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Öğe Accurate absolute free energies for ligand-protein binding based on non-equilibrium approaches(Nature Research, 2021) Gapsys, Vytautas; Yildirim, Ahmet; Aldeghi, Matteo; Khalak, Yuriy; van der Spoel, David; de Groot, Bert L.Molecular dynamics-based approaches to calculate absolute protein-ligand binding free energy often rely on equilibrium free energy perturbation (FEP) protocols. Here, the authors study ligands binding to bromodomains and T4 lysozyme and find that both equilibrium and non-equilibrium approaches converge to the same results with the non-equilibrium method converging faster than FEP. The accurate calculation of the binding free energy for arbitrary ligand-protein pairs is a considerable challenge in computer-aided drug discovery. Recently, it has been demonstrated that current state-of-the-art molecular dynamics (MD) based methods are capable of making highly accurate predictions. Conventional MD-based approaches rely on the first principles of statistical mechanics and assume equilibrium sampling of the phase space. In the current work we demonstrate that accurate absolute binding free energies (ABFE) can also be obtained via theoretically rigorous non-equilibrium approaches. Our investigation of ligands binding to bromodomains and T4 lysozyme reveals that both equilibrium and non-equilibrium approaches converge to the same results. The non-equilibrium approach achieves the same level of accuracy and convergence as an equilibrium free energy perturbation (FEP) method enhanced by Hamiltonian replica exchange. We also compare uni- and bi-directional non-equilibrium approaches and demonstrate that considering the work distributions from both forward and reverse directions provides substantial accuracy gains. In summary, non-equilibrium ABFE calculations are shown to yield reliable and well-converged estimates of protein-ligand binding affinity.Öğe Binding of Pollutants to Biomolecules: A Simulation Study(2016) Yildirim, Ahmet; Zhang, Jin; Manzetti, Sergio; van der Spoel, DavidA number of cases around the world have been reported where animals were found dead or dying with symptoms resembling a thiamine (vitamin B) deficiency, and for some of these, a link to pollutants has been suggested. Here, we investigate whether biomolecules involved in thiamin binding and transport could be blocked by a range of different pollutants. We used in silico docking of five compound classes (25 compounds in total) to each of five targets (prion protein, ECF-type ABC transporter, thi-box riboswitch receptor, thiamin pyrophosphokinase, and YKoF protein) and subsequently performed molecular dynamics (MD) simulations to assess the stability of the complexes. The compound classes were thiamin analogues (control), pesticides, veterinary medicines, polychlorinated biphenyls, and dioxins, all of which are prevalent in the environment to some extent. A few anthropogenic compounds were found to bind the ECF-type ABC transporter, but none binds stably to prion protein. For the riboswitch, most compounds remained in their binding pockets during 50 ns of MD simulation, indicating that RNA provides a promiscuous binding site. In both YKoF and thiamin pyrophosphokinase (TPK), most compounds remain tightly bound. However, TPK biomolecules undergo pollutant-induced conformational changes. Although most compounds are found to bind to some of these targets, a larger data set is needed along with more quantitative methods like free energy perturbation calculations before firm conclusions can be drawn. This study is in part a test bed for large-scale quantitative computational screening of interactions between biological entities and pollutant molecules.Öğe Propagation of uncertainty in physicochemical data to force field predictions(Amer Physical Soc, 2020) Yildirim, Ahmet; Ghahremanpour, Mohammad Mehdi; van der Spoel, DavidThe solvation free energy (SFE) is a key property in the thermodynamics of chemical processes. It can be evaluated using molecular simulations with good statistical accuracy. However, force field predictions exhibit systematic errors due to uncertainties in the parametrization. Here we evaluate how the uncertainty in physicochemical data underlying force fields propagates to SFE predictions. We find that the data contribution to the uncertainty in SFE is up to 25 times larger than the statistical uncertainty. The total uncertainty in the SFE in water is higher than in cyclohexane.Öğe Properties of Organic Liquids when Simulated with Long-Range Lennard-Jones Interactions(2015) M. Fischer, Nina; J. van Maaren, Paul; C. Ditz, Jonas; Yildirim, Ahmet; van der Spoel, DavidIn order to increase the accuracy of classical computer simulations, existing methodologies may need to be adapted. Hitherto, most force fields employ a truncated potential function to model van der Waals interactions, sometimes augmented with an analytical correction. Although such corrections are accurate for homogeneous systems with a long cutoff, they should not be used in inherently inhomogeneous systems such as biomolecular and interface systems. For such cases, a variant of the particle mesh Ewald algorithm (Lennard-Jones PME) was already proposed 20 years ago (Essmann et al. J. Chem. Phys. 1995, 103, 8577-8593), but it was implemented only recently (Wennberg et al. J. Chem. Theory Comput. 2013, 9, 3527-3537) in a major simulation code (GROMACS). The availability of this method allows surface tensions of liquids as well as bulk properties to be established, such as density and enthalpy of vaporization, without approximations due to truncation. Here, we report on simulations of ≈150 liquids (taken from a force field benchmark: Caleman et al. J. Chem. Theory Comput. 2012, 8, 61-74) using three different force fields and compare simulations with and without explicit long-range van der Waals interactions. We find that the density and enthalpy of vaporization increase for most liquids using the generalized Amber force field (GAFF, Wang et al. J. Comput. Chem. 2004, 25, 1157-1174) and the Charmm generalized force field (CGenFF, Vanommeslaeghe et al. J. Comput. Chem. 2010, 31, 671-690) but less so for OPLS/AA (Jorgensen and Tirado-Rives, Proc. Natl. Acad. Sci. U.S.A. 2005, 102, 6665-6670), which was parametrized with an analytical correction to the van der Waals potential. The surface tension increases by ≈10(-2) N/m for all force fields. These results suggest that van der Waals attractions in force fields are too strong, in particular for the GAFF and CGenFF. In addition to the simulation results, we introduce a new version of a web server, http://virtualchemistry.org, aimed at facilitating sharing and reuse of input files for molecular simulations.Öğe Statistical efficiency of methods for computing free energy of hydration(2018) Yildirim, Ahmet; A. Wassenaar, Tsjerk; van der Spoel, DavidThe hydration free energy (HFE) is a critical property for predicting and understanding chemical and biological processes in aqueous solution. There are a number of computational methods to derive HFE, generally classified into the equilibrium or non-equilibrium methods, based on the type of calculations used. In the present study, we compute the hydration free energies of 34 small, neutral, organic molecules with experimental HFE between +2 and -16 kcal/mol. The one-sided non-equilibrium methods Jarzynski Forward (JF) and Backward (JB), the two-sided non-equilibrium methods Jarzynski mean based on the average of JF and JB, Crooks Gaussian Intersection (CGI), and the Bennett Acceptance Ratio (BAR) are compared to the estimates from the two-sided equilibrium method Multistate Bennett Acceptance Ratio (MBAR), which is considered as the reference method for HFE calculations, and experimental data from the literature. Our results show that the estimated hydration free energies from all the methods are consistent with MBAR results, and all methods provide a mean absolute error of ∼0.8 kcal/mol and root mean square error of ∼1 kcal for the 34 organic molecules studied. In addition, the results show that one-sided methods JF and JB result in systematic deviations that cannot be corrected entirely. The statistical efficiency ε of the different methods can be expressed as the one over the simulation time times the average variance in the HFE. From such an analysis, we conclude that ε(MBAR) > ε(BAR) ≈ ε(CGI) > ε(JX), where JX is any of the Jarzynski methods. In other words, the non-equilibrium methods tested here for the prediction of HFE have lower computational efficiency than the MBAR method.
