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      Binding affinities of the farnesoid X receptor in the D3R Grand Challenge 2 estimated by free-energy perturbation and docking

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          Abstract

          We have studied the binding of 102 ligands to the farnesoid X receptor within the D3R Grand Challenge 2016 blind-prediction competition. First, we employed docking with five different docking software and scoring functions. The selected docked poses gave an average root-mean-squared deviation of 4.2 Å. Consensus scoring gave decent results with a Kendall’s τ of 0.26 ± 0.06 and a Spearman’s ρ of 0.41 ± 0.08. For a subset of 33 ligands, we calculated relative binding free energies with free-energy perturbation. Five transformations between the ligands involved a change of the net charge and we implemented and benchmarked a semi-analytic correction (Rocklin et al., J Chem Phys 139:184103, 2013) for artifacts caused by the periodic boundary conditions and Ewald summation. The results gave a mean absolute deviation of 7.5 kJ/mol compared to the experimental estimates and a correlation coefficient of R 2 = 0.1. These results were among the four best in this competition out of 22 submissions. The charge corrections were significant (7–8 kJ/mol) and always improved the results. By employing 23 intermediate states in the free-energy perturbation, there was a proper overlap between all states and the precision was 0.1–0.7 kJ/mol. However, thermodynamic cycles indicate that the sampling was insufficient in some of the perturbations.

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          The online version of this article (doi:10.1007/s10822-017-0056-z) contains supplementary material, which is available to authorized users.

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          Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field.

          Designing tight-binding ligands is a primary objective of small-molecule drug discovery. Over the past few decades, free-energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (∼5× in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.
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            Accurate Calculation of Hydration Free Energies Using Macroscopic Solvent Models

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              Statistically optimal analysis of samples from multiple equilibrium states

              We present a new estimator for computing free energy differences and thermodynamic expectations as well as their uncertainties from samples obtained from multiple equilibrium states via either simulation or experiment. The estimator, which we term the multistate Bennett acceptance ratio (MBAR) estimator because it reduces to the Bennett acceptance ratio when only two states are considered, has significant advantages over multiple histogram reweighting methods for combining data from multiple states. It does not require the sampled energy range to be discretized to produce histograms, eliminating bias due to energy binning and significantly reducing the time complexity of computing a solution to the estimating equations in many cases. Additionally, an estimate of the statistical uncertainty is provided for all estimated quantities. In the large sample limit, MBAR is unbiased and has the lowest variance of any known estimator for making use of equilibrium data collected from multiple states. We illustrate this method by producing a highly precise estimate of the potential of mean force for a DNA hairpin system, combining data from multiple optical tweezer measurements under constant force bias.
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                Author and article information

                Contributors
                +46 - 46 2224502 , Ulf.Ryde@teokem.lu.se
                Journal
                J Comput Aided Mol Des
                J. Comput. Aided Mol. Des
                Journal of Computer-Aided Molecular Design
                Springer International Publishing (Cham )
                0920-654X
                1573-4951
                6 September 2017
                6 September 2017
                2018
                : 32
                : 1
                : 211-224
                Affiliations
                [1 ]ISNI 0000 0001 0930 2361, GRID grid.4514.4, Department of Theoretical Chemistry, Chemical Centre, , Lund University, ; P. O. Box 124, 221 00 Lund, Sweden
                [2 ]ISNI 0000 0001 0943 7661, GRID grid.10939.32, Institute of Chemistry, , University of Tartu, ; Ravila 14a, 50411 Tartu, Estonia
                Author information
                http://orcid.org/0000-0001-7653-8489
                Article
                56
                10.1007/s10822-017-0056-z
                5767205
                28879536
                3d11d62d-c72e-41cd-864c-328b77f3c1cd
                © The Author(s) 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 2 June 2017
                : 29 August 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004359, Vetenskapsrådet;
                Award ID: 2014-5540
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004063, Knut och Alice Wallenbergs Stiftelse;
                Award ID: KAW 2013.0022
                Award Recipient :
                Funded by: Estonian Ministry of Education and Research
                Award ID: A.T.G.-S. IUT34-14
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer International Publishing AG, part of Springer Nature 2018

                Biomedical engineering
                ligand binding,docking,quantum-polarised ligand docking,free-energy perturbation,bennett acceptance ratio,periodic boundary conditions,charge transformations,drug design data resource,d3r grand challenge 2016

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