Generalized Born Model Based on a Surface Integral Formulation

Ghosh, Avijit; Rapp, Chaya S.; Friesner, Richard A.

doi:10.1021/jp982533o

Cited by 397 publications

(455 citation statements)

References 11 publications

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“…13 23 used a similar energy function on a large database of loop decoys. The energy function used here is similar (OPLS all-atom force field 24 -26 and Surface Generalized Born model of solvation 27,28 ), but the sampling algorithms used are entirely new and make it possible to apply a realistic, well-validated solvent model to a large test set of loops, with modest computational expense.…”

Section: Overview Of Methodologymentioning

confidence: 99%

“…The OPLS torsional energy parameters were recently refined by using high-level quantum chemical calculations 26 and validated by using protein sidechain prediction 24 ; the updated parameters are used here. The solvation free energy was estimated by using an implicit solvent model consisting of the Surface Generalized Born (SGB) model of polar solvation 28 and a nonpolar estimator developed by Gallicchio and coworkers. 27 Correction terms have also been developed to improve the agreement between SGB and Poisson-Boltzmann solvation free energy calculations.…”

Section: Details Of Energy Functionmentioning

confidence: 99%

“…27 Correction terms have also been developed to improve the agreement between SGB and Poisson-Boltzmann solvation free energy calculations. 28 Two surfaces (extending over the entire simulation region, including the symmetry copies) were calculated to perform the SGB surface integrals: one high resolution (330 points per sphere) and one low resolution (10 points per sphere). The distributions of points on the spheres used to construct these surfaces were determined by using the spiral points algorithm.…”

Section: Details Of Energy Functionmentioning

confidence: 99%

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A hierarchical approach to all‐atom protein loop prediction

et al. 2004

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The application of all-atom force fields (and explicit or implicit solvent models) to protein homology-modeling tasks such as side-chain and loop prediction remains challenging both because of the expense of the individual energy calculations and because of the difficulty of sampling the rugged all-atom energy surface. Here we address this challenge for the problem of loop prediction through the development of numerous new algorithms, with an emphasis on multiscale and hierarchical techniques. As a first step in evaluating the performance of our loop prediction algorithm, we have applied it to the problem of reconstructing loops in native structures; we also explicitly include crystal packing to provide a fair comparison with crystal structures. In brief, large numbers of loops are generated by using a dihedral angle-based buildup procedure followed by iterative cycles of clustering, side-chain optimization, and complete energy minimization of selected loop structures. We evaluate this method by using the largest test set yet used for validation of a loop prediction method, with a total of 833 loops ranging from 4 to 12 residues in length. Average/median backbone rootmean-square deviations (RMSDs) to the native structures (superimposing the body of the protein, not the loop itself) are 0.42/0.24 Å for 5 residue loops, 1.00/0.44 Å for 8 residue loops, and 2.47/1.83 Å for 11 residue loops. Median RMSDs are substantially lower than the averages because of a small number of outliers; the causes of these failures are examined in some detail, and many can be attributed to errors in assignment of protonation states of titratable residues, omission of ligands from the simulation, and, in a few cases, probable errors in the experimentally determined structures. When these obvious problems in the data sets are filtered out, average RMSDs to the native structures improve to 0.43 Å for 5 residue loops, 0.84 Å for 8 residue loops, and 1.63 Å for 11 residue loops. In the vast majority of cases, the method locates energy minima that are lower than or equal to that of the minimized native loop, thus indicating that sampling rarely limits prediction accuracy. The overall results are, to our knowledge, the best reported to date, and we attribute this success to the combination of an accurate all-atom energy function, efficient methods for loop buildup and side-chain optimization, and, especially for the longer loops, the hierarchical refinement protocol.

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Section: Overview Of Methodologymentioning

confidence: 99%

Section: Details Of Energy Functionmentioning

confidence: 99%

Section: Details Of Energy Functionmentioning

confidence: 99%

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A hierarchical approach to all‐atom protein loop prediction

et al. 2004

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“…The SGB implementation used in this work includes further correction terms that bring the SGB reaction field energy into closer agreement with exact PB results. 51 Coupled with the SGB model is a function describing the nonpolar interactions between the solute and solvent which is based on two terms: the van der Waals interaction between solute and solvent and the work to form the cavity in the solvent. The full solvation model is referred to as SGB/NP.…”

Section: Sgb/np Implicit Solventmentioning

confidence: 99%

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

Felts

Gallicchio

Chekmarev

et al. 2008

J. Chem. Theory Comput.

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The OPLS-AA all-atom force field and the Analytical Generalized Born plus Non-Polar (AGBNP) implicit solvent model, in conjunction with torsion angle conformational search protocols based on the Protein Local Optimization Program (PLOP), are shown to be effective in predicting the native conformations of 57 9-residue and 35 13-residue loops of a diverse series of proteins with low sequence identity. The novel nonpolar solvation free energy estimator implemented in AGBNP augmented by correction terms aimed at reducing the occurrence of ion pairing are important to achieve the best prediction accuracy. Extended versions of the previously developed PLOP-based conformational search schemes based on calculations in the crystal environment are reported that are suitable for application to loop homology modeling without the crystal environment. Our results suggest that in general the loop backbone conformation is not strongly influenced by crystal packing. The application of the temperature Replica Exchange Molecular Dynamics (T-REMD) sampling method for a few examples where PLOP sampling is insufficient are also reported. The results reported indicate that the OPLS-AA/AGBNP effective potential is suitable for high-resolution modeling of proteins in the final stages of homology modeling and/or protein crystallographic refinement.

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“…Second, to achieve computational tractability, we employ a continuum solvation model, using the surface generalized Born (SGB) approach. 9 Details of the solvation model (including treatment of bound waters) have a substantial effect on the results. In this paper, we briefly discuss modeling of both the crystalline and aqueous environments, leaving however a more extensive exploration of these topics to other publications.…”

Section: Introductionmentioning

confidence: 99%

Force Field Validation Using Protein Side Chain Prediction

et al. 2002

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The prediction of protein side chain conformations is used to evaluate the accuracy of force field parameters. Specifically, new torsional parameters have recently been reported for the OPLS-AA force field, which achieved substantially better accuracy with respect to high level gas-phase quantum chemical calculations [J. Phys. Chem. B 2001, 105, 6474]. Here we demonstrate that these new parameters also lead to qualitatively improved side chain prediction accuracy. The primary emphasis is on the prediction of single side chain conformations, with the rest of the protein held fixed at the native configuration. Errors due to incomplete sampling can thus be essentially eliminated, using a combination of rotamer search and energy minimization. In addition, the protein environment is modeled realistically using implicit solvation and an explicit representation of crystal packing effects. Aided by the development of new algorithms, these calculations have been performed with modest computational requirements (a cluster of PCs) on a database of 36 proteins (∼5000 total residues). The side chain prediction tests that we employ are quite general and can be used to evaluate nonbonded or solvation parameters as well. As such, they provide a useful complement to decoy studies for force field validation.

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Generalized Born Model Based on a Surface Integral Formulation

Cited by 397 publications

References 11 publications

A hierarchical approach to all‐atom protein loop prediction

A hierarchical approach to all‐atom protein loop prediction

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

Force Field Validation Using Protein Side Chain Prediction

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