Large-scale virtual screening for discovering leads in the postgenomic era

2001 ◽  
Vol 40 (2) ◽  
pp. 360-376 ◽  
Author(s):  
B. Waszkowycz ◽  
T. D. J. Perkins ◽  
R. A. Sykes ◽  
J. Li
Keyword(s):  
Virtual Screening ◽  
Large Scale

scholarly journals A novel ligand of the translationally controlled tumor protein (TCTP) identified by virtual drug screening for cancer differentiation therapy

2021 ◽  
Author(s):  
Nicolas Fischer ◽  
Ean-Jeong Seo ◽  
Sara Abdelfatah ◽  
Edmond Fleischer ◽  
Anette Klinger ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Virtual Screening ◽  
Small Molecules ◽  
Large Scale ◽  
Differentiation Therapy ◽  
P53 Expression ◽  
Microscale Thermophoresis ◽  
Cycle Arrest ◽  
G1 Cell Cycle Arrest ◽  
Mcf 7

SummaryIntroduction Differentiation therapy is a promising strategy for cancer treatment. The translationally controlled tumor protein (TCTP) is an encouraging target in this context. By now, this field of research is still at its infancy, which motivated us to perform a large-scale screening for the identification of novel ligands of TCTP. We studied the binding mode and the effect of TCTP blockade on the cell cycle in different cancer cell lines. Methods Based on the ZINC-database, we performed virtual screening of 2,556,750 compounds to analyze the binding of small molecules to TCTP. The in silico results were confirmed by microscale thermophoresis. The effect of the new ligand molecules was investigated on cancer cell survival, flow cytometric cell cycle analysis and protein expression by Western blotting and co-immunoprecipitation in MOLT-4, MDA-MB-231, SK-OV-3 and MCF-7 cells. Results Large-scale virtual screening by PyRx combined with molecular docking by AutoDock4 revealed five candidate compounds. By microscale thermophoresis, ZINC10157406 (6-(4-fluorophenyl)-2-[(8-methoxy-4-methyl-2-quinazolinyl)amino]-4(3H)-pyrimidinone) was identified as TCTP ligand with a KD of 0.87 ± 0.38. ZINC10157406 revealed growth inhibitory effects and caused G0/G1 cell cycle arrest in MOLT-4, SK-OV-3 and MCF-7 cells. ZINC10157406 (2 × IC50) downregulated TCTP expression by 86.70 ± 0.44% and upregulated p53 expression by 177.60 ± 12.46%. We validated ZINC10157406 binding to the p53 interaction site of TCTP and replacing p53 by co-immunoprecipitation. Discussion ZINC10157406 was identified as potent ligand of TCTP by in silico and in vitro methods. The compound bound to TCTP with a considerably higher affinity compared to artesunate as known TCTP inhibitor. We were able to demonstrate the effect of TCTP blockade at the p53 binding site, i.e. expression of TCTP decreased, whereas p53 expression increased. This effect was accompanied by a dose-dependent decrease of CDK2, CDK4, CDK, cyclin D1 and cyclin D3 causing a G0/G1 cell cycle arrest in MOLT-4, SK-OV-3 and MCF-7 cells. Our findings are supposed to stimulate further research on TCTP-specific small molecules for differentiation therapy in oncology.

scholarly journals Computational generation of an annotated gigalibrary of synthesizable, composite peptidic macrocycles

2020 ◽  
Vol 117 (40) ◽  
pp. 24679-24690
Author(s):  
Ishika Saha ◽  
Eric K. Dang ◽  
Dennis Svatunek ◽  
Kendall N. Houk ◽  
Patrick G. Harran
Keyword(s):  
Virtual Screening ◽  
Open Source ◽  
Open Source Software ◽  
Large Scale ◽  
Three Dimensional ◽  
Pharmacological Properties ◽  
Robust Methods ◽  
Small Peptide ◽  
Multistep Reaction ◽  
Condensed Heterocycles

Peptidomimetic macrocycles have the potential to regulate challenging therapeutic targets. Structures of this type having precise shapes and drug-like character are particularly coveted, but are relatively difficult to synthesize. Our laboratory has developed robust methods that integrate small-peptide units into designed scaffolds. These methods create macrocycles and embed condensed heterocycles to diversify outcomes and improve pharmacological properties. The hypothetical scope of the methodology is vast and far outpaces the capacity of our experimental format. We now describe a computational rendering of our methodology that creates an in silico three-dimensional library of composite peptidic macrocycles. Our open-source platform, CPMG (Composite Peptide Macrocycle Generator), has algorithmically generated a library of 2,020,794,198 macrocycles that can result from the multistep reaction sequences we have developed. Structures are generated based on predicted site reactivity and filtered on the basis of physical and three-dimensional properties to identify maximally diverse compounds for prioritization. For conformational analyses, we also introduce ConfBuster++, an RDKit port of the open-source software ConfBuster, which allows facile integration with CPMG and ready parallelization for better scalability. Our approach deeply probes ligand space accessible via our synthetic methodology and provides a resource for large-scale virtual screening.

Large-scale virtual screening for the identification of new Helicobacter pylori urease inhibitor scaffolds

2011 ◽  
Vol 18 (7) ◽  
pp. 2917-2927 ◽  
Author(s):  
Homa Azizian ◽  
Farzaneh Nabati ◽  
Amirhossein Sharifi ◽  
Farideh Siavoshi ◽  
Mohammad Mahdavi ◽  
...  
Keyword(s):  
Helicobacter Pylori ◽  
Virtual Screening ◽  
Large Scale ◽  
Urease Inhibitor ◽  
Inhibitor Scaffolds

Large-Scale Systematic Analysis of 2D Fingerprint Methods and Parameters to Improve Virtual Screening Enrichments

2010 ◽  
Vol 50 (5) ◽  
pp. 771-784 ◽  
Author(s):  
Madhavi Sastry ◽  
Jeffrey F. Lowrie ◽  
Steven L. Dixon ◽  
Woody Sherman
Keyword(s):  
Virtual Screening ◽  
Large Scale ◽  
Systematic Analysis

scholarly journals A Multi-Pronged Approach Targeting SARS-CoV-2 Proteins Using Ultra-Large Virtual Screening

2020 ◽  
Author(s):  
Christoph Gorgulla ◽  
Krishna PadmanabhaDas ◽  
Kendra E. Leigh ◽  
Marco Cespugli ◽  
Patrick D. Fischer ◽  
...  
Keyword(s):  
Virtual Screening ◽  
In Silico ◽  
Active Sites ◽  
Large Scale ◽  
Protein Protein Interaction ◽  
The Face ◽  
Computing Platforms ◽  
Screening Platform ◽  
Novel Coronavirus ◽  
Further Development

<p>Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), previously known as 2019 novel coronavirus (2019-nCoV), has spread rapidly across the globe, creating an unparalleled global health burden and spurring a deepening economic crisis. As of July 7th, 2020, almost seven months into the outbreak, there are no approved vaccines and few treatments available. Developing drugs that target multiple points in the viral life cycle could serve as a strategy to tackle the current as well as future coronavirus pandemics. Here we leverage the power of our recently developed <i>in silico</i> screening platform, VirtualFlow, to identify inhibitors that target SARS-CoV-2. VirtualFlow is able to efficiently harness the power of computing clusters and cloud-based computing platforms to carry out ultra-large scale virtual screens. In this unprecedented structure-based multi-target virtual screening campaign, we have used VirtualFlow to screen an average of approximately 1 billion molecules against each of 40 different target sites on 17 different potential viral and host targets in the cloud. In addition to targeting the active sites of viral enzymes, we also target critical auxiliary sites such as functionally important protein-protein interaction interfaces. This multi-target approach not only increases the likelihood of finding a potent inhibitor, but could also help identify a collection of anti-coronavirus drugs that would retain efficacy in the face of viral mutation. Drugs belonging to different regimen classes could be combined to develop possible combination therapies, and top hits that bind at highly conserved sites would be potential candidates for further development as coronavirus drugs. Here, we present the top 200 <i>in silico</i> hits for each target site. While in-house experimental validation of some of these compounds is currently underway, we want to make this array of potential inhibitor candidates available to researchers worldwide in consideration of the pressing need for fast-tracked drug development.</p>

scholarly journals ScaffComb: A Phenotype‐Based Framework for Drug Combination Virtual Screening in Large‐Scale Chemical Datasets

2021 ◽  
pp. 2102092
Author(s):  
Zhaofeng Ye ◽  
Fengling Chen ◽  
Jiangyang Zeng ◽  
Juntao Gao ◽  
Michael Q. Zhang
Keyword(s):  
Virtual Screening ◽  
Drug Combination ◽  
Large Scale

scholarly journals Multiscale virtual screening optimization for shotgun drug repurposing using the CANDO platform

2020 ◽  
Author(s):  
Matthew L. Hudson ◽  
Ram Samudrala
Keyword(s):  
Virtual Screening ◽  
Decision Tree ◽  
Protein Interactions ◽  
Large Scale ◽  
Therapeutic Potential ◽  
Protein Structures ◽  
Drug Repurposing ◽  
Clinical Indication ◽  
Docking Method ◽  
Useful Signal

AbstractDrug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multidisease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines via large scale modelling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is then compared to all other signatures that are then sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions in the platform used to create the drug-proteome signatures may be determined by any screening or docking method but the primary approach used thus far has been an in house similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and cheminformatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the corresponding two docking-based pipelines it was synthesized from, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking based signature generation methods can capture unique and useful signal for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.

Discovery of Novel Human Histamine H4 Receptor Ligands by Large-Scale Structure-Based Virtual Screening

2008 ◽  
Vol 51 (11) ◽  
pp. 3145-3153 ◽  
Author(s):  
Róbert Kiss ◽  
Béla Kiss ◽  
Árpád Könczöl ◽  
Ferenc Szalai ◽  
Ivett Jelinek ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Large Scale ◽  
Large Scale Structure ◽  
Scale Structure ◽  
Receptor Ligands ◽  
Histamine H4 Receptor ◽  
H4 Receptor ◽  
Human Histamine
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