A Large-Scale Test of Free-Energy Simulation Estimates of Protein–Ligand Binding Affinities

Mikulskis, Paulius; Genheden, Samuel; Ryde, Ulf

doi:10.1021/ci5004027

Cited by 60 publications

(106 citation statements)

References 73 publications

(140 reference statements)

Supporting

Mentioning

101

Contrasting

Order By: Relevance

“…The alchemical perturbation simulations were performed in the following way [10]: the system at each lambda value was subjected to 100 cycles of steepest-descent minimisation, with all atoms, except water molecules and hydrogen atoms, restrained to their start position with a force constant of 418 kJ/mol/Å 2 . This was followed by 50 ps NPT simulation and a 500 ps NPT equilibration without any restraints.…”

Section: Methodsmentioning

confidence: 99%

“…To estimate the convergence of the various perturbations, six different overlap measures were employed [10]. We calculated the Bhattacharyya coefficient for the energy distribution overlap (Ω) [103], the Wu & Kofke overlap measures of the energy probability distributions ( K AB ) and their bias metrics (Π) [104, 105], the weight of the maximum term in the exponential average ( w max ) [22], the difference of the forward and backward exponential average estimate (ΔΔ G EA ), and the difference between the BAR and TI estimates) [10].…”

Section: Methodsmentioning

confidence: 99%

“…We calculated the Bhattacharyya coefficient for the energy distribution overlap (Ω) [103], the Wu & Kofke overlap measures of the energy probability distributions ( K AB ) and their bias metrics (Π) [104, 105], the weight of the maximum term in the exponential average ( w max ) [22], the difference of the forward and backward exponential average estimate (ΔΔ G EA ), and the difference between the BAR and TI estimates) [10]. Ω goes from 0, no overlap to 1, perfect overlap [103], and we consider values higher than 0.7 acceptable [10]. K AB goes from 0—no overlap, via 1—full overlap, to 2—the first distribution is completely inside the second distribution [104, 105], and again values larger than 0.7 are accepted.…”

Section: Methodsmentioning

confidence: 99%

“…Comparisons of different approaches for the same test case are less common and often half-hearted in the meaning that the authors are experts or developers of one approach and include other methods mainly to show that they are worse [10, 13, 14]. In this respect, blind-test competitions are important to judge the true performance of different approaches, allowing experts to provide predictions that are not biased by the experimental results.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Binding-affinity predictions of HSP90 in the D3R Grand Challenge 2015 with docking, MM/GBSA, QM/MM, and free-energy simulations

Ignjatović

Caldararu

Dong

et al. 2016

J Comput Aided Mol Des

Self Cite

View full text Add to dashboard Cite

We have estimated the binding affinity of three sets of ligands of the heat-shock protein 90 in the D3R grand challenge blind test competition. We have employed four different methods, based on five different crystal structures: first, we docked the ligands to the proteins with induced-fit docking with the Glide software and calculated binding affinities with three energy functions. Second, the docked structures were minimised in a continuum solvent and binding affinities were calculated with the MM/GBSA method (molecular mechanics combined with generalised Born and solvent-accessible surface area solvation). Third, the docked structures were re-optimised by combined quantum mechanics and molecular mechanics (QM/MM) calculations. Then, interaction energies were calculated with quantum mechanical calculations employing 970–1160 atoms in a continuum solvent, combined with energy corrections for dispersion, zero-point energy and entropy, ligand distortion, ligand solvation, and an increase of the basis set to quadruple-zeta quality. Fourth, relative binding affinities were estimated by free-energy simulations, using the multi-state Bennett acceptance-ratio approach. Unfortunately, the results were varying and rather poor, with only one calculation giving a correlation to the experimental affinities larger than 0.7, and with no consistent difference in the quality of the predictions from the various methods. For one set of ligands, the results could be strongly improved (after experimental data were revealed) if it was recognised that one of the ligands displaced one or two water molecules. For the other two sets, the problem is probably that the ligands bind in different modes than in the crystal structures employed or that the conformation of the ligand-binding site or the whole protein changes.Electronic supplementary materialThe online version of this article (doi:10.1007/s10822-016-9942-z) contains supplementary material, which is available to authorized users.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Binding-affinity predictions of HSP90 in the D3R Grand Challenge 2015 with docking, MM/GBSA, QM/MM, and free-energy simulations

Ignjatović

Caldararu

Dong

et al. 2016

J Comput Aided Mol Des

Self Cite

View full text Add to dashboard Cite

show abstract

“…3,7,11-15 These methods include Thermodynamic Integration (TI) 11,16-18 and Free Energy Perturbation (FEP), 11,19-22 as well as analysis through Bennett’s acceptance ratio and its variants (BAR/MBAR) 23-28 and are augmented with enhanced sampling such as replica exchange molecular dynamics, 29 metadynamics, 30 driven adiabatic free energy dynamics, 31 orthogonal space random walk, 32 adaptive integration, 33 and other methods. 34,35 Advanced alchemical free energy methods for binding affinity prediction 9,10,36 have evolved to the point that they approach quantitative predictive accuracy for ligand-binding affinities, 27,37,38 although much work remains, such as addressing sampling and convergence issues, 27,37 to improve precision so as to afford meaningful comparison with experimental uncertainties. 39 …”

mentioning

confidence: 99%

Toward Fast and Accurate Binding Affinity Prediction with pmemdGTI: An Efficient Implementation of GPU-Accelerated Thermodynamic Integration

Sherborne³

et al. 2017

J. Chem. Theory Comput.

113

121

View full text Add to dashboard Cite

We report the implementation of the thermodynamic integration method on the pmemd module of the AMBER 16 package on GPUs (pmemdGTI). The pmemdGTI code typically delivers over 2 orders of magnitude of speed-up relative to a single CPU core for the calculation of ligand–protein binding affinities with no statistically significant numerical differences and thus provides a powerful new tool for drug discovery applications.

show abstract

Binding affinities by alchemical perturbation using QM/MM with a large QM system and polarizable MM model

2015

Self Cite

View full text Add to dashboard Cite

The most general way to improve the accuracy of binding-affinity calculations for protein-ligand systems is to use quantum-mechanical (QM) methods together with rigorous alchemical-perturbation (AP) methods. We explore this approach by calculating the relative binding free energy of two synthetic disaccharides binding to galectin-3 at a reasonably high QM level (dispersion-corrected density functional theory with a triple-zeta basis set) and with a sufficiently large QM system to include all short-range interactions with the ligand (744-748 atoms). The rest of the protein is treated as a collection of atomic multipoles (up to quadrupoles) and polarizabilities. Several methods for evaluating the binding free energy from the 3600 QM calculations are investigated in terms of stability and accuracy. In particular, methods using QM calculations only at the endpoints of the transformation are compared with the recently proposed non-Boltzmann Bennett acceptance ratio (NBB) method that uses QM calculations at several stages of the transformation. Unfortunately, none of the rigorous approaches give sufficient statistical precision. However, a novel approximate method, involving the direct use of QM energies in the Bennett acceptance ratio method, gives similar results as NBB but with better precision, ∼3 kJ/mol. The statistical error can be further reduced by performing a greater number of QM calculations.

show abstract

A Large-Scale Test of Free-Energy Simulation Estimates of Protein–Ligand Binding Affinities

Cited by 60 publications

References 73 publications

Binding-affinity predictions of HSP90 in the D3R Grand Challenge 2015 with docking, MM/GBSA, QM/MM, and free-energy simulations

Binding-affinity predictions of HSP90 in the D3R Grand Challenge 2015 with docking, MM/GBSA, QM/MM, and free-energy simulations

Toward Fast and Accurate Binding Affinity Prediction with pmemdGTI: An Efficient Implementation of GPU-Accelerated Thermodynamic Integration

Binding affinities by alchemical perturbation using QM/MM with a large QM system and polarizable MM model

Contact Info

Product

Resources

About