Binding-affinity predictions of HSP90 in the D3R Grand Challenge 2015 with docking, MM/GBSA, QM/MM, and free-energy simulations

Prioritizing Small Sets of Molecules for Synthesis through in-silico Tools: A Comparison of Common Ranking Methods

Prioritizing molecules for synthesis is a key role of computational methods within medicinal chemistry. Multiple tools exist for ranking molecules, from the cheap and popular molecular docking methods to more computationally expensive molecular-dynamics (MD)-based methods. It is often questioned whether the accuracy of the more rigorous methods justifies the higher computational cost and associated calculation time. Here, we compared the performance on ranking the binding of small molecules for seven scoring functions from five docking programs, one end-point method (MM/GBSA), and two MD-based free energy methods (PMX, FEP+). We investigated 16 pharmaceutically relevant targets with a total of 423 known binders. The performance of docking methods for ligand ranking was strongly system dependent. We observed that MD-based methods predominantly outperformed docking algorithms and MM/GBSA calculations. Based on our results, we recommend the application of MD-based free energy methods for prioritization of molecules for synthesis in lead optimization, whenever feasible.

Identification of Drug Combination Therapies for SARS-CoV-2: A Molecular Dynamics Simulations Approach

Purpose

The development of effective treatments for coronavirus infectious disease 19 (COVID-19) caused by SARS-Coronavirus-2 was hindered by the little data available about this virus at the start of the pandemic. Drug repurposing provides a good strategy to explore approved drugs' possible SARS-CoV-2 antiviral activity. Moreover, drug synergism is essential in antiviral treatment due to improved efficacy and reduced toxicity. In this work, we studied the effect of approved and investigational drugs on one of SARS-CoV-2 essential proteins, the main protease (Mpro), in search of antiviral treatments and/or drug combinations.

Methods

Different possible druggable sites of Mpro were identified and screened against an in-house library of more than 4000 chemical compounds. Molecular dynamics simulations were carried out to explore conformational changes induced by different ligands' binding. Subsequently, the inhibitory effect of the identified compounds and the suggested drug combinations on the Mpro were established using a 3CL protease (SARS-CoV-2) assay kit.

Results

Three potential inhibitors in three different binding sites were identified; favipiravir, cefixime, and carvedilol. Molecular dynamics simulations predicted the synergistic effect of two drug combinations: favipiravir/cefixime, and favipiravir/carvedilol. The in vitro inhibitory effect of the predicted drug combinations was established on this enzyme.

Conclusion

In this work, we could study one of the promising SARS-CoV-2 viral protein targets in searching for treatments for COVID-19. The inhibitory effect of several drugs on Mpro was established in silico and in vitro assays. Molecular dynamics simulations showed promising results in predicting the synergistic effect of drug combinations.

Mebendazole's Conformational Space and Its Predicted Binding to Human Heat-Shock Protein 90

Recent experimental evidence suggests that mebendazole, a popular antiparasitic drug, binds to heat shock protein 90 (Hsp90) and inhibits acute myeloid leukemia cell growth. In this study we use quantum mechanics (QM), molecular similarity, and molecular dynamics (MD) calculations to predict possible binding poses of mebendazole to the adenosine triphosphate (ATP) binding site of Hsp90. Extensive conformational searches and minimization of the five mebendazole tautomers using the MP2/aug-cc-pVTZ theory level resulted in 152 minima. Mebendazole-Hsp90 complex models were subsequently created using the QM optimized conformations and protein coordinates obtained from experimental crystal structures that were chosen through similarity calculations. Nine different poses were identified from a total of 600 ns of explicit solvent, all-atom MD simulations using two different force fields. All simulations support the hypothesis that mebendazole is able to bind to the ATP binding site of Hsp90.

Structure-based pharmacophore modeling, virtual screening approaches to identifying the potent hepatitis C viral protease and polymerase novel inhibitors

Hepatitis C is an infectious disease that leads to acute and chronic liver illnesses. Currently, there are no effective vaccines against this deadly virus. Direct acting antiviral (DAA) drugs are given in the combination with ribavirin and pegylated interferon which lead to adverse effects. Through in silico analysis, the structure-based docking study was performed against NS3/4A protease and NS5B polymerase proteins of HCV. In the current study, multiple e-pharmacophore-based virtual screening methods such as HTVS, SP, and XP were carried out to screen natural compounds and enamine databases. Our result outcomes revealed that CID AE-848/13196185 and CID AE-848/36959205 compounds show good binding interactions with protease protein. In addition, CID 15081408 and CID 173568 show better binding interactions with the polymerase protein. Further to validate the docking results, we performed molecular dynamics simulation for the top hit compounds bound with protease and polymerase proteins to illustrate conformational differences in the stability compared with the active site of the cocrystal inhibitor. Thus, the current study emphasizes these compounds could be an effective drug to treat HCV.

AA-Score: a New Scoring Function Based on Amino Acid-Specific Interaction for Molecular Docking

The protein-ligand scoring function plays an important role in computer-aided drug discovery and is heavily used in virtual screening and lead optimization. In this study, we developed a new empirical protein-ligand scoring function with amino acid-specific interaction components for hydrogen bond, van der Waals, and electrostatic interactions. In addition, hydrophobic, π-stacking, π-cation, and metal-ligand interactions are also included in the new scoring function. To better evaluate the performance of the AA-Score, we generated several new test sets for evaluation of scoring, ranking, and docking performances, respectively. Extensive tests show that AA-Score performs well on scoring, docking, and ranking as compared to other widely used traditional scoring functions. The performance improvement of AA-Score benefits from the decomposition of individual interaction into amino acid-specific types. To facilitate applications, we developed an easy-to-use tool to analyze protein-ligand interaction fingerprint and predict binding affinity using the AA-Score. The source code and associated running examples can be found at https://github.com/xundrug/AA-Score-Tool.

Molecular Docking and Molecular Dynamics Simulation of Fisetin, Galangin, Hesperetin, Hesperidin, Myricetin, and Naringenin against Polymerase of Dengue Virus

Dengue fever is a disease spread by the DENV virus through mosquitoes. This disease is dangerous because there is no specific drug, vaccine, or antiviral against the DENV virus, insisting on drug discovery for dengue fever. RNA-dependent RNA polymerase (RdRp) enzyme in DENV can be a drug target because it has an important role in the virus replication process. In this research, in silico simulations were carried out on bioflavonoid compounds, namely, Fisetin, Galangin, Hesperetin, Hesperidin, Myricetin, and Naringenin with Quercetin as control ligand. QSAR analysis showed that all ligand has the probability to be antiviral and RNA synthesis inhibitor. Docking scores showed that Myricetin, Hesperidin, and Fisetin show strong performance while Hesperidin, Hesperetin, and Naringenin showed strong performance in MM/GBSA. Only Hesperidin showed strong performance in both scorings. Further investigation by ADMET analysis was done to investigate toxicology and pharmacological properties. Our molecular dynamics study through RMSD showed that even though Quercetin does not give good scoring values in both docking score and MM/GBSA, it has robust stable interaction to RdRp. The strong performance of Hesperidin was also validated by protein-ligand contact fraction in 5 ns. Overall, we observed that Hesperidin shows good potential as a DENV-3-RdRp inhibitor in par with Quercetin, although further in vitro study should be conducted.

Reliable Prediction of the Protein-Ligand Binding Affinity Using a Charge Penetration Corrected AMOEBA Force Field: A Case Study of Drug Resistance Mutations in Abl Kinase

Protein mutations that directly impair drug binding are related to therapeutic resistance, and accurate prediction of their impact on drug binding would benefit drug design and clinical practice. Here, we have developed a scoring strategy that predicts the effect of the mutations on the protein-ligand binding affinity. In view of the critical importance of electrostatics in protein-ligand interactions, the charge penetration corrected AMOEBA force field (AMOEBA_CP model) was employed to improve the accuracy of the calculated electrostatic energy. We calculated the electrostatic energy using an energy decomposition analysis scheme based on the generalized Kohn-Sham (GKS-EDA). The AMOEBA_CP model was validated by a protein-fragment-ligand complex data set (Abl236) constructed from the co-crystal structures of the cancer target Abl kinase with six inhibitors. To predict ligand binding affinity changes upon protein mutation of Abl kinase, we used sampling protocol with multistep simulated annealing to search conformations of mutant proteins. The scoring strategy based on AMOEBA_CP model has achieved considerable performance in predicting resistance for 8 kinase inhibitors across 144 clinically identified point mutations. Overall, this study illustrates that the AMOEBA_CP model, which accurately treats electrostatics through penetration correction, enables the accurate prediction of the mutation-induced variation of protein-ligand binding affinity.

Phytochemical Compound Screening to Identify Novel Small Molecules against Dengue Virus: A Docking and Dynamics Study

The spread of the Dengue virus over the world, as well as multiple outbreaks of different serotypes, has resulted in a large number of deaths and a medical emergency, as no viable medications to treat Dengue virus patients have yet been found. In this paper, we provide an in silico virtual screening and molecular dynamics-based analysis to uncover efficient Dengue infection inhibitors. Based on a Google search and literature mining, a large phytochemical library was generated and employed as ligand molecules. In this investigation, the protein target NS2B/NS3 from Dengue was employed, and around 27 compounds were evaluated in a docking study. Phellodendroside (−63 kcal/mole), quercimeritrin (−59.5 kcal/mole), and quercetin-7-O-rutinoside (−54.1 kcal/mole) were chosen based on their binding free energy in MM-GBSA. The tested compounds generated numerous interactions at Lys74, Asn152, and Gln167 residues in the active regions of NS2B/NS3, which is needed for the protein’s inhibition. As a result, the stable mode of docked complexes is defined by various descriptors from molecular dynamics simulations, such as RMSD, SASA, Rg, RMSF, and hydrogen bond. The pharmacological properties of the compounds were also investigated, and no toxicity was found in computational ADMET properties calculations. As a result, this computational analysis may aid fellow researchers in developing innovative Dengue virus inhibitors.

Efficient calculation of protein-ligand binding free energy with GFN methods: the power of cluster model

The protein-ligand interactions are crucial in many biochemical processes and biomedical applications, yet it still remains challenging to accurately calculating the binding free energy of their interactions. In this work,...

Profiling of phytochemical and antioxidant activity of wild mushrooms: Evidence from the in vitro study and phytoconstituent's binding affinity to the human erythrocyte catalase and human glutathione reductase

This study was undertaken to evaluate the appearance of phytochemicals and antioxidant activity of seven wild mushrooms of the University of Chittagong campus. Phytochemical screening was performed using standard methods, whereas DPPH radical scavenging assay was used to elucidate the antioxidant effect. Besides, in silico studies were implemented using the targets of human erythrocyte catalase 3-amino-1,2,4-triazole, human glutathione reductase, and selected compounds. Again, the absorption, distribution, metabolism, elimination and toxicity (ADME/T) analysis has been determined by using online tools. Both Ganoderma lucidum (Curtis) Karst. and Ganoderma applanatum (Pers.) Pat. showed a significant (p < .001) increase in the percentage of scavenging activity at 400 μg/ml concentration when compared with ascorbic acid. The methanol extract of G. lucidum, G. applanatum, and Rhodofomes cajanderi (P. Karst.) B. K. Cui, M. L. Han & Y. C. Dai showed strong antioxidant activity with an IC50 value. In addition, molecular docking studies of the previously isolated compounds from three selective mushrooms revealed that the targeted compounds along with positive controls were able to interact strongly (range: -3.498 to -8.655) with the enzymes. The study concludes that the G . lucidum, G. applanatum, and R. cajanderi mushrooms can be a strong source in the management of oxidative stress-induced diseases.

Restoring chemo-sensitivity to temozolomide via targeted inhibition of poly (ADP-ribose) polymerase-1 by naringin in glioblastoma

Inclining mortality with a constant plummet in the survival rates associated with glioblastoma still stands as an inveterate predicament. The only promising therapy with temozolomide (TMZ) is now side-lined due to escalated resistance mediated by Poly (ADP-ribose) Polymerase-1 (PARP-1). In the light of this, the very study was designed to evaluate the potential of an active phyto component named naringin, in inhibiting PARP-1, using in silico and in vitro methods. Under in silico settings, inhibitor bound crystal structure of PARP-1, i.e., 4UND was retrieved and molecular docking studies were performed against naringin using Schrodinger software. In vitro cytotoxicity and apoptotic detection assay were performed using C6 glioma cells. Docking studies revealed high affinity and low binding energy at the inhibition site with good stability. An increase in cytotoxicity to C6 cells was observed with TMZ and naringin combination when compared to TMZ alone. Isobologram plot confirmed the synergistic effect of the drug combination. A significant increase in the number of apoptotic cells with combination drugs, as evaluated by acridine orange and ethidium bromide staining reassured the reversal of resistance. In conclusion, chemosensitivity to TMZ was restored by successful inhibition of PARP-1 using naringin and the drug combination was hence proven effective in reversing TMZ resistance.

Kidney injury molecule-1 is a potential receptor for SARS-CoV-2

COVID-19 patients present high incidence of kidney abnormalities, which are associated with poor prognosis and mortality. The identification of SARS-CoV-2 in the kidney of COVID-19 patients suggests renal tropism of SARS-CoV-2. However, whether there is a specific target of SARS-CoV-2 in the kidney remains unclear. Herein, by using in silico simulation, coimmunoprecipitation, fluorescence resonance energy transfer, fluorescein isothiocyanate labeling, and rational design of antagonist peptides, we demonstrate that kidney injury molecule-1 (KIM1), a molecule dramatically upregulated upon kidney injury, binds with the receptor-binding domain (RBD) of SARS-CoV-2 and facilitates its attachment to cell membrane, with the immunoglobulin variable Ig-like (Ig V) domain of KIM1 playing a key role in this recognition. The interaction between SARS-CoV-2 RBD and KIM1 is potently blockaded by a rationally designed KIM1-derived polypeptide AP2. In addition, our results also suggest interactions between KIM1 Ig V domain and the RBDs of SARS-CoV and MERS-CoV, pathogens of two severe infectious respiratory diseases. Together, these findings suggest KIM1 as a novel receptor for SARS-CoV-2 and other coronaviruses. We propose that KIM1 may thus mediate and exacerbate the renal infection of SARS-CoV-2 in a ‘vicious cycle’, and KIM1 could be further explored as a therapeutic target.

Emerging computational approaches for the study of regio- and stereoselectivity in organic synthesis

Computational chemistry has become important in organic synthesis as it provides a detailed understanding of molecular structures and properties and detailed reaction mechanisms.

DockingApp RF: A State-of-the-Art Novel Scoring Function for Molecular Docking in a User-Friendly Interface to AutoDock Vina

Motivation: Bringing a new drug to the market is expensive and time-consuming. To cut the costs and time, computer-aided drug design (CADD) approaches have been increasingly included in the drug discovery pipeline. However, despite traditional docking tools show a good conformational space sampling ability, they are still unable to produce accurate binding affinity predictions. This work presents a novel scoring function for molecular docking seamlessly integrated into DockingApp, a user-friendly graphical interface for AutoDock Vina. The proposed function is based on a random forest model and a selection of specific features to overcome the existing limits of Vina’s original scoring mechanism. A novel version of DockingApp, named DockingApp RF, has been developed to host the proposed scoring function and to automatize the rescoring procedure of the output of AutoDock Vina, even to nonexpert users. Results: By coupling intermolecular interaction, solvent accessible surface area features and Vina’s energy terms, DockingApp RF’s new scoring function is able to improve the binding affinity prediction of AutoDock Vina. Furthermore, comparison tests carried out on the CASF-2013 and CASF-2016 datasets demonstrate that DockingApp RF’s performance is comparable to other state-of-the-art machine-learning- and deep-learning-based scoring functions. The new scoring function thus represents a significant advancement in terms of the reliability and effectiveness of docking compared to AutoDock Vina’s scoring function. At the same time, the characteristics that made DockingApp appealing to a wide range of users are retained in this new version and have been complemented with additional features.

Thermodynamic Integration in 3n Dimensions without Biases or Alchemy for Protein Interactions

Thermodynamic integration (TI), a powerful formalism for computing Gibbs free energy, has been implemented for many biophysical processes with alchemical schemes that require delicate human efforts to choose/design biasing potentials for sampling the desired biophysical events and to remove their artifactitious consequences afterwards. Theoretically, an alchemical scheme is exact but practically, an unsophisticated implementation of this exact formula can cause error amplifications. Small relative errors in the input parameters can be amplified many times in their propagation into the computed free energy [due to subtraction of similar numbers such as (105 ± 5)‒(100 ± 5) = 5 ± 7]. In this paper, we present an unsophisticated implementation of TI in 3n dimensions (3nD) (n=1,2,3…) for the potential of mean force along a 3nD path connecting one state in the bound state ensemble to one state in the unbound state ensemble. Fluctuations in these 3nD are integrated in the bound and unbound state ensembles but not along the 3nD path. Using TI3nD, we computed the standard binding free energies of three protein complexes: trometamol in Salmonella effector SpvD (n=1), biotin-avidin (n=2), and Colicin E9 endonuclease with cognate immunity protein Im9 (n=3). We employed three different protocols in three independent computations of E9-Im9 to show TI3nD's robustness. We also computed the hydration energies of ten biologically relevant compounds (n=1 for water, acetamide, urea, glycerol, trometamol, ammonium and n=2 for erythritol, 1,3-propanediol, xylitol, biotin). Each of the 15 computations is accomplishable within one (for hydration) to ten (for E9-Im9) days on an inexpensive GPU workstation. The computed results all agree with the available experimental data.

D3R Grand Challenge 4: ligand similarity and MM-GBSA-based pose prediction and affinity ranking for BACE-1 inhibitors

The Drug Design Data Resource (D3R) Grand Challenges present an opportunity to assess, in the context of a blind predictive challenge, the accuracy and the limits of tools and methodologies designed to help guide pharmaceutical drug discovery projects. Here, we report the results of our participation in the D3R Grand Challenge 4 (GC4), which focused on predicting the binding poses and affinity ranking for compounds targeting the [Formula: see text]-amyloid precursor protein (BACE-1). Our ligand similarity-based protocol using HYBRID (OpenEye Scientific Software) successfully identified poses close to the native binding mode for most of the ligands with less than 2 Å RMSD accuracy. Furthermore, we compared the performance of our HYBRID-based approach to that of AutoDock Vina and DOCK 6 and found that using a reference ligand to guide the docking process is a better strategy for pose prediction and helped HYBRID to perform better here. We also conducted end-point free energy estimates on molecules dynamics based ensembles of protein-ligand complexes using molecular mechanics combined with generalized Born surface area method (MM-GBSA). We found that the binding affinity ranking based on MM-GBSA scores have poor correlation with the experimental values. Finally, the main lessons from our participation in D3R GC4 are: (i) the generation of the macrocyclic conformers is a key step for successful pose prediction, (ii) the protonation states of the BACE-1 binding site should be treated carefully, (iii) the MM-GBSA method could not discriminate well between different predicted binding poses, and (iv) the MM-GBSA method does not perform well at predicting protein-ligand binding affinities here.

Binding Free Energy Calculation Using Quantum Mechanics Aimed for Drug Lead Optimization

The routine use of in silico tools is already established in drug lead design. Besides the use of molecular docking methods to screen large chemical libraries and thus prioritize compounds for purchase or synthesis, more accurate calculations of protein-ligand binding free energy has shown the potential to guide lead optimization, thus saving time and resources. Theoretical developments and advances in computing power have allowed quantum mechanical-based methods applied to calculations on biomacromolecules to be increasingly explored and used, with the purpose of providing a more accurate description of protein-ligand interactions and an enhanced level of accuracy in the calculation of binding affinities. It should be noted that the quantum mechanical formulation includes, in principle, all contributions to the energy, considering terms usually neglected in molecular mechanics force fields, such as electronic polarization, metal coordination, and covalent binding; moreover, quantum mechanical approaches are systematically improvable. By treating all elements and interactions on equal footing, and avoiding the need of system-dependent parameterizations, they provide a greater degree of transferability. In this review, we illustrate the increasing relevance of quantum mechanical methods for binding free energy calculation in the context of structure-based drug lead optimization, showing representative applications of the different approaches available.

Water structure in solution and crystal molecular dynamics simulations compared to protein crystal structures

The function of proteins is influenced not only by the atomic structure but also by the detailed structure of the solvent surrounding it. Computational studies of protein structure also critically depend on the water structure around the protein. Herein we compare the water structure obtained from molecular dynamics (MD) simulations of galectin-3 in complex with two ligands to crystallographic water molecules observed in the corresponding crystal structures. We computed MD trajectories both in a water box, which mimics a protein in solution, and in a crystallographic unit cell, which mimics a protein in a crystal. The calculations were compared to crystal structures obtained at both cryogenic and room temperature. Two types of analyses of the MD simulations were performed. First, the positions of the crystallographic water molecules were compared to peaks in the MD density after alignment of the protein in each snapshot. The results of this analysis indicate that all simulations reproduce the crystallographic water structure rather poorly. However, if we define the crystallographic water sites based on their distances to nearby protein atoms and follow these sites throughout the simulations, the MD simulations reproduce the crystallographic water sites much better. This shows that the failure of MD simulations to reproduce the water structure around proteins in crystal structures observed both in this and previous studies is caused by the problem of identifying water sites for a flexible and dynamic protein (traditionally done by overlaying the structures). Our local clustering approach solves the problem and shows that the MD simulations reasonably reproduce the water structure observed in crystals. Furthermore, analysis of the crystal MD simulations indicates a few water molecules that are close to unmodeled electron density peaks in the crystal structures, suggesting that crystal MD could be used as a complementary tool for identifying and modelling water in protein crystallography.

Molecular dynamics simulations can reproduce the water structure around proteins in crystal structure only if a local clustering is performed.

Comparison of affinity ranking using AutoDock-GPU and MM-GBSA scores for BACE-1 inhibitors in the D3R Grand Challenge 4

Molecular docking has been successfully used in computer-aided molecular design projects for the identification of ligand poses within protein binding sites. However, relying on docking scores to rank different ligands with respect to their experimental affinities might not be sufficient. It is believed that the binding scores calculated using molecular mechanics combined with the Poisson-Boltzman surface area (MM-PBSA) or generalized Born surface area (MM-GBSA) can predict binding affinities more accurately. In this perspective, we decided to take part in Stage 2 of the Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) to compare the performance of a quick scoring function, AutoDock4, to that of MM-GBSA in predicting the binding affinities of a set of [Formula: see text]-Amyloid Cleaving Enzyme 1 (BACE-1) ligands. Our results show that re-scoring docking poses using MM-GBSA did not improve the correlation with experimental affinities. We further did a retrospective analysis of the results and found that our MM-GBSA protocol is sensitive to details in the protein-ligand system: (i) neutral ligands are more adapted to MM-GBSA calculations than charged ligands, (ii) predicted binding affinities depend on the initial conformation of the BACE-1 receptor, (iii) protonating the aspartyl dyad of BACE-1 correctly results in more accurate binding affinity predictions.

Rational Identification of Hsp90 Inhibitors as Anticancer Lead Molecules by Structure Based Drug Designing Approach

BACKGROUND

Heat shock protein 90 (Hsp90) is an encouraging anticancer target for the development of clinically significant molecules. Schiff bases play a crucial role in anticancer research because of their ease of synthesis and excellent antiproliferative effect against multiple cancer cell lines. Therefore, we started our research work with the discovery of resorcinol/4-chloro resorcinol derived Schiff bases as Hsp90 inhibitors, which resulted in the discovery of a viable anticancer lead molecule.

OBJECTIVE

The objective of the study is to discover more promising lead molecules using our previously established drug discovery program, wherein the rational drug design is achieved by molecular docking studies.

METHODS

The docking studies were carried out by using Surflex Geom X programme of Sybyl X-1.2 version software. The molecules with good docking scores were synthesized and their structures were confirmed by IR, 1H NMR and mass spectral analysis. Subsequently, the molecules were evaluated for their potential to attenuate Hsp90 ATPase activity by Malachite green assay. The anticancer effect of the molecules was examined on PC3 prostate cancer cell lines by utilizing 3-(4,5-dimethythiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay methodology.

RESULTS

Schiff bases 11, 12, 20, 23 and 27 exhibiting IC50 value below 1μM and 15μM, in malachite green assay and MTT assay, respectively, emerged as viable lead molecules for future optimization.

CONCLUSION

The research work will pave the way for the rational development of cost-effective Schiff bases as Hsp90 inhibitors as the method employed for the synthesis of the molecules is simple, economic and facile.

Exploring the potent inhibitors and binding modes of phospholipase A2 through in silico investigation

Snake venom of Naja naja comprises of several types of enzymes, and among them, water-soluble proteolytic enzyme, phospholipase A2 (PLA₂), is noteworthy for its numerous adverse effects, such as cytotoxicity, cardiotoxicity, hemolytic, anti-coagulant, and hypotensive effects, including being highly potent as a neurotoxin. Limited anti-venom therapy (with their lower efficacy) has attracted considerable pharmacological interest to develop potent inhibitors of PLA₂. Thus, 34 experimentally proven and diverse synthetic inhibitors of PLA₂ were screened primarily on the basis of Glide extra precision docking and MM-GBSA rescoring function. Then, ten potential hits were subjected to induced fit docking, in which top three potential inhibitors were considered, and those were found to interact with Ca²⁺, disulfide binding site, and phosphatidylcholine activation sites, thereby, possibly disrupting the catalytic activity of Ca²⁺ as well as the inflammatory functions of PLA₂. These compounds showed positive remarks on various physiochemical properties and pharmacologically relevant descriptors. Gap energy and thermodynamic properties were investigated by employing density functional theory for all compounds to understand their chemical reactivity and thermodynamic stability. Molecular dynamics simulation was performed for 100 ns in order to evaluate the stability and binding modes of docked complexes, and the energy of binding was calculated through MM-PBSA analysis. On the whole, the proposed compounds could be used for targeted inhibition. Communicated by Ramaswamy H. Sarma.

Structure-based virtual screening, molecular dynamics simulation and MM-PBSA toward identifying the inhibitors for two-component regulatory system protein NarL of Mycobacterium Tuberculosis

The nitrate/nitrite response regulatory protein NarL belongs to the two-component regulatory system of Mycobacterium tuberculosis (MTB), plays a crucial role in anaerobic survival of mycobacteria in host. The absence of this protein in humans, makes it an attractive drug target for MTB treatment. However, the specific drug molecules targeting NarL are yet to be identified. In this study, we identified the promising drug candidates using structure based virtual screening of compounds from chemical libraries (ChEMBL and ZINC), followed by the extensive physicochemical properties analyses and molecular dynamics (MD) simulation. As the initial results, we obtained 4,754 bioactive compounds from ChEMBL having anti-tuberculosis activity which is finally narrowed down to the best 10 hits. A similar approach was applied to search for structurally similar compounds from ZINC data, corresponding to the top hits obtained from ChEMBL. Our collective results show that two compounds, ChEMBL509609 (Gscore - 5.054 kcal/mol, Xscore - 6.47 kcal/mol) and ZINC01843143 (Gscore - 5.114 kcal/mol, Xscore - 6.46 kcal/mol) having the best docking score and ADMET profile. The structural stability and dynamics of lead molecules at active site of NarL were examined using MD simulation and the binding free energies were estimated with MM-PBSA. Essential dynamics and MM-PBSA demonstrated that NarL-ChEMBL509609 complex remains the most stable during simulation of 100 ns with the higher binding free energy which may be a suitable candidate for further experimental analysis. AbbreviationsADMEAbsorption, Distribution, Metabolism, And ExcretionBCGBacillus Calmette-GuerinCNSCentral nervous systemDOTSDirectly observed treatment, short courseEDEssential dynamicsHIVHuman immunodeficiency virusHKHistidine kinaseHOAHuman oral absorptionHTVSHigh throughput virtual screeningIRRIIrritationMDMolecular dynamicsMDRMultidrug resistantMTBMycobacterium tuberculosisMUTMutagenicityMWMolecular weightPHOAPercentage of human oral absorptionREPReproductive developmentRgRadius of gyrationRMSDRoot mean square deviationRMSFRoot mean square fluctuationRO5Lipinski's rule of fiveRRResponse regulatorSPStandard precisionSPGStandard precision glideTBTuberculosisTCSTwo-component regulatory systemTDRTotally drug-resistantTUMOTumorigenicityWHOWorld health organizationXDRExtensively drug-resistantXPExtra precisionCommunicated by Ramaswamy H. Sarma.

Investigation of Drug Interaction Potentials and Binding Modes on Direct Renin Inhibitors: A Computational Modeling Studies

Background:

Hypertension is one of the key risk factors for cardiovascular disease, it is regulated through Renin Angiotensin Aldosterone System (RAAS) cascade. Renin catalyzes the initial rate-limiting step in RAAS system, that influences the synthesis of angiotensin I from precursor angiotensin. Renin inhibition could be a potential step for the blood pressure lowering mechanism as well as for organ protection.

Methods:

In order to understand the structure-activity association of direct renin inhibitors (DRIs), we have carried out three-dimensional quantitative structure activity relationship (3D-QSAR), molecular docking studies and Density Functional Theory (DFT) analysis to identify the attractive compounds. Five-point pharmacophore model of one acceptor, three hydrophobic groups and one aromatic ring was chosen for the dataset of 40 compounds.

Results:

The generated 3D-QSAR model shows that the alignment has a good correlation coefficient for the training set compounds, which comprise the value of R2 = 0.96, SD = 0.1, and F = 131.3. The test compounds had Q2 = 0.91, RMSE = 0.25, and Pearson-R = 0.97, which describes the predicted model was reliable.

Discussion:

External validations were carried out to validate the predicted QSAR model. Further, the significant compounds were studied using different in silico approaches in order to explore the difference in the atomic configuration and binding mechanism of the identified compounds.

Conclusion:

The molecular dynamics simulation of the complex was analyzed and confirmed the stability of the compounds in the protein. The outcome of the result could be useful to improve the safety and efficacy of DRIs that can be projected to clinical trials.

Modeling caspase-1 inhibition: Implications for catalytic mechanism and drug design

The metabolic product of caspase-1, IL-1β, is an important mediator in inflammation and pyroptosis cell death process. Alzheimer's disease, septic shock and rheumatoid arthritis are IL-1β mediated diseases, making the caspase-1 an interesting target of pharmacological value. Many inhibitors have been developed until now, most of them are peptidomimetic with improved potency. In the present study, all-atom molecular dynamics simulations and the MM/GBSA method were employed to reproduce and interpret the results obtained by in vitro experiments for a series of inhibitors. The analysis shows that the tautomeric state of the catalytic His237 impact significantly the performance of the prediction protocol, providing evidence for a His237 tautomeric state different to the proposed in the putative mechanism. Additionally, analysis of inhibitor-enzyme interactions indicates that the differences in the inhibitory potency of the tested ligands can be explained mainly by the interaction of the inhibitors with the S2-S4 protein region. These results provide guidelines for subsequent studies of caspase-1 catalytic reaction mechanism and for the design of novel inhibitors.

Improving small molecule virtual screening strategies for the next generation of therapeutics

The new generation of post-genomic targets, such as protein-protein interactions (PPIs), often require new chemotypes not well represented in current compound libraries. This is one reason for why traditional high throughput screening (HTS) approaches are not more successful in delivering medicinal chemistry starting points for PPIs. In silico screening methods of an expanded chemical space are then potential alternatives for developing novel chemical probes to modulate PPIs. In this review, we report on the state-of-the-art pipelines for virtual screening, emphasizing prospectively validated methods capable of addressing the challenge of drugging difficult targets in the human interactome. Collectively, we show that optimal strategies for structure based virtual screening vary depending on receptor structure and degree of flexibility.

Quantum Chemical Approaches in Structure-Based Virtual Screening and Lead Optimization

Today computational chemistry is a consolidated tool in drug lead discovery endeavors. Due to methodological developments and to the enormous advance in computer hardware, methods based on quantum mechanics (QM) have gained great attention in the last 10 years, and calculations on biomacromolecules are becoming increasingly explored, aiming to provide better accuracy in the description of protein-ligand interactions and the prediction of binding affinities. In principle, the QM formulation includes all contributions to the energy, accounting for terms usually missing in molecular mechanics force-fields, such as electronic polarization effects, metal coordination, and covalent binding; moreover, QM methods are systematically improvable, and provide a greater degree of transferability. In this mini-review we present recent applications of explicit QM-based methods in small-molecule docking and scoring, and in the calculation of binding free-energy in protein-ligand systems. Although the routine use of QM-based approaches in an industrial drug lead discovery setting remains a formidable challenging task, it is likely they will increasingly become active players within the drug discovery pipeline.

Binding affinities of the farnesoid X receptor in the D3R Grand Challenge 2 estimated by free-energy perturbation and docking

We have studied the binding of 102 ligands to the farnesoid X receptor within the D3R Grand Challenge 2016 blind-prediction competition. First, we employed docking with five different docking software and scoring functions. The selected docked poses gave an average root-mean-squared deviation of 4.2 Å. Consensus scoring gave decent results with a Kendall's τ of 0.26 ± 0.06 and a Spearman's ρ of 0.41 ± 0.08. For a subset of 33 ligands, we calculated relative binding free energies with free-energy perturbation. Five transformations between the ligands involved a change of the net charge and we implemented and benchmarked a semi-analytic correction (Rocklin et al., J Chem Phys 139:184103, 2013) for artifacts caused by the periodic boundary conditions and Ewald summation. The results gave a mean absolute deviation of 7.5 kJ/mol compared to the experimental estimates and a correlation coefficient of R 2 = 0.1. These results were among the four best in this competition out of 22 submissions. The charge corrections were significant (7-8 kJ/mol) and always improved the results. By employing 23 intermediate states in the free-energy perturbation, there was a proper overlap between all states and the precision was 0.1-0.7 kJ/mol. However, thermodynamic cycles indicate that the sampling was insufficient in some of the perturbations.

Optimal affinity ranking for automated virtual screening validated in prospective D3R grand challenges

The goal of virtual screening is to generate a substantially reduced and enriched subset of compounds from a large virtual chemistry space. Critical in these efforts are methods to properly rank the binding affinity of compounds. Prospective evaluations of ranking strategies in the D3R grand challenges show that for targets with deep pockets the best correlations (Spearman ρ ~ 0.5) were obtained by our submissions that docked compounds to the holo-receptors with the most chemically similar ligand. On the other hand, for targets with open pockets using multiple receptor structures is not a good strategy. Instead, docking to a single optimal receptor led to the best correlations (Spearman ρ ~ 0.5), and overall performs better than any other method. Yet, choosing a suboptimal receptor for crossdocking can significantly undermine the affinity rankings. Our submissions that evaluated the free energy of congeneric compounds were also among the best in the community experiment. Error bars of around 1 kcal/mol are still too large to significantly improve the overall rankings. Collectively, our top of the line predictions show that automated virtual screening with rigid receptors perform better than flexible docking and other more complex methods.