Calculations of Relative Hydration Free Energies: A Comparative Study Using Thermodynamic Integration and an Extrapolation Method Based on a Single Reference State

Identification of Optimal Ligand Growth Vectors Using an Alchemical Free-Energy Method

In this work, a novel method to rationally design inhibitors with improved steric contacts and enhanced binding free energies is presented. This new method uses alchemical single step perturbation calculations to rapidly optimize the van der Waals interactions of a small molecule in a protein-ligand complex in order to maximize its binding affinity. The results of the optimizer are used to predict beneficial growth vectors on the ligand, and good agreement is found between the predictions from the optimizer and a more rigorous free energy calculation, with a Spearman's rank order correlation of 0.59. The advantage of the method presented here is the significant speed up of over 10-fold compared to traditional free energy calculations and sublinear scaling with the number of growth vectors assessed. Where experimental data were available, mutations from hydrogen to a methyl group at sites highlighted by the optimizer were calculated with MBAR, and the mean unsigned error between experimental and calculated values of the binding free energy was 0.83 kcal/mol.

An Alternative to Conventional λ-Intermediate States in Alchemical Free Energy Calculations: λ-Enveloping Distribution Sampling

Alchemical free energy calculations typically rely on intermediate states to bridge between the relevant phase spaces of the two end states. These intermediate states are usually created by mixing the energies or parameters of the end states according to a coupling parameter λ. The choice of the procedure has a strong impact on the efficiency of the calculation, as it affects both the encountered energy barriers and the phase space overlap between the states. The present work builds on the connection between the minimum variance pathway (MVP) and enveloping distribution sampling (EDS). It is shown that both methods can be regarded as special cases of a common scheme referred to as λ-EDS, which can also reproduce the behavior of conventional λ-intermediate states. A particularly attractive feature of λ-EDS is its ability to emulate the use of soft core potentials (SCP) while avoiding the associated computational overhead when applying efficient free energy estimators such as the multistate Bennett's acceptance ratio (MBAR). The method is illustrated for both relative and absolute free energy calculations considering five benchmark systems. The first two systems (charge inversion and cavity creation in a dipolar solvent) demonstrate the use of λ-EDS as an alternative coupling scheme in the context of thermodynamic integration (TI). The three other systems (change of bond length, change of dihedral angles, and cavity creation in water) investigate the efficiency and optimal choice of parameters in the context of free energy perturbation (FEP) and Bennett's acceptance ratio (BAR). It is shown that λ-EDS allows larger steps along the alchemical pathway than conventional intermediate states.

Optimization of Protein-Ligand Electrostatic Interactions Using an Alchemical Free-Energy Method

We present an explicit solvent alchemical free-energy method for optimizing the partial charges of a ligand to maximize the binding affinity with a receptor. This methodology can be applied to known ligand–protein complexes to determine an optimized set of ligand partial atomic changes. Three protein–ligand complexes have been optimized in this work: FXa, P38, and the androgen receptor. The sets of optimized charges can be used to identify design principles for chemical changes to the ligands which improve the binding affinity for all three systems. In this work, beneficial chemical mutations are generated from these principles and the resulting molecules tested using free-energy perturbation calculations. We show that three quarters of our chemical changes are predicted to improve the binding affinity, with an average improvement for the beneficial mutations of approximately 1 kcal/mol. In the cases where experimental data are available, the agreement between prediction and experiment is also good. The results demonstrate that charge optimization in explicit solvent is a useful tool for predicting beneficial chemical changes such as pyridinations, fluorinations, and oxygen to sulfur mutations.

Computational Fluorine Scanning Using Free-Energy Perturbation

We present perturbative fluorine scanning, a computational fluorine scanning approach using free-energy perturbation. This method can be applied to molecular dynamics simulations of a single compound and make predictions for the best binders out of numerous fluorinated analogues. We tested the method on nine test systems: renin, DPP4, menin, P38, factor Xa, CDK2, AKT, JAK2, and androgen receptor. The predictions were in excellent agreement with more rigorous alchemical free-energy calculations and in good agreement with experimental data for most of the test systems. However, the agreement with experiment was very poor in some of the test systems, and this highlights the need for improved force fields in addition to accurate treatment of tautomeric and protonation states. The method is of particular interest due to the wide use of fluorine in medicinal chemistry to improve binding affinity and ADME properties. The promising results on this test case suggest that perturbative fluorine scanning will be a useful addition to the available arsenal of free-energy methods.

Density functional theory calculations on entire proteins for free energies of binding: application to a model polar binding site

In drug optimization calculations, the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method can be used to compute free energies of binding of ligands to proteins. The method involves the evaluation of the energy of configurations in an implicit solvent model. One source of errors is the force field used, which can potentially lead to large errors due to the restrictions in accuracy imposed by its empirical nature. To assess the effect of the force field on the calculation of binding energies, in this article we use large-scale density functional theory (DFT) calculations as an alternative method to evaluate the energies of the configurations in a "QM-PBSA" approach. Our DFT calculations are performed with a near-complete basis set and a minimal parameter implicit solvent model, within the self-consistent calculation, using the ONETEP program on protein-ligand complexes containing more than 2600 atoms. We apply this approach to the T4-lysozyme double mutant L99A/M102Q protein, which is a well-studied model of a polar binding site, using a set of eight small aromatic ligands. We observe that there is very good correlation between the MM and QM binding energies in vacuum but less so in the solvent. The relative binding free energies from DFT are more accurate than the ones from the MM calculations, and give markedly better agreement with experiment for six of the eight ligands. Furthermore, in contrast to MM-PBSA, QM-PBSA is able to correctly predict a nonbinder.

Path-breaking schemes for nonequilibrium free energy calculations

We propose a path-breaking route to the enhancement of unidirectional nonequilibrium simulations for the calculation of free energy differences via Jarzynski's equality [C. Jarzynski, Phys. Rev. Lett. 78, 2690 (1997)]. One of the most important limitations of unidirectional nonequilibrium simulations is the amount of realizations necessary to reach suitable convergence of the work exponential average featuring the Jarzynski's relationship. In this respect, a significant improvement of the performances could be obtained by finding a way of stopping trajectories with negligible contribution to the work exponential average, before their normal end. This is achieved using path-breaking schemes which are essentially based on periodic checks of the work dissipated during the pulling trajectories. Such schemes can be based either on breaking trajectories whose dissipated work exceeds a given threshold or on breaking trajectories with a probability increasing with the dissipated work. In both cases, the computer time needed to carry out a series of nonequilibrium trajectories is reduced up to a factor ranging from 2 to more than 10, at least for the processes under consideration in the present study. The efficiency depends on several aspects, such as the type of process, the number of check-points along the pathway and the pulling rate as well. The method is illustrated through radically different processes, i.e., the helix-coil transition of deca-alanine and the pulling of the distance between two methane molecules in water solution.

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.

Efficient Combination of Environment Change and Alchemical Perturbation within the Enveloping Distribution Sampling (EDS) Scheme: Twin-System EDS and Application to the Determination of Octanol-Water Partition Coefficients

The methodology of Enveloping Distribution Sampling (EDS) is extended to probe a single-simulation alternative to the thermodynamic cycle that is standardly used for measuring the effect of a modification of a chemical compound, e.g. from a given species to a chemical derivative for a ligand or solute molecule, on the free-enthalpy change associated with a change in environment, e.g. from the unbound state to the bound state for a protein-ligand system or from one solvent to another one for a solute molecule. This alternative approach relies on the coupled simulation of two systems (computational boxes) 1 and 2, and the method is therefore referred to as twin-system EDS. Systems 1 and 2 account for the two choices of environment. The end states of the alchemical perturbation for the twin-system associate the two alternative forms X and Y of the molecule to systems 1 and 2 or 2 and 1, respectively. In this way, the processes of transforming one molecule into the other are carried out simultaneously in opposite directions in the two environments, leading to a change in free enthalpy that is smaller than for the two individual processes and to an energy-difference distribution that is more symmetric. As an illustration, the method is applied to the calculation of octanol-water partition coefficients for C4 to C8 alkanes, 1-hexanol and 1,2-dimethoxyethane. It is shown in particular that the consideration of the residual hydration of octanol leads to calculated partition coefficients that are in better agreement with reported experimental numbers.

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.

Accurate estimation of solvation free energy using polynomial fitting techniques

This report details an approach to improve the accuracy of free energy difference estimates using thermodynamic integration data (slope of the free energy with respect to the switching variable λ) and its application to calculating solvation free energy. The central idea is to utilize polynomial fitting schemes to approximate the thermodynamic integration data to improve the accuracy of the free energy difference estimates. Previously, we introduced the use of polynomial regression technique to fit thermodynamic integration data (Shyu and Ytreberg, J Comput Chem, 2009, 30, 2297). In this report we introduce polynomial and spline interpolation techniques. Two systems with analytically solvable relative free energies are used to test the accuracy of the interpolation approach. We also use both interpolation and regression methods to determine a small molecule solvation free energy. Our simulations show that, using such polynomial techniques and nonequidistant λ values, the solvation free energy can be estimated with high accuracy without using soft-core scaling and separate simulations for Lennard-Jones and partial charges. The results from our study suggest that these polynomial techniques, especially with use of nonequidistant λ values, improve the accuracy for ΔF estimates without demanding additional simulations. We also provide general guidelines for use of polynomial fitting to estimate free energy. To allow researchers to immediately utilize these methods, free software and documentation is provided via http://www.phys.uidaho.edu/ytreberg/software.

Computing Relative Free Energies of Solvation using Single Reference Thermodynamic Integration Augmented with Hamiltonian Replica Exchange

This paper introduces an efficient single-topology variant of Thermodynamic Integration (TI) for computing relative transformation free energies in a series of molecules with respect to a single reference state. The presented TI variant that we refer to as Single-Reference TI (SR-TI) combines well-established molecular simulation methodologies into a practical computational tool. Augmented with Hamiltonian Replica Exchange (HREX), the SR-TI variant can deliver enhanced sampling in select degrees of freedom. The utility of the SR-TI variant is demonstrated in calculations of relative solvation free energies for a series of benzene derivatives with increasing complexity. Noteworthy, the SR-TI variant with the HREX option provides converged results in a challenging case of an amide molecule with a high (13-15 kcal/mol) barrier for internal cis/trans interconversion using simulation times of only 1 to 4 ns.

Reducing the bias and uncertainty of free energy estimates by using regression to fit thermodynamic integration data

This report presents the application of polynomial regression for estimating free energy differences using thermodynamic integration data, i.e., slope of free energy with respect to the switching variable lambda. We employ linear regression to construct a polynomial that optimally fits the thermodynamic integration data, and thus reduces the bias and uncertainty of the resulting free energy estimate. Two test systems with analytical solutions were used to verify the accuracy and precision of the approach. Our results suggest that use of regression with high degree of polynomials provides the most accurate free energy difference estimates, but often with slightly larger uncertainty, compared to commonly used quadrature techniques. High degree polynomials possess the flexibility to closely fit the thermodynamic integration data but are often sensitive to small changes in the data points. Thus, we also used Chebyshev nodes to guide in the selection of nonequidistant lambda values for use in thermodynamic integration. We conclude that polynomial regression with nonequidistant lambda values delivers the most accurate and precise free energy estimates for thermodynamic integration data for the systems considered here. Software and documentation is available at http://www.phys.uidaho.edu/ytreberg/software.

Comparison of free energy methods for molecular systems

We present a detailed comparison of computational efficiency and precision for several free energy difference (DeltaF) methods. The analysis includes both equilibrium and nonequilibrium approaches, and distinguishes between unidirectional and bidirectional methodologies. We are primarily interested in comparing two recently proposed approaches, adaptive integration, and single-ensemble path sampling to more established methodologies. As test cases, we study relative solvation free energies of large changes to the size or charge of a Lennard-Jones particle in explicit water. The results show that, for the systems used in this study, both adaptive integration and path sampling offer unique advantages over the more traditional approaches. Specifically, adaptive integration is found to provide very precise long-simulation DeltaF estimates as compared to other methods used in this report, while also offering rapid estimation of DeltaF. The results demonstrate that the adaptive integration approach is the best overall method for the systems studied here. The single-ensemble path sampling approach is found to be superior to ordinary Jarzynski averaging for the unidirectional, "fast-growth" nonequilibrium case. Closer examination of the path sampling approach on a two-dimensional system suggests it may be the overall method of choice when conformational sampling barriers are high. However, it appears that the free energy landscapes for the systems used in this study have rather modest configurational sampling barriers.

Peptide conformational equilibria computed via a single-stage shifting protocol

We study the conformational equilibria of two peptides using a novel statistical mechanics approach designed for calculating free energy differences between highly dissimilar conformational states. Our results elucidate the contrasting roles of entropy in implicitly solvated leucine dipeptide and decaglycine. The method extends earlier work by Voter and overcomes the notorious "overlap" problem in free energy computations by constructing a mathematically equivalent calculation with high conformational similarity. The approach requires only equilibrium simulations of the two states of interest, without the need for sampling transition states. We discuss possible extensions and optimizations of the approach.

Single-ensemble nonequilibrium path-sampling estimates of free energy differences

We introduce a straightforward, single-ensemble, path sampling approach to calculate free energy differences based on Jarzynski's relation. For a two-dimensional "toy" test system, the new (minimally optimized) method performs roughly one hundred times faster than either optimized "traditional" Jarzynski calculations or conventional thermodynamic integration. The simplicity of the underlying formalism suggests the approach will find broad applicability in molecular systems.

Calculation of affinities of peptides for proteins

Several methodologies were employed to calculate the Gibbs standard free energy of binding for a collection of protein-ligand complexes, where the ligand is a peptide and the protein is representative for various protein families. Almost 40 protein-ligand complexes were employed for a continuum approach, which considers the protein and the peptide at the atomic level, but includes solvent as a polarizable continuum. Five protein-ligand complexes were employed for an all-atom approach that relies on a combination of the double decoupling method with thermodynamic integration and molecular dynamics. These affinities were also computed by means of the linear interaction energy method. Although it generally proved rather difficult to predict the absolute free energies correctly, for some protein families the experimental ranking order was correctly reproduced by the continuum and all-atom approach. Considerable attention has also been given to correctly analyze the affinities of charged peptides, where it is required to judge the effect of one or more ions that are being decoupled in an all-atom approach to preserve electroneutrality. The various methods are further judged upon their merits.

Determination of solvation free energies by adaptive expanded ensemble molecular dynamics

A new method of calculating absolute free energies is presented. It was developed as an extension to the expanded ensemble molecular dynamics scheme and uses probability density estimation to continuously optimize the expanded ensemble parameters. The new method is much faster as it removes the time-consuming and expertise-requiring step of determining balancing factors. Its efficiency and accuracy are demonstrated for the dissolution of three qualitatively very different chemical species in water: methane, ionic salts, and benzylamine. A recently suggested optimization scheme by Wang and Landau [Phys. Rev. Lett. 86, 2050 (2001)] was also implemented and found to be computationally less efficient than the proposed adaptive expanded ensemble method.

Efficient use of nonequilibrium measurement to estimate free energy differences for molecular systems

A promising method for calculating free energy differences DeltaF is to generate nonequilibrium data via "fast-growth" simulations or by experiments--and then use Jarzynski's equality. However, a difficulty with using Jarzynski's equality is that DeltaF estimates converge very slowly and unreliably due to the nonlinear nature of the calculation--thus requiring large, costly data sets. The purpose of the work presented here is to determine the best estimate for DeltaF given a (finite) set of work values previously generated by simulation or experiment. Exploiting statistical properties of Jarzynski's equality, we present two fully automated analyses of nonequilibrium data from a toy model, and various simulated molecular systems. Both schemes remove at least several k(B)T of bias from DeltaF estimates, compared to direct application of Jarzynski's equality, for modest sized data sets (100 work values), in all tested systems. Results from one of the new methods suggest that good estimates of DeltaF can be obtained using 5-40-fold less data than was previously possible. Extending previous work, the new results exploit the systematic behavior of bias due to finite sample size. A key innovation is better use of the more statistically reliable information available from the raw data.

Protein flexibility and computer-aided drug design

Although computational techniques are increasingly being used in computer-aided drug design, the effects due to protein flexibility are still ignored in many applications. This review revisits rigorous statistical mechanical methods for predicting binding affinity, discusses some recent developments for improving their speed and reliability, and examines faster approximate models for facilitating virtual screening and lead optimization.