Scoring binding affinity of multiple ligands using implicit solvent and a single molecular dynamics trajectory: application to influenza neuraminidase

Evaluating the performance of MM/PBSA for binding affinity prediction using class A GPCR crystal structures

The recent expansion of GPCR crystal structures provides the opportunity to assess the performance of structure-based drug design methods for the GPCR superfamily. Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA)-based methods are commonly used for binding affinity prediction, as they provide an intermediate compromise of speed and accuracy between the empirical scoring functions used in docking and more robust free energy perturbation methods. In this study, we systematically assessed the performance of MM/PBSA in predicting experimental binding free energies using twenty Class A GPCR crystal structures and 934 known ligands. Correlations between predicted and experimental binding free energies varied significantly between individual targets, ranging from r = - 0.334 in the inactive-state CB₁ cannabinoid receptor to r = 0.781 in the active-state CB₁ cannabinoid receptor, while average correlation across all twenty targets was relatively poor (r = 0.183). MM/PBSA provided better predictions of binding free energies compared to docking scores in eight out of the twenty GPCR targets while performing worse for four targets. MM/PBSA binding affinity predictions calculated using a single, energy minimized structure provided comparable predictions to sampling from molecular dynamics simulations and may be more efficient when computational cost becomes restrictive. Additionally, we observed that restricting MM/PBSA calculations to ligands with a high degree of structural similarity to the crystal structure ligands improved performance in several cases. In conclusion, while MM/PBSA remains a valuable tool for GPCR structure-based drug design, its performance in predicting the binding free energies of GPCR ligands remains highly system-specific as demonstrated in a subset of twenty Class A GPCRs, and validation of MM/PBSA-based methods for each individual case is recommended before prospective use.

Intermolecular interactions between DNA and methamphetamine, amphetamine, ecstasy and their major metabolites

In this work, we carried out a theoretical investigation regarding amphetamine-type stimulants, which can cause central nervous system degeneration, interacting with human DNA. These include amphetamine, methamphetamine, 3,4-Methylenedioxymethamphetamine (also known as ecstasy), as well as their main metabolites. The studies were performed through molecular docking and molecular dynamics simulations, where molecular interactions of the receptor-ligand systems, along with their physical-chemical energies, were reported. Our results show that 3,4-Methylenedioxymethamphetamine and 3,4-Dihydroxymethamphetamine (ecstasy) present considerable reactivity with the receptor (DNA), suggesting that these molecules may cause damage due to human-DNA. These results were indicated by free Gibbs change of bind (ΔG_bind) values referring to intermolecular interactions between the drugs and the minor grooves of DNA, which were predominant for all simulations. In addition, it was observed that 3,4-Dihydroxymethamphetamine (ΔG_bind = -13.15 kcal/mol) presented greater spontaneity in establishing interactions with DNA in comparison to 3,4-Methylenedioxymethamphetamine (ΔG_bind = -8.61 kcal/mol). Thus, according with the calculations performed our results suggest that the 3,4-Methylenedioxymethamphetamine and 3,4-Dihydroxymethamphetamine have greater probability to provide damage to human DNA fragments.

Pentacycloundecane lactam vs lactone norstatine type protease HIV inhibitors: binding energy calculations and DFT study

Background

Novel pentacycloundecane (PCU)-lactone-CO-EAIS peptide inhibitors were designed, synthesized, and evaluated against wild-type C-South African (C-SA) HIV-1 protease. Three compounds are reported herein, two of which displayed IC₅₀ values of less than 1.00 μM. A comparative MM-PB(GB)SA binding free energy of solvation values of PCU-lactam and lactone models and their enantiomers as well as the PCU-lactam-NH-EAIS and lactone-CO-EAIS peptide inhibitors and their corresponding diastereomers complexed with South African HIV protease (C-SA) was performed. This will enable us to rationalize the considerable difference between inhibitory concentration (IC₅₀) of PCU-lactam-NH-EAIS and PCU-lactone-CO-EAIS peptides.

Results

The PCU-lactam model exhibited more negative calculated binding free energies of solvation than the PCU-lactone model. The same trend was observed for the PCU-peptide inhibitors, which correspond to the experimental activities for the PCU-lactam-NH-EAIS peptide (IC₅₀ = 0.076 μM) and the PCU-lactone-CO-EAIS peptide inhibitors (IC₅₀ = 0.850 μM). Furthermore, a density functional theory (DFT) study on the natural atomic charges of the nitrogen and oxygen atoms of the three PCU-lactam, PCU-lactim and PCU-lactone models were performed using natural bond orbital (NBO) analysis. Electrostatic potential maps were also used to visualize the electron density around electron-rich regions. The asymmetry parameter (η) and quadrupole coupling constant (χ) values of the nitrogen and oxygen nuclei of the model compounds were calculated at the same level of theory. Electronic molecular properties including polarizability and electric dipole moments were also calculated and compared. The Gibbs theoretical free solvation energies of solvation (∆G_solv) were also considered.

Conclusions

A general trend is observed that the lactam species appears to have a larger negative charge distribution around the heteroatoms, larger quadrupole constant, dipole moment and better solvation energy, in comparison to the PCU-lactone model. It can be argued that these characteristics will ensure better eletronic interaction between the lactam and the receptor, corresponding to the observed HIV protease activities in terms of experimental IC₅₀ data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12929-015-0115-5) contains supplementary material, which is available to authorized users.

SUSCEPTIBILITY OF COMMERCIAL NEURAMINIDASE INHIBITORS AGAINST 2013 A/H7N9 INFLUENZA VIRUS: A DOCKING AND MOLECULAR DYNAMICS STUDY

The latest influenza A ( H 7 N 9) virus attracted a worldwide attention due to the first report of human infections and the continuing reported cases in China. In this work, homology modeling, docking and molecular dynamics simulations were combined to study the interactions between neuraminidase ( N 9_2013, from novel A/ H 7 N 9 virus) and agents zanamivir, oseltamivir, peramivir. It was found that N 9_2013 protein is structurally close to the template (PDB code: 1F8B), especially the active site. The binding properties of N 9_2013 protein were nearly identical to those of template. As a result, the three available drugs should be still efficacious for the new emerging A ( H 7 N 9) virus. However, the stabilities of docked complexes and binding affinities (Eint) were slightly reduced, in contrast to the corresponding inhibitor-template complexes, with the values of -82.27 (-84.30), -78.84 (-80.28) and -77.52 (-81.94) kcal mol-1, respectively. Besides, R292K mutation might induce the resistance of the novel virus to the commercial inhibitors. Thus, it arouses the need for continuous monitoring of antiviral drug susceptibilities.

Inhibitory effect and possible mechanism of action of patchouli alcohol against influenza A (H2N2) virus

In the present study, the anti-influenza A (H2N2) virus activity of patchouli alcohol was studied in vitro, in vivo and in silico. The CC₅₀ of patchouli alcohol was above 20 µM. Patchouli alcohol could inhibit influenza virus with an IC₅₀ of 4.03 ± 0.23 µM. MTT assay showed that the inhibition by patchouli alcohol appears strongly after penetration of the virus into the cell. In the influenza mouse model, patchouli alcohol showed obvious protection against the viral infection at a dose of 5 mg/kg/day. Flexible docking and molecular dynamic simulations indicated that patchouli alcohol was bound to the neuraminidase protein of influenza virus, with an interaction energy of –40.38 kcal mol^–1. The invariant key active-site residues Asp151, Arg152, Glu119, Glu276 and Tyr406 played important roles during the binding process. Based on spatial and energetic criteria, patchouli alcohol interfered with the NA functions. Results presented here suggest that patchouli alcohol possesses anti-influenza A (H2N2) virus properties, and therefore is a potential source of anti-influenza agents for the pharmaceutical industry.

Physics-based scoring of protein–ligand interactions: explicit polarizability, quantum mechanics and free energies

The ability to accurately predict the interaction of a ligand with its receptor is a key limitation in computer-aided drug design approaches such as virtual screening and de novo design. In this article, we examine current strategies for a physics-based approach to scoring of protein–ligand affinity, as well as outlining recent developments in force fields and quantum chemical techniques. We also consider advances in the development and application of simulation-based free energy methods to study protein–ligand interactions. Fuelled by recent advances in computational algorithms and hardware, there is the opportunity for increased integration of physics-based scoring approaches at earlier stages in computationally guided drug discovery. Specifically, we envisage increased use of implicit solvent models and simulation-based scoring methods as tools for computing the affinities of large virtual ligand libraries. Approaches based on end point simulations and reference potentials allow the application of more advanced potential energy functions to prediction of protein–ligand binding affinities. Comprehensive evaluation of polarizable force fields and quantum mechanical (QM)/molecular mechanical and QM methods in scoring of protein–ligand interactions is required, particularly in their ability to address challenging targets such as metalloproteins and other proteins that make highly polar interactions. Finally, we anticipate increasingly quantitative free energy perturbation and thermodynamic integration methods that are practical for optimization of hits obtained from screened ligand libraries

Computer-based de novo designs of tripeptides as novel neuraminidase inhibitors

The latest influenza A (H1N1) pandemic attracted worldwide attention and called for the urgent development of novel antiviral drugs. Here, seven tripeptides are designed and explored as neuraminidase (NA) inhibitors on the structural basis of known inhibitors. Their interactions with NA are studied and compared with each other, using flexible docking and molecular dynamics simulations. The various composed tripeptides have respective binding specificities and their interaction energies with NA decrease in the order of FRI > FRV > FRT > FHV > FRS > FRG > YRV (letters corresponding to amino acid code). The Arg and Phe portions of the tripeptides play important roles during the binding process: Arg has strong electrostatic interactions with the key residues Asp151, Glu119, Glu227 and Glu277, whereas Phe fits well in the hydrophobic cave within the NA active site. Owing to the introduction of hydrophobic property, the interaction energies of FRV and FRI are larger; in particular, FRI demonstrates the best binding quality and shows potential as a lead compound. In addition, the influence of the chemical states of the terminal amino acids are clarified: it is revealed that the charged states of the N-terminus (NH(3) (+)) and C-terminus (COO(-)) are crucial for the tripeptide inhibitory activities and longer peptides may not be appropriate. In addition, the medium inhibiting activity by acetylation of the N-terminus indicates the possible chemical modifications of FRI. Experimental efforts are expected in order to actualize the tripeptides as potent NA inhibitors in the near future.

MM-GBSA binding free energy decomposition and T cell receptor engineering

Recognition by the T-cell receptor (TCR) of immunogenic peptides (p) presented by class I major histocompatibility complexes (MHC) is the key event in the immune response against virus infected cells or tumor cells. The major determinant of T cell activation is the affinity of the TCR for the peptide-MHC complex, though kinetic parameters are also important. A study of the 2C TCR/SIYR/H-2Kb system using a binding free energy decomposition (BFED) based on the MM-GBSA approach had been performed to assess the performance of the approach on this system. The results showed that the TCR-p-MHC BFED including entropic terms provides a detailed and reliable description of the energetics of the interaction (Zoete and Michielin, 2007). Based on these results, we have developed a new approach to design sequence modifications for a TCR recognizing the human leukocyte antigen (HLA)-A2 restricted tumor epitope NY-ESO-1. NY-ESO-1 is a cancer testis antigen expressed not only in melanoma, but also on several other types of cancers. It has been observed at high frequencies in melanoma patients with unusually positive clinical outcome and, therefore, represents an interesting target for adoptive transfer with modified TCR. Sequence modifications of TCR potentially increasing the affinity for this epitope have been proposed and tested in vitro. T cells expressing some of the proposed TCR mutants showed better T cell functionality, with improved killing of peptide-loaded T2 cells and better proliferative capacity compared to the wild type TCR expressing cells. These results open the door of rational TCR design for adoptive transfer cancer therapy.

Combining crystallographic information and an aspherical-atom data bank in the evaluation of the electrostatic interaction energy in an enzyme-substrate complex: influenza neuraminidase inhibition

Although electrostatic interactions contribute only a part of the interaction energies between macromolecules, unlike dispersion forces they are highly directional and therefore dominate the nature of molecular packing in crystals and in biological complexes and contribute significantly to differences in inhibition strength among related enzyme inhibitors. In the reported study, a wide range of complexes of influenza neuraminidases with inhibitor molecules (sialic acid derivatives and others) have been analyzed using charge densities from a transferable aspherical-atom data bank. The strongest interactions of the residues are with the acidic group at the C2 position of the inhibitor ( approximately -300 kJ mol(-1) for -COO(-) in non-aromatic inhibitors, approximately -120-210 kJ mol(-1) for -COO(-) in aromatic inhibitors and approximately -450 kJ mol(-1) for -PO(3)(2-)) and with the amino and guanidine groups at C4 ( approximately -250 kJ mol(-1)). Other groups contribute less than approximately 100 kJ mol(-1). Residues Glu119, Asp151, Glu227, Glu276 and Arg371 show the largest variation in electrostatic energies of interaction with different groups of inhibitors, which points to their important role in the inhibitor recognition. The Arg292-->Lys mutation reduces the electrostatic interactions of the enzyme with the acidic group at C2 for all inhibitors that have been studied (SIA, DAN, 4AM, ZMR, G20, G28, G39 and BCZ), but enhances the interactions with the glycerol group at C6 for inhibitors that contain it. This is in agreement with the lower level of resistance of the mutated virus to glycerol-containing inhibitors compared with the more hydrophobic derivatives.

Characterizing loop dynamics and ligand recognition in human- and avian-type influenza neuraminidases via generalized born molecular dynamics and end-point free energy calculations

The comparative dynamics and inhibitor binding free energies of group-1 and group-2 pathogenic influenza A subtype neuraminidase (NA) enzymes are of fundamental biological interest and relevant to structure-based drug design studies for antiviral compounds. In this work, we present seven generalized Born molecular dynamics simulations of avian (N1)- and human (N9)-type NAs in order to probe the comparative flexibility of the two subtypes, both with and without the inhibitor oseltamivir bound. The enhanced sampling obtained through the implicit solvent treatment suggests several provocative insights into the dynamics of the two subtypes, including that the group-2 enzymes may exhibit similar motion in the 430-binding site regions but different 150-loop motion. End-point free energy calculations elucidate the contributions to inhibitor binding free energies and suggest that entropic considerations cannot be neglected when comparing across the subtypes. We anticipate the findings presented here will have broad implications for the development of novel antiviral compounds against both seasonal and pandemic influenza strains.

Homology modeling of human transketolase: description of critical sites useful for drug design and study of the cofactor binding mode

Transketolase, the most critical enzyme of the non-oxidative branch of the pentose phosphate pathway, has been reported as a new target protein for cancer research. However, since the crystal structure of human Transketolase is unknown, no structure-based methods can be used to identify new inhibitors. We performed homology modeling of human Transketolase using the crystal structure of yeast as a template, and then refined the model through molecular dynamics simulations. Based on the resulting structure we propose five critical sites containing arginines (Arg 101, Arg 318, Arg 395, Arg 401 and Arg 474) that contribute to dimer stability or catalytic activity. In addition, an interaction analysis of its cofactor (thiamine pyrophosphate) and a binding site description were carried out, suggesting the substrate channel already identified in yeast Transketolase. A binding free energy calculation of its cofactor was performed to establish the main driving forces of binding. In summary, we describe a reliable model of human Transketolase that can be used in structure-based drug design and in the search for new Transketolase inhibitors that disrupt dimer stability and cover the critical sites found.

Electrostatic contributions drive the interaction between Staphylococcus aureus protein Efb-C and its complement target C3d

The C3-inhibitory domain of Staphylococcus aureus extracellular fibrinogen-binding protein (Efb-C) defines a novel three-helix bundle motif that regulates complement activation. Previous crystallographic studies of Efb-C bound to its cognate subdomain of human C3 (C3d) identified Arg-131 and Asn-138 of Efb-C as key residues for its activity. In order to characterize more completely the physical and chemical driving forces behind this important interaction, we employed in this study a combination of structural, biophysical, and computational methods to analyze the interaction of C3d with Efb-C and the single-point mutants R131A and N138A. Our results show that while these mutations do not drastically affect the structure of the Efb-C/C3d recognition complex, they have significant adverse effects on both the thermodynamic and kinetic profiles of the resulting complexes. We also characterized other key interactions along the Efb-C/C3d binding interface and found an intricate network of salt bridges and hydrogen bonds that anchor Efb-C to C3d, resulting in its potent complement inhibitory properties.

Comparative evaluation of MMPBSA and XSCORE to compute binding free energy in XIAP-peptide complexes

Evaluation of binding free energy in receptor-ligand complexes is one of the most important challenges in theoretical drug design. Free energy is directly correlated to the thermodynamic affinity constant, and, as a first step in druglikeness, a lead compound must have this constant in the range of micro- to nanomolar activity. Many efforts have been made to calculate it by rigorous computational approaches, such as free energy perturbation or linear response approximation. However, these methods are still computationally expensive. We focus our work on XIAP, an antiapoptotic protein whose inhibition can lead to new drugs against cancer disease. We report here a comparative evaluation of two completely different methodologies to estimate binding free energy, MMPBSA (a force field based function) and XSCORE (an empirical scoring function), in seven XIAP-peptide complexes using a representative set of structures generated by previous molecular dynamics simulations. Both methods are able to predict the experimental binding free energy with acceptable errors, but if one needs to identify slight differences upon binding, MMPBSA performs better, although XSCORE is not a bad choice taking into account the low computational cost of this method.

Combining docking, scoring and molecular field analyses to probe influenza neuraminidase-ligand interactions

In this project, several docking conditions, scoring functions and corresponding protein-aligned molecular field analysis (CoMFA) models were evaluated for a diverse set of neuraminidase (NA) inhibitors. To this end, a group of inhibitors were docked into the active site of NA. The docked structures were utilized to construct a corresponding protein-aligned CoMFA models by employing probe-based (H+, OH, CH3) energy grids and genetic partial least squares (G/PLS) statistical analysis. A total of 16 different docking configurations were evaluated, of which some succeeded in producing self-consistent and predictive CoMFA models. However, the best model coincided with docking the ionized ligands into the hydrated form of the binding site via PLP1 scoring function (r2LOO=0.735, r2PRESS against 24 test compounds=0.828). The highest-ranking CoMFA models were employed to probe NA-ligand interactions. Further validation by comparison with a co-crystallized ligand-NA crystallographic structure was performed. This combination of docking/scoring/CoMFA modeling provided interesting insights into the binding of different NA inhibitors.

Analytical electrostatics for biomolecules: Beyond the generalized Born approximation

The modeling and simulation of macromolecules in solution often benefits from fast analytical approximations for the electrostatic interactions. In our previous work [G. Sigalov et al., J. Chem. Phys. 122, 094511 (2005)], we proposed a method based on an approximate analytical solution of the linearized Poisson-Boltzmann equation for a sphere. In the current work, we extend the method to biomolecules of arbitrary shape and provide computationally efficient algorithms for estimation of the parameters of the model. This approach, which we tentatively call ALPB here, is tested against the standard numerical Poisson-Boltzmann (NPB) treatment on a set of 579 representative proteins, nucleic acids, and small peptides. The tests are performed across a wide range of solvent/solute dielectrics and at biologically relevant salt concentrations. Over the range of the solvent and solute parameters tested, the systematic deviation (from the NPB reference) of solvation energies computed by ALPB is 0.5-3.5 kcal/mol, which is 5-50 times smaller than that of the conventional generalized Born approximation widely used in this context. At the same time, ALPB is equally computationally efficient. The new model is incorporated into the AMBER molecular modeling package and tested on small proteins.

Molecular mechanics methods for predicting protein–ligand binding

Ligand binding affinity prediction is one of the most important applications of computational chemistry. However, accurately ranking compounds with respect to their estimated binding affinities to a biomolecular target remains highly challenging. We provide an overview of recent work using molecular mechanics energy functions to address this challenge. We briefly review methods that use molecular dynamics and Monte Carlo simulations to predict absolute and relative ligand binding free energies, as well as our own work in which we have developed a physics-based scoring method that can be applied to hundreds of thousands of compounds by invoking a number of simplifying approximations. In our previous studies, we have demonstrated that our scoring method is a promising approach for improving the discrimination between ligands that are known to bind and those that are presumed not to, in virtual screening of large compound databases. In new results presented here, we explore several improvements to our computational method including modifying the dielectric constant used for the protein and ligand interiors, and empirically scaling energy terms to compensate for deficiencies in the energy model. Future directions for further improving our physics-based scoring method are also discussed.