ParDOCK: an all atom energy based Monte Carlo docking protocol for protein-ligand complexes

We report here an all-atom energy based Monte Carlo docking procedure tested on a dataset of 226 protein-ligand complexes. Average root mean square deviation (RMSD) from crystal conformation was observed to be approximately 0.53 A. The correlation coefficient (r(2)) for the predicted binding free energies calculated using the docked structures against experimental binding affinities was 0.72. The docking protocol is web-enabled as a free software at www.scfbio-iitd.res.in/dock.     

Prediction of estrogen receptor β ligands potency and selectivity by docking and MM-GBSA scoring methods using three different scaffolds

This study aimed to identify the docking and molecular mechanics-generalized born surface area (MM-GBSA) re-scoring parameters which can correlate the binding affinity and selectivity of the ligands towards oestrogen receptor β (ERβ). Three different series of ERβ ligands were used as dataset and the compounds were docked against ERβ (protein data bank (PDB) ID: 1QKM) using Glide and ArgusLab. Glide docking showed superior results when compared with ArgusLab. Docked poses were then rescored using Prime-MM-GBSA to calculate free energy binding. Correlations were made between observed activities of ERβ ligands with computationally predicted values from docking, binding energy parameters. ERβ ligands experimental binding affinity/selectivity did not correlate well with Glide and ArgusLab score. Whereas calculated Glide energy (coulomb-van der Waal interaction energy) correlated significantly with binding affinity of ERβ ligands (r²?=?0.66). MM-GBSA re-scoring showed correlation of r²?=?0.74 with selectivity of ERβ ligands. These results will aid the discovery of novel ERβ ligands with isoform selectivity.     

Validation of an automated procedure for the prediction of relative free energies of binding on a set of aldose reductase inhibitors

Among the available methods for predicting free energies of binding of ligands to a protein, the molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) and molecular mechanics generalized Born surface area (MM-GBSA) approaches have been validated for a relatively limited number of targets and compounds in the training set. Here, we report the results of an extensive study on a series of 28 inhibitors of aldose reductase with experimentally determined crystal structures and inhibitory activities, in which we evaluate the ability of MM-PBSA and MM-GBSA methods in predicting binding free energies using a number of different simulation conditions. While none of the methods proved able to predict absolute free energies of binding in quantitative agreement with the experimental values, calculated and experimental free energies of binding were significantly correlated. Comparing the predicted and experimental ΔG of binding, MM-PBSA proved to perform better than MM-GBSA, and within the MM-PBSA methods, the PBSA of Amber performed similarly to Delphi. In particular, significant relationships between experimental and computed free energies of binding were obtained using Amber PBSA and structures minimized with a distance-dependent dielectric function. Importantly, while free energy predictions are usually made on large collections of equilibrated structures sampled during molecular dynamics in water, we have found that a single minimized structure is a reasonable approximation if relative free energies of binding are to be calculated. This finding is particularly relevant, considering that the generation of equilibrated MD ensembles and the subsequent free energy analysis on multiple snapshots is computationally intensive, while the generation and analysis of a single minimized structure of a protein–ligand complex is relatively fast, and therefore suited for high-throughput virtual screening studies. At this aim, we have developed an automated workflow that integrates all the necessary steps required to generate structures and calculate free energies of binding. The procedure is relatively fast and able to screen automatically and iteratively molecules contained in databases and libraries of compounds. Taken altogether, our results suggest that the workflow can be a valuable tool for ligand identification and optimization, being able to automatically and efficiently refine docking poses, which sometimes may not be accurate, and rank the compounds based on more accurate scoring functions.     

Investigation into mechanism of orotidine 5′-monophosphate decarboxylase enzyme by MM-PBSA/MM-GBSA and molecular docking

In this work, we studied the binding affinity of orotidine 5′-monophosphate (OMP) and 6-hydroxy-UMP (BMP) for Saccharomyces cerevisiae orotidine 5′-monophosphate decarboxylase (OMPDC) enzyme by using Molecular Mechanics-Poisson–Boltzmann Surface Area (MM-PBSA) and the Molecular Mechanics-Generalised Born Surface Area (MM-GBSA) calculations. In all simulations, Asp91, which is an important residue in the enzyme active site, was considered in both anionic (present in the native form of the enzyme) and neutral states. A series of 10-ns molecular dynamics simulations were performed for the four OMPDC–ligand complexes, two ligand-free enzymes and two free ligands, followed by MM-PBSA/MM-GBSA calculations on the collected snapshots, and molecular docking calculations using the free enzymes and ligands. The results of MM-PBSA/MM-GBSA calculations indicate that all of the OMPDC–ligand complexes form favourable systems in water, which is in agreement with corresponding experimental data. The results of the MM-PBSA and molecular docking methods also showed that OMPDC–BMP complexes, transition state analogue and inhibitor of the OMPDC enzyme have the highest binding affinities. The fact that in the native anionic state BMP shows a higher binding affinity compared with the substrate suggests the contribution of a transition state stabilisation mechanism in the debatable catalytic mechanism of the OMPDC enzyme.     

Accounting for Ligand Conformational Restriction in Calculations of Protein-Ligand Binding Affinities

The conformation adopted by a ligand on binding to a receptor may differ from its lowest-energy conformation in solution. In addition, the bound ligand is more conformationally restricted, which is associated with a configurational entropy loss. The free energy change due to these effects is often neglected or treated crudely in current models for predicting binding affinity. We present a method for estimating this contribution, based on perturbation theory using the quasi-harmonic model of Karplus and Kushick as a reference system. The consistency of the method is checked for small model systems. Subsequently we use the method, along with an estimate for the enthalpic contribution due to ligand-receptor interactions, to calculate relative binding affinities. The AMBER force field and generalized Born implicit solvent model is used. Binding affinities were estimated for a test set of 233 protein-ligand complexes for which crystal structures and measured binding affinities are available. In most cases, the ligand conformation in the bound state was significantly different from the most favorable conformation in solution. In general, the correlation between measured and calculated ligand binding affinities including the free energy change due to ligand conformational change is comparable to or slightly better than that obtained by using an empirically-trained docking score. Both entropic and enthalpic contributions to this free energy change are significant.     

Computational analyses of interactions between ALK-5 and bioactive ligands: insights for the design of potential anticancer agents

Activin Receptor-Like Kinase 5 (ALK-5) is related to some types of cancer, such as breast, lung, and pancreas. In this study, we have used molecular docking, molecular dynamics simulations, and free energy calculations in order to explore key interactions between ALK-5 and six bioactive ligands with different ranges of biological activity. The motivation of this work is the lack of crystal structure for inhibitor–protein complexes for this set of ligands. The understanding of the molecular structure and the protein–ligand interaction could give support for the development of new drugs against cancer. The results show that the calculated binding free energy using MM-GBSA, MM-PBSA, and SIE is correlated with experimental data with r² = 0.88, 0.80, and 0.94, respectively, which indicates that the calculated binding free energy is in excellent agreement with experimental data. In addition, the results demonstrate that H bonds with Lys232, Glu245, Tyr249, His283, Asp351, and one structural water molecule play an important role for the inhibition of ALK-5. Overall, we discussed the main interactions between ALK-5 and six inhibitors that may be used as starting points for designing new molecules to the treatment of cancer.     

Estimating binding affinities by docking/scoring methods using variable protonation states

To investigate the effects of multiple protonation states on protein-ligand recognition, we generated alternative protonation states for selected titratable groups of ligands and receptors. The selection of states was based on the predicted pK(a) of the unbound receptor and ligand and the proximity of titratable groups of the receptor to the binding site. Various ligand tautomer states were also considered. An independent docking calculation was run for each state. Several protocols were examined: using an ensemble of all generated states of ligand and receptor, using only the most probable state of the unbound ligand/receptor, and using only the state giving the most favorable docking score. The accuracies of these approaches were compared, using a set of 176 protein-ligand complexes (15 receptors) for which crystal structures and measured binding affinities are available. The best agreement with experiment was obtained when ligand poses from experimental crystal structures were used. For 9 of 15 receptors, using an ensemble of all generated protonation states of the ligand and receptor gave the best correlation between calculated and measured affinities.     

Evaluation of protein–ligand affinity prediction using steered molecular dynamics simulations

In computational drug design, ranking a series of compound analogs in a manner that is consistent with experimental affinities remains a challenge. In this study, we evaluated the prediction of protein–ligand binding affinities using steered molecular dynamics simulations. First, we investigated the appropriate conditions for accurate predictions in these simulations. A conic harmonic restraint was applied to the system for efficient sampling of work values on the ligand unbinding pathway. We found that pulling velocity significantly influenced affinity predictions, but that the number of collectable trajectories was less influential. We identified the appropriate pulling velocity and collectable trajectories for binding affinity predictions as 1.25 Å/ns and 100, respectively, and these parameters were used to evaluate three target proteins (FK506 binding protein, trypsin, and cyclin-dependent kinase 2). For these proteins using our parameters, the accuracy of affinity prediction was higher and more stable when Jarzynski’s equality was employed compared with the second-order cumulant expansion equation of Jarzynski’s equality. Our results showed that steered molecular dynamics simulations are effective for predicting the rank order of ligands; thus, they are a potential tool for compound selection in hit-to-lead and lead optimization processes.     

Deciphering the Glycan Preference of Bacterial Lectins by Glycan Array and Molecular Docking with Validation by Microcalorimetry and Crystallography

Recent advances in glycobiology revealed the essential role of lectins for deciphering the glycocode by specific recognition of carbohydrates. Integrated multiscale approaches are needed for characterizing lectin specificity: combining on one hand high-throughput analysis by glycan array experiments and systematic molecular docking of oligosaccharide libraries and on the other hand detailed analysis of the lectin/oligosaccharide interaction by x-ray crystallography, microcalorimetry and free energy calculations. The lectins LecB from Pseudomonas aeruginosa and BambL from Burkholderia ambifaria are part of the virulence factors used by the pathogenic bacteria to invade the targeted host. These two lectins are not related but both recognize fucosylated oligosaccharides such as the histo-blood group oligosaccharides of the ABH(O) and Lewis epitopes. The specificities were characterized using semi-quantitative data from glycan array and analyzed by molecular docking with the Glide software. Reliable prediction of protein/oligosaccharide structures could be obtained as validated by existing crystal structures of complexes. Additionally, the crystal structure of BambL/Lewis x was determined at 1.6 Å resolution, which confirms that Lewis x has to adopt a high-energy conformation so as to bind to this lectin. Free energies of binding were calculated using a procedure combining the Glide docking protocol followed by free energy rescoring with the Prime/Molecular Mechanics Generalized Born Surface Area (MM-GBSA) method. The calculated data were in reasonable agreement with experimental free energies of binding obtained by titration microcalorimetry. The established predictive protocol is proposed to rationalize large sets of data such as glycan arrays and to help in lead discovery projects based on such high throughput technology.     

Structure-activity relationship for extracellular block of K+ channels by tetraalkylammonium ions

External tetraalkylammonium ion binding to potassium channels is studied using microscopic molecular modelling methods and the experimental structure of the KcsA channel. Relative binding free energies of the KcsA complexes with Me4N+, Et4N+, and n-Pr4N+ are calculated with the molecular dynamics free energy perturbation approach together with automated ligand docking. The four-fold symmetry of the entrance cavity formed by the Tyr82 residues is found to provide stronger binding for the D2d than for the S4 conformation of the ligands. In agreement with experiment the Et4N+ blocker shows several kcal/mol better binding than the other tetraalkylammonium ions.     

An In Silico Analysis of the Binding Modes and Binding Affinities of Small Molecule Modulators of PDZ-Peptide Interactions

Inhibitors of PDZ-peptide interactions have important implications in a variety of biological processes including treatment of cancer and Parkinson’s disease. Even though experimental studies have reported characterization of peptidomimetic inhibitors of PDZ-peptide interactions, the binding modes for most of them have not been characterized by structural studies. In this study we have attempted to understand the structural basis of the small molecule-PDZ interactions by in silico analysis of the binding modes and binding affinities of a set of 38 small molecules with known K_i or K_d values for PDZ2 and PDZ3 domains of PSD-95 protein. These two PDZ domains show differential selectivity for these compounds despite having a high degree of sequence similarity and almost identical peptide binding pockets. Optimum binding modes for these ligands for PDZ2 and PDZ3 domains were identified by using a novel combination of semi-flexible docking and explicit solvent molecular dynamics (MD) simulations. Analysis of the binding modes revealed most of the peptidomimectic ligands which had high K_i or K_d moved away from the peptide binding pocket, while ligands with high binding affinities remained in the peptide binding pocket. The differential specificities of the PDZ2 and PDZ3 domains primarily arise from differences in the conformation of the loop connecting βB and βC strands, because this loop interacts with the N-terminal chemical moieties of the ligands. We have also computed the MM/PBSA binding free energy values for these 38 compounds with both the PDZ domains from multiple 5 ns MD trajectories on each complex i.e. a total of 228 MD trajectories of 5 ns length each. Interestingly, computational binding free energies show good agreement with experimental binding free energies with a correlation coefficient of approximately 0.6. Thus our study demonstrates that combined use of docking and MD simulations can help in identification of potent inhibitors of PDZ-peptide complexes.     

Molecular modeling and evaluation of binding mode and affinity of artemisinin-quinine hybrid and its congeners with Fe-protoporphyrin-IX as a putative receptor

A recent rational approach to anti-malarial drug design is characterized as "covalent biotherapy" involves linking of two molecules with individual intrinsic activity into a single agent, thus packaging dual activity into a single hybrid molecule. In view of this background and reported anti malaria synergism between artemisinin and quinine; we describe the computer-assisted docking to predict molecular interaction and binding affinity of Artemisinin-Quinine hybrid and its derivatives with the intraparasitic haeme group of human haemoglobin. Starting from a crystallographic structure of Fe-protoporphyrin-IX, binding modes, orientation of peroxide bridge (Fe-O distance), docking score and interaction energy are predicted using the docking molecular mechanics based on generalized Born/surface area (MM-GBSA) solvation model. Seven new ligands were identified with a favourable glide score (XP score) and binding free energy (ΔG) with reference to the experimental structure from a data set of thirty four hybrid derivatives. The result shows the conformational property of the drug-receptor interaction and may lead to rational design and synthesis of improved potent artemisinin based hybrid antimalarial that target haemozoin formation.     

Flexible docking using tabu search and an empirical estimate of binding affinity

This article describes the implementation of a new docking approach. The method uses a Tabu search methodology to dock flexibly ligand molecules into rigid receptor structures. It uses an empirical objective function with a small number of physically based terms derived from fitting experimental binding affinities for crystallographic complexes. This means that docking energies produced by the searching algorithm provide direct estimates of the binding affinities of the ligands. The method has been tested on 50 ligand-receptor complexes for which the experimental binding affinity and binding geometry are known. All water molecules are removed from the structures and ligand molecules are minimized in vacuo before docking. The lowest energy geometry produced by the docking protocol is within 1.5 Å root-mean square of the experimental binding mode for 86% of the complexes. The lowest energies produced by the docking are in fair agreement with the known free energies of binding for the ligands. Proteins 33:367–382, 1998. © 1998 Wiley-Liss, Inc.     

Ligands Binding and Molecular Simulation: the Potential Investigation of a Biosensor Based on an Insect Odorant Binding Protein

Based on mimicking biological olfaction, biosensors have been applied for the detection of various ligands in complex environment, which could represent one of the most promising research fields. In this study, the basic characters of one insect odorant binding protein (OBP) as a biosensor were explored. To explore the molecular recognition process, the tertiary structure of the protein was modeled and the protein-ligand interactions with 1,536,550 chemicals were investigated by the molecular docking. The availability of large amount of recombinant SlitOBP1 overcame the difficulty to obtain biological sensing material. After obtained the purified recombinant protein, the result of fluorescence binding assays proved the candidate protein has good affinities with the majority of the tested chemicals. With the aid of simulation docking, the key conserved amino acids within the binding site were identified and then mutated to alanine. After mutation, the protein-ligand binding characteristics were recorded, and the competitive binding assays were carried out to provide experimental verification. The detailed information on its structure and affinities investigated in this study could allow the design of specific mutants with desired characteristics, which provides a solid base for tailoring OBP for biosensor and provides a role model for screening the other elements in olfactory system for different applications.     

Binding free energy calculations of galectin-3-ligand interactions

Galectins show remarkable binding specificity towards beta-galactosides. A recently developed method for calculating binding free energies between a protein and its substrates has been used to evaluate the binding specificity of galectin-3. Five disaccharides and a tetrasaccharide were used as the substrates. The calculated binding free energies agree quite well with the experimental data and the ranking of binding affinities is well reproduced. For all the six protein-ligand complexes it was observed that electrostatic interactions oppose binding whereas the non-polar contributions drive complex formation. The observed binding specificity of galectin-3 for galactosides rather than glucosides is discussed in light of our results.     

Unravelling the binding affinity between model transport protein and a prospective tuberculosis therapeutic agent: a spectroscopic and theoretical simulation exploration

Abstract

Haloxyfop was reported to exhibit inhibition effect targeting Mycobacterium tuberculosis and pathogenic parasites. To pave its way for drug development, more research is required to determine the affinities interacting with biological receptors in vivo. In this work, the interactions of Haloxyfop with two model transport proteins were investigated by spectroscopic techniques and theoretical simulation. The interaction mechanism, thermodynamic properties and the impact of Haloxyfop-induced conformational change in serum albumins were revealed by series of fluorescence, UV-Vis absorption and circular dichroic spectroscopy. The specific binding sites were determined by site-competitive replacement experiment. Molecular docking and dynamic simulation provided a visual screening in the microscopic binding mode. The structure of Haloxyfop was roughly divided into three parts that exhibit different covalent interaction affinities. The two isomers of Haloxyfop showed a certain degree of affinity difference. Hydrophobic, polar interaction and π-effect were analyzed in detail, and the surface electrostatic potential energy maps were simulated to provide references. The free energy, calculated by the molecular mechanics-generalized born surface area (MM-GBSA) and molecular mechanics-Poisson Boltzmann surface area (MM-PBSA) methods, was decomposed to per-residues, which intuitively revealed relevant contributions in binding process. The role of water existence was explored through molecular dynamic refinement, and the frontier molecular orbital analysis explored the ionic interaction mechanism in electronic level. In general, multiple chemistry method was adopted to fully unravel the properties of Haloxyfop-binding for the sake of rationalizing the applicability as a therapeutic agent.

Communicated by Ramaswamy H. Sarma     

Affinity and specificity of protein U1A-RNA complex formation based on an additive component free energy model

An MM-GBSA computational protocol was used to investigate wild-type U1A-RNA and F56 U1A mutant experimental binding free energies. The trend in mutant binding free energies compared to wild-type is well-reproduced. Following application of a linear-response-like equation to scale the various energy components, the binding free energies agree quantitatively with observed experimental values. Conformational adaptation contributes to the binding free energy for both the protein and the RNA in these systems. Small differences in DeltaGs are the result of different and sometimes quite large relative contributions from various energetic components. Residual free energy decomposition indicates differences not only at the site of mutation, but throughout the entire protein. MM-GBSA and ab initio calculations performed on model systems suggest that stacking interactions may nearly, but not completely, account for observed differences in mutant binding affinities. This study indicates that there may be different underlying causes of ostensibly similar experimentally observed binding affinities of different mutants, and thus recommends caution in the interpretation of binding affinities and specificities purely by inspection.     

HIV-1跨膜蛋白gp41与N-取代吡咯衍生物的结合模式研究

采用分子对接,分子动力学(MD)模拟和分子力学/泊松-波尔兹曼溶剂可有面积方法与分子力学/广义伯恩溶剂可及面积方法(MM-PBSA/MM-GBSA),预测两种N-取代吡咯衍生物与HIV-1 跨膜蛋白gp41疏水口袋的结合模式与作用机理．分子对接采用多种受体构象,并从结果中选取几种可能的结合模式进行MD 模拟,然后通过MM-PBSA计算结合能的方法识别最优的结合模式. MM-PBSA计算结果表明,范德华相互作用是结合的主要驱动力,而极性相互作用决定了配体在结合过程中的取向．进一步的结合能分解显示,配体的羧基与gp41残基Arg579的静电相互作用对结合有重要贡献．上述工作为进一步优化N-取代吡咯衍生物类的HIV-1融合抑制剂建立了良好的理论基础．     

Structure-based virtual screening,molecular docking,ADMET and molecular simulations to develop benzoxaborole analogs as potential inhibitor against Leishmania donovani trypanothione reductase

Visceral leishmaniasis (VL) is the most fatal form of leishmaniasis and it affects 70 countries worldwide. Increasing drug resistant for antileishmanial drugs such as miltefosine, sodium stibogluconate and pentamidine has been reported in the VL endemic region. Amphotericin B has shown potential antileishmanial activity in different formulations but its cost of treatment and associated nephrotoxicity have limited its use by affected people living in the endemic zone. To control the VL infection in the affected countries, it is necessary to develop new antileishmanial compounds with high efficacy and negligible toxicity. Computer aided programs such as binding free energy estimation; ADMET prediction and molecular dynamics simulation can be used to investigate novel antileishmanial molecules in shorter duration. To develop antileishmanial lead molecule, we performed standard precision (SP) docking for 1160 benzoxaborole analogs along with reference inhibitors against trypanothione reductase of Leishmania parasite. Furthermore, extra precision (XP) docking, ADMET prediction, prime MM-GBSA was conducted over 115 ligands, showing better docking score than reference inhibitors to get potential antileishmanial compounds. Simultaneously, area under the curve (AUC) was estimated using ROC plot to validate the SP and XP docking protocol. Later on, two benzoxaborole analogs with best MM-GBSA ΔG-bind were subjected to molecular simulation and docking confirmation to ensure the ligand interaction with TR. The presented drug discovery based on computational study confirms that BOB27 can be used as a potential drug candidate and warrants further experimental investigation to fight against VL in endemic areas.