Protein-ligand Interaction Energy Research Articles

A hierarchical method for molecular docking using cloud computing

Discovering small molecules that interact with protein targets will be a key part of future drug discovery efforts. Molecular docking of drug-like molecules is likely to be valuable in this field; however, the great number of such molecules makes the potential size of this task enormous. In this paper, a method to screen small molecular databases using cloud computing is proposed. This method is called the hierarchical method for molecular docking and can be completed in a relatively short period of time. In this method, the optimization of molecular docking is divided into two subproblems based on the different effects on the protein–ligand interaction energy. An adaptive genetic algorithm is developed to solve the optimization problem and a new docking program (FlexGAsDock) based on the hierarchical docking method has been developed. The implementation of docking on a cloud computing platform is then discussed. The docking results show that this method can be conveniently used for the efficient molecular design of drugs.

An Efficient Method to Evaluate Intermolecular Interaction Energies in Large Systems Using Overlapping Multicenter ONIOM and the Fragment Molecular Orbital Method

We propose an approach based on the overlapping multicenter ONIOM to evaluate intermolecular interaction energies in large systems and demonstrate its accuracy on several representative systems in the complete basis set limit at the MP2 and CCSD(T) level of theory. In the application to the intermolecular interaction energy between insulin dimer and 4'-hydroxyacetanilide at the MP2/CBS level, we use the fragment molecular orbital method for the calculation of the entire complex assigned to the lowest layer in three-layer ONIOM. The developed method is shown to be efficient and accurate in the evaluation of the protein-ligand interaction energies.

In silico studies of Echinococcus granulosus FABPs

Fatty acid (FA) binding proteins are small intracellular proteins whose members exhibit great diversity and low similarity at the primary structure level, but a highly conserved three-dimensional structure. Characterised by a high-affinity non-covalent binding of hydrophobic ligands, these proteins have a molecular mass of 14–15 kDa with a characteristic β-barrel structure. Members of this family have been identified along the zoological scale, with Platyhelminthes being the more primitive organisms where they have been reported. Two FA binding proteins (FABPs), EgFABP1 and EgFABP2, with 88% similarity have been identified in Echinococcus granulosus. In an effort to understand why two such similar proteins are expressed by this organism, we performed an in silico analysis of the binding capabilities of both proteins. The crystallographic structure of EgFABP1 was utilised as a template to model EgFABP2, and both were docked against palmitate, oleate, linoleate and arachidonate. The docked structures were submitted to 4 ns molecular dynamics simulations, and their protein–ligand interaction energies were measured. The collected data demonstrated that linoleate and arachidonate had the higher interaction energies when bound to EgFABP1 and that palmitate and linoleate had the higher interaction energies when bound to EgFABP2. External and internal binding surfaces were analysed, showing differences at both levels. Internal surface compositions suggested that both proteins could have preferences for certain FAs. Comparisons of the holo and apo forms of each protein indicated that the ligand imposed subtle, but specific modifications that could trigger surface signals. The differences found between the proteins under study suggest that they could have functional uniqueness in the parasite's metabolism.

Correlation between biological activity and binding energy in systems of integrin with cyclic RGD-containing binders: a QM/MM molecular dynamics study

We here report a combined quantum mechanical/molecular mechanical (QM/MM) molecular dynamics (MD) study on the binding interactions between the α(V)β₃ integrin and eight cyclic arginine-glycine-aspartate (RGD) containing peptides. The initial conformation of each peptide within the binding site of the integrin was determined by docking the ligand to the reactive site of the integrin crystal structure with the aid of docking software FRED. The subsequent QM/MM MD simulations of the complex structures show that these eight cyclic RGD-peptides have a generally similar interaction mode with the binding site of the integrin to the cyclo(RGDf-N[M]V) analog found in the crystal structure. Still, there are subtle differences in the interactions of peptide ligands with the integrin, which contribute to the different inhibition activities. The averaged QM/MM protein-ligand interaction energy (IE) is remarkably correlated to the biological activity of the ligand. The IE, as well as a three-variable model which is somewhat interpretable, thus can be used to predict the bioactivity of a new ligand quantitatively, at least within a family of analogs. The present study establishes a helpful protocol for advancing lead compounds to potent inhibitors.

In silico designing and characterization of Amiloride derivatives as ion channel modulator

The purpose of this study is to design and characterize derivatives of Amiloride, an ion channel modulating agent with the help of computing softwares and docking procedures. Amiloride is a potassium-sparing diuretics used in the management of hypertension and congestive heart failure. It works directly by blocking the epithelial sodium channel which results in inhibiting sodium reabsorption in the distal convoluted tubules, connecting and collecting ducts in the kidney. It results in promotion of the loss of sodium and water from the body. Molecular docking approaches are routinely used in modern drug design to help understand drug–receptor interaction. This study has been performed with the help of Chemdraw Ultra 7.0, ArgusLab softwares, Molegro Virtual Docker, Molinspiron online software, MedChem Designer, EPA DSSTox Structure Browser v2.0 by docking procedures. Results reveal that the protein–ligand interaction energy of derivatives AM12 and AM14 were −119.111 and −126.706 which is better than the original Amiloride molecule (CID no. 16231) as −118.965, so the derivatives which better binds with the urokinase plasminogen activator may be used in concern with cancer and vascular diseases as it links with the proteolysis cascade.

Fully ab initio protein‐ligand interaction energies with dispersion corrected density functional theory

Dispersion corrected density functional theory (DFT-D3) is used for fully ab initio protein-ligand (PL) interaction energy calculation via molecular fractionation with conjugated caps (MFCC) and applied to PL complexes from the PDB comprising 3680, 1798, and 1060 atoms. Molecular fragments with n amino acids instead of one in the original MFCC approach are considered, thereby allowing for estimating the three-body and higher many-body terms. n > 1 is recommended both in terms of accuracy and efficiency of MFCC. For neutral protein side-chains, the computed PL interaction energy is visibly independent of the fragment length n. The MFCC fractionation error is determined by comparison to a full-system calculation for the 1060 atoms containing PL complex. For charged amino acid side-chains, the variation of the MFCC result with n is increased. For these systems, using a continuum solvation model with a dielectricity constant typical for protein environments (ϵ = 4) reduces both the variation with n and improves the stability of the DFT calculations considerably. The PL interaction energies for two typical complexes obtained ab initio for the first time are found to be rather large (-30 and -54 kcal/mol).

Polarization effects in protein–ligand calculations extend farther than the actual induction energy

The various roles that polarizabilities play in the calculation of protein–ligand interaction energies with a polarizable force field are investigated, and the importance and distance dependence of some common approximations is determined for each of these roles separately, using quantum-mechanical calculations as the reference. It is found that the pure induction energy, if defined as the energetic gain from the charge redistribution upon interaction between the protein and ligand, is a rather short-ranged effect that becomes independent of the exact implementation at distances above ∼4 A. On the other hand, the polarization between the protein residues in the assembly of a protein from separately computed fragments (as is routinely done in force field development) has a significant effect on the computed interaction energies, even for residues as far as 15 A from the ligand. Finally, polarization improves the transferability of partial charges, but the simple polarization model used in, for example, the Amber force field explains only 14–19% of the conformational variation of the charges. In all cases, more advanced polarization models, especially involving anisotropic polarizabilities, seem to give significantly better descriptions of these effects. The study suggests that an accurate treatment of polarization can be important even in systems where the actual induction energy is small in magnitude.

Trans-Disciplinary Protein Binding Of Drug: Docking Approach

AbstractThe purpose of present work is to introduce the binding of existing drugs to the trans-disciplinary protein with the help of docking procedures. Docking procedures allows virtually screening a database of compounds and predict the strongest binder based on various scoring functions. This work has been performed with the help of Molegro Virtual docker, in which two drugs are docked with their corresponding and non-corresponding (trans-disciplinary) protein. Results reveals that the protein-ligand interaction energy, hydrogen bond energy and MolDock scores provided by molegro virtual docker, between legands (Levodopa and Acyclovir) and trans-disciplinary proteins have been equivalent or even better than that of between legands and corresponding proteins.

Protein–Ligand Interaction Energies with Dispersion Corrected Density Functional Theory and High-Level Wave Function Based Methods

With dispersion-corrected density functional theory (DFT-D3) intermolecular interaction energies for a diverse set of noncovalently bound protein-ligand complexes from the Protein Data Bank are calculated. The focus is on major contacts occurring between the drug molecule and the binding site. Generalized gradient approximation (GGA), meta-GGA, and hybrid functionals are used. DFT-D3 interaction energies are benchmarked against the best available wave function based results that are provided by the estimated complete basis set (CBS) limit of the local pair natural orbital coupled-electron pair approximation (LPNO-CEPA/1) and compared to MP2 and semiempirical data. The size of the complexes and their interaction energies (ΔE(PL)) varies between 50 and 300 atoms and from -1 to -65 kcal/mol, respectively. Basis set effects are considered by applying extended sets of triple- to quadruple-ζ quality. Computed total ΔE(PL) values show a good correlation with the dispersion contribution despite the fact that the protein-ligand complexes contain many hydrogen bonds. It is concluded that an adequate, for example, asymptotically correct, treatment of dispersion interactions is necessary for the realistic modeling of protein-ligand binding. Inclusion of the dispersion correction drastically reduces the dependence of the computed interaction energies on the density functional compared to uncorrected DFT results. DFT-D3 methods provide results that are consistent with LPNO-CEPA/1 and MP2, the differences of about 1-2 kcal/mol on average (<5% of ΔE(PL)) being on the order of their accuracy, while dispersion-corrected semiempirical AM1 and PM3 approaches show a deviating behavior. The DFT-D3 results are found to depend insignificantly on the choice of the short-range damping model. We propose to use DFT-D3 as an essential ingredient in a QM/MM approach for advanced virtual screening approaches of protein-ligand interactions to be combined with similarly "first-principle" accounts for the estimation of solvation and entropic effects.

Transferability of conformational dependent charges from protein simulations

AbstractWe have studied the transferability of atomic charges for proteins, fitted to the quantum mechanical electrostatic potential and extensively averaged over a set of structures sampled by molecular dynamics (MD) and over all residues of the same kind in the protein sequence (xAvESP). Previously, such charges were obtained for one single protein (avidin). In this study, we use five additional proteins. The aim of this study is fourfold. First, we provide xAvESP charges for all amino acids, including amino‐ and carboxy‐terminal variants of all, as well as alternative protonation states of His, Asp, Glu, Lys, Arg, Cys, and Tyr. Second, we show that the xAvESP charges averaged over the five new proteins are similar to charges obtained in the same way for avidin, with a correlation coefficient of 0.997. This shows that the charges are transferable and system‐independent. Electrostatic protein–ligand interaction energies calculated with charges obtained from different proteins differ by only 1–3 kJ/mol on average. The xAvESP charges correlate rather well with Amber charges (except for the N atom of amino‐terminal residues that are erroneous in Amber), although they are obtained in a more general way. Third, the conformational dependence of the charges is significant and gives rise to quite large differences in energies. However, these differences are to a large extent screened by solvation effects. For example, the solvent‐screened electrostatic interaction energy between the protein galectin‐3 and five different ligands varies with the charge sets by less than 3 kJ/mol on average. Finally, we show that the xAvESP charges give a comparable root‐mean‐squared deviation as the Amber charges for the MD simulations of 18 protein–ligand complexes, they give comparable or slightly worse backbone NH order parameters for two galectin‐3 complexes, but they give a better correlation between calculated and experimental affinities for the binding of seven biotin analogues to avidin and for nine inhibitors of factor Xa. © 2011 Wiley Periodicals, Inc. Int J Quantum Chem 112:1768–1785, 2012

Prediction of cyclin-dependent kinase 2 inhibitor potency using the fragment molecular orbital method.

BackgroundThe reliable and robust estimation of ligand binding affinity continues to be a challenge in drug design. Many current methods rely on molecular mechanics (MM) calculations which do not fully explain complex molecular interactions. Full quantum mechanical (QM) computation of the electronic state of protein-ligand complexes has recently become possible by the latest advances in the development of linear-scaling QM methods such as the ab initio fragment molecular orbital (FMO) method. This approximate molecular orbital method is sufficiently fast that it can be incorporated into the development cycle during structure-based drug design for the reliable estimation of ligand binding affinity. Additionally, the FMO method can be combined with approximations for entropy and solvation to make it applicable for binding affinity prediction for a broad range of target and chemotypes.ResultsWe applied this method to examine the binding affinity for a series of published cyclin-dependent kinase 2 (CDK2) inhibitors. We calculated the binding affinity for 28 CDK2 inhibitors using the ab initio FMO method based on a number of X-ray crystal structures. The sum of the pair interaction energies (PIE) was calculated and used to explain the gas-phase enthalpic contribution to binding. The correlation of the ligand potencies to the protein-ligand interaction energies gained from FMO was examined and was seen to give a good correlation which outperformed three MM force field based scoring functions used to appoximate the free energy of binding. Although the FMO calculation allows for the enthalpic component of binding interactions to be understood at the quantum level, as it is an in vacuo single point calculation, the entropic component and solvation terms are neglected. For this reason a more accurate and predictive estimate for binding free energy was desired. Therefore, additional terms used to describe the protein-ligand interactions were then calculated to improve the correlation of the FMO derived values to experimental free energies of binding. These terms were used to account for the polar and non-polar solvation of the molecule estimated by the Poisson-Boltzmann equation and the solvent accessible surface area (SASA), respectively, as well as a correction term for ligand entropy. A quantitative structure-activity relationship (QSAR) model obtained by Partial Least Squares projection to latent structures (PLS) analysis of the ligand potencies and the calculated terms showed a strong correlation (r2 = 0.939, q2 = 0.896) for the 14 molecule test set which had a Pearson rank order correlation of 0.97. A training set of a further 14 molecules was well predicted (r2 = 0.842), and could be used to obtain meaningful estimations of the binding free energy.ConclusionsOur results show that binding energies calculated with the FMO method correlate well with published data. Analysis of the terms used to derive the FMO energies adds greater understanding to the binding interactions than can be gained by MM methods. Combining this information with additional terms and creating a scaled model to describe the data results in more accurate predictions of ligand potencies than the absolute values obtained by FMO alone.

Polarizable Atomic Multipole X-Ray Refinement: Hydration Geometry and Application to Macromolecules

We recently developed a polarizable atomic multipole refinement method assisted by the AMOEBA force field for macromolecular crystallography. Compared to standard refinement procedures, the method uses a more rigorous treatment of x-ray scattering and electrostatics that can significantly improve the resultant information contained in an atomic model. We applied this method to high-resolution lysozyme and trypsin data sets, and validated its utility for precisely describing biomolecular electron density, as indicated by a 0.4–0.6% decrease in the R- and Rfree-values, and a corresponding decrease in the relative energy of 0.4–0.8 Kcal/mol/residue. The re-refinements illustrate the ability of force-field electrostatics to orient water networks and catalytically relevant hydrogens, which can be used to make predictions regarding active site function, activity, and protein-ligand interaction energies. Re-refinement of a DNA crystal structure generates the zigzag spine pattern of hydrogen bonding in the minor groove without manual intervention. The polarizable atomic multipole electrostatics model implemented in the AMOEBA force field is applicable and informative for crystal structures solved at any resolution.

Limits of Free Energy Computation for Protein-Ligand Interactions.

A detailed error analysis is presented for the computation of protein-ligand interaction energies. In particular, we show that it is probable that even highly accurate computed binding free energies have errors that represent a large percentage of the target free energies of binding. This is due to the observation that the error for computed energies quasi-linearly increases with the increasing number of interactions present in a protein-ligand complex. This principle is expected to hold true for any system that involves an ever increasing number of inter or intra-molecular interactions (e.g. ab initio protein folding). We introduce the concept of best-case scenario errors (BCS(errors)) that can be routinely applied to docking and scoring exercises and used to provide errors bars for the computed binding free energies. These BCS(errors) form a basis by which one can evaluate the outcome of a docking and scoring exercise. Moreover, the resultant error analysis enables the formation of an hypothesis that defines the best direction to proceed in order to improve scoring functions used in molecular docking studies.

Effects of Ligand Density on Hydrophobic Charge Induction Chromatography: Molecular Dynamics Simulation

High ligand density is usually required in hydrophobic charge induction chromatography (HCIC) for high adsorption capacity. However, it is not clear to what extent the ligand density alters the adsorption and desorption behaviors, or if this leads to the protein conformational transition within adsorbent pores. In the present study, molecular dynamics simulation is performed to examine the effects of ligand density in HCIC using a 46-bead beta-barrel coarse-grained model protein and a coarse-grained adsorbent pore model established in our earlier work. Four ligand densities (1.474, 1.769, 2.212, and 2.949 micromol/m(2)) are simulated at 298.15 K. The simulations indicate that both the capacity and irreversibility of adsorption increase with ligand density. However, it is found that the fastest adsorption occurs at a ligand density of 2.212 micromol/m(2) rather than at the highest density studied. Analyses of adsorption trajectories, protein-ligand interaction energy, and the free energy map indicate that there is repulsion of protein when unfavorable contacts of the protein and ligands occur. There is an enhanced repulsion at 2.949 micromol/m(2), which increases the energy barrier to the transition region and reduces the opportunities to get stable adsorption, thus leading to the decreased adsorption rate. At 2.212 micromol/m(2), however, the repulsion is mild and the high ligand coverage provides abundant opportunities for the protein to get the fastest adsorption and thus causes the maximum unfolding. In the following simulations, complete and irreversible desorption is observed at all ligand densities, in agreement with the easy pH-induced elution behavior of HCIC observed experimentally. It is found that there is a suitable balance between hydrophobic attraction and electrostatic repulsion at 2.212 micromol/m(2), which leads to the slowest desorption kinetics and causes the maximum unfolding. Moreover, analysis of unfolded protein distribution indicates that unfolding occurs mainly on the ligand surface in both adsorption and desorption. The behaviors have been comprehensively elucidated by molecular and thermodynamic analyses.

Aromatic Interactions in the Binding of Ligands to HMGCoA Reductase

3-Hydroxy-3-methyglutaryl-coenzyme A (HMGCoA) reductase is the enzyme that catalyzes the rate-determining step in cholesterol synthesis; it is also the target for statin drugs, which are competitive inhibitors of the enzyme. We examine potentially important enzyme-ligand interactions currently not incorporated into statin drug design: weak, induction/dispersion interactions between ligands and residue tyrosine 479 in the HMGCoA reductase active site. HMGCoA is a large molecule with a long coenzyme A "tail", and in order to study the interactions of interest, it was necessary to find the smallest possible portion of the HMGCoA molecule that would serve as a reasonable model for the entire molecule. Using this minimal model, we calculated BSSE-corrected electronic interaction energies between the residue and the ligand molecule using several DFT methods (local, hybrid, and gradient-corrected DFT methods) as well as MP2. We also performed several in silico mutations of the tyrosine 479 residue to determine the potential effects of these changes on protein-ligand interaction energies. Our work shows that this previously unexploited protein-ligand interaction between tyrosine residue 479 and HMGCoA can be important in the design of future statin drugs. Per our previous work, our results show that local DFT methods more closely match MP2 energy values for aromatic binding than do hybrid or gradient-corrected DFT methods.

Elastic Potential Grids: Accurate and Efficient Representation of Intermolecular Interactions for Fully Flexible Docking

Elastic Potential Grids: Accurate and Efficient Representation of Intermolecular Interactions for Fully Flexible Docking

Combining a genetic algorithm with a linear scaling semiempirical method for protein–ligand docking

In the search for more energetically precise docking methods and to avoid the problem of defining suitable ligand force fields, we have built a software to perform rigid molecular docking by combining a genetic algorithm with a linear scaling semiempirical program. The resulting AlgoGen-DivCon software uses the Divide and Conquer linear scaling methodology to evaluate protein–ligand interaction energies using quantum semiempirical hamiltonians while conformational search is performed by a genetic algorithm which optimizes the position of a mobile ligand relative to a fixed protein system. Tests have been performed to assess the performance of AlgoGen-DivCon. Our results on the docking of 8-azaxanthine, oxonic acid and uric acid in a 700 atom Urate Oxidase model are structurally very similar to known crystallographic structures and prove that it is now possible to perform in a reasonable amount of time molecular dockings of relatively large protein–ligand complexes using a quantum description of the interaction energy.

A fast method of large-scale serial semiempirical calculations of docking complexes

A brief survey of the state of the art in methods of calculations of protein—ligand interaction energies in docking complexes is presented. A new computational technique is proposed that allows one to fundamentally improve the performance of large-scale serial calculations of docking complexes using the AM1/PM3 semiempirical methods. The technique explicitly allows for a specific feature of docking problems, viz., the need for calculating numerous ligand complexes with a specified protein whose noninteracting part remains “frozen” during computations. The interaction energies calculated using the new method differ only slightly from the results of complete AM1 calculations and the performance attained is high enough to solve practical drug design problems.

A Quantum Mechanic/Molecular Mechanic Study of the Wild-Type and N155S Mutant HIV-1 Integrase Complexed with Diketo Acid

Integrase (IN) is one of the three human immunodeficiency virus type 1 (HIV-1) enzymes essential for effective viral replication. Recently, mutation studies have been reported that have shown that a certain degree of viral resistance to diketo acids (DKAs) appears when some amino acid residues of the IN active site are mutated. Mutations represent a fascinating experimental challenge, and we invite theoretical simulations for the disclosure of still unexplored features of enzyme reactions. The aim of this work is to understand the molecular mechanisms of HIV-1 IN drug resistance, which will be useful for designing anti-HIV inhibitors with unique resistance profiles. In this study, we use molecular dynamics simulations, within the hybrid quantum mechanics/molecular mechanics (QM/MM) approach, to determine the protein-ligand interaction energy for wild-type and N155S mutant HIV-1 IN, both complexed with a DKA. This hybrid methodology has the advantage of the inclusion of quantum effects such as ligand polarization upon binding, which can be very important when highly polarizable groups are embedded in anisotropic environments, for example in metal-containing active sites. Furthermore, an energy terms decomposition analysis was performed to determine contributions of individual residues to the enzyme-inhibitor interactions. The results reveal that there is a strong interaction between the Lys-159, Lys-156, and Asn-155 residues and Mg2+ cation and the DKA inhibitor. Our calculations show that the binding energy is higher in wild-type than in the N155S mutant, in accordance with the experimental results. The role of the mutated residue has thus been checked as maintaining the structure of the ternary complex formed by the protein, the Mg2+ cation, and the inhibitor. These results might be useful to design compounds with more interesting anti-HIV-1 IN activity on the basis of its three-dimensional structure.

Computational design of novel cyclic urea as HIV-1 protease inhibitor

AbstractA series of novel cyclic urea molecules 5,6-dihydroxy-1,3-diazepane-2,4,7-trione as HIV-1 protease inhibitors were designed using computational techniques. The designed molecules were compared with the known cyclic urea molecules by performing docking studies, calculating their ADME (Absorption, Distribution, Metabolism, and Excretion) properties and protein ligand interaction energy. These novel molecules were designed by substituting the P 1/P′ 1 positions (4th and 7th position of 1, 3-diazepan-2-one) with double bonded oxygens. This reduces the molecular weight and increases the bioavailability, indicating better ADME properties. The docking studies showed good binding affinity towards HIV-1 protease. The biological activity of these inhibitors were predicted by a model equation generated by the regression analysis between biological activity (log 1/K i ) of known inhibitors and their protein ligand interaction energy. The synthetic studies are in progress.