Flavonoids as Multi-target Inhibitors for Proteins Associated with Ebola Virus: In Silico Discovery Using Virtual Screening and Molecular Docking Studies

Raj, Utkarsh; Varadwaj, Pritish Kumar

doi:10.1007/s12539-015-0109-8

Cited by 79 publications

(45 citation statements)

References 32 publications

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“…These hits are likely non-specific compounds and are found because of assay artefacts such as aggregation. When Gossypentin and Taxifolin were screened as active multitarget inhibitors, these compounds were found to be 94% and 91%, respectively, similar to compounds that have been reported as aggregators [47,67,68,69,70]. Being a predominant mechanism for artefactual inhibition of proteins through non-specific interactions, several controls against this are now widely employed to screen for virtual hits with previously reported aggregators [71].…”

Section: Discussionmentioning

confidence: 98%

Integrated Computational Approach for Virtual Hit Identification against Ebola Viral Proteins VP35 and VP40

Mirza

Ikram

2016

IJMS

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The Ebola virus (EBOV) has been recognised for nearly 40 years, with the most recent EBOV outbreak being in West Africa, where it created a humanitarian crisis. Mortalities reported up to 30 March 2016 totalled 11,307. However, up until now, EBOV drugs have been far from achieving regulatory (FDA) approval. It is therefore essential to identify parent compounds that have the potential to be developed into effective drugs. Studies on Ebola viral proteins have shown that some can elicit an immunological response in mice, and these are now considered essential components of a vaccine designed to protect against Ebola haemorrhagic fever. The current study focuses on chemoinformatic approaches to identify virtual hits against Ebola viral proteins (VP35 and VP40), including protein binding site prediction, drug-likeness, pharmacokinetic and pharmacodynamic properties, metabolic site prediction, and molecular docking. Retrospective validation was performed using a database of non-active compounds, and early enrichment of EBOV actives at different false positive rates was calculated. Homology modelling and subsequent superimposition of binding site residues on other strains of EBOV were carried out to check residual conformations, and hence to confirm the efficacy of potential compounds. As a mechanism for artefactual inhibition of proteins through non-specific compounds, virtual hits were assessed for their aggregator potential compared with previously reported aggregators. These systematic studies have indicated that a few compounds may be effective inhibitors of EBOV replication and therefore might have the potential to be developed as anti-EBOV drugs after subsequent testing and validation in experiments in vivo.

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Section: Discussionmentioning

confidence: 98%

Integrated Computational Approach for Virtual Hit Identification against Ebola Viral Proteins VP35 and VP40

Mirza

Ikram

2016

IJMS

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“…A follow up to this study proposed ibuprofen for testing 21 . Others have also used computational docking studies to propose multi-target inhibitors of VP40, VP35, VP30 and VP24 22 , inhibitors of VP40 23 or have suggested molecules to test in the absence of computational approaches 24, 25 . We are unaware of any validation of these compounds.…”

Section: Introductionmentioning

confidence: 99%

Machine learning models identify molecules active against the Ebola virus in vitro

et al. 2016

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The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC 50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

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“…152 Moreover, the recent resolution of the structure of the VP24-KPNA5 complex (PDB-ID: 4U2X) allowed to perform a number of in silico studies to identify possible inhibitors. [159][160][161] In the same in silico screening, a strong interaction was identified between VP24 and ST060285 ortaxifolin, a flavonoid commonly present in different conifer species including Taxus chinensis and Larix sibirica, whose antiviral properties have been known for a long time. 7), and theaflavin-3,3 0 -digallate.…”

Section: Vp24 Inhibitorsmentioning

confidence: 99%

“…[154][155][156] Docking studies showed a certain VP24 binding affinity to some plant polyphenols such as epigallocatechin gallate, 1,2,3,6-tetragalloyl glucose (1,2,3,6-TGG) (Fig. [161][162][163] While gossypetin and taxifolin have been found to interact also with other protein targets, VP40, VP35, and VP30, the flavonoid ST101866 (Fig. 157 Three small molecules extracted from Indian medicinal plants, limonin (Fig.…”

Section: Vp24 Inhibitorsmentioning

confidence: 99%

Antiviral Agents Against Ebola Virus Infection: Repositioning Old Drugs and Finding Novel Small Molecules

Fanunza

Frau

Corona

et al. 2018

Annual Reports in Medicinal Chemistry

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Flavonoids as Multi-target Inhibitors for Proteins Associated with Ebola Virus: In Silico Discovery Using Virtual Screening and Molecular Docking Studies

Cited by 79 publications

References 32 publications

Integrated Computational Approach for Virtual Hit Identification against Ebola Viral Proteins VP35 and VP40

Integrated Computational Approach for Virtual Hit Identification against Ebola Viral Proteins VP35 and VP40

Machine learning models identify molecules active against the Ebola virus in vitro

Antiviral Agents Against Ebola Virus Infection: Repositioning Old Drugs and Finding Novel Small Molecules

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