Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset

Shirts, Michael R.; Klein, Christoph; Swails, Jason M.; Yin, Jian; Gilson, Michael K.; Mobley, David L.; Case, David A.; Zhong, Ellen D.

doi:10.1007/s10822-016-9977-1

Cited by 263 publications

(246 citation statements)

References 26 publications

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“…The input files for plausibly docked host–guest complexes solvated in TIP3P water with counterions were provided to participants in Amber [42], Gromacs [43], Desmond [44], and LAMMPS [45] formats, along with the other SAMPL5 starting data. The procedures used to ascertain that all four standard setups were equivalent across all four software packages are detailed in another paper in this issue [46]. …”

Section: Methodsmentioning

confidence: 99%

Overview of the SAMPL5 host–guest challenge: Are we doing better?

Yin

Henriksen

Slochower

et al. 2016

J Comput Aided Mol Des

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186

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The ability to computationally predict protein-small molecule binding affinities with high accuracy would accelerate drug discovery and reduce its cost by eliminating rounds of trial-and-error synthesis and experimental evaluation of candidate ligands. As academic and industrial groups work toward this capability, there is an ongoing need for datasets that can be used to rigorously test new computational methods. Although protein–ligand data are clearly important for this purpose, their size and complexity make it difficult to obtain well-converged results and to troubleshoot computational methods. Host–guest systems offer a valuable alternative class of test cases, as they exemplify noncovalent molecular recognition but are far smaller and simpler. As a consequence, host–guest systems have been part of the prior two rounds of SAMPL prediction exercises, and they also figure in the present SAMPL5 round. In addition to being blinded, and thus avoiding biases that may arise in retrospective studies, the SAMPL challenges have the merit of focusing multiple researchers on a common set of molecular systems, so that methods may be compared and ideas exchanged. The present paper provides an overview of the host–guest component of SAMPL5, which centers on three different hosts, two octa-acids and a glycoluril-based molecular clip, and two different sets of guest molecules, in aqueous solution. A range of methods were applied, including electronic structure calculations with implicit solvent models; methods that combine empirical force fields with implicit solvent models; and explicit solvent free energy simulations. The most reliable methods tend to fall in the latter class, consistent with results in prior SAMPL rounds, but the level of accuracy is still below that sought for reliable computer-aided drug design. Advances in force field accuracy, modeling of protonation equilibria, electronic structure methods, and solvent models, hold promise for future improvements.

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Section: Methodsmentioning

confidence: 99%

Overview of the SAMPL5 host–guest challenge: Are we doing better?

Yin

Henriksen

Slochower

et al. 2016

J Comput Aided Mol Des

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186

243

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“…After a short equilibration phase to relax the solvent, the final frame was saved for distribution to all SAMPL5 participants as the AMBER standard input files for OAH and OAMe. Standard input files in the format of GROMACS, Desmond and LAMPPs formats were converted from the AMBER files with custom scripts [24]. …”

Section: Methodsmentioning

confidence: 99%

The SAMPL5 host–guest challenge: computing binding free energies and enthalpies from explicit solvent simulations by the attach-pull-release (APR) method

Yin

Henriksen

Slochower

et al. 2016

J Comput Aided Mol Des

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The absolute binding free energies and binding enthalpies of twelve host-guest systems in the SAMPL5 blind challenge were computed using our attach-pull-release (APR) approach. This method has previously shown good correlations between experimental and calculated binding data in retrospective studies of cucurbit[7]uril (CB7) and β-cyclodextrin (βCD) systems. In the present work, the computed binding free energies for host octa acid (OA or OAH) and tetra-endo-methyl octa-acid (TEMOA or OAMe) with guests are in good agreement with prospective experimental data, with a coefficient of determination (R2) of 0.8 and root-mean-squared error (RMSE) of 1.7 kcal/mol using the TIP3P water model. The binding enthalpy calculations achieve moderate correlations, with R2 of 0.5 and RMSE of 2.5 kcal/mol, for TIP3P water. Calculations using the newly developed OPC water model also show good performance. Furthermore, the present calculations semi-quantitatively capture the experimental trend of enthalpy-entropy compensation observed, and successfully predict guests with the strongest and weakest binding affinity. The most populated binding poses of all twelve systems, based on clustering analysis of 750 ns molecular dynamics (MD) trajectories, were extracted and analyzed. Computational methods using MD simulations and explicit solvent models in a rigorous statistical thermodynamic framework, like APR, can generate reasonable predictions of binding thermodynamics. Especially with continuing improvement in simulation force fields, such methods hold the promise of making substantial contributions to hit identification and lead optimization in the drug discovery process.

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“…It is crucial yet nontrivial to validate that a simulation package correctly implements and applies the desired methods (111), and benchmark cases can help with this. First, all software packages could be tested for their ability to generate correct potential energies for a single configuration of the specified molecular system and force field.…”

Section: The Need For Well-chosen Benchmark Systemsmentioning

confidence: 99%

Predicting Binding Free Energies: Frontiers and Benchmarks

Mobley

Gilson

2017

Annu. Rev. Biophys.

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320

325

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Binding free energy calculations based on molecular simulations provide predicted affinities for biomolecular complexes. These calculations begin with a detailed description of a system, including its chemical composition and the interactions between its components. Simulations of the system are then used to compute thermodynamic information, such as binding affinities. Because of their promise for guiding molecular design, these calculations have recently begun to see widespread applications in early stage drug discovery. However, many challenges remain to make them a robust and reliable tool. Here, we highlight key challenges facing these calculations, describe known examples of these challenges, and call for the designation of standard community benchmark test systems that will help the research community generate and evaluate progress. In our view, progress will require careful assessment and evaluation of new methods, force fields, and modeling innovations on well-characterized benchmark systems, and we lay out our vision for how this can be achieved.

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Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset

Cited by 263 publications

References 26 publications

Overview of the SAMPL5 host–guest challenge: Are we doing better?

Overview of the SAMPL5 host–guest challenge: Are we doing better?

The SAMPL5 host–guest challenge: computing binding free energies and enthalpies from explicit solvent simulations by the attach-pull-release (APR) method

Predicting Binding Free Energies: Frontiers and Benchmarks

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