Relative Free Energies Research Articles

Absolute binding free energy calculations improve enrichment of actives in virtual compound screening

We determined the effectiveness of absolute binding free energy (ABFE) calculations to refine the selection of active compounds in virtual compound screening, a setting where the more commonly used relative binding free energy approach is not readily applicable. To do this, we conducted baseline docking calculations of structurally diverse compounds in the DUD-E database for three targets, BACE1, CDK2 and thrombin, followed by ABFE calculations for compounds with high docking scores. The docking calculations alone achieved solid enrichment of active compounds over decoys. Encouragingly, the ABFE calculations then improved on this baseline. Analysis of the results emphasizes the importance of establishing high quality ligand poses as starting points for ABFE calculations, a nontrivial goal when processing a library of diverse compounds without informative co-crystal structures. Overall, our results suggest that ABFE calculations can play a valuable role in the drug discovery process.

The wetting transition of low surface tension droplet on the special-shaped microstructure surface

The hydrophobic surfaces have been widely used in many field due to their excellent properties. However, the droplet with low surface tension can easily undergo wetting transition and highly infiltrate on a surface with conventional structure, so it is difficult to achieve the hydrophobic state. Preliminary bionics studies showed that the microstructure surface with a reentrant angle could suspend the droplet with low surface tension to achieve the hydrophobic property. But, the wetting mechanism of droplet on the reentrant microstructure surface was not clear. The wettability of the droplet on the reentrant microstructure surface was studied with the numerical method of VOF-CSF in this paper. It was found that the reentrant structure could effectively inhibit the occurrence of wetting transition compared with the conventional columnar-shaped structure, so that the hydrophilic droplet remained suspended and showed a large apparent contact angle. The mechanical model and the relative surface free energy of the droplet in the wetting process on the reentrant microstructure surface were analyzed theoretically. It was found that the geometrical parameters of reentrant structure affected the breakthrough pressure and energy barrier, and then affected the wetting transition. The results showed that the performance of doubly-reentrant structure was better than that of singly-reentrant structure in inhibiting wetting transition. For the reentrant structure with larger width and smaller spacing, the advantage of inhibiting wetting transition was more obvious, and the effect of width gradually disappeared with the increase of spacing. For the droplet that has been suspended on the reentrant structure surface, a slight change of structure width will greatly affect the stability of the small droplet.

Relative binding free Energy calculations in GROMACS between dissimilar molecules using bidirectional nonequilibrium dual topology schemes

Relative binding free Energy calculations in GROMACS between dissimilar molecules using bidirectional nonequilibrium dual topology schemes

Relative binding free Energy calculations in GROMACS between dissimilar molecules using bidirectional nonequilibrium dual topology schemes

Relative binding free Energy calculations in GROMACS between dissimilar molecules using bidirectional nonequilibrium dual topology schemes

Relative binding free Energy calculations in GROMACS between dissimilar molecules using bidirectional nonequilibrium dual topology schemes

Relative binding free Energy calculations in GROMACS between dissimilar molecules using bidirectional nonequilibrium dual topology schemes

Drug Discovery by Automated Adaptation of Chemical Structure and Identity.

Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.

Efficient Quantification of Lipid Packing Defect Sensing by Amphipathic Peptides: Comparing Martini 2 and 3 with CHARMM36

In biological systems, proteins can be attracted to curved or stretched regions of lipid bilayers by sensing hydrophobic defects in the lipid packing on the membrane surface. Here, we present an efficient end-state free energy calculation method to quantify such sensing in molecular dynamics simulations. We illustrate that lipid packing defect sensing can be defined as the difference in mechanical work required to stretch a membrane with and without a peptide bound to the surface. We also demonstrate that a peptide’s ability to concurrently induce excess leaflet area (tension) and elastic softening—a property we call the “characteristic area of sensing” (CHAOS)—and lipid packing sensing behavior are in fact two sides of the same coin. In essence, defect sensing displays a peptide’s propensity to generate tension. The here-proposed mechanical pathway is equally accurate yet, computationally, about 40 times less costly than the commonly used alchemical pathway (thermodynamic integration), allowing for more feasible free energy calculations in atomistic simulations. This enabled us to directly compare the Martini 2 and 3 coarse-grained and the CHARMM36 atomistic force fields in terms of relative binding free energies for six representative peptides including the curvature sensor ALPS and two antiviral amphipathic helices (AH). We observed that Martini 3 qualitatively reproduces experimental trends while producing substantially lower (relative) binding free energies and shallower membrane insertion depths compared to atomistic simulations. In contrast, Martini 2 tends to overestimate (relative) binding free energies. Finally, we offer a glimpse into how our end-state-based free energy method can enable the inverse design of optimal lipid packing defect sensing peptides when used in conjunction with our recently developed evolutionary molecular dynamics (Evo-MD) method. We argue that these optimized defect sensors—aside from their biomedical and biophysical relevance—can provide valuable targets for the development of lipid force fields.

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.

Molecular Dynamics Study of the Thermodynamics of Integer Charge Transfer vs Charge-Transfer Complex Formation in Doped Conjugated Polymers.

Molecular dopants such as 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4TCNQ) can interact with conjugated polymers such as poly(3-hexylthiophene-2,5-diyl) (P3HT) in two different ways: they can undergo integer charge transfer (ICT) or they can form a partial-charge-transfer complex (CTC). Both are seen experimentally, but the CTC has been challenging to characterize, making it difficult to answer questions such as the following. Which polymorph is more stable? Do they have similar barriers for formation? Is there a thermodynamic route to convert one to the other? Here, we study the structure and the thermodynamics of bulk F4TCNQ-doped P3HT with all-atom molecular dynamics simulations, using thermodynamic integration to calculate the relative free energies. We find that the ICT and CTC polymorphs have similar thermodynamic stabilities. The barrier to create the ICT polymorph, however, is lower than that to make the CTC polymorph, because the ICT polymorph has a small critical nucleus, but the critical nucleus for the CTC polymorph is larger than what we can simulate. Moreover, simulated thermal annealing shows that the activation barrier for converting the CTC polymorph to the ICT polymorph is relatively modest. Overall, the simulations explain both the observed structures and the thermodynamics of F4TCNQ-doped P3HT and offer guidelines for targeting the production of a desired polymorph for different applications.

Relative Binding Free Energy between Chemically Distant Compounds Using a Bidirectional Nonequilibrium Approach.

In the context of advanced hit-to-lead drug design based on atomistic molecular dynamics simulations, we propose a dual topology alchemical approach for calculating the relative binding free energy (RBFE) between two chemically distant compounds. The method (termed NE-RBFE) relies on the enhanced sampling of the end-states in bulk and in the bound state via Hamiltonian Replica Exchange, alchemically connected by a series of independent and fast nonequilibrium (NE) simulations. The technique has been implemented in a bidirectional fashion, applying the Crooks theorem to the NE work distributions for RBFE predictions. The dissipation of the NE process, negatively affecting accuracy, has been minimized by introducing a smooth regularization based on shifted electrostatic and Lennard-Jones non bonded potentials. As a challenging testbed, we have applied our method to the calculation of the RBFEs in the recent host–guest SAMPL international contest, featuring a macrocyclic host with guests varying in the net charge, volume, and chemical fingerprints. Closure validation has been successfully verified in cycles involving compounds with disparate Tanimoto coefficients, volume, and net charge. NE-RBFE is specifically tailored for massively parallel facilities and can be used with little or no code modification on most of the popular software packages supporting nonequilibrium alchemical simulations, such as Gromacs, Amber, NAMD, or OpenMM. The proposed methodology bypasses most of the entanglements and limitations of the standard single topology RBFE approach for strictly congeneric series based on free-energy perturbation, such as slowly relaxing cavity water, sampling issues along the alchemical stratification, and the need for highly overlapping molecular fingerprints.

Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision, and Reproducibility.

The binding free energy between a ligand and its target protein is an essential quantity to know at all stages of the drug discovery pipeline. Assessing this value computationally can offer insight into where efforts should be focused in the pursuit of effective therapeutics to treat a myriad of diseases. In this work, we examine the computation of alchemical relative binding free energies with an eye for assessing reproducibility across popular molecular dynamics packages and free energy estimators. The focus of this work is on 54 ligand transformations from a diverse set of protein targets: MCL1, PTP1B, TYK2, CDK2, and thrombin. These targets are studied with three popular molecular dynamics packages: OpenMM, NAMD2, and NAMD3 alpha. Trajectories collected with these packages are used to compare relative binding free energies calculated with thermodynamic integration and free energy perturbation methods. The resulting binding free energies show good agreement between molecular dynamics packages with an average mean unsigned error between them of 0.50 kcal/mol. The correlation between packages is very good, with the lowest Spearman’s, Pearson’s and Kendall’s tau correlation coefficients being 0.92, 0.91, and 0.76, respectively. Agreement between thermodynamic integration and free energy perturbation is shown to be very good when using ensemble averaging.

A computationally affordable approach for accurate prediction of the binding affinity of JAK2 inhibitors.

Janus kinase 2 (JAK2) inhibitors are potential anticancer drugs in the treatment of lymphoma, leukemia, thrombocytosis and particularly myeloproliferative diseases.However, the resemblance among JAK family members has challenged the identification of highly selective inhibitors for JAK2 to reduce undesired side effects. As a result, a robust search for promising JAK2 inhibitors using a computational approach that can effectively nominate new potential candidates to be further analyzed through laborious experimental operations has become necessary. In this study, the binding affinities of JAK2 inhibitors were rapidly and precisely estimated using the fast pulling of ligand (FPL) simulations combined with a modified linear interaction energy (LIE) method. The approach correlates with the experimental binding affinities of JAK2 inhibitors with a correlation coefficient of R = 0.82 and a root-mean-square error of 0.67kcal•mol-1. The data reveal that the FPL/LIE method is highly approximate in anticipating the relative binding free energies of known JAK2 inhibitors with an affordable consumption of computational resources, and thus, it is very promising to be applied in in silico screening for new potential JAK2 inhibitors from a large number of molecules available.

Palladium-Catalyzed One-Step Synthesis of Stereodefined Difunctionalized Glycals

Palladium-Catalyzed One-Step Synthesis of Stereodefined Difunctionalized Glycals

Kinetics of Initial Charge Separation in the Photosynthetic Reaction Centers of Rhodobacter sphaeroides.

A stochastic kinetic study of the initial electron transfer reaction in the reaction centers of wild-type and different mutants of photosynthetic bacterium Rhodobacter sphaeroides is suggested. The present approach to the disorder-driven complex kinetics skilfully offers an alternative to the earlier dynamic analyses. Exploiting a rational description of the reaction coordinate, effected by the relaxation of the surrounding protein environment, the measured kinetics were reproduced quantitatively. Notably, comparisons of the extracted relative free energies of electron transfer for the selected mutants and the available independent electrochemical estimates show exact agreement in some cases.

Ensemble Simulations and Experimental Free Energy Distributions: Evaluation and Characterization of Isoxazole Amides as SMYD3 Inhibitors.

Optimization of binding affinities for ligands to their target protein is a primary objective in rational drug discovery. Herein, we report on a collaborative study that evaluates various compounds designed to bind to the SET and MYND domain-containing protein 3 (SMYD3). SMYD3 is a histone methyltransferase and plays an important role in transcriptional regulation in cell proliferation, cell cycle, and human carcinogenesis. Experimental measurements using the scintillation proximity assay show that the distributions of binding free energies from a large number of independent measurements exhibit non-normal properties. We use ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and TIES (thermodynamic integration with enhanced sampling) protocols to predict the binding free energies and to provide a detailed chemical insight into the nature of ligand–protein binding. Our results show that the 1-trajectory ESMACS protocol works well for the set of ligands studied here. Although one unexplained outlier exists, we obtain excellent statistical ranking across the set of compounds from the ESMACS protocol and good agreement between calculations and experiments for the relative binding free energies from the TIES protocol. ESMACS and TIES are again found to be powerful protocols for the accurate comparison of the binding free energies.

Alchemical free energy simulations without speed limits. A generic framework to calculate free energy differences independent of the underlying molecular dynamics program.

We describe the theory of the so‐called common‐core/serial‐atom‐insertion (CC/SAI) approach to compute alchemical free energy differences and its practical implementation in a Python package called Transformato. CC/SAI is not tied to a specific biomolecular simulation program and does not rely on special purpose code for alchemical transformations. To calculate the alchemical free energy difference between several small molecules, the physical end‐states are mutated into a suitable common core. Since this only requires turning off interactions, the setup of intermediate states is straightforward to automate. Transformato currently supports CHARMM and OpenMM as back ends to carry out the necessary molecular dynamics simulations, as well as post‐processing calculations. We validate the method by computing a series of relative solvation free energy differences.

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.

Structure-based virtual screening, ADMET profiling, and molecular dynamics simulation studies on HIV-1 protease for identification of active phytocompounds as potential anti-HIV agents

ABSTRACT HIV-1 protease (HIV-pr) acts as the most promising drug target commonly used for anti-HIV therapy. However, the efficiency of the protease inhibitors (PIs) has been greatly reduced by mutations assisted drug resistance. Because of the richness and greatest diversity of plants, the current study aims at exploring phytocompounds as PIs with potential inhibitory properties against HIV-pr through multi-faceted in silico tactics. Virtually screened phytocompounds (Epicatechin, Epigallocatechol, D-Catechin, Afrormosin and Afzelechin) were employed via docking to the WT and I50V mutant. Molecular dynamics of complexed HIV-pr with 50 ns trajectory expose that the nominated phytocompounds can efficiently bind to the HIV-pr active site and control the flap dynamics. The relative binding free energies for the five phytocompounds to both WT and I50V mutant were calculated by MM-GBSA method. The increased binding affinity was witnessed for most of the PIs (except AFZ), and on account of the robust binding, which may be primarily accredited due to the upsurge in ΔE ele and ΔE vdw, the I50V mutant might be well acclimatised by the PIs. Overall, this study led to the identification of potential hit compounds like Epigallocatechol with favourable pharmacokinetic properties for both WT and I50 V may lead to the discovery of robust new drugs against HIV-pr.

Improving ΔΔG Predictions with a Multitask Convolutional Siamese Network.

The lead optimization phase of drug discovery refines an initial hit molecule for desired properties, especially potency. Synthesis and experimental testing of the small perturbations during this refinement can be quite costly and time-consuming. Relative binding free energy (RBFE, also referred to as ΔΔG) methods allow the estimation of binding free energy changes after small changes to a ligand scaffold. Here, we propose and evaluate a Siamese convolutional neural network (CNN) for the prediction of RBFE between two bound ligands. We show that our multitask loss is able to improve on a previous state-of-the-art Siamese network for RBFE prediction via increased regularization of the latent space. The Siamese network architecture is well suited to the prediction of RBFE in comparison to a standard CNN trained on the same data (Pearson's R of 0.553 and 0.5, respectively). When evaluated on a left-out protein family, our Siamese CNN shows variability in its RBFE predictive performance depending on the protein family being evaluated (Pearson's R ranging from -0.44 to 0.97). RBFE prediction performance can be improved during generalization by injecting only a few examples (few-shot learning) from the evaluation data set during model training.

Vanadium(IV) coordination complexes with excellent biological activities: a synthetic, characterization, and density functional theory approach

A set of new oxidovanadium(IV) complexes, [VO(4-NO2C6H4CH = CHCONHO)2] (1), [VO(4-NO2C6H4CH = CHCONHO)2(2–CNC5H4N)] (2) and [VO(4-NO2C6H4CH = CHCONHO)2(4-CNC6H4NH2)] (3), have been synthesized and characterized by different analytical techniques (magnetic susceptibility, molar conductivity, elemental analysis) and spectroscopic techniques, viz. FTIR, UV–vis, EPR, and mass spectrometry. The magnetic susceptibility, EPR, and ESI-MS data indicate that 1 exists as monomer and a distorted square pyramidal geometry around vanadium is ascertained. The electrochemical study of 1 has shown that it is electrochemically active exhibiting VOV/VOIV quasi-reversible redox couple. The biological activity of 1–3 has been studied against various pathogenic bacteria Staphylococcus epidermidis, Klebsiella pneumoniae, Staphylococcus aureus, Escherichia coli, Salmonella paratyphi, and Salmonella typhi, and fungi Brachypsectra fulva, Candida albicans, and Fusarium oxysporum by minimum inhibitory concentration (MIC) method. In some cases, the synthesized complexes showed superior antibacterial and antifungal activity than the well-known standard drugs tetracycline hydrochloride and fluconazole. The cytotoxicity of 1–3 has been studied on a human cervix carcinoma HeLa cell derivative (mammalian transformed cell line Hep2c) by MTT assay. Density functional theory (DFT) studies have been carried out to determine their relative free energy of formation and optimized molecular structures of 1–3. Time-dependent density functional theory (TD-DFT) based calculations have been performed to find out the frontier molecular orbitals and corroborate with the experimentally observed electronic transitions of 1. Other parameters like HOMO, LUMO energies, and global reactivity descriptors clearly support higher biological activity of 2 and 3 than 1.