Implicit and Explicit Solvent: An Efficient Method To Compute Conformational and Solvation Free Energies

Deng

Zhang

Levy

2015

J. Chem. Theory Comput.

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The ability to accurately model solvent effects on free energy surfaces is important for understanding many biophysical processes including protein folding and misfolding, allosteric transitions and protein-ligand binding. Although all-atom simulations in explicit solvent can provide an accurate model for biomolecules in solution, explicit solvent simulations are hampered by the slow equilibration on rugged landscapes containing multiple basins separated by barriers. In many cases, implicit solvent models can be used to significantly speed up the conformational sampling; however, implicit solvent simulations do not fully capture the effects of a molecular solvent, and this can lead to loss of accuracy in the estimated free energies. Here we introduce a new approach to compute free energy changes in which the molecular details of explicit solvent simulations are retained while also taking advantage of the speed of the implicit solvent simulations. In this approach, the slow equilibration in explicit solvent, due to the long waiting times before barrier crossing, is avoided by using a thermodynamic cycle which connects the free energy basins in implicit solvent and explicit solvent using a localized decoupling scheme. We test this method by computing conformational free energy differences and solvation free energies of the model system alanine dipeptide in water. The free energy changes between basins in explicit solvent calculated using fully explicit solvent paths agree with the corresponding free energy differences obtained using the implicit/explicit thermodynamic cycle to within 0.3 kcal/mol out of ~3 kcal/mol at only ~8 % of the computational cost. We note that WHAM methods can be used to further improve the efficiency and accuracy of the explicit/implicit thermodynamic cycle.

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“…3). Hence, if the homology is low between ECL2 in the receptor of interest and any of the available crystal structures, it is potentially better to look at alternative means of modeling such as the use of loop libraries or de novo modeling techniques [62][63][64][65]. The issue has been explored in some detail.…”

Section: Homology Model Buildingmentioning

confidence: 99%

GPCRHomology Model Development and Application

Garland

Blaney

2010

Burger's Medicinal Chemistry and Drug Discovery

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G‐protein‐coupled receptors (GPCRs) are a very important class of proteins for drug discovery. Exciting progress has been made recently in the determination of X‐ray crystal structures. However, despite this, structural coverage of the receptor family is still thin. We lack direct structural knowledge of many important subfamilies, we have only modest detail of the conformational changes associated with receptor activation and we lack robust crystallographic systems for the rapid determination of ligand binding modes. As a consequence, the ability to build and apply homology models for drug discovery at these targets is very valuable. The accuracy of the models has improved in parallel with the increase in available structural data and the models have been successfully applied to a wide range of lead generation and optimization problems.

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Prediction of Protein Loop Conformations Using the AGBNP Implicit Solvent Model and Torsion Angle Sampling

Cited by 62 publications

References 73 publications

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

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

Connecting Free Energy Surfaces in Implicit and Explicit Solvent: An Efficient Method To Compute Conformational and Solvation Free Energies

GPCRHomology Model Development and Application

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