Peptide Similarity Search Based and Virtual Screening Based Strategies to Identify Small Molecules to Inhibit CarD–RNAP Interaction in M. tuberculosis

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.

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Binding Ensembles of p53-MDM2 Peptide Inhibitors by Combining Bayesian Inference and Atomistic Simulations

Molecules ◽

10.3390/molecules26010198 ◽

2021 ◽

Vol 26 (1) ◽

pp. 198

Author(s):

Lijun Lang ◽

Alberto Perez

Keyword(s):

Bayesian Inference ◽

Virtual Screening ◽

Statistical Mechanics ◽

Small Molecules ◽

Rational Design ◽

Driving Forces ◽

Peptide Inhibitors ◽

Atomistic Simulations ◽

Binding Mechanism ◽

Intrinsically Disordered

Designing peptide inhibitors of the p53-MDM2 interaction against cancer is of wide interest. Computational modeling and virtual screening are a well established step in the rational design of small molecules. But they face challenges for binding flexible peptide molecules that fold upon binding. We look at the ability of five different peptides, three of which are intrinsically disordered, to bind to MDM2 with a new Bayesian inference approach (MELD × MD). The method is able to capture the folding upon binding mechanism and differentiate binding preferences between the five peptides. Processing the ensembles with statistical mechanics tools depicts the most likely bound conformations and hints at differences in the binding mechanism. Finally, the study shows the importance of capturing two driving forces to binding in this system: the ability of peptides to adopt bound conformations (ΔGconformation) and the interaction between interface residues (ΔGinteraction).

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Identification of Novel Inhibitors for Tobacco Mosaic Virus Infection in Solanaceae Plants

Advances in Bioinformatics ◽

10.1155/2015/198214 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

Archana Prabahar ◽

Subashini Swaminathan ◽

Arul Loganathan ◽

Ramalingam Jegadeesan

Keyword(s):

Virtual Screening ◽

Rna Polymerase ◽

Tobacco Mosaic Virus ◽

Small Molecules ◽

Mosaic Virus ◽

Catalytic Domain ◽

Docking Analysis ◽

Catalytic Sites ◽

Capping Enzyme ◽

Tobacco Mosaic Virus Infection

Tobacco mosaic virus (TMV) infects several crops of economic importance (e.g., tomato) and remains as one of the major concerns to the farmers. TMV enters the host cell and produces the capping enzyme RNA polymerase. The viral genome replicates further to produce multiple mRNAs which encodes several proteins, including the coat protein and an RNA-dependent RNA polymerase (RdRp), as well as the movement protein. TMV replicase domain was chosen for the virtual screening studies against small molecules derived from ligand databases such as PubChem and ChemBank. Catalytic sites of the RdRp domain were identified and subjected to docking analysis with screened ligands derived from virtual screening LigandFit. Small molecules that interact with the target molecule at the catalytic domain region amino acids, GDD, were chosen as the best inhibitors for controlling the TMV replicase activity.

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Computational Characterization of Small Molecules Binding to the Human XPF Active Site and Virtual Screening to Identify Potential New DNA Repair Inhibitors Targeting the ERCC1-XPF Endonuclease

International Journal of Molecular Sciences ◽

10.3390/ijms19051328 ◽

2018 ◽

Vol 19 (5) ◽

pp. 1328 ◽

Cited By ~ 9

Author(s):

Francesco Gentile ◽

Khaled Barakat ◽

Jack Tuszynski

Keyword(s):

Dna Repair ◽

Virtual Screening ◽

Small Molecules ◽

Active Site ◽

Dna Repair Inhibitors

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Development of Small-Molecules Targeting Receptor Activator of Nuclear Factor-κB Ligand (RANKL)—Receptor Activator of Nuclear Factor-κB (RANK) Protein–Protein Interaction by Structure-Based Virtual Screening and Hit Optimization

Journal of Medicinal Chemistry ◽

10.1021/acs.jmedchem.8b02027 ◽

2019 ◽

Vol 62 (11) ◽

pp. 5370-5381 ◽

Cited By ~ 5

Author(s):

Min Jiang ◽

Lei Peng ◽

Kai Yang ◽

Tianqi Wang ◽

Xueming Yan ◽

...

Keyword(s):

Virtual Screening ◽

Small Molecules ◽

Protein Interaction ◽

Nuclear Factor Κb ◽

Nuclear Factor ◽

Protein Protein Interaction ◽

Receptor Activator

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Accelerating Drug Discovery by Early Protein Drug Target Prediction Based on a Multi-Fingerprint Similarity Search †

Molecules ◽

10.3390/molecules24122233 ◽

2019 ◽

Vol 24 (12) ◽

pp. 2233 ◽

Cited By ~ 11

Author(s):

Michele Montaruli ◽

Domenico Alberga ◽

Fulvio Ciriaco ◽

Daniela Trisciuzzi ◽

Anna Rita Tondo ◽

...

Keyword(s):

Drug Discovery ◽

Small Molecules ◽

Similarity Search ◽

Drug Targets ◽

Search Algorithm ◽

Confidence Score ◽

Database Management System ◽

Therapeutic Drug ◽

Protein Drug ◽

Early Protein

In this continuing work, we have updated our recently proposed Multi-fingerprint Similarity Search algorithm (MuSSel) by enabling the generation of dominant ionized species at a physiological pH and the exploration of a larger data domain, which included more than half a million high-quality small molecules extracted from the latest release of ChEMBL (version 24.1, at the time of writing). Provided with a high biological assay confidence score, these selected compounds explored up to 2822 protein drug targets. To improve the data accuracy, samples marked as prodrugs or with equivocal biological annotations were not considered. Notably, MuSSel performances were overall improved by using an object-relational database management system based on PostgreSQL. In order to challenge the real effectiveness of MuSSel in predicting relevant therapeutic drug targets, we analyzed a pool of 36 external bioactive compounds published in the Journal of Medicinal Chemistry from October to December 2018. This study demonstrates that the use of highly curated chemical and biological experimental data on one side, and a powerful multi-fingerprint search algorithm on the other, can be of the utmost importance in addressing the fate of newly conceived small molecules, by strongly reducing the attrition of early phases of drug discovery programs.

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