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Molecular docking, pivotal in predicting small-molecule ligand binding modes, struggles with accurately identifying binding conformations and affinities. This is particularly true for neonicotinoids, insecticides whose impacts on ecosystems require precise molecular interaction modeling. This study scrutinizes the effectiveness of prominent docking software (Ledock, ADFR, Autodock Vina, CDOCKER) in simulating interactions of environmental chemicals, especially neonicotinoid-like molecules with nicotinic acetylcholine receptors (nAChRs) and acetylcholine binding proteins (AChBPs). We aimed to assess the accuracy and reliability of these tools in reproducing crystallographic data, focusing on semi-flexible and flexible docking approaches. Our analysis identified Ledock as the most accurate in semi-flexible docking, while Autodock Vina with Vinardo scoring function proved most reliable. However, no software consistently excelled in both accuracy and reliability. Additionally, our evaluation revealed that none of the tools could establish a clear correlation between docking scores and experimental dissociation constants (Kd) for neonicotinoid-like compounds. In contrast, a strong correlation was found with drug-like compounds, bringing to light a bias in considered software towards pharmaceuticals, thus limiting their applicability to environmental chemicals. The comparison between semi-flexible and flexible docking revealed that the increased computational complexity of the latter did not result in enhanced accuracy. In fact, the higher computational cost of flexible docking with its lack of enhanced predictive accuracy, rendered this approach useless for this class of compounds. Conclusively, our findings emphasize the need for continued development of docking methodologies, particularly for environmental chemicals. This study not only illuminates current software capabilities but also underscores the urgency for advancements in computational molecular docking as it is a relevant tool to environmental sciences.

Mohammad H Fetian, Qosay A Al-Balas Department of Medicinal Chemistry and Pharmacognosy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, JordanCorrespondence: Qosay A Al-Balas, Department of Medicinal Chemistry & Pharmacognosy, Faculty of Pharmacy, Jordan University of Science and Technology, P.O. Box 3030, Irbid, 22110, Jordan, Tel +962 2 720 1000, Fax +962 2 720 1071, Email qabalas@just.edu.joBackground: Glyoxalase system detoxifies methylglyoxal and other ketoaldehydes to produce innocuous metabolites that allow the cells to function normally. Its inhibition in cancer cells causes these toxic metabolites to accumulate, and the cancer cells enter the apoptotic stage.Methods: The techniques of Computer-Aided Drug Design (CADD) were used, and the compounds possessing a zinc-binding group from commercial databases were extracted, using the pharmacophore search protocol. These compounds were subjected to robust docking using the CDOCKER protocol within the Discovery Studio. Docking was performed on both Glo-I twin active sites. The biological activities of candidate hits were assessed using an in vitro assay against Glo-I.Results: Compounds containing zinc-binding groups were extracted from ASINEX® commercial database, which contains (91,001 compounds). This step has helped to retrieve 1809 ligands, which then were prepared and docked at the two active sites of Glo-I. The fourteen compounds, which have showed the highest scores in docking and returned acceptable Total Binding Energy values, were purchased and tested against the enzyme in vitro. Two compounds out of the fourteen, which were selected in the final step, possess tetrazole ring as zinc chelating moiety, and have showed moderate activity with an IC50 of 48.18μM for SYN 25285236 and 48.77 μM for SYN 22881895.Conclusion: Two hits with moderate activity are identified as the lead compounds against Glo-I. Both compounds possess a negatively ionized tetrazole ring as the zinc-binding moiety. These compounds will lead to the development of inhibitors with improved activities. Keywords: Glo-I, CDOCKER, zinc-binding group, tetrazole ring, structure-based drug design

This study aimed to identify novel Glyoxalase-I (Glo-I) inhibitors with potential anticancer properties, focusing on anthraquinone amide-based derivatives. We synthesized a series of these derivatives and conducted in silico docking studies to predict their binding interactions with Glo-I. In vitro assessments were performed to evaluate the anti-Glo-I activity of the synthesized compounds. A comprehensive structure-activity relationship (SAR) analysis identified key features responsible for specific binding affinities of anthraquinone amide-based derivatives to Glo-I. Additionally, a 100 ns molecular dynamics simulation assessed the stability of the most potent compound compared to a co-crystallized ligand. Compound MQ3 demonstrated a remarkable inhibitory effect against Glo-I, with an IC50 concentration of 1.45 μM. The inhibitory potency of MQ3 may be attributed to the catechol ring, amide functional group, and anthraquinone moiety, collectively contributing to a strong binding affinity with Glo-I. Anthraquinone amide-based derivatives exhibit substantial potential as Glo-I inhibitors with prospective anticancer activity. The exceptional inhibitory efficacy of compound MQ3 indicates its potential as an effective anticancer agent. These findings underscore the significance of anthraquinone amide-based derivatives as a novel class of compounds for cancer therapy, supporting further research and advancements in targeting the Glo-I enzyme to combat cancer. [ABSTRACT FROM AUTHOR]

Molecular docking, pivotal in predicting small-molecule ligand binding modes, struggles with accurately identifying binding conformations and affinities. This is particularly true for neonicotinoids, insecticides whose impacts on ecosystems require precise molecular interaction modeling. This study scrutinizes the effectiveness of prominent docking software (Ledock, ADFR, Autodock Vina, CDOCKER) in simulating interactions of environmental chemicals, especially neonicotinoid-like molecules with nicotinic acetylcholine receptors (nAChRs) and acetylcholine binding proteins (AChBPs). We aimed to assess the accuracy and reliability of these tools in reproducing crystallographic data, focusing on semi-flexible and flexible docking approaches. Our analysis identified Ledock as the most accurate in semi-flexible docking, while Autodock Vina with Vinardo scoring function proved most reliable. However, no software consistently excelled in both accuracy and reliability. Additionally, our evaluation revealed that none of the tools could establish a clear correlation between docking scores and experimental dissociation constants (K d) for neonicotinoid-like compounds. In contrast, a strong correlation was found with drug-like compounds, bringing to light a bias in considered software towards pharmaceuticals, thus limiting their applicability to environmental chemicals. The comparison between semi-flexible and flexible docking revealed that the increased computational complexity of the latter did not result in enhanced accuracy. In fact, the higher computational cost of flexible docking with its lack of enhanced predictive accuracy, rendered this approach useless for this class of compounds. Conclusively, our findings emphasize the need for continued development of docking methodologies, particularly for environmental chemicals. This study not only illuminates current software capabilities but also underscores the urgency for advancements in computational molecular docking as it is a relevant tool to environmental sciences. [Display omitted] • Benchmarked docking tools for neonicotinoids using 54 PDB protein-ligand systems with nAChRs and AChBPs. • Flexible docking appeared to be less accurate and more computationally demanding than semi-flexible docking. • Revealed biases in current software, highlighting the need for tools tailored to insecticides. [ABSTRACT FROM AUTHOR]

The opportunity to prospectively predict ligand bound poses and free energies of binding to the Farnesoid X Receptor in the D3R Grand Challenge 2 provided a useful exercise to evaluate CHARMM based docking (CDOCKER) and [Formula: see text]-dynamics methodologies for use in "real-world" applications in computer aided drug design. In addition to measuring their current performance, several recent methodological developments have been analyzed retrospectively to highlight best procedural practices in future applications. For pose prediction with CDOCKER, when the protein structure used for rigid receptor docking was close to the crystallographic holo structure, reliable poses were obtained. Benzimidazoles, with a known holo receptor structure, were successfully docked with an average RMSD of 0.97 [Formula: see text]. Other non-benzimidazole ligands displayed less accuracy largely because the receptor structures we chose for docking were too different from the experimental holo structures. However, retrospective analysis has shown that when these ligands were re-docked into their holo structures, the average RMSD dropped to 1.18 [Formula: see text] for all ligands. When sulfonamides and spiros were docked with the apo structure, which agrees more with their holo structure than the structures we chose, five out of six ligands were correctly docked. These docking results emphasize the need for flexible receptor docking approaches. For [Formula: see text]-dynamics techniques, including multisite [Formula: see text]-dynamics (MS[Formula: see text]D), reasonable agreement with experiment was observed for the 33 ligands investigated; root mean square errors of 2.08 and 1.67 kcal/mol were obtained for free energy sets 1 and 2, respectively. Retrospectively, soft-core potentials, adaptive landscape flattening, and biasing potential replica exchange (BP-REX) algorithms were critical to model large substituent perturbations with sufficient precision and within restrictive timeframes, such as was required with participation in Grand Challenge 2. These developments, their associated benefits, and proposed procedures for their use in future applications are discussed.

This computational study investigates 21 bioactive compounds from the Asteraceae family as potential inhibitors targeting the Spike protein (S protein) of SARS-CoV-2. Employing in silico methods and simulations, particularly CDOCKER and MM-GBSA, the study identifies two standout compounds, pterodontic acid and cichoric acid, demonstrating robust binding affinities (-46.1973 and -39.4265 kcal/mol) against the S protein. Comparative analysis with Favipiravir underscores their potential as promising inhibitors. Remarkably, these bioactives exhibit favorable ADMET properties, suggesting safety and efficacy. Molecular dynamics simulations validate their stability and interactions, signifying their potential as effective SARS-CoV-2 inhibitors., (© 2024 Wiley Periodicals LLC.)

Background: Klebsiella species have emerged as well-known opportunistic pathogens causing nosocomial infections with β-lactamase-mediated resistance as a prevalent antibiotic resistance mechanism. The discovery and emergence of metallo-β-lactamases, mainly new- Delhi metallo-β-lactamases (NDMs), have increased the threat and challenges in healthcare facilities., Objective: A computational screening was conducted using 570 natural compounds from Dr. Duke's Phytochemical and Ethnobotanical data to discover promising inhibitors for NDM-6, NDM-9, and NDM-23 of the Klebsiella species., Methods: Using homology modeling on the Raptor-X web server, the structures of the three NDM variants were predicted. The structures were validated using various computational tools and MD simulation for 50 ns. Lipinski - Vebers' Filter and ADMET Screening were used to screen 570 compounds, followed by docking in Biovia Discovery Studio 2019 using the CDOCKER module. GROMACS was used to simulate the compounds with the highest scores with the proteins for 50 ns. Using the MM-PBSA method and g_mmpbsa tool, binding free energies were estimated and per-residue decomposition analysis was conducted., Results: The three structures predicted were found stable after the 50 ns MD Simulation run. The compounds Budmunchiamine-A and Rhamnocitrin were found to have the best binding energy towards NDM-6, NDM-9, and NDM-23, respectively. From the results of MD Simulation, MM-PBSA binding free energy calculations, and per-residue decomposition analysis, the Protein-ligand complex of NDM-6 with Budmunchiamine A and NDM-9 with Rhamnocitrin was relatively more stable than the complex of NDM-23 and Rhamnocitrin., Conclusion: The study suggests that Budmunchiamine-A and Rhamnocitrin are potential inhibitors of NDM-6 and NDM-9, respectively, and may pave a path for in-vivo and in-vitro studies in the future., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

SHP2 is a potential target for the development of novel therapies for SHP2-dependent cancers. In our research, with the aid of the ‘Receptor-Ligand Pharmacophore’ technique, a 3D-QSAR method was carried out to explore structure activity relationship of SHP2 allosteric inhibitors. Structure-based drug design was employed to optimize SHP099, an efficacious, potent, and selective SHP2 allosteric inhibitor. A novel class of selective SHP2 allosteric inhibitors was discovered by using the powerful ‘SBP’, ‘ADMET’ and ‘CDOCKER’ techniques. By means of molecular dynamics simulations, it was observed that these novel inhibitors not only had the same function as SHP099 did in inhibiting SHP2, but also had more favorable conformation for binding to the receptor. Thus, this report may provide a new method in discovering novel and selective SHP2 allosteric inhibitors. [ABSTRACT FROM AUTHOR]

The opportunity to prospectively predict ligand bound poses and free energies of binding to the Farnesoid X Receptor in the D3R Grand Challenge 2 provided a useful exercise to evaluate CHARMM based docking (CDOCKER) and $$\lambda$$ -dynamics methodologies for use in 'real-world' applications in computer aided drug design. In addition to measuring their current performance, several recent methodological developments have been analyzed retrospectively to highlight best procedural practices in future applications. For pose prediction with CDOCKER, when the protein structure used for rigid receptor docking was close to the crystallographic holo structure, reliable poses were obtained. Benzimidazoles, with a known holo receptor structure, were successfully docked with an average RMSD of 0.97 $$\AA$$ . Other non-benzimidazole ligands displayed less accuracy largely because the receptor structures we chose for docking were too different from the experimental holo structures. However, retrospective analysis has shown that when these ligands were re-docked into their holo structures, the average RMSD dropped to 1.18 $$\AA$$ for all ligands. When sulfonamides and spiros were docked with the apo structure, which agrees more with their holo structure than the structures we chose, five out of six ligands were correctly docked. These docking results emphasize the need for flexible receptor docking approaches. For $$\lambda$$ -dynamics techniques, including multisite $$\lambda$$ -dynamics (MS $$\lambda$$ D), reasonable agreement with experiment was observed for the 33 ligands investigated; root mean square errors of 2.08 and 1.67 kcal/mol were obtained for free energy sets 1 and 2, respectively. Retrospectively, soft-core potentials, adaptive landscape flattening, and biasing potential replica exchange (BP-REX) algorithms were critical to model large substituent perturbations with sufficient precision and within restrictive timeframes, such as was required with participation in Grand Challenge 2. These developments, their associated benefits, and proposed procedures for their use in future applications are discussed. [ABSTRACT FROM AUTHOR]

The Plasmodium falciparum Lactate Dehydrogenase enzyme (PfLDH) catalyzes inter-conversion of pyruvate to lactate during glycolysis producing the energy required for parasitic growth. The PfLDH has been studied as a potential molecular target for development of anti-malarial agents. In an attempt to find the potent inhibitor of PfLDH, we have used Discovery studio to perform molecular docking in the active binding pocket of PfLDH by CDOCKER, followed by three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic guanidine batzelladine compounds, which were previously synthesized in our laboratory. Docking studies showed that there is a very strong correlation between in silico and in vitro results. Based on docking results, a highly predictive 3D-QSAR model was developed with q2 of 0.516. The model has predicted r2 of 0.91 showing that predicted IC50 values are in good agreement with experimental IC50 values. The results obtained from this study revealed the developed model can be used to design new anti-malarial compounds based on tricyclic guanidine derivatives and to predict activities of new inhibitors.

Nine pairs of megastigmane enantiomers ( 1a/ 1b- 9a/ 9b), comprising two new compounds (6 S,9 R)-blumenol C ( 7b), (6 S,9 S)-blumenol C ( 8b), two pairs of enantiomers (+)-(6 R)-eucomegastigmane A ( 1a), (−)-(6 S)-eucomegastigmane A ( 1b), (+)-(3 S,4 S)-eucomegastigmane B ( 5a), (−)-(3 R,4 R)-eucomegastigmane B ( 5b) isolated by chiral resolution firstly, and twelve known compounds, were isolated from the leaves of Eucommia ulmoides Oliver. Their structures were elucidated based on extensive spectroscopic analysis. Absolute configurations of the megastigmane enantiomers were assigned by comparing experimental ECD and OR with calculated ECD and OR. Docking-based virtual screening of all compounds showed that megastigmane enantiomers have weak intermolecular interactions with the binding site residues of angiotensin-converting enzyme (ACE) and angiotensin II type 1 receptor (ATR). [ABSTRACT FROM AUTHOR]

Coronavirus has left severe health impacts on the human population, globally. Still a significant number of cases are reported daily as no specific medications are available for its effective treatment. The presence of the CD147 receptor (human basigin) on the host cell facilitates the severe acute respiratory disease coronavirus 2 (SARS-CoV-2) infection. Therefore, the drugs that efficiently alter the formation of CD147 and spike protein complex could be the right drug candidate to inhibit the replication of SARS-CoV-2. Hence, an e-Pharmacophore model was developed based on the receptor-ligand cavity of CD147 protein which was further mapped against pre-existing drugs of coronavirus disease treatment. A total of seven drugs were found to be suited as pharmacophores out of 11 drugs screened which was further docked with CD147 protein using CDOCKER of Biovia discovery studio. The active site sphere of the prepared protein was 101.44, 87.84, and 97.17 along with the radius being 15.33 and the root-mean-square deviation value obtained was 0.73 Å. The protein minimization energy was calculated to be -30,328.81547 kcal/mol. The docking results showed ritonavir as the best fit as it demonstrated a higher CDOCKER energy (-57.30) with correspond to CDOCKER interaction energy (-53.38). However, authors further suggest in vitro studies to understand the potential activity of the ritonavir.

Farnesyltransferase enzyme (FTase) is considered an essential enzyme in the Ras signaling pathway associated with cancer. Thus, designing inhibitors for this enzyme might lead to the discovery of compounds with effective anticancer activity. In an attempt to obtain effective FTase inhibitors, pharmacophore hypotheses were generated using structure-based and ligand-based approaches built in Discovery Studio v3.1. Knowing the presence of the zinc feature is essential for inhibitor’s binding to the active site of FTase enzyme; further customization was applied to include this feature in the generated pharmacophore hypotheses. These pharmacophore hypotheses were thoroughly validated using various procedures such as ROC analysis and ligand pharmacophore mapping. The validated pharmacophore hypotheses were used to screen 3D databases to identify possible hits. Those which were both high ranked and showed sufficient ability to bind the zinc feature in active site, were further refined by applying drug-like criteria such as Lipiniski’s “rule of five” and ADMET filters. Finally, the two candidate compounds (ZINC39323901 and ZINC01034774) were allowed to dock using CDOCKER and GOLD in the active site of FTase enzyme to optimize hit selection.

Within this study, a unique 3D structure-based pharmacophore model of the enzyme glyoxalase-1 (Glo-1) has been revealed. Glo-1 is considered a zinc metalloenzyme in which the inhibitor binding with zinc atom at the active site is crucial. To our knowledge, this is the first pharmacophore model that has a selective feature for a “zinc binding group” which has been customized within the structure-based pharmacophore model of Glo-1 to extract ligands that possess functional groups able to bind zinc atom solely from database screening. In addition, an extensive 2D similarity search using three diverse similarity techniques (Tanimoto, Dice, Cosine) has been performed over the commercially available “Zinc Clean Drug-Like Database” that contains around 10 million compounds to help find suitable inhibitors for this enzyme based on known inhibitors from the literature. The resultant hits were mapped over the structure based pharmacophore and the successful hits were further docked using three docking programs with different pose fitting and scoring techniques (GOLD, LibDock, CDOCKER). Nine candidates were suggested to be novel Glo-1 inhibitors containing the “zinc binding group” with the highest consensus scoring from docking.

Dipeptidyl peptidase-4 (DPP-4) is the vital enzyme that is responsible for inactivating intestinal peptides glucagon like peptide-1 (GLP-1) and Gastric inhibitory polypeptide (GIP), which stimulates a decline in blood glucose levels. The aim of this study was to explore the inhibition activity of small molecule inhibitors to DPP-4 following a computational strategy based on docking studies and molecular dynamics simulations. The thorough docking protocol we applied allowed us to derive good correlation parameters between the predicted binding affinities (pKi) of the DPP-4 inhibitors and the experimental activity values (pIC50). Based on molecular docking receptor-ligand interactions, pharmacophore generation was carried out in order to identify the binding modes of structurally diverse compounds in the receptor active site. Consideration of the permanence and flexibility of DPP-4 inhibitor complexes by means of molecular dynamics (MD) simulation specified that the inhibitors maintained the binding mode observed in the docking study. The present study helps generate new information for further structural optimization and can influence the development of new DPP-4 inhibitors discoveries in the treatment of type-2 diabetes. [ABSTRACT FROM AUTHOR]

Dipeptidyl peptidase-4 (DPP-4) is the vital enzyme that is responsible for inactivating intestinal peptides glucagon like peptide-1 (GLP-1) and Gastric inhibitory polypeptide (GIP), which stimulates a decline in blood glucose levels. The aim of this study was to explore the inhibition activity of small molecule inhibitors to DPP-4 following a computational strategy based on docking studies and molecular dynamics simulations. The thorough docking protocol we applied allowed us to derive good correlation parameters between the predicted binding affinities (pKi) of the DPP-4 inhibitors and the experimental activity values (pIC50). Based on molecular docking receptor-ligand interactions, pharmacophore generation was carried out in order to identify the binding modes of structurally diverse compounds in the receptor active site. Consideration of the permanence and flexibility of DPP-4 inhibitor complexes by means of molecular dynamics (MD) simulation specified that the inhibitors maintained the binding mode observed in the docking study. The present study helps generate new information for further structural optimization and can influence the development of new DPP-4 inhibitors discoveries in the treatment of type-2 diabetes.

Abstract: The lipase from Malassezia globosa (SMG1) has specific activity on mono- and diacylglycerol but not on triacylglycerol. The structural analysis of SMG1 structure shows that two bulky aromatic residues, W116 and W229, lie at the entrance of the active site. To study the functions of these two residues in the substrate recognition and the catalytic reaction, they were mutated to a series of amino acids. Subsequently, biochemical properties of these mutants were investigated. Although the activities decrease, W229L and W116A show a significant shift in substrate preference. W229L has an increased preference for short-chain substrates whereas W116A has preference for long-chain substrates. Besides, the half-lives of W116A and W116H at 45 °C are 346.6 min and 115.5 min respectively, which improve significantly compared to that of native enzyme. Moreover, the optimum substrate of W116A, W116F and W229F mutants shifted from p-nitrophenyl caprylate to p-nitrophenyl myristate. These findings not only shed light onto the lipase structure/function relationship but also lay the framework for the potential industrial applications. [Copyright &y& Elsevier]

One of the significant human health concerns today is the emergence of drug-resistant bacteria and their propagation worldwide, rendering all available treatment options useless. One of the molecular mechanisms behind the evolution and dissemination of multidrug-resistant species is the overexpression of efflux pumps. AcrB is a major component of the AcrAB-TolC efflux pump belonging to the RND division and responsible for the extrusion of antibiotics from the bacterial cell leading to resistance. In this study, we applied the reverse chemogenomics technique to find inhibitors of AcrB to combat the issue of drug resistance. A total of 102 compounds from five different plants having medicinal properties were passed through Lipinski filter and ADMET screening was done to check their drug likeliness before docking against the AcrB protein and based on the -cDocker energy scores and toxicity analysis report, the compounds with best values were analyzed. A comparison was made between them and known inhibitors as well as antibiotics. Heat maps, frequency histograms, 2   D diagrams were generated, and the molecules were simulated for 60   ns using GROMACS. From the study, we have found Dihydrocapsaicin and Garcinexanthone-A to be a potential efflux pump inhibitors having all the characters of a promising drug candidate.Communicated by Ramaswamy H. Sarma.

Within this study, a unique 3D structure-based pharmacophore model of the enzyme glyoxalase-1 (Glo-1) has been revealed. Glo-1 is considered a zinc metalloenzyme in which the inhibitor binding with zinc atom at the active site is crucial. To our knowledge, this is the first pharmacophore model that has a selective feature for a "zinc binding group" which has been customized within the structure-based pharmacophore model of Glo-1 to extract ligands that possess functional groups able to bind zinc atom solely from database screening. In addition, an extensive 2D similarity search using three diverse similarity techniques (Tanimoto, Dice, Cosine) has been performed over the commercially available "Zinc Clean Drug-Like Database" that contains around 10 million compounds to help find suitable inhibitors for this enzyme based on known inhibitors from the literature. The resultant hits were mapped over the structure based pharmacophore and the successful hits were further docked using three docking programs with different pose fitting and scoring techniques (GOLD, LibDock, CDOCKER). Nine candidates were suggested to be novel Glo-1 inhibitors containing the "zinc binding group" with the highest consensus scoring from docking. [ABSTRACT FROM AUTHOR]

Vibrio fischeri is a marine luminous bacterium that has been widely used in toxicity bioassays, while Vibrio qinghaiensis sp.-Q67 is a newly found freshwater species which is more suitable for the tests on freshwater samples. However, there is a sensitive difference between these two species due to the media, chemical modes of action and the tested species. It remains unclear how these factors induce toxicity changes in luminous bioassays. Therefore, by using molecular docking between reactive chemicals and the target proteins of Vibrio fischeri and Vibrio qinghaiensis sp.-Q67 respectively, the sensitive difference was explored from the angle of amino acid residues that involved in the interactions. Mutation of amino acid residues was performed to investigate the role of these amino acids in the interactions and the most important amino acid residues in toxicity effect were found. The results suggested tat the most important amino acid residues in toxicity effect would affect the binding affinity between chemicals and target proteins of Vibrio fischeri and Vibrio qinghaiensis sp.-Q67, and then induce distinct toxic effect on them. As there are fewer toxicity data for freshwater Vibrio qinghaiensis sp.-Q67 than for Vibrio fischeri, this study helps to take advantage of the plentiful toxicity data of Vibrio fischeri to predict toxicities of freshwater samples. [ABSTRACT FROM AUTHOR]

A quasi 4D-QSAR has been carried out on a series of potent Gram-negative LpxC inhibitors. This approach makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package. This new methodology is based on the generation of a conformational ensemble profile, CEP, for each compound instead of only one conformation, followed by the calculation intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are independent variables employed in a QSAR analysis. The comparison of the proposed methodology to comparative molecular field analysis (CoMFA) formalism was performed. This methodology explores jointly the main features of CoMFA and 4D-QSAR models. Step-wise multiple linear regression was used for the selection of the most informative variables. After variable selection, multiple linear regression (MLR) and partial least squares (PLS) methods used for building the regression models. Leave-N-out cross-validation (LNO), and Y-randomization were performed in order to confirm the robustness of the model in addition to analysis of the independent test set. Best models provided the following statistics : $${{R}^{2}= 0.943, {q}^{2}_{\rm LOO}= 0.802, {q}^{2}_{\rm LNO}= 0.798, {R}^{2}_{\rm Pred}= 0.936}$$ (PLS) and $${{R}^{2 }= 0.948, {q}^{2}_{\rm LOO}= 0.823, {q}^{2}_{\rm LNO}= 0.818, {R}^{2}_{\rm Pred} = 0.928}$$ (MLR). Docking study was applied to investigate the major interactions in protein-ligand complex with CDOCKER algorithm. Visualization of the descriptors of the best model helps us to interpret the model from the chemical point of view, supporting the applicability of this new approach in rational drug design. [ABSTRACT FROM AUTHOR]

Using generated conformations from docking analysis by CDOCKER algorithm, some 3D-QSAR models; CoMFA region focusing (CoMFA-RF) and CoMSIA have been created on a series of a new class of potent and non-chiral renin inhibitors. The satisfactory predictions were obtained by CoMFA-RF and CoMSIA based on docking alignment in comparison to CoMFA. Robustness and predictability of the models were further verified by using the test set, cross validation (leave one out and leave ten out), bootstrapping, and progressive scrambling. All-orientation search (AOS) strategy was used to acquire the best orientation and minimize the effect of the initial orientation of aligned compounds. The results of 3D-QSAR models are in agreement with docking results. Moreover, the resulting 3D CoMFA-RF/ CoMSIA contour maps and corresponding models were applied to design new and more active inhibitors. [ABSTRACT FROM AUTHOR]

Abstract: Chemical features based 3D pharmacophore model for REarranged during Transfection (RET) tyrosine kinase were developed by using a training set of 26 structurally diverse known RET inhibitors. The best pharmacophore hypothesis, which identified inhibitors with an associated correlation coefficient of 0.90 between their experimental and estimated anti-RET values, contained one hydrogen-bond acceptor, one hydrogen-bond donor, one hydrophobic, and one ring aromatic features. The model was further validated by a testing set, Fischer’s randomization test, and goodness of hit (GH) test. We applied this pharmacophore model to screen NCI database for potential RET inhibitors. The hits were docked to RET with GOLD and CDOCKER after filtering by Lipinski’s rules. Ultimately, 24 molecules were selected as potential RET inhibitors for further investigation. [Copyright &y& Elsevier]

Succumbing to Multi-Drug Resistant (MDR) bacteria is a great distress to the recent health care system. Out of the several attempts that have been made to kill MDR pathogens, a few gained short-lived success. The failures, of the discovered or innovated antimicrobials, were mostly due to their high level of toxicity to hosts and the phenomenal rate of developing resistance by the pathogens against the new arsenal. Recently, a few quantum dots were tested against the pathogenic bacteria and therefore, justified for potential stockpiling of next-generation antibacterial agents. The key players for antimicrobial properties of quantum dots are considered to be Reactive Oxygen Species (ROS). The mechanism of reaction between bacteria and quantum dots needs to be better understood. They are generally targeted towards the cell wall and membrane components as lipoteichoic acid and phosphatidyl glycerol of bacteria have been documented here. In this paper, we have attempted to simulate ZnS quantum dots and have analysed their mechanism of reaction as well as binding potential to the above bacterial membrane components using CDOCKER. Results have shown a high level of antibacterial activity towards several pathogenic bacteria which specify their potentiality for future generation antibacterial drug development., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Kinesin spindle protein (KSP) belongs to the kinesin superfamily of microtubule-based motor proteins. KSP is responsible for the establishment of the bipolar mitotic spindle which mediates cell division. Inhibition of KSP expedites the blockade of the normal cell cycle during mitosis through the generation of monoastral MT arrays that finally cause apoptotic cell death. As KSP is highly expressed in proliferating/cancer cells, it has gained considerable attention as a potential drug target for cancer chemotherapy. Therefore, this study envisaged to design novel KSP inhibitors by employing computational techniques/tools such as pharmacophore modelling, virtual database screening, molecular docking and molecular dynamics. Initially, the pharmacophore models were generated from the data-set of highly potent KSP inhibitors and the pharmacophore models were validated against in house test set ligands. The validated pharmacophore model was then taken for database screening (Maybridge and ChemBridge) to yield hits, which were further filtered for their drug-likeliness. The potential hits retrieved from virtual database screening were docked using CDOCKER to identify the ligand binding landscape. The top-ranked hits obtained from molecular docking were progressed to molecular dynamics (AMBER) simulations to deduce the ligand binding affinity. This study identified MB-41570 and CB-10358 as potential hits and evaluated these experimentally using in vitro KSP ATPase inhibition assays.

The Plasmodium falciparum Lactate Dehydrogenase enzyme ( Pf LDH) catalyzes inter-conversion of pyruvate to lactate during glycolysis producing the energy required for parasitic growth. The Pf LDH has been studied as a potential molecular target for development of anti-malarial agents. In an attempt to find the potent inhibitor of Pf LDH, we have used Discovery studio to perform molecular docking in the active binding pocket of Pf LDH by CDOCKER, followed by three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic guanidine batzelladine compounds, which were previously synthesized in our laboratory. Docking studies showed that there is a very strong correlation between in silico and in vitro results. Based on docking results, a highly predictive 3D-QSAR model was developed with q 2 of 0.516. The model has predicted r 2 of 0.91 showing that predicted IC 50 values are in good agreement with experimental IC 50 values. The results obtained from this study revealed the developed model can be used to design new anti-malarial compounds based on tricyclic guanidine derivatives and to predict activities of new inhibitors.