Virtual Screening with Gnina 1.0.
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ABSTRACT:
SUBMITTER: Sunseri J
PROVIDER: S-EPMC8659095 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:The virtual screening (VS) of lead compounds using molecular docking and pharmacophore detection is now an important tool in drug discovery. VS tasks typically require a combination of several software tools and a molecular graphics system. Thus, the integration of all the requisite tools in a single operating environment could reduce the complexity of running VS experiments. However, only a few freely available integrated software platforms have been developed. RESULTS:A free open-source platform, IVSPlat 1.0, was developed in this study for the management and automation of VS tasks. We integrated several VS-related programs into a molecular graphics system to provide a comprehensive platform for the solution of VS tasks based on molecular docking, pharmacophore detection, and a combination of both methods. This tool can be used to visualize intermediate and final results of the VS execution, while also providing a clustering tool for the analysis of VS results. A case study was conducted to demonstrate the applicability of this platform. CONCLUSIONS:IVSPlat 1.0 provides a plug-in-based solution for the management, automation, and visualization of VS tasks. IVSPlat 1.0 is an open framework that allows the integration of extra software to extend its functionality and modified versions can be freely distributed. The open source code and documentation are available at http://kyc.nenu.edu.cn/IVSPlat/.
| S-EPMC3264508 | biostudies-literature
Project description:AIM: This study was conducted to compare the efficiencies of two virtual screening approaches, pharmacophore-based virtual screening (PBVS) and docking-based virtual screening (DBVS) methods. METHODS: All virtual screens were performed on two data sets of small molecules with both actives and decoys against eight structurally diverse protein targets, namely angiotensin converting enzyme (ACE), acetylcholinesterase (AChE), androgen receptor (AR), D-alanyl-D-alanine carboxypeptidase (DacA), dihydrofolate reductase (DHFR), estrogen receptors alpha (ERalpha), HIV-1 protease (HIV-pr), and thymidine kinase (TK). Each pharmacophore model was constructed based on several X-ray structures of protein-ligand complexes. Virtual screens were performed using four screening standards, the program Catalyst for PBVS and three docking programs (DOCK, GOLD and Glide) for DBVS. RESULTS: Of the sixteen sets of virtual screens (one target versus two testing databases), the enrichment factors of fourteen cases using the PBVS method were higher than those using DBVS methods. The average hit rates over the eight targets at 2% and 5% of the highest ranks of the entire databases for PBVS are much higher than those for DBVS. CONCLUSION: The PBVS method outperformed DBVS methods in retrieving actives from the databases in our tested targets, and is a powerful method in drug discovery.
| S-EPMC4007494 | biostudies-literature
Project description:Effective virtual screening relies on our ability to make accurate prediction of protein-ligand binding, which remains a great challenge. In this work, utilizing the molecular-mechanics Poisson-Boltzmann (or Generalized Born) surface area approach, we have evaluated the binding affinity of a set of 156 ligands to seven families of proteins, trypsin ?, thrombin ?, cyclin-dependent kinase (CDK), cAMP-dependent kinase (PKA), urokinase-type plasminogen activator, ?-glucosidase A, and coagulation factor Xa. The effect of protein dielectric constant in the implicit-solvent model on the binding free energy calculation is shown to be important. The statistical correlations between the binding energy calculated from the implicit-solvent approach and experimental free energy are in the range of 0.56-0.79 across all the families. This performance is better than that of typical docking programs especially given that the latter is directly trained using known binding data whereas the molecular mechanics is based on general physical parameters. Estimation of entropic contribution remains the barrier to accurate free energy calculation. We show that the traditional rigid rotor harmonic oscillator approximation is unable to improve the binding free energy prediction. Inclusion of conformational restriction seems to be promising but requires further investigation. On the other hand, our preliminary study suggests that implicit-solvent based alchemical perturbation, which offers explicit sampling of configuration entropy, can be a viable approach to significantly improve the prediction of binding free energy. Overall, the molecular mechanics approach has the potential for medium to high-throughput computational drug discovery.
| S-EPMC3092865 | biostudies-literature
Project description:The practice of virtual screening (VS) to identify chemical leads to known or novel targets is becoming a core function of the computational chemist within industry. By employing a range of techniques, when attempting to identify compounds with activity against a biological target, a small focused subset of a larger collection of compounds can be identified and tested, often with results much better than selecting a similar number of compounds at random. We will review the key methods available, their relative success, and provide practical insights into best practices and key gaps. We will also argue that the capability of VS methods has grown to a point where fuller integration with experimental methods, including HTS, could increase the effectiveness of both.
| S-EPMC7176223 | biostudies-literature
Project description:BackgroundThere are three main problems associated with the virtual screening of bioassay data. The first is access to freely-available curated data, the second is the number of false positives that occur in the physical primary screening process, and finally the data is highly-imbalanced with a low ratio of Active compounds to Inactive compounds. This paper first discusses these three problems and then a selection of Weka cost-sensitive classifiers (Naive Bayes, SVM, C4.5 and Random Forest) are applied to a variety of bioassay datasets.ResultsPharmaceutical bioassay data is not readily available to the academic community. The data held at PubChem is not curated and there is a lack of detailed cross-referencing between Primary and Confirmatory screening assays. With regard to the number of false positives that occur in the primary screening process, the analysis carried out has been shallow due to the lack of cross-referencing mentioned above. In six cases found, the average percentage of false positives from the High-Throughput Primary screen is quite high at 64%. For the cost-sensitive classification, Weka's implementations of the Support Vector Machine and C4.5 decision tree learner have performed relatively well. It was also found, that the setting of the Weka cost matrix is dependent on the base classifier used and not solely on the ratio of class imbalance.ConclusionsUnderstandably, pharmaceutical data is hard to obtain. However, it would be beneficial to both the pharmaceutical industry and to academics for curated primary screening and corresponding confirmatory data to be provided. Two benefits could be gained by employing virtual screening techniques to bioassay data. First, by reducing the search space of compounds to be screened and secondly, by analysing the false positives that occur in the primary screening process, the technology may be improved. The number of false positives arising from primary screening leads to the issue of whether this type of data should be used for virtual screening. Care when using Weka's cost-sensitive classifiers is needed - across the board misclassification costs based on class ratios should not be used when comparing differing classifiers for the same dataset.
| S-EPMC2820499 | biostudies-literature
Project description:alpha-Cobratoxin (Cbtx), the neurotoxin isolated from the venom of the Thai cobra Naja kaouthia , causes paralysis by preventing acetylcholine (ACh) binding to nicotinic acetylcholine receptors (nAChRs). In the current study, the region of the Cbtx molecule that is directly involved in binding to nAChRs is used as the target for anticobratoxin drug design. The crystal structure (1YI5) of Cbtx in complex with the acetylcholine binding protein (AChBP), a soluble homolog of the extracellular binding domain of nAChRs, was selected to prepare an alpha-cobratoxin active binding site for docking. The amino acid residues (Ser182-Tyr192) of the AChBP structure, the binding site of Cbtx, were used as the positive control to validate the prepared Cbtx active binding site (root mean square deviation < 1.2 A). Virtual screening of the National Cancer Institute diversity set, a library of 1990 compounds with nonredundant pharmacophore profiles, using AutoDock against the Cbtx active site, revealed 39 potential inhibitor candidates. The adapted in vitro radioligand competition assays using [(3)H]epibatidine and [(125)I]bungarotoxin against the AChBPs from the marine species, Aplysia californica (Ac), and from the freshwater snails, Lymnaea stagnalis (Ls) and Bolinus truncates (Bt), revealed 4 compounds from the list of inhibitor candidates that had micromolar to nanomolar interferences for the toxin binding to AChBPs. Three hits (NSC42258, NSC121865, and NSC134754) can prolong the survival time of the mice if administered 30 min before injection with Cbtx, but only NSC121865 and NSC134754 can prolong the survival time if injected immediately after injection with Cbtx. These inhibitors serve as novel templates/scaffolds for the development of more potent and specific anticobratoxin.
| S-EPMC3191909 | biostudies-literature
Project description:The H3D virtual screening cascade contains models for Mycobacterium tuberculosis and Plasmodium falciparum IC50 predictions, as well as ADME, cytotoxicity, and solubility assays.
Model Type: Predictive machine learning model.
Model Relevance: This panel of models provides predictions for the H3D virtual screening cascade.
Model Encoded by: Miquel Duran-Frigola (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam
Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos7kpb
2024-05-13 | MODEL2403270001 | BioModels
Project description:The nicotinic acetylcholine receptors (nAChRs) are a member of the ligand-gated ion channel family and play a key role in the transfer of information across neurological networks. The X-ray crystal structure of agonist-bound ?(7) acetylcholine binding protein (AChBP) has been recognized as the most appropriate template to model the ligand-binding domain of nAChR for studying the molecular mechanism of the receptor-ligand interactions. Virtual screening of the National Cancer Institute diversity set, a library of 1990 compounds with nonredundant pharmacophore profiles, using AutoDock against AChBPs revealed 51 potential candidates. In vitro radioligand competition assays using [(3)H] epibatidine against the AChBPs from the freshwater snails, Lymnaea stagnalis, and from the marine species, Aplysia californica and the mutant (AcY55W), revealed seven compounds from the list of candidates that had micromolar to nanomolar affinities for the AChBPs. Further investigation on ?(7)nAChR expressing in Xenopus oocytes and on the recombinant receptors with fluorescence resonance energy transfer (FRET)-based calcium sensor expressing in HEK cells showed that seven compounds were antagonists of ?(7)nAChR, only one compound (NSC34352) demonstrated partial agonistic effect at low dose (10 µM), and two compounds (NSC36369 and NSC34352) were selective antagonists on ?(7)nAchR with moderate potency. These hits serve as novel templates/scaffolds for development of more potent and specific in the AChR systems.
| S-EPMC4762448 | biostudies-literature
Project description:Virtual high-throughput screening (vHTS) is an invaluable method in modern drug discovery. It permits screening large datasets or databases of chemical structures for those structures binding possibly to a drug target. Virtual screening is typically performed by docking code, which often runs sequentially. Processing of huge vHTS datasets can be parallelized by chunking the data because individual docking runs are independent of each other. The goal of this work is to find an optimal splitting maximizing the speedup while considering overhead and available cores on Distributed Computing Infrastructures (DCIs). We have conducted thorough performance studies accounting not only for the runtime of the docking itself, but also for structure preparation. Performance studies were conducted via the workflow-enabled science gateway MoSGrid (Molecular Simulation Grid). As input we used benchmark datasets for protein kinases. Our performance studies show that docking workflows can be made to scale almost linearly up to 500 concurrent processes distributed even over large DCIs, thus accelerating vHTS campaigns significantly.
| S-EPMC4083208 | biostudies-literature
Project description:Computational approaches for predicting protein-ligand interactions can facilitate drug lead discovery and drug target determination. We have previously developed a threading/structural-based approach, FINDSITEcomb, for the virtual ligand screening of proteins that has been extensively experimentally validated. Even when low resolution predicted protein structures are employed, FINDSITEcomb has the advantage of being faster and more accurate than traditional high-resolution structure-based docking methods. It also overcomes the limitations of traditional QSAR methods that require a known set of seed ligands that bind to the given protein target. Here, we further improve FINDSITEcomb by enhancing its template ligand selection from the PDB/DrugBank/ChEMBL libraries of known protein-ligand interactions by (1) parsing the template proteins and their corresponding binding ligands in the DrugBank and ChEMBL libraries into domains so that the ligands with falsely matched domains to the targets will not be selected as template ligands; (2) applying various thresholds to filter out falsely matched template structures in the structure comparison process and thus their corresponding ligands for template ligand selection. With a sequence identity cutoff of 30% of target to templates and modeled target structures, FINDSITEcomb2.0 is shown to significantly improve upon FINDSITEcomb on the DUD-E benchmark set by increasing the 1% enrichment factor from 16.7 to 22.1, with a p-value of 4.3 × 10-3 by the Student t-test. With an 80% sequence identity cutoff of target to templates for the DUD-E set and modeled target structures, FINDSITEcomb2.0, having a 1% ROC enrichment factor of 52.39, also outperforms state-of-the-art methods that employ machine learning such as a deep convolutional neural network, CNN, with an enrichment of 29.65. Thus, FINDSITEcomb2.0 represents a significant improvement in the state-of-the-art. The FINDSITEcomb2.0 web service is freely available for academic users at http://pwp.gatech.edu/cssb/FINDSITE-COMB-2 .
| S-EPMC6437778 | biostudies-literature