Advances and Continuing Challenges in Achieving Realistic and Predictive Simulations of the Properties of Organic and Biological Molecules

Kollman, Peter A.

doi:10.1021/ar9500675

Cited by 266 publications

(209 citation statements)

References 98 publications

Supporting

Mentioning

206

Contrasting

Unclassified

Order By: Relevance

“…Trialanine was simulated in the ff96 (27), ff99 (28), ff99sbnmr-ildn (29,30), CHARMM27 (31,32), and OPLS-AA (33) force fields, as previously reported (11). Simulations were performed using Gromacs 4.5 (34) and run at constant temperature (300 K) and pressure (1.01 atm).…”

Section: Methods Molecular Dynamics Simulationsmentioning

confidence: 99%

Bayesian Energy Landscape Tilting: Towards Concordant Models of Molecular Ensembles

Beauchamp¹,

Pande²,

Das³

2014

Preprint

View full text Add to dashboard Cite

Predicting biological structure has remained challenging for systems such as disordered proteins that take on myriad conformations. Hybrid simulation/experiment strategies have been undermined by difficulties in evaluating errors from computational model inaccuracies and data uncertainties. Building on recent proposals from maximum entropy theory and nonequilibrium thermodynamics, we address these issues through a Bayesian Energy Landscape Tilting (BELT) scheme for computing Bayesian "hyperensembles" over conformational ensembles. BELT uses Markov chain Monte Carlo to directly sample maximum-entropy conformational ensembles consistent with a set of input experimental observables. To test this framework, we apply BELT to model trialanine, starting from disagreeing simulations with the force fields ff96, ff99, ff99sbnmr-ildn, CHARMM27, and OPLS-AA. BELT incorporation of limited chemical shift and 3 J measurements gives convergent values of the peptide's α, β, and P P II conformational populations in all cases. As a test of predictive power, all five BELT hyperensembles recover set-aside measurements not used in the fitting and report accurate errors, even when starting from highly inaccurate simulations. BELT's principled framework thus enables practical predictions for complex biomolecular systems from discordant simulations and sparse data.

show abstract

Section: Methods Molecular Dynamics Simulationsmentioning

confidence: 99%

Bayesian Energy Landscape Tilting: Towards Concordant Models of Molecular Ensembles

Beauchamp¹,

Pande²,

Das³

2014

Preprint

View full text Add to dashboard Cite

show abstract

“…This was prepared with the GROMACS 38,39 3.3 utility PDB2GMX with default protonation states, using a GROMACS port 40 of the AMBER 96 41 force field. Since the cavity that makes up the binding site is completely hydrophobic without any nearby titratable groups, these protonation states present no difficulties.…”

Section: System Preparationmentioning

confidence: 99%

Predicting Absolute Ligand Binding Free Energies to a Simple Model Site

Mobley

Graves

Chodera

et al. 2007

Journal of Molecular Biology

310

501

View full text Add to dashboard Cite

A central challenge in structure-based ligand design is the accurate prediction of binding free energies. Here we apply alchemical free energy calculations in explicit solvent to predict ligand binding in a model cavity in T4 lysozyme. Even in this simple site, there are challenges. We made systematic improvements, beginning with single poses from docking, then including multiple poses, additional protein conformational changes, and using an improved charge model. Computed absolute binding free energies had an RMS error of 1.9 kcal/mol relative to previously determined experimental values. In blind prospective tests, the methods correctly discriminated between several true ligands and decoys in a set of putative binders identified by docking. In these prospective tests, the RMS error in predicted binding free energies relative to those subsequently determined experimentally was only 0.6 kcal/mol. X-ray crystal structures of the new ligands bound in the cavity corresponded closely to predictions from the free energy calculations, but sometimes differed from those predicted by docking. Finally, we examined the impact of holding the protein rigid, as in docking, with a view to learning how approximations made in docking affect accuracy and how they may be improved.

show abstract

“…An exponential averaging formula does not work for the fully coupled case, since the high-energy, exponential tail is not adequately sampled during simulations; direct implementation of the exponential averaging formula leads to a 16 kJ/mol error in the free energy. Larger cases such as CF 4 become even more problematic, 50 and no detectable overlap would occur for a small peptide, for example.…”

Section: Introductionmentioning

confidence: 99%

Modeling molecular and ionic absolute solvation free energies with quasichemical theory bounds

Rogers

Beck

2008

The Journal of Chemical Physics

View full text Add to dashboard Cite

A recently developed statistical mechanical Quasi-Chemical Theory (QCT) has led to significant insights into solvation phenomena for both hydrophilic and hydrophobic solutes. The QCT exactly partitions solvation free energies into three components: 1) inner-shell chemical, 2) outer-shell packing, and 3) outer-shell long-ranged contributions. In this paper, we discuss efficient methods for computing each of the three parts of the free energy. A Bayesian estimation approach is developed to compute the inner-shell chemical and outer-shell packing contributions. We derive upper and lower bounds on the outer-shell long-ranged portion of the free energy by expressing this component in two equivalent ways. Local, high energy contacts between solute and solvent are eliminated by spatial conditioning in this free energy piece, leading to near-Gaussian distributions of solute-solvent interactions energies. Thus, the average of the two mean-field bounds yields an accurate and efficient free energy estimate. Aqueous solvation free energy results are presented for several solutes, including methane, perfluoromethane, water, and the sodium and chloride ions.The results demonstrate the accuracy and efficiency of the methods. The approach should prove useful in computing solvation free energies in inhomogeneous, restricted environments.

show abstract

Advances and Continuing Challenges in Achieving Realistic and Predictive Simulations of the Properties of Organic and Biological Molecules

Abstract: have contributed a great deal. In the area of conformational sampling, the work of Yax Sun, Carlos Simmerling, and Steve DeBolt is featured prominently. Free energy calculations involved studies by

Cited by 266 publications

References 98 publications

Bayesian Energy Landscape Tilting: Towards Concordant Models of Molecular Ensembles

Bayesian Energy Landscape Tilting: Towards Concordant Models of Molecular Ensembles

Predicting Absolute Ligand Binding Free Energies to a Simple Model Site

Modeling molecular and ionic absolute solvation free energies with quasichemical theory bounds

Contact Info

Product

Resources

About