Molecular docking based screening of predicted potential inhibitors for VP40 from Ebola virus

Abazari, Danya; Moghtadaei, Mehrad; Behvarmanesh, Ali; Ghannadi, Bahareh; Aghaei, Monireh; Behruznia, Mahboobeh; Rigi, Garshasb

doi:10.6026/97320630011243

Cited by 18 publications

(16 citation statements)

References 23 publications

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“…Both Ebola targets were considered as having the potential for screening large compound libraries in early drug discovery processes and several high-throughput studies have been performed recently on drug repurposing. However, to avoid the risk of rapid development of drug resistance, repurposed drugs must follow strict criteria because a few mutations can drastically alter the biological properties of RNA viruses [24,26,27,30,53,54,55,56]. …”

Section: Discussionmentioning

confidence: 99%

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: 99%

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

Mirza

Ikram

2016

IJMS

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“…In Ebola's case, several studies have been conducted to find new drug candidates from various target proteins through this method [22,[27][28][29][30]. Natural products have been considered as one of the most potential lead compound sources because of their interesting bioactivities.…”

Section: Introductionmentioning

confidence: 99%

Identification of novel Ebola virus (EBOV) VP24 inhibitor from Indonesian natural products through in silico drug design approach

Tambunan¹,

Nasution²

2017

AIP Conference Proceedings

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Sensitivity differences between microporous NaY and hierarchical ZSM-5 modified electrodes as ammonia gas sensor AIP Conference Proceedings 1862, 030081 (2017) Abstract. Ebola remains as one of the deadliest diseases in the world, with almost 29,000 cases were reported and kill 11,000 of them, and yet neither treatment nor vaccine that can combat this disease effectively. This disease is caused by ebolavirus (EBOV), a primary member of Filoviridae family. The life cycle of this virus has been operated by several key proteins, one of them is VP24 protein, which has been known for its crucial role in the transcription and replication of EBOV. Therefore, targeting VP24 protein can be a solution for treating this pathogenic disease. In this study, virtual screening of Indonesian natural products as EBOV VP24 inhibitor was performed. About 2,020 ligands from many sources, including HerbalDB database, were obtained and screened by using DataWarrior software to measure its molecular and pharmacological properties, resulting 301 ligands in the process. Then, the molecular docking simulation was performed to check the ligand's binding interaction and affinity with EBOV VP24 protein; this simulation was done by using MOE 2014.09 software. This study resulted that cycloartocarpin was the best ligand to inhibit the EBOV VP24 protein. Therefore, this ligand should be checked its stability through molecular dynamics simulation and performed in vitro test to verify its bioactivity against the EBOV VP24 protein.

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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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Molecular docking based screening of predicted potential inhibitors for VP40 from Ebola virus

Cited by 18 publications

References 23 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

Identification of novel Ebola virus (EBOV) VP24 inhibitor from Indonesian natural products through in silico drug design approach

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

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