Relative Free Energies Of Hydration Research Articles

Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations.

Relative binding free energy (RBFE) calculations have emerged as a powerful tool that supports ligand optimization in drug discovery. Despite many successes, the use of RBFEs can often be limited by automation problems, in particular, the setup of such calculations. Atom mapping algorithms are an essential component in setting up automatic large-scale hybrid-topology RBFE calculation campaigns. Traditional algorithms typically employ a 2D subgraph isomorphism solver (SIS) in order to estimate the maximum common substructure. SIS-based approaches can be limited by time-intensive operations and issues with capturing geometry-linked chemical properties, potentially leading to suboptimal solutions. To overcome these limitations, we have developed Kartograf, a geometric-graph-based algorithm that uses primarily the 3D coordinates of atoms to find a mapping between two ligands. In free energy approaches, the ligand conformations are usually derived from docking or other previous modeling approaches, giving the coordinates a certain importance. By considering the spatial relationships between atoms related to the molecule coordinates, our algorithm bypasses the computationally complex subgraph matching of SIS-based approaches and reduces the problem to a much simpler bipartite graph matching problem. Moreover, Kartograf effectively circumvents typical mapping issues induced by molecule symmetry and stereoisomerism, making it a more robust approach for atom mapping from a geometric perspective. To validate our method, we calculated mappings with our novel approach using a diverse set of small molecules and used the mappings in relative hydration and binding free energy calculations. The comparison with two SIS-based algorithms showed that Kartograf offers a fast alternative approach. The code for Kartograf is freely available on GitHub (https://github.com/OpenFreeEnergy/kartograf). While developed for the OpenFE ecosystem, Kartograf can also be utilized as a standalone Python package.

Replica-Exchange Enveloping Distribution Sampling Using Generalized AMBER Force-Field Topologies: Application to Relative Hydration Free-Energy Calculations for Large Sets of Molecules

Free-energy differences between pairs of end-states can be estimated based on molecular dynamics (MD) simulations using standard pathway-dependent methods such as thermodynamic integration (TI), free-energy perturbation, or Bennett’s acceptance ratio. Replica-exchange enveloping distribution sampling (RE-EDS), on the other hand, allows for the sampling of multiple end-states in a single simulation without the specification of any pathways. In this work, we use the RE-EDS method as implemented in GROMOS together with generalized AMBER force-field (GAFF) topologies, converted to a GROMOS-compatible format with a newly developed GROMOS++ program amber2gromos, to compute relative hydration free energies for a series of benzene derivatives. The results obtained with RE-EDS are compared to the experimental data as well as calculated values from the literature. In addition, the estimated free-energy differences in water and in vacuum are compared to values from TI calculations carried out with GROMACS. The hydration free energies obtained using RE-EDS for multiple molecules are found to be in good agreement with both the experimental data and the results calculated using other free-energy methods. While all considered free-energy methods delivered accurate results, the RE-EDS calculations required the least amount of total simulation time. This work serves as a validation for the use of GAFF topologies with the GROMOS simulation package and the RE-EDS approach. Furthermore, the performance of RE-EDS for a large set of 28 end-states is assessed with promising results.

Replica-Exchange Enveloping Distribution Sampling: Calculation of Relative Free Energies in GROMOS

Molecular dynamics (MD) simulations have become an important tool to investigate biological systems. Free-energy calculations based on MD are playing an increasingly important role for computer-aided drug design and material discovery in recent years. Free-energy differences between pairs of end-states can be estimated using well-established methods such as thermodynamic integration (TI) or Bennett’s acceptance ratio (BAR). An attractive alternative is the recently developed replica-exchange enveloping distribution sampling (RE-EDS) method, which enables estimating relative free-energy differences between multiple molecules from a single simulation. Here, we provide an introduction to the principles underlying RE-EDS and give an overview of the RE-EDS pipeline. In addition, we provide a description of the two complementary tools RestraintMaker and amber2gromos. We briefly discuss the findings of three recent applications of RE-EDS to calculate relative binding or hydration free energies. In all three studies, good agreement was found between the results obtained using RE-EDS and experimental values as well as values obtained using other free-energy methods.

RestraintMaker: a graph-based approach to select distance restraints in free-energy calculations with dual topology

The calculation of relative binding free energies (RBFE) involves the choice of the end-state/system representation, of a sampling approach, and of a free-energy estimator. System representations are usually termed “single topology” or “dual topology”. As the terminology is often used ambiguously in the literature, a systematic categorization of the system representations is proposed here. In the dual-topology approach, the molecules are simulated as separate molecules. Such an approach is relatively easy to automate for high-throughput RBFE calculations compared to the single-topology approach. Distance restraints are commonly applied to prevent the molecules from drifting apart, thereby improving the sampling efficiency. In this study, we introduce the program RestraintMaker, which relies on a greedy algorithm to find (locally) optimal distance restraints between pairs of atoms based on geometric measures. The algorithm is further extended for multi-state methods such as enveloping distribution sampling (EDS) or multi-site lambda -dynamics. The performance of RestraintMaker is demonstrated for toy models and for the calculation of relative hydration free energies. The Python program can be used in script form or through an interactive GUI within PyMol. The selected distance restraints can be written out in GROMOS or GROMACS file formats. Additionally, the program provides a human-readable JSON format that can easily be parsed and processed further. The code of RestraintMaker is freely available on GitHub https://github.com/rinikerlab/restraintmaker.

Scaffold Hopping Transformations Using Auxiliary Restraints for Calculating Accurate Relative Binding Free Energies.

In silico screening of drug-target interactions is a key part of the drug discovery process. Changes in the drug scaffold via contraction or expansion of rings, the breaking of rings, and the introduction of cyclic structures from acyclic structures are commonly applied by medicinal chemists to improve binding affinity and enhance favorable properties of candidate compounds. These processes, commonly referred to as scaffold hopping, are challenging to model computationally. Although relative binding free energy (RBFE) calculations have shown success in predicting binding affinity changes caused by perturbing R-groups attached to a common scaffold, applications of RBFE calculations to modeling scaffold hopping are relatively limited. Scaffold hopping inevitably involves breaking and forming bond interactions of quadratic functional forms, which is highly challenging. A novel method for handling ring opening/closure/contraction/expansion and linker contraction/expansion is presented here. To the best of our knowledge, RBFE calculations on linker contraction/expansion have not been previously reported. The method uses auxiliary restraints to hold the atoms at the ends of a bond in place during the breaking and forming of the bonds. The broad applicability of the method was demonstrated by examining perturbations involving small-molecule macrocycles and mutations of proline in proteins. High accuracy was obtained using the method for most of the perturbations studied. The rigor of the method was isolated from the force field by validating the method using relative and absolute hydration free energy calculations compared to standard simulation results. Unlike other methods that rely on λ-dependent functional forms for bond interactions, the method presented here can be employed using modern molecular dynamics software without modification of codes or force field functions.

Single-Ion Thermodynamics from First Principles: Calculation of the Absolute Hydration Free Energy and Single-Electrode Potential of Aqueous Li+ Using ab Initio Quantum Mechanical/Molecular Mechanical Molecular Dynamics Simulations

A recently proposed thermodynamic integration (TI) approach formulated in the framework of quantum mechanical/molecular mechanical molecular dynamics (QM/MM MD) simulations is applied to study the structure, dynamics, and absolute intrinsic hydration free energy Δs GM+,wat◦ of the Li+ ion at a correlated ab initio level of theory. Based on the results, standard values (298.15 K, ideal gas at 1 bar, ideal solute at 1 molal) for the absolute intrinsic hydration free energy [Formula: see text] of the proton, the surface electric potential jump χwat◦ upon entering bulk water, and the absolute single-electrode potential [Formula: see text] of the reference hydrogen electrode are calculated to be -1099.9 ± 4.2 kJ·mol-1, 0.13 ± 0.08 V, and 4.28 ± 0.04 V, respectively, in excellent agreement with the standard values recommended by Hünenberger and Reif on the basis of an extensive evaluation of the available experimental data (-1100 ± 5 kJ·mol-1, 0.13 ± 0.10 V, and 4.28 ± 0.13 V). The simulation results for Li+ are also compared to those for Na+ and K+ from a previous study in terms of relative hydration free energies ΔΔs GM+,wat◦ and relative electrode potentials [Formula: see text]. The calculated values are found to agree extremely well with the experimental differences in standard conventional hydration free energies ΔΔs GM+,wat• and redox potentials [Formula: see text]. The level of agreement between simulation and experiment, which is quantitative within error bars, underlines the substantial accuracy improvement achieved by applying a highly demanding QM/MM approach at the resolution-of-identity second-order Møller-Plesset perturbation (RIMP2) level over calculations relying on purely molecular mechanical or density functional theory (DFT) descriptions. A detailed analysis of the structural and dynamical properties of the Li+ hydrate indicates that a correct description of the solvation structure and dynamics is achieved as well at this level of theory. Consideration of the QM/MM potential-energy components also shows that the partitioning into QM and MM zones does not induce any significant energetic artifact for the system considered.

Tinker-OpenMM: Absolute and relative alchemical free energies using AMOEBA on GPUs.

The capabilities of the polarizable force fields for alchemical free energy calculations have been limited by the high computational cost and complexity of the underlying potential energy functions. In this work, we present a GPU-based general alchemical free energy simulation platform for polarizable potential AMOEBA. Tinker-OpenMM, the OpenMM implementation of the AMOEBA simulation engine has been modified to enable both absolute and relative alchemical simulations on GPUs, which leads to a ∼200-fold improvement in simulation speed over a single CPU core. We show that free energy values calculated using this platform agree with the results of Tinker simulations for the hydration of organic compounds and binding of host-guest systems within the statistical errors. In addition to absolute binding, we designed a relative alchemical approach for computing relative binding affinities of ligands to the same host, where a special path was applied to avoid numerical instability due to polarization between the different ligands that bind to the same site. This scheme is general and does not require ligands to have similar scaffolds. We show that relative hydration and binding free energy calculated using this approach match those computed from the absolute free energy approach. © 2017 Wiley Periodicals, Inc.

Explicit Solvent Hydration Benchmark for Proteins with Application to the PBSA Method.

Explicit and implicit solvent models have a proven record of delivering hydration free energies of small, druglike solutes in reasonable agreement with experiment. Hydration of macromolecules, such as proteins, is to a large extent uncharted territory, with few results shedding light on quantitative consistency between different solvent models, let alone their ability to reproduce real water. In this work, based on extensive explicit solvent simulations employing TIP3P and SPC/E water models we analyze hydration free energy changes between fixed conformations of 5 diverse proteins, including large multidomain structures. For the two solvent models we find better agreement in electrostatic rather than nonpolar contributions (RMSE of 2.3 and 2.7 kcal/mol, respectively), even though absolute values of the latter are typically an order of magnitude smaller. We also highlight the importance of finite size corrections to relative protein hydration free energies, which turn out to be rather large, on the order of several kcal/mol, and are necessary for proper interpretation of results obtained under periodic boundary conditions. We further compare gathered data with predictions of the implicit solvent approach based on the Poisson equation and the surface or volume based nonpolar term. We find definitely lesser consistency than between the two explicit models (RMSE between implicit and TIP3 results of 11.3 and 8.4 kcal/mol for electrostatic and nonpolar contributions, respectively). In the process we determine the value of the protein dielectric constant and the geometric model for the dielectric boundary that provide for the best agreement. Finally, we evaluate the usefulness of surface and volume based models of nonpolar contributions to hydration free energy of large biomolecules.

Parametrization of halogen bonds in the CHARMM general force field: Improved treatment of ligand–protein interactions

A halogen bond is a highly directional, non-covalent interaction between a halogen atom and another electronegative atom. It arises due to the formation of a small region of positive electrostatic potential opposite the covalent bond to the halogen, called the ‘sigma hole.’ Empirical force fields in which the electrostatic interactions are represented by atom-centered point charges cannot capture this effect because halogen atoms usually carry a negative charge and therefore interact unfavorably with other electronegative atoms. A strategy to overcome this problem is to attach a positively charged virtual particle to the halogen. In this work, we extend the additive CHARMM General Force Field (CGenFF) to include such interactions in model systems of phenyl-X, with X being Cl, Br or I including di- and trihalogenated species. The charges, Lennard-Jones parameters, and halogen-virtual particle distances were optimized to reproduce the orientation dependence of quantum mechanical interaction energies with water, acetone, and N-methylacetamide as well as experimental pure liquid properties and relative hydration free energies with respect to benzene. The resulting parameters were validated in molecular dynamics simulations on small-molecule crystals and on solvated protein–ligand complexes containing halogenated compounds. The inclusion of positive virtual sites leads to better agreement across experimental observables, including preservation of ligand binding poses as a direct result of the improved representation of halogen bonding.

A Python tool to set up relative free energy calculations in GROMACS.

Free energy calculations based on molecular dynamics (MD) simulations have seen a tremendous growth in the last decade. However, it is still difficult and tedious to set them up in an automated manner, as the majority of the present-day MD simulation packages lack that functionality. Relative free energy calculations are a particular challenge for several reasons, including the problem of finding a common substructure and mapping the transformation to be applied. Here we present a tool, alchemical-setup.py, that automatically generates all the input files needed to perform relative solvation and binding free energy calculations with the MD package GROMACS. When combined with Lead Optimization Mapper (LOMAP;Liu et al. in J Comput Aided Mol Des 27(9):755-770, 2013), recently developed in our group, alchemical-setup.py allows fully automated setup of relative free energy calculations in GROMACS. Taking a graph of the planned calculations and a mapping, both computed by LOMAP, our tool generates the topology and coordinate files needed to perform relative free energy calculations for a given set of molecules, and provides a set of simulation input parameters. The tool was validated by performing relative hydration free energy calculations for a handful of molecules from the SAMPL4 challenge (Mobley et al. in J Comput Aided Mol Des 28(4):135-150, 2014). Good agreement with previously published results and the straightforward way in which free energy calculations can be conducted make alchemical-setup.py a promising tool for automated setup of relative solvation and binding free energy calculations.

Rapid Alchemical Free Energy Calculation Employing a Generalized Born Implicit Solvent Model

Generalized Born (GB) implicit solvent models are typically used in postprocessing of molecular dynamics trajectories obtained from explicit solvent simulations to estimate binding free energies or effects of mutations in proteins. The possibility to employ a GB implicit solvent model for the calculation of rigorous free energy changes associated with alchemical transformations has been explored. During free energy perturbation (FEP) simulations, Lennard-Jones, Coulomb, and Born radii parameters are transformed gradually in a single-topology series. The FEP calculations are embedded in a replica exchange scheme allowing rapid convergence. The method was tested on the calculation of relative hydration free energies, relative binding free energies of a ligand-receptor system, and in silico alanine scanning of a peptide-protein complex. In all cases, good agreement with available experimental data was obtained. On medium sized protein-ligand systems and using a cluster of graphical processing units, the approach allows the calculation of relative free energy changes associated with a chemical modification of a binding partner within a few minutes of computer time and opens the possibility for systematic in silico studies.

Free Energies of Binding from Large-Scale First-Principles Quantum Mechanical Calculations: Application to Ligand Hydration Energies

Schemes of increasing sophistication for obtaining free energies of binding have been developed over the years, where configurational sampling is used to include the all-important entropic contributions to the free energies. However, the quality of the results will also depend on the accuracy with which the intermolecular interactions are computed at each molecular configuration. In this context, the energy change associated with the rearrangement of electrons (electronic polarization and charge transfer) upon binding is a very important effect. Classical molecular mechanics force fields do not take this effect into account explicitly, and polarizable force fields and semiempirical quantum or hybrid quantum-classical (QM/MM) calculations are increasingly employed (at higher computational cost) to compute intermolecular interactions in free-energy schemes. In this work, we investigate the use of large-scale quantum mechanical calculations from first-principles as a way of fully taking into account electronic effects in free-energy calculations. We employ a one-step free-energy perturbation (FEP) scheme from a molecular mechanical (MM) potential to a quantum mechanical (QM) potential as a correction to thermodynamic integration calculations within the MM potential. We use this approach to calculate relative free energies of hydration of small aromatic molecules. Our quantum calculations are performed on multiple configurations from classical molecular dynamics simulations. The quantum energy of each configuration is obtained from density functional theory calculations with a near-complete psinc basis set on over 600 atoms using the ONETEP program.

QM/MM molecular dynamics simulations of the hydration of Mg(II) and Zn(II) ions

The hydration of Mg2+ and Zn2+ is examined using molecular dynamics simulations using 3 computational approaches of increasing complexity: the CHARMM nonpolarizable force field based on the TIP3P water model, the Drude polarizable force field based on the SWM4-NDP water model, and a combined QM/MM approach in which the inner coordination sphere is represented using a high-quality density functional theory (DFT) model (PBE/def2-TZVPP), and the remainder of the bulk water solvent is represented using the polarizable SWM4-NDP water model. The characteristic structural distribution functions (radial, angular, and tilt) are comparedand show very good agreement between the polarizable force field and QM/MM approaches. They predict an average Mg–O distance of 2.11 Å and an Zn–O distance of 2.13 Å, in good agreement with the available experimental neutron scattering and EXAFS data, while the Mg–O distances calculated using the nonpolarizable force field are 0.1 Å too short. Mg2+ (aq) and Zn2+ (aq) both have a coordination number of 6 and have a remarkably similar octahedral coordination mode, despite the chemical differences between these ions. Thermodynamic integration was used to calculate the relative hydration free energies of these ions (ΔΔGhydr). The nonpolarizable model is in error by 60 kcal mol– 1 and incorrectly predicts that Mg2+ has the more negative hydration energy. The Drude polarizable model predicts a ΔΔGhydr of only –13.2 kcal kcal mol– 1, an improvement over the results of the nonpolarizable force field, but still signficantly different than the experimental value of –30.1 kcal mol–1. The combined QM/MM approach performs much better, predicting a ΔΔGhydr of –34.8 kcal mol–1 in excellent agreement with experiment. These calculations support the experimental observation that Zn2+ has more favourable solvation free energy than Mg2+ despite having a very similar solvation structure.

Prediction of Phase Equilibrium and Hydration Free Energy of Carboxylic Acids by Monte Carlo Simulations

In this work, a new transferable united-atom force field has been developed to predict phase equilibrium and hydration free energy of carboxylic acids. To take advantage of the transferability of the AUA4 force field, all Lennard-Jones parameters of groups involved in the carboxylic acid chemical function are reused from previous parametrizations of this force field. Only a unique set of partial electrostatic charges is proposed to reproduce the experimental gas phase dipole moment, saturated liquid densities and vapor pressures. Phase equilibrium properties of various pure carboxylic acids (acetic acid, propanoic acid, butanoic acid, pentanoic acid, hexanoic acid) and one diacid (1,5-pentanedioic) are studied through Monte Carlo simulations in the Gibbs ensemble. A good accuracy is obtained for pure compound saturated liquid densities and vapor pressures (average deviation of 2% and 6%, respectively), as well as for critical points. The vaporization enthalpy is, however, poorly predicted for short acids, probably due to a limitation of the force field to correctly describe the significant dimerization in the vapor phase. Pressure-composition diagrams for two binary mixtures (acetic acid + n-butane and propanoic acid + pentanoic acid) are also computed with a good accuracy, showing the transferability of the proposed force field to mixtures. Hydration free energies are calculated for three carboxylic acids using thermodynamic integration. A systematic overestimation of around 10 kJ/mol is observed compared to experimental data. This new force field parametrized only on saturated equilibrium properties appears insufficient to reach an acceptable precision for this property, and only relative hydration free energies between two carboxylic acids can be correctly predicted. This highlights the limitation of the transferability feature of force fields to properties not included in the parametrization database.

Local Sampling in Steered Monte Carlo Simulations Decreases Dissipation and Enhances Free Energy Estimates via Nonequilibrium Work Theorems

Configurational freezing (J. Chem. Theory Comput.2011, 7, 582) is a method devised for steered Monte Carlo simulations aimed at improving free energy estimates via nonequilibrium work theorems (see Jarzynski in Phys. Rev. Lett.1997, 78, 2690 and Crooks in J. Stat. Phys.1998, 90, 1481). The basic idea is to limit the sampling to particles located in the region of space where dissipation occurs, while leaving the other particles fixed. Therefore, the method is based on the reasonable assumption that dissipation is a local phenomenon in single-molecule nonequilibrium processes, a statement which holds for many processes including, for example, folding of biopolymers and protein-ligand binding/unbinding. In this article, the configurational freezing approach, based on the sampling of particles located around hot-spot sites encompassing the high dissipation domain, is supplemented by the possibility of selecting such particles (for trial Monte Carlo moves) dependent on their distance from the hot spots. This is accomplished by exploiting an extension of the Owicki's preferential sampling (J. Am. Chem. Soc.1977, 99, 7413) in the original configurational freezing machinery. The combined strategy is shown to improve the accuracy of free energy estimates in physically sound cases: the calculation of the water to methane relative hydration free energy and the calculation of the potentials of mean force of two solvated methane molecules and two solvated benzene molecules along the direction connecting the centers of mass.

Site-Specific Fragment Identification Guided by Single-Step Free Energy Perturbation Calculations

The in-silico Site Identification by Ligand Competitive Saturation (SILCS) approach identifies the binding sites of representative chemical entities on the entire protein surface, information that can be applied for computational fragment-based drug design. In this study, we report an efficient computational protocol that uses sampling of the protein-fragment conformational space obtained from the SILCS simulations and performs single step free energy perturbation (SSFEP) calculations to identify site-specific favorable chemical modifications of benzene involving substitutions of ring hydrogens with individual non-hydrogen atoms. The SSFEP method is able to capture the experimental trends in relative hydration free energies of benzene analogues and for two datasets of experimental relative binding free energies of congeneric series of ligands of the proteins α-thrombin and P38 MAP kinase. The approach includes a protocol in which data obtained from SILCS simulations of the proteins is first analyzed to identify favorable benzene binding sites following which an ensemble of benzene-protein conformations for that site is obtained. The SSFEP protocol applied to that ensemble results in good reproduction of experimental free energies of the α-thrombin ligands, but not for P38 MAP kinase ligands. Comparison with results from a P38 full-ligand simulation and analysis of conformations reveals the reason for the poor agreement being the connectivity with the remainder of the ligand, a limitation inherent in fragment-based methods. Since the SSFEP approach can identify favorable benzene modifications as well as identify the most favorable fragment conformations, the obtained information can be of value for fragment linking or structure-based optimization.

Multisite λ Dynamics for Simulated Structure–Activity Relationship Studies

Multi-Site λ-dynamics (MSλD) is a new free energy simulation method that is based on λ-dynamics. It has been developed to enable multiple substituents at multiple sites on a common ligand core to be modeled simultaneously and their free energies assessed. The efficacy of MSλD for estimating relative hydration free energies and relative binding affinties is demonstrated using three test systems. Model compounds representing multiple identical benzene, dihydroxybenzene and dimethoxybenzene molecules show total combined MSλD trajectory lengths of ~1.5 ns are sufficient to reliably achieve relative hydration free energy estimates within 0.2 kcal/mol and are less sensitive to the number of trajectories that are used to generate these estimates for hybrid ligands that contain up to ten substituents modeled at a single site or five substituents modeled at each of two sites. Relative hydration free energies among six benzene derivatives calculated from MSλD simulations are in very good agreement with those from alchemical free energy simulations (with average unsigned differences of 0.23 kcal/mol and R(2)=0.991) and experiment (with average unsigned errors of 1.8 kcal/mol and R(2)=0.959). Estimates of the relative binding affinities among 14 inhibitors of HIV-1 reverse transcriptase obtained from MSλD simulations are in reasonable agreement with those from traditional free energy simulations and experiment (average unsigned errors of 0.9 kcal/mol and R(2)=0.402). For the same level of accuracy and precision MSλD simulations are achieved ~20-50 times faster than traditional free energy simulations and thus with reliable force field parameters can be used effectively to screen tens to hundreds of compounds in structure-based drug design applications.

A Simple QM/MM Approach for Capturing Polarization Effects in Protein−Ligand Binding Free Energy Calculations

We present a molecular simulation protocol to compute free energies of binding, which combines a QM/MM correction term with rigorous classical free energy techniques, thereby accounting for electronic polarization effects. Relative free energies of binding are first computed using classical force fields, Monte Carlo sampling, and replica exchange thermodynamic integration. Snapshots of the configurations at the end points of the perturbation are then subjected to DFT-QM/MM single-point calculations using the B3LYP functional and a range of basis sets. The resulting quantum mechanical energies are then processed using the Zwanzig equation to give free energies incorporating electronic polarization. Our approach is conceptually simple and does not require tightly coupled QM and MM software. The method has been validated by calculating the relative free energies of hydration of methane and water and the relative free energy of binding of two inhibitors of cyclooxygenase-2. Closed thermodynamic cycles are obtained across different pathways, demonstrating the correctness of the technique, although significantly more sampling is required for the protein-ligand system. Our method offers a simple and effective way to incorporate quantum mechanical effects into computed free energies of binding.

Exploiting Configurational Freezing in Nonequilibrium Monte Carlo Simulations.

To achieve acceptable accuracy in fast-switching free energy estimates by Jarzynski equality [ Phys. Rev. Lett. 1997 , 78 , 2690 ] or Crooks fluctuation theorem [ J. Stat. Phys. 1998 , 90 , 1481 ], it is often necessary to realize a large number of externally driven trajectories. This is basically due to inefficient calculation of path-ensemble averages arising from the work dissipated during the nonequilibrium paths. We propose a computational technique, addressed to Monte Carlo simulations, to improve free energy estimates by lowering the dissipated work. The method is inspired by the dynamical freezing approach, recently developed in the context of molecular dynamics simulations [ Phys. Rev. E 2009 , 80 , 041124 ]. The idea is to limit the configurational sampling to particles of a well-established region of the sample (namely, the region where dissipation is supposed to occur), while leaving fixed (frozen) the other particles. Therefore, the method, called configurational freezing, is based on the reasonable assumption that dissipation is a local phenomenon in single-molecule nonequilibrium processes, a statement which is satisfied by most processes, including folding of biopolymers, molecular docking, alchemical transformations, etc. At variance with standard simulations, in configurational freezing simulations the computational cost is not correlated with the size of the whole system, but rather with that of the reaction site. The method is illustrated in two examples, i.e., the calculation of the water to methane relative hydration free energy and the calculation of the potential of mean force of two methane molecules in water solution as a function of their distance.

Prediction of Relative Stability between TACE/Gelastatin and TACE/Gelastatin Hydroxamate

A gelastatins (1), natural MMP inhibitors, and their hydroxamate analogues (2) in TACE enzyme evaluated for discovery of potent TACE inhibitors. We have employed molecular dynamics simulations to compute the relative free energy of hydration and binding to TACE for gelastatin (1) and its hydroxamate analogue (2). The relative free energy difference is directly described in this article using the free energy perturbation approach as a means to accurately predict the TACE inhibitor of gelastatin analogues. The results show that the good agreement between the experimental and theoretical relative free energies of binding, gelastatin hydroxamate (2) binds stronger to TACE by -3.37 kcal/mol. The desolvation energy costs significantly reduced binding affinity, hydroxamate group associated with high desolvation energy formed strong favorable interactions with TACE with more than compensated for the solvation costs and therefore led to an improvement in relative binding affinity.