Intuitive,but not simple: Including explicit water molecules in protein–protein docking simulations improves model quality

Characterizing the nature of interaction between proteins that have not been experimentally cocrystallized requires a computational docking approach that can successfully predict the spatial conformation adopted in the complex. In this work, the Hydropathic INTeractions (HINT) force field model was used for scoring docked models in a data set of 30 high‐resolution crystallographically characterized “dry” protein–protein complexes and was shown to reliably identify native‐like models. However, most current protein–protein docking algorithms fail to explicitly account for water molecules involved in bridging interactions that mediate and stabilize the association of the protein partners, so we used HINT to illuminate the physical and chemical properties of bridging waters and account for their energetic stabilizing contributions. The HINT water Relevance metric identified the “truly” bridging waters at the 30 protein–protein interfaces and we utilized them in “solvated” docking by manually inserting them into the input files for the rigid body ZDOCK program. By accounting for these interfacial waters, a statistically significant improvement of ～24% in the average hit‐count within the top‐10 predictions the protein–protein dataset was seen, compared to standard “dry” docking. The results also show scoring improvement, with medium and high accuracy models ranking much better than incorrect ones. These improvements can be attributed to the physical presence of water molecules that alter surface properties and better represent native shape and hydropathic complementarity between interacting partners, with concomitantly more accurate native‐like structure predictions. Proteins 2014; 82:916–932. © 2013 Wiley Periodicals, Inc.     

Molecular docking and structure-based virtual screening studies of potential drug target,CAAX prenyl proteases,of Leishmania donovani

Targeting CAAX prenyl proteases of Leishmania donovani can be a good approach towards developing a drug molecule against Leishmaniasis. We have modeled the structure of CAAX prenyl protease I and II of L. donovani, using homology modeling approach. The structures were further validated using Ramachandran plot and ProSA. Active site prediction has shown difference in the amino acid residues present at the active site of CAAX prenyl protease I and CAAX prenyl protease II. The electrostatic potential surface of the CAAX prenyl protease I and II has revealed that CAAX prenyl protease I has more electropositive and electronegative potentials as compared CAAX prenyl protease II suggesting significant difference in their activity. Molecular docking with known bisubstrate analog inhibitors of protein farnesyl transferase and peptidyl (acyloxy) methyl ketones reveals significant binding of these molecules with CAAX prenyl protease I, but comparatively less binding with CAAX prenyl protease II. New and potent inhibitors were also found using structure-based virtual screening. The best docked compounds obtained from virtual screening were subjected to induced fit docking to get best docked configurations. Prediction of drug-like characteristics has revealed that the best docked compounds are in line with Lipinski’s rule. Moreover, best docked protein–ligand complexes of CAAX prenyl protease I and II are found to be stable throughout 20 ns simulation. Overall, the study has identified potent drug molecules targeting CAAX prenyl protease I and II of L. donovani whose drug candidature can be verified further using biochemical and cellular studies.     

Identification of ALK5 inhibitor via structure-based virtual screening and ADMET prediction

Abstract

TGF-β plays a critical role in the initiation and progression of fibrosis in various organ systems such as kidney, heart, lung and liver. TGF-β and its receptors (ALK5 and TβR II) are able to control the cellular growth and promote several biological responses. To date, many pharmaceutical companies have employed virtual screening to identify potent inhibitors against ALK5. Nevertheless, none of these studies had involved the in silico ADMET evaluation and Raccoon filtering. In our experiment, all 57423 molecules were downloaded from TCM database and were filtered and converted to PDBQT formats by Raccoon software. Then 24?189 structures were run through AutoDock Vina in PyRx 0.8, 164 molecules were selected and further evaluated by ADMET Predictor 6.5, and 56 structures were selected and docked by Glide 6.2. Finally, the top 10 hits were identified as promising oral ALK5 inhibitors according to their Glide scores. The Glide scores of the best two compounds, 40686 and 33534, were ?10.75 and ?10.30?kcal/mol, respectively. This research provides a set of combined and detailed virtual screening protocol and is helpful for explaining the mechanism of receptor–ligand interactions.     

Organic dyes as small molecule protein–protein interaction inhibitors for the CD40–CD154 costimulatory interaction

It is becoming increasingly clear that small molecules can often act as effective protein–protein interaction (PPI) inhibitors, an area of increasing interest for its many possible therapeutic applications. We have identified several organic dyes and related small molecules that (i) concentration‐dependently inhibit the important CD40–CD154 costimulatory interaction with activities in the low micromolar (µM) range, (ii) show selectivity toward this particular PPI, (iii) seem to bind on the surface of CD154, and (iv) concentration‐dependently inhibit the CD154‐induced B cell proliferation. They were identified through an iterative activity screening/structural similarity search procedure starting with suramin as lead, and the best smaller compounds, the main focus of the present work, achieved an almost 3‐fold increase in ligand efficiency (ΔG⁰/nonhydrogen atom = 0.8 kJ/N_nHa) approaching the average of known promising small‐molecule PPI inhibitors (～1.0 kJ/N_nHa). Since CD154 is a member of the tumor necrosis factor (TNF) superfamily of cell surface interaction molecules, inhibitory activities on the TNF‐R1–TNF‐α interactions were also determined to test for specificity, and the compounds selected here all showed more than 30‐fold selectivity toward the CD40–CD154 interaction. Because of their easy availability in various structural scaffolds and because of their good protein‐binding ability, often explored for tissue‐specific staining and other purposes, such organic dyes can provide a valuable addition to the chemical space searched to identify small molecule PPI inhibitors in general. Copyright © 2009 John Wiley & Sons, Ltd.     

Discovery of binding proteins for a protein target using protein–protein docking‐based virtual screening

Target structure‐based virtual screening, which employs protein‐small molecule docking to identify potential ligands, has been widely used in small‐molecule drug discovery. In the present study, we used a protein–protein docking program to identify proteins that bind to a specific target protein. In the testing phase, an all‐to‐all protein–protein docking run on a large dataset was performed. The three‐dimensional rigid docking program SDOCK was used to examine protein–protein docking on all protein pairs in the dataset. Both the binding affinity and features of the binding energy landscape were considered in the scoring function in order to distinguish positive binding pairs from negative binding pairs. Thus, the lowest docking score, the average Z‐score, and convergency of the low‐score solutions were incorporated in the analysis. The hybrid scoring function was optimized in the all‐to‐all docking test. The docking method and the hybrid scoring function were then used to screen for proteins that bind to tumor necrosis factor‐α (TNFα), which is a well‐known therapeutic target for rheumatoid arthritis and other autoimmune diseases. A protein library containing 677 proteins was used for the screen. Proteins with scores among the top 20% were further examined. Sixteen proteins from the top‐ranking 67 proteins were selected for experimental study. Two of these proteins showed significant binding to TNFα in an in vitro binding study. The results of the present study demonstrate the power and potential application of protein–protein docking for the discovery of novel binding proteins for specific protein targets. Proteins 2014; 82:2472–2482. © 2014 Wiley Periodicals, Inc.     

Developing novel approaches to improve binding energy estimation and virtual screening: a PARP case study

Poly-(ADP-ribose)-polymerase (PARP) is a promising anti-cancer target as it plays a crucial role in the cellular reparation and survival mechanisms. However, the development of a robust and cost effective experimental technique to screen PARP inhibitors is still a scientific challenge owing to the difficulties in quantitative detection of the enzyme activity. In this work we demonstrate that the computational chemistry tools including molecular docking and scoring can perform on par with the experimental studies in assessing binding constants and in the recovery of active compounds in virtual screening. Using the recently introduced Lead Finder software we were able to dock a set of 142 well characterized PARP inhibitors and obtain a good correlation between the calculated and experimentally measured binding energies with the rmsd of 1.67 kcal mol⁻¹. Additionally, fine-tuning of the energy scaling coefficients within the Lead Finder scoring function has further decreased rmsd to the value of 0.88 kcal mol⁻¹. Moreover, we were able to reproduce the selectivity of ligand binding between the two isoforms of the enzyme-PARP1 and PARP2-suggesting that the Lead Finder software can be used to design isoform-selective inhibitors of PARP. An impressive enrichment was obtained in the virtual screening experiment, in which the mentioned set of PARP inhibitors was mixed with a commercial library of 300,000 compounds. We also demonstrate that the virtual screening performance can be significantly improved by an additional structural filtration of the docked ligand poses through detection of the crucial hydrogen bonding interactions with the enzyme.     

AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility

Automated docking of drug-like molecules into receptors is an essential tool in structure-based drug design. While modeling receptor flexibility is important for correctly predicting ligand binding, it still remains challenging. This work focuses on an approach in which receptor flexibility is modeled by explicitly specifying a set of receptor side-chains a-priori. The challenges of this approach include the: 1) exponential growth of the search space, demanding more efficient search methods; and 2) increased number of false positives, calling for scoring functions tailored for flexible receptor docking. We present AutoDockFR–AutoDock for Flexible Receptors (ADFR), a new docking engine based on the AutoDock4 scoring function, which addresses the aforementioned challenges with a new Genetic Algorithm (GA) and customized scoring function. We validate ADFR using the Astex Diverse Set, demonstrating an increase in efficiency and reliability of its GA over the one implemented in AutoDock4. We demonstrate greatly increased success rates when cross-docking ligands into apo receptors that require side-chain conformational changes for ligand binding. These cross-docking experiments are based on two datasets: 1) SEQ17 –a receptor diversity set containing 17 pairs of apo-holo structures; and 2) CDK2 –a ligand diversity set composed of one CDK2 apo structure and 52 known bound inhibitors. We show that, when cross-docking ligands into the apo conformation of the receptors with up to 14 flexible side-chains, ADFR reports more correctly cross-docked ligands than AutoDock Vina on both datasets with solutions found for 70.6% vs. 35.3% systems on SEQ17, and 76.9% vs. 61.5% on CDK2. ADFR also outperforms AutoDock Vina in number of top ranking solutions on both datasets. Furthermore, we show that correctly docked CDK2 complexes re-create on average 79.8% of all pairwise atomic interactions between the ligand and moving receptor atoms in the holo complexes. Finally, we show that down-weighting the receptor internal energy improves the ranking of correctly docked poses and that runtime for AutoDockFR scales linearly when side-chain flexibility is added.     

Structural insight into Mycobacterium tuberculosis maltosyl transferase inhibitors: pharmacophore-based virtual screening,docking, and molecular dynamics simulations

Pharmacophore-based virtual screening, subsequent docking, and molecular dynamics (MD) simulations have been done to identify potential inhibitors of maltosyl transferase of Mycobacterium tuberculosis (mtb GlgE). Ligand and structure-based pharmacophore models representing its primary binding site (pbs) and unique secondary binding site 2 (sbs2), respectively, were constructed based on the three dimensional structure of mtb GlgE. These pharmacophore models were further used for screening of ZINC and antituberculosis compounds database (ATD). Virtually screened molecules satisfying Lipinski’s rule of five were then analyzed using docking studies and have identified 23 molecules with better binding affinity than its natural substrate, maltose. Four top scoring ligands from ZINC and ATD that either binds to pbs or sbs2 have been subjected to 10 ns each MD simulations and binding free energy calculations. Results of these studies have confirmed stable protein ligand binding. Results reported in the article are likely to be helpful in antitubercular therapeutic development research.     

Strategies for lead discovery: Application of footprint similarity targeting HIVgp41

A highly-conserved binding pocket on HIVgp41 is an important target for development of anti-viral inhibitors. Holden et al. (Bioorg. Med. Chem. Lett. 2012, 22, 3011) recently reported 7 experimentally-verified leads identified through a computational screen to the gp41 pocket in conjunction with a new DOCK scoring method (termed FPS scoring) developed in our laboratory. The method employs molecular footprints based on per-residue van der Waals interactions, electrostatic interactions, or the sum. In this work, we critically examine the gp41 screening results, prioritized using different scoring methods, in terms of two main criteria: (1) ligand pose properties which include footprint and energy score decompositions, MW, number of rotatable bonds, ligand efficiency, formal charge, and volume overlap, and (2) ligand pose stability which includes footprint stability (changes in footprint overlap) and rmsd stability (changes in geometry). Relative to standard DOCK scoring, pose property analyses demonstrate how FPS scoring can be used to identify ligands that mimic a known reference (derived here from the native gp41 substrate), while pose stability analyses demonstrate how FPS scoring can be used to enrich for compounds with greater overall stability during molecular dynamics (MD) simulations. Compellingly, of the 115 compounds tested experimentally, the 7 active compounds, as a group, more closely mimic the footprints made by the reference and show greater MD stability compared to the inactive group. Extensive studies using 116 protein–ligand complexes as controls reveal that ligands in their crystallographic binding pose also maintain higher FPS scores and smaller rmsds than do accompanying decoys, confirming that native poses are indeed ‘stable’ under the same conditions and that monitoring FPS variability during compound prioritization is likely to be beneficial. Overall, the results suggest the new scoring method will complement current virtual screening approaches for both the identification (FPS-ranking) and prioritization (FPS-stability) of target-compatible molecules in a quantitative and logical way.     

Towards in silico lead optimization: scores from ensembles of protein/ligand conformations reliably correlate with biological activity

Accurately ranking protein/ligand interactions and distinguishing subtle differences between homologous compounds in a virtual focused library in silico is essential in a structure-based drug discovery program. In order to establish a predictive model to design novel inhibitors of dihydrofolate reductase (DHFR) from the parasitic protozoa, Cryptosporidium hominis, we docked a series of 30 DHFR inhibitors with measured inhibition constants against the crystal structure of the protein. By including protein flexibility and averaging the energies of the 25 lowest protein/ligand conformers we obtained more accurate total nonbonded energies from which we calculated a predicted biological activity. The calculated and measured biological activities showed reliable correlations of 72.9%. Additionally, visual analysis of the ensemble of protein/ligand conformations revealed alternative ligand binding pockets in the active site. Using the same principles we then created a homology model of DHFR from Toxoplasma gondii and docked 11 inhibitors. A correlation of 50.2% between docking score and activity validates both the method and the model. The correlations presented here are particularly compelling considering the high structural similarity of the ligands and the fact that we have used structures derived from crystallographic data and homology modeling. These docking principles may be useful in any lead optimization study where accurate ranking of similar compounds is desired.     

Optimizing the affinity and specificity of ligand binding with the inclusion of solvation effect

Solvation effect is an important factor for protein–ligand binding in aqueous water. Previous scoring function of protein–ligand interactions rarely incorporates the solvation model into the quantification of protein–ligand interactions, mainly due to the immense computational cost, especially in the structure‐based virtual screening, and nontransferable application of independently optimized atomic solvation parameters. In order to overcome these barriers, we effectively combine knowledge‐based atom–pair potentials and the atomic solvation energy of charge‐independent implicit solvent model in the optimization of binding affinity and specificity. The resulting scoring functions with optimized atomic solvation parameters is named as specificity and affinity with solvation effect (SPA‐SE). The performance of SPA‐SE is evaluated and compared to 20 other scoring functions, as well as SPA. The comparative results show that SPA‐SE outperforms all other scoring functions in binding affinity prediction and “native” pose identification. Our optimization validates that solvation effect is an important regulator to the stability and specificity of protein–ligand binding. The development strategy of SPA‐SE sets an example for other scoring function to account for the solvation effect in biomolecular recognitions. Proteins 2015; 83:1632–1642. © 2015 Wiley Periodicals, Inc.     

Pharmmaker: Pharmacophore modeling and hit identification based on druggability simulations

Recent years have seen progress in druggability simulations, that is, molecular dynamics simulations of target proteins in solutions containing drug‐like probe molecules to characterize their drug‐binding abilities, if any. An important consecutive step is to analyze the trajectories to construct pharmacophore models (PMs) to use for virtual screening of libraries of small molecules. While considerable success has been observed in this type of computer‐aided drug discovery, a systematic tool encompassing multiple steps from druggability simulations to pharmacophore modeling, to identifying hits by virtual screening of libraries of compounds, has been lacking. We address this need here by developing a new tool, Pharmmaker, building on the DruGUI module of our ProDy application programming interface. Pharmmaker is composed of a suite of steps: (Step 1) identification of high affinity residues for each probe molecule type; (Step 2) selecting high affinity residues and hot spots in the vicinity of sites identified by DruGUI; (Step 3) ranking of the interactions between high affinity residues and specific probes; (Step 4) obtaining probe binding poses and corresponding protein conformations by collecting top‐ranked snapshots; and (Step 5) using those snapshots for constructing PMs. The PMs are then used as filters for identifying hits in structure‐based virtual screening. Pharmmaker, accessible online at http://prody.csb.pitt.edu/pharmmaker , can be used in conjunction with other tools available in ProDy.     

Structure‐guided screening of chemical database to identify NS3‐NS2B inhibitors for effective therapeutic application in dengue infection

Dengue infection is the most common arthropod‐borne disease caused by dengue viruses, predominantly affecting millions of human beings annually. To find out promising chemical entities for therapeutic application in Dengue, in the current research, a multi‐step virtual screening effort was conceived to screen out the entire “screening library” of the Asinex database. Initially, through “Lipinski rule of five” filtration criterion almost 0.6 million compounds were collected and docked with NS3‐NS2B protein. Thereby, the chemical space was reduced to about 3500 compounds through the analysis of binding affinity obtained from molecular docking study in AutoDock Vina. Further, the “Virtual Screening Workflow” (VSW) utility of Schrödinger suite was used, which follows a stepwise multiple docking programs such as ‐ high‐throughput virtual screening (HTVS), standard precision (SP), and extra precision (XP) docking, and in postprocessing analysis the MM‐GBSA based free binding energy calculation. Finally, five potent molecules were proposed as potential inhibitors for the dengue NS3‐NS2B protein based on the investigation of molecular interactions map and protein‐ligand fingerprint analyses. Different pharmacokinetics and drug‐likeness parameters were also checked, which favour the potentiality of selected molecules for being drug‐like candidates. The molecular dynamics (MD) simulation analyses of protein‐ligand complexes were explained that NS3‐NS2B bound with proposed molecules quite stable in dynamic states as observed from the root means square deviation (RMSD) and root means square fluctuation (RMSF) parameters. The binding free energy was calculated using MM‐GBSA method from the MD simulation trajectories revealed that all proposed molecules possess such a strong binding affinity towards the dengue NS3‐NS2B protein. Therefore, proposed molecules may be potential chemical components for effective inhibition of dengue NS3‐NS2B protein subjected to experimental validation.     

In vitro antimalarial activity and molecular docking analysis of 4-aminoquinoline-clubbed 1,3,5-triazine derivatives

Aims: Present report describes the in vitro antimalarial activity and docking analysis of seven 4‐aminoquinoline‐clubbed 1,3,5‐triazine derivatives on pf‐DHFR‐TS. Methods and Results: The antimalarial activity was evaluated in vitro against chloroquine‐sensitive 3D7 strain of Plasmodium falciparum. Compounds were docked onto the active site of pf‐DHFR‐TS using docking server to explicate necessary structural requirements for antimalarial activity. Conclusion: Title molecules demonstrated considerable bioactivity against the malaria parasite. Docking analysis revealed deep engulfment of the molecules into the inner groove of pf‐DHFR‐TS active site by making stable ligand–receptor posses. Hydrophobic interaction was identified as the only major interacting force playing a role between ligand–receptor interaction and minor with hydrogen bonds. Signi?cance and Impact of the study: The study provided the novel insight into the necessary structural requirement for rationale‐based antimalarial drug discovery.     

Structural insights on identification of potential lead compounds targeting WbpP in Vibrio vulnificus through structure-based approaches

WbpP encoding UDP-GlcNAC C4 epimerase is responsible for the activation of virulence factor in marine pathogen Vibrio vulnificus (V. vulnificus) and it is linked to many aquatic diseases, thus making it a potential therapeutic target. There are few reported compounds that include several natural products and synthetic compounds targeting Vibrio sp, but specific inhibitor targeting WbpP are unavailable. Here, we performed structure-based virtual screening using chemical libraries such as Binding, TOSLab and Maybridge to identify small molecule inhibitors of WbpP with better drug-like properties. Deficient structural information forced to model the structure and the stable protein structure was obtained through 30?ns of MD simulations. Druggability regions are focused for new lead compounds and our screening protocol provides fast docking of entire small molecule library with screening criteria of ADME/Lipinski filter/Docking followed by re-docking of top hits using a method that incorporates both ligand and protein flexibility. Docking conformations of lead molecules interface displays strong H-bond interactions with the key residues Gly101, Ser102, Val195, Tyr165, Arg298, Val209, Ser142, Arg233 and Gln200. Subsequently, the top-ranking compounds were prioritized using the molecular dynamics simulation-based conformation and stability studies. Our study suggests that the proposed compounds may aid as a starting point for the rational design of novel therapeutic agents.     

Virtual screening using the ligand ZINC database for novel lipoxygenase-3 inhibitors

The leukotrienes constitute a group of arachidonic acid-derived compounds with biologic activities suggesting important roles in inflammation and immediate hypersensitivity. Epidermis-type lipoxygenase-3 (ALOXE3), a distinct subclass within the multigene family of mammalian lipoxygenases, is a novel isoenzyme involved in the metabolism of leukotrienes and plays a very important role in skin barrier functions. Lipoxygenase selective inhibitors such as azelastine and zileuton are currently used to reduce inflammatory response. Nausea, pharyngolaryngeal pain, headache, nasal burning and somnolence are the most frequently reported adverse effects of these drugs. Therefore, there is still a need to develop more potent lipoxygenase inhibitors. In this paper, we report the screening of various compounds from the ZINC database (contains over 21 million compounds) using the Molegro Virtual Docker software against the ALOXE3 protein. Screening was performed using molecular constraints tool to filter compounds with physico-chemical properties similar to the 1N8Q bound ligand protocatechuic acid. The analysis resulted in 4319 Lipinski compliant hits which are docked and scored to identify structurally novel ligands that make similar interactions to those of known ligands or may have different interactions with other parts of the binding site. Our screening approach identified four molecules ZINC84299674; ZINC76643455; ZINC84299122 & ZINC75626957 with MolDock score of -128.901, -120.22, -116.873 & - 102.116 kcal/mol, respectively. Their energy scores were better than the 1N8Q bound co-crystallized ligand protocatechuic acid (with MolDock score of -77.225 kcal/mol). All the ligands were docked within the binding pocket forming interactions with amino acid residues.     

Identification of InhA inhibitors: A combination of virtual screening,molecular dynamics simulations and quantum chemical studies

In the present work, multiple pharmacophore-based virtual screening of the SPECS natural product database was carried out to identify novel inhibitors of the validated biological target, InhA. The pharmacophore models were built from the five different groups of the co-crystallized ligands present within the active site. The generated models with the same features from each group were pooled and subjected to the test set validation, receiver–operator characteristic analysis and Güner–Henry studies. A set of five hypotheses with sensitivity > 0.5, specificity > 0.5, area under curve (AUC) > 0.7, and goodness of hit score > 0.7 were retrieved and exploited for the virtual screening. The common hits (87 molecules) obtained from these hypotheses were processed via drug-likeness filters. The filtered molecules (27 molecules) were compared for the binding modes and the top scored molecules (12 molecules) along with the reference (triclosan (TCL), docking score = ?11.65 kcal/mol) were rescored and reprioritized via molecular mechanics-generalized Born surface area approach. Eventually, the stability of reprioritized (10 molecules) docked complexes was scrutinized via molecular dynamics simulations. Moreover, the quantum chemical studies of the dynamically stable compounds (9 molecules) were performed to understand structural features essential for the activity. Overall, the protocol resulted in the recognition of nine lead compounds that can be targeted against InhA.     

Detecting the native ligand orientation by interfacial rigidity: SiteInterlock

Understanding the physical attributes of protein‐ligand interfaces, the source of most biological activity, is a fundamental problem in biophysics. Knowing the characteristic features of interfaces also enables the design of molecules with potent and selective interactions. Prediction of native protein‐ligand interactions has traditionally focused on the development of physics‐based potential energy functions, empirical scoring functions that are fit to binding data, and knowledge‐based potentials that assess the likelihood of pairwise interactions. Here we explore a new approach, testing the hypothesis that protein‐ligand binding results in computationally detectable rigidification of the protein‐ligand interface. Our SiteInterlock approach uses rigidity theory to efficiently measure the relative interfacial rigidity of a series of small‐molecule ligand orientations and conformations for a number of protein complexes. In the majority of cases, SiteInterlock detects a near‐native binding mode as being the most rigid, with particularly robust performance relative to other methods when the ligand‐free conformation of the protein is provided. The interfacial rigidification of both the protein and ligand prove to be important characteristics of the native binding mode. This measure of rigidity is also sensitive to the spatial coupling of interactions and bond‐rotational degrees of freedom in the interface. While the predictive performance of SiteInterlock is competitive with the best of the five other scoring functions tested, its measure of rigidity encompasses cooperative rather than just additive binding interactions, providing novel information for detecting native‐like complexes. SiteInterlock shows special strength in enhancing the prediction of native complexes by ruling out inaccurate poses. Proteins 2016; 84:1888–1901. © 2016 Wiley Periodicals, Inc.     

New molecular scaffolds for the design of Mycobacterium tuberculosis type II dehydroquinase inhibitors identified using ligand and receptor based virtual screening

Using ligand and receptor based virtual screening approaches we have identified potential virtual screening hits targeting type II dehydroquinase from Mycobacterium tuberculosis, an effective and validated anti-mycobacterial target. Initially, we applied a virtual screening workflow based on a combination of 2D structural fingerprints, 3D pharmacophore and molecular docking to identify compounds that rigidly match specific aspects of ligand bioactive conformation. Subsequently, the resulting compounds were ranked and prioritized using receptor interaction fingerprint based scoring and quantitative structure activity relationship model developed using already known actives. The virtual screening hits prioritized belong to several classes of molecular scaffolds with several available substitution positions that could allow chemical modification to enhance binding affinity. Finally, identified hits may be useful to a medicinal chemist or combinatorial chemist to pick up the new molecular starting points for medicinal chemistry optimization for the design of novel type II dehydroquinase inhibitors.     

Virtual screening to identify novel potential inhibitors for Glutamine synthetase of Mycobacterium tuberculosis

Abstract

Glutamine synthetase (GS) of Mycobacterium tuberculosis (Mtb) is an essential enzyme which is involved in nitrogen metabolism and cell wall synthesis. It is involved in the inhibition of phagosome-lysosome fusion by preventing acidification. Targeting GS can be helpful to control the infection of Mtb. In order to identify potential inhibitors, we screened chemical libraries (56,400 compounds of ChEMBL anti-mycobacterial, 1596 FDA approved drugs, 419 Natural product and 916 phytochemical) against this target using the virtual screening approach. Screening by molecular docking identified ten top-ranked compounds as GSMtb inhibitors and they were compared with known inhibitors (as control). Since GS enzyme (GSHs) is also present in human. We have compared the protein sequence of GS from Mtb and human using the P-BLAST in NCBI. We found ～27% identity in between these two sequences, so we also compared the binding affinity of inhibitor between Mtb and human. Finally, we identified top two compounds namely CHEMBL387509, CHEMBL226198 from ChEMBL anti-mycobacterial dataset, and Eriocitrin and Malvidin from phytochemical dataset which showed lees binding affinity towards GSHs whereas Pamidronate, and Phentermine from FDA approved drugs and (-)-Quinic Acid, Hexopyranuronic acid, Quebrachit, and Castanospermine from natural product showed protein-ligand interaction with Mtb protein while no interaction with GSHs. The top two docked complexes were subjected to molecular dynamic simulation to understand the stability of the molecule. Further, we calculated the binding free energy of the docked complex and analyzed hydrogen bond, salt bridge, pie stacking, and hydrophobic interaction in the docking region. These ligands exhibited very good binding affinity GSMtb enzymes. Therefore, these ligands are novel and drug-likeness compounds, and they may be potential inhibitors of M tuberculosis.

Communicated by Ramaswamy H. Sarma