scholarly journals Ensemble Learning for Large Scale Virtual Screening on Apache Spark

2018 ◽  
pp. 244-256
Author(s):  
Karima Sid ◽  
Mohamed Batouche
Keyword(s):  
Virtual Screening ◽  
Ensemble Learning ◽  
Large Scale ◽  
Apache Spark

scholarly journals Large-scale virtual screening on public cloud resources with Apache Spark

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Marco Capuccini ◽  
Laeeq Ahmed ◽  
Wesley Schaal ◽  
Erwin Laure ◽  
Ola Spjuth
Keyword(s):  
Virtual Screening ◽  
Large Scale ◽  
Apache Spark ◽  
Public Cloud ◽  
Cloud Resources

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 Apache Spark Accelerated Deep Learning Inference for Large Scale Satellite Image Analytics

2020 ◽  
Vol 13 ◽  
pp. 271-283 ◽  
Author(s):  
Dalton Lunga ◽  
Jonathan Gerrand ◽  
Lexie Yang ◽  
Christopher Layton ◽  
Robert Stewart
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Satellite Image ◽  
Apache Spark
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