Automatic GROMACS Topology Generation and Comparisons of Force Fields for Solvation Free Energy Calculations

Lundborg, Magnus; Lindahl, Erik

doi:10.1021/jp505332p

Cited by 101 publications

(85 citation statements)

References 90 publications

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“…31,32 The Generalized Amber force field is attractive for force field calculations since derivation of new models is almost completely automated using the Antechamber package, 48 facilitating highthroughput computational predictions. We also notice STaGE, a newly developed program that can generate GROMACS topology for force fields such as CGenFF and OPLS-AA automaticlly, 78 would faciliate a large scale force field free energy benchmark in the future. Here, we tested the performance of the GAFF for predicting the solvation free energy of organic molecules in organic solvents and compared the results to experimental data and to the two methods mentioned above.…”

Section: ■ Discussionmentioning

confidence: 99%

Force Field Benchmark of Organic Liquids. 2. Gibbs Energy of Solvation

Jin

Tuguldur

Spoel

2015

J. Chem. Inf. Model.

146

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Quantitative prediction of physical properties of liquids is a longstanding goal of molecular simulation. Here, we evaluate the predictive power of the Generalized Amber Force Field (Wang et al. J. Comput. Chem. 2004, 25, 1157-1174) for the Gibbs energy of solvation of organic molecules in organic solvents using the thermodynamics integration (TI) method. The results are compared to experimental data, to a model based on quantitative structure property relations (QSPR), and to the conductor-like screening models for realistic solvation (COSMO-RS) model. Although the TI calculations yield slightly better correlation to experimental results than the other models, in all fairness we should conclude that the difference between the models is minor since both QSPR and COSMO-RS yield a slightly lower RMSD from that of the experiment (<3.5 kJ/mol). By analyzing which molecules (either as solvents or solutes) are outliers in the TI calculations, we can pinpoint where additional parametrization efforts are needed. For the force field based TI calculations, deviations from the experiment occur in particular when compounds containing nitro or ester groups are solvated into other liquids, suggesting that the interaction between these groups and solvents may be too strong. In the COSMO-RS calculations, outliers mainly occur when compounds containing (in particular aromatic) rings are solvated despite using a ring correction term in the calculations.

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Section: ■ Discussionmentioning

confidence: 99%

Force Field Benchmark of Organic Liquids. 2. Gibbs Energy of Solvation

Jin

Tuguldur

Spoel

2015

J. Chem. Inf. Model.

146

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“…To further analyze the implications of the T109M mutation, we used GROMACS simulation software 27 to perform a 50-ns molecular-dynamics simulation with the PFN1 T109M :PLP complex, as well as a similar simulation with the PFN1 WT :PLP complex (see Methods for details). When we superimposed the structural models of PFN1 and the T109M mutant, the overall conformation of the mutant was almost identical to the wild type (Fig.…”

Section: Resultsmentioning

confidence: 99%

ALS-causing mutations in profilin-1 alter its conformational dynamics: A computational approach to explain propensity for aggregation

Kiaei

Balasubramaniam

Kumar

et al. 2018

Sci Rep

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Profilin-1 (PFN1) is a 140-amino-acid protein with two distinct binding sites―one for actin and one for poly-L-proline (PLP). The best-described function of PFN1 is to catalyze actin elongation and polymerization. Thus far, eight DNA mutations in the PFN1 gene encoding the PFN1 protein are associated with human amyotrophic lateral sclerosis (ALS). We and others recently showed that two of these mutations (Gly118Val or G118V and Cys71Gly or C71G) cause ALS in rodents. In vitro studies suggested that Met114Thr and Thr109Met cause the protein to behave abnormally and cause neurotoxicity. The mechanism by which a single amino acid change in human PFN1 causes the degeneration of motor neurons is not known. In this study, we investigated the structural perturbations of PFN1 caused by each ALS-associated mutation. We used molecular dynamics simulations to assess how these mutations alter the secondary and tertiary structures of human PFN1. Herein, we present our in silico data and analysis on the effect of G118V and T109M mutations on PFN1 and its interactions with actin and PLP. The substitution of valine for glycine reduces the conformational flexibility of the loop region between the α-helix and β-strand and enhances the hydrophobicity of the region. Our in silico analysis of T109M indicates that this mutation alters the shape of the PLP-binding site and reduces the flexibility of this site. Simulation studies of PFN1 in its wild type (WT) and mutant forms (both G118V and T109M mutants) revealed differential fluctuation patterns and the formation of salt bridges and hydrogen bonds between critical residues that may shed light on differences between WT and mutant PFN1. In particular, we hypothesize that the flexibility of the actin- and PLP-binding sites in WT PFN1 may allow the protein to adopt slightly different conformations in its free and bound forms. These findings provide new insights into how each of these mutations in PFN1 might increase its propensity for misfolding and aggregation, leading to its dysfunction.

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“…12,88,89 Credit must be given, however, to developers of freely available simulation software who keep improving the capabilities of these programs for free energy calculations. 90−92 We are confident that, gradually, as interest in these calculations increases thanks to further developments and testing, workflows will become more and more automated and user-friendly. The reader who is already interested in learning how to run these calculations can find some additional considerations, and references to background information, in SI Text T3.…”

Section: Discussionmentioning

confidence: 99%

Predictions of Ligand Selectivity from Absolute Binding Free Energy Calculations

et al. 2017

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Binding selectivity is a requirement for the development of a safe drug, and it is a critical property for chemical probes used in preclinical target validation. Engineering selectivity adds considerable complexity to the rational design of new drugs, as it involves the optimization of multiple binding affinities. Computationally, the prediction of binding selectivity is a challenge, and generally applicable methodologies are still not available to the computational and medicinal chemistry communities. Absolute binding free energy calculations based on alchemical pathways provide a rigorous framework for affinity predictions and could thus offer a general approach to the problem. We evaluated the performance of free energy calculations based on molecular dynamics for the prediction of selectivity by estimating the affinity profile of three bromodomain inhibitors across multiple bromodomain families, and by comparing the results to isothermal titration calorimetry data. Two case studies were considered. In the first one, the affinities of two similar ligands for seven bromodomains were calculated and returned excellent agreement with experiment (mean unsigned error of 0.81 kcal/mol and Pearson correlation of 0.75). In this test case, we also show how the preferred binding orientation of a ligand for different proteins can be estimated via free energy calculations. In the second case, the affinities of a broad-spectrum inhibitor for 22 bromodomains were calculated and returned a more modest accuracy (mean unsigned error of 1.76 kcal/mol and Pearson correlation of 0.48); however, the reparametrization of a sulfonamide moiety improved the agreement with experiment.

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Automatic GROMACS Topology Generation and Comparisons of Force Fields for Solvation Free Energy Calculations

Cited by 101 publications

References 90 publications

Force Field Benchmark of Organic Liquids. 2. Gibbs Energy of Solvation

Force Field Benchmark of Organic Liquids. 2. Gibbs Energy of Solvation

ALS-causing mutations in profilin-1 alter its conformational dynamics: A computational approach to explain propensity for aggregation

Predictions of Ligand Selectivity from Absolute Binding Free Energy Calculations

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