Identification of drugs targeting multiple viral and human proteins using computational analysis for repurposing against COVID-19

The SARS-CoV2 is a highly contagious pathogen that causes a respiratory disease named COVID-19. The COVID-19 was declared a pandemic by the WHO on 11th March 2020. It has affected about 5.38 million people globally (identified cases as on 24th May 2020), with an average lethality of ~3%. Unfortunately, there is no standard cure for the disease, although some drugs are under clinical trial. Thus, there is an urgent need of drugs for the treatment of COVID-19. The molecularly targeted therapies have proven their utility in various diseases such as HIV, SARS, and HCV. Therefore, a lot of efforts are being directed towards the identification of molecules that can be helpful in the management of COVID-19. In the current studies, we have used state of the art bioinformatics techniques to screen the FDA approved drugs against thirteen SARS-CoV2 proteins in order to identify drugs for quick repurposing. The strategy was to identify potential drugs that can target multiple viral proteins simultaneously. Our strategy originates from the fact that individual viral proteins play specific role in multiple aspects of viral lifecycle such as attachment, entry, replication, morphogenesis and egress and targeting them simultaneously will have better inhibitory effect. Additionally, we analyzed if the identified molecules can also affect the host proteins whose expression is differentially modulated during SARS-CoV2 infection. The differentially expressed genes (DEGs) were identified using analysis of NCBI-GEO data (GEO-ID: GSE-147507). A pathway and protein-protein interaction network analysis of the identified DEGs led to the identification of network hubs that may play important roles in SARS-CoV2 infection. Therefore, targeting such genes may also be a beneficial strategy to curb disease manifestation. We have identified 29 molecules that can bind to various SARS-CoV2 and human host proteins. We hope that this study will help researchers in the identification and repurposing of multipotent drugs, simultaneously targeting the several viral and host proteins, for the treatment of COVID-19.

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Identification of Drugs Targeting Multiple Viral and Human Proteins Using Computational Analysis for Repurposing Against COVID-19

10.26434/chemrxiv.12366938 ◽

2020 ◽

Author(s):

Sugandh Kumar ◽

Pratima Kumari ◽

Geetanjali Agnihotri ◽

Preethy VijayKumar ◽

Shaheerah Khan ◽

...

Keyword(s):

Interaction Network ◽

Viral Proteins ◽

Inhibitory Effect ◽

Host Proteins ◽

Disease Manifestation ◽

Protein Protein Interaction ◽

Human Proteins ◽

Approved Drugs ◽

Viral Lifecycle ◽

Fda Approved Drugs

The SARS-CoV2 is a highly contagious pathogen that causes a respiratory disease named COVID-19. The COVID-19 was declared a pandemic by the WHO on 11th March 2020. It has affected about 5.38 million people globally (identified cases as on 24th May 2020), with an average lethality of ~3%. Unfortunately, there is no standard cure for the disease, although some drugs are under clinical trial. Thus, there is an urgent need of drugs for the treatment of COVID-19. The molecularly targeted therapies have proven their utility in various diseases such as HIV, SARS, and HCV. Therefore, a lot of efforts are being directed towards the identification of molecules that can be helpful in the management of COVID-19. In the current studies, we have used state of the art bioinformatics techniques to screen the FDA approved drugs against thirteen SARS-CoV2 proteins in order to identify drugs for quick repurposing. The strategy was to identify potential drugs that can target multiple viral proteins simultaneously. Our strategy originates from the fact that individual viral proteins play specific role in multiple aspects of viral lifecycle such as attachment, entry, replication, morphogenesis and egress and targeting them simultaneously will have better inhibitory effect. Additionally, we analyzed if the identified molecules can also affect the host proteins whose expression is differentially modulated during SARS-CoV2 infection. The differentially expressed genes (DEGs) were identified using analysis of NCBI-GEO data (GEO-ID: GSE-147507). A pathway and protein-protein interaction network analysis of the identified DEGs led to the identification of network hubs that may play important roles in SARS-CoV2 infection. Therefore, targeting such genes may also be a beneficial strategy to curb disease manifestation. We have identified 29 molecules that can bind to various SARS-CoV2 and human host proteins. We hope that this study will help researchers in the identification and repurposing of multipotent drugs, simultaneously targeting the several viral and host proteins, for the treatment of COVID-19.

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Multi-Targeted Approaches and Drug Repurposing Reveal Possible SARS-CoV-2 Inhibitors

Vaccines ◽

10.3390/vaccines10010024 ◽

2021 ◽

Vol 10 (1) ◽

pp. 24

Author(s):

Khalid Mashay Alanazi ◽

Mohammad Abul Farah ◽

Yan-Yan Hor

Keyword(s):

Experimental Validation ◽

Computational Analysis ◽

Treatment Options ◽

Drug Repurposing ◽

Binding Affinities ◽

Normal Model ◽

Inhibitory Potential ◽

Us Fda ◽

Approved Drugs ◽

Fda Approved Drugs

The COVID-19 pandemic caused by SARS-CoV-2 is unprecedented in recent memory owing to the non-stop escalation in number of infections and deaths in almost every country of the world. The lack of treatment options further worsens the scenario, thereby necessitating the exploration of already existing US FDA-approved drugs for their effectiveness against COVID-19. In the present study, we have performed virtual screening of nutraceuticals available from DrugBank against 14 SARS-CoV-2 proteins. Molecular docking identified several inhibitors, two of which, rutin and NADH, displayed strong binding affinities and inhibitory potential against SARS-CoV-2 proteins. Further normal model-based simulations were performed to gain insights into the conformational transitions in proteins induced by the drugs. The computational analysis in the present study paves the way for experimental validation and development of multi-target guided inhibitors to fight COVID-19.

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Interfering Reactive Cysteine Proteome in Covid-19 Disease

Current Medicinal Chemistry ◽

10.2174/0929867328666210623142811 ◽

2021 ◽

Vol 28 ◽

Author(s):

Marcos Martinez-Banaclocha

Keyword(s):

Immune Response ◽

Life Cycle ◽

Therapeutic Strategy ◽

Viral Proteins ◽

Viral Life Cycle ◽

Biological Potential ◽

Inflammatory Immune Response ◽

Approved Drugs ◽

Fda Approved Drugs

: Although vaccination against SARS-CoV-2 infection has been initiated, effective therapies for severe Covid-19 disease are still needed. A promising therapeutic strategy is using FDA-approved drugs that have the biological potential to interfere with or modify some of the viral proteins capable of changing the disease's course. Recent studies highlight that some clinically safe drugs can suppress the viral life cycle while potentially promoting an adequate host inflammatory/immune response by interfering with the disease's cysteine proteome.

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Different Subtypes of Influenza Viruses Target Different Human Proteins and Pathways Leading to Different Pathogenic Phenotypes

BioMed Research International ◽

10.1155/2019/4794910 ◽

2019 ◽

Vol 2019 ◽

pp. 1-7

Author(s):

Yujie Wang ◽

Ting Song ◽

Kaiwu Li ◽

Yuan Jin ◽

Junjie Yue ◽

...

Keyword(s):

Protein Interactions ◽

Influenza A ◽

Viral Proteins ◽

Influenza Viruses ◽

Host Protein ◽

Protein Protein Interactions ◽

Influenza A Viruses ◽

Host Proteins ◽

Pathogenic Mechanisms ◽

Human Proteins

Different subtypes of influenza A viruses (IAVs) cause different pathogenic phenotypes after infecting human bodies. Analysis of the interactions between viral proteins and the host proteins may provide insights into the pathogenic mechanisms of the virus. In this paper, we found that the same proteins (nucleoprotein and neuraminidase) of H1N1 and H5N1 have different impacts on the NF-κB activation. By further examining the virus–host protein–protein interactions, we found that both NP and NA proteins of the H1N1 and H5N1 viruses target different host proteins. These results indicate that different subtypes of influenza viruses target different human proteins and pathways leading to different pathogenic phenotypes.

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Combined Deep Learning and Molecular Docking Simulations Approach Identifies Potentially Effective FDA Approved Drugs for Repurposing Against SARS-CoV-2

10.26434/chemrxiv.12227363.v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Muhammad Umer Anwar ◽

Farjad Adnan ◽

Asma Abro ◽

Muhammad Rayyan Khan ◽

Asad Ur Rehman ◽

...

Keyword(s):

Deep Learning ◽

Molecular Docking ◽

Active Sites ◽

Drug Repurposing ◽

Viral Proteins ◽

Penicillin G ◽

Computational Approaches ◽

Docking Simulations ◽

Approved Drugs ◽

Fda Approved Drugs

The ongoing pandemic of Coronavirus Disease 2019 (COVID-19), the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed a serious threat to global public health. Currently no approved drug or vaccine exists against SARS-CoV-2. Drug repurposing, represented as an effective drug discovery strategy from existing drugs, is a time efficient approach to find effective drugs against SARS-CoV-2 in this emergency situation. Both experimental and computational approaches are being employed in drug repurposing with computational approaches becoming increasingly popular and efficient. In this study, we present a robust experimental design combining deep learning with molecular docking experiments to identify most promising candidates from the list of FDA approved drugs that can be repurposed to treat COVID-19. We have employed a deep learning based Drug Target Interaction (DTI) model, called DeepDTA, with few improvements to predict drug-protein binding affinities, represented as KIBA scores, for 2,440 FDA approved and 8,168 investigational drugs against 24 SARS-CoV-2 viral proteins. FDA approved drugs with the highest KIBA scores were selected for molecular docking simulations. We ran docking simulations for 168 selected drugs against 285 total predicted and/or experimentally proven active sites of all 24 SARS-CoV-2 viral proteins. We used a recently published open source AutoDock based high throughput screening platform virtualflow to reduce the time required to run around 50,000 docking simulations. A list of 49 most promising FDA approved drugs with best consensus KIBA scores and AutoDock vina binding affinity values against selected SARS-CoV-2 viral proteins is generated. Most importantly, anidulafungin, velpatasvir, glecaprevir, rifabutin, procaine penicillin G, tadalafil, riboflavin 5’-monophosphate, flavin adenine dinucleotide, terlipressin, desmopressin, elbasvir, oxatomide, enasidenib, edoxaban and selinexor demonstrate highest predicted inhibitory potential against key SARS-CoV-2 viral proteins.

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Combined Deep Learning and Molecular Docking Simulations Approach Identifies Potentially Effective FDA Approved Drugs for Repurposing Against SARS-CoV-2

10.26434/chemrxiv.12227363 ◽

2020 ◽

Author(s):

Muhammad Umer Anwar ◽

Farjad Adnan ◽

Asma Abro ◽

Muhammad Rayyan Khan ◽

Asad Ur Rehman ◽

...

Keyword(s):

Deep Learning ◽

Molecular Docking ◽

Active Sites ◽

Drug Repurposing ◽

Viral Proteins ◽

Penicillin G ◽

Computational Approaches ◽

Docking Simulations ◽

Approved Drugs ◽

Fda Approved Drugs

The ongoing pandemic of Coronavirus Disease 2019 (COVID-19), the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed a serious threat to global public health. Currently no approved drug or vaccine exists against SARS-CoV-2. Drug repurposing, represented as an effective drug discovery strategy from existing drugs, is a time efficient approach to find effective drugs against SARS-CoV-2 in this emergency situation. Both experimental and computational approaches are being employed in drug repurposing with computational approaches becoming increasingly popular and efficient. In this study, we present a robust experimental design combining deep learning with molecular docking experiments to identify most promising candidates from the list of FDA approved drugs that can be repurposed to treat COVID-19. We have employed a deep learning based Drug Target Interaction (DTI) model, called DeepDTA, with few improvements to predict drug-protein binding affinities, represented as KIBA scores, for 2,440 FDA approved and 8,168 investigational drugs against 24 SARS-CoV-2 viral proteins. FDA approved drugs with the highest KIBA scores were selected for molecular docking simulations. We ran docking simulations for 168 selected drugs against 285 total predicted and/or experimentally proven active sites of all 24 SARS-CoV-2 viral proteins. We used a recently published open source AutoDock based high throughput screening platform virtualflow to reduce the time required to run around 50,000 docking simulations. A list of 49 most promising FDA approved drugs with best consensus KIBA scores and AutoDock vina binding affinity values against selected SARS-CoV-2 viral proteins is generated. Most importantly, anidulafungin, velpatasvir, glecaprevir, rifabutin, procaine penicillin G, tadalafil, riboflavin 5’-monophosphate, flavin adenine dinucleotide, terlipressin, desmopressin, elbasvir, oxatomide, enasidenib, edoxaban and selinexor demonstrate highest predicted inhibitory potential against key SARS-CoV-2 viral proteins.

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The proximal proteome of 17 SARS-CoV-2 proteins links to disrupted antiviral signaling and host translation

10.1101/2021.02.23.432450 ◽

2021 ◽

Author(s):

Jordan M Meyers ◽

Muthukumar Ramanathan ◽

Ronald L Shanderson ◽

Laura Donohue ◽

Ian Ferguson ◽

...

Keyword(s):

Protein Translation ◽

Viral Proteins ◽

Innate Immune ◽

Host Protein ◽

Host Proteins ◽

Immune Signaling ◽

Data Resource ◽

Regulatory Variants ◽

Innate Immune Signaling ◽

Human Proteins

AbstractViral proteins localize within subcellular compartments to subvert host machinery and promote pathogenesis. To study SARS-CoV-2 biology, we generated an atlas of 2422 human proteins vicinal to 17 SARS-CoV-2 viral proteins using proximity proteomics. This identified viral proteins at specific intracellular locations, such as association of accessary proteins with intracellular membranes, and projected SARS-CoV-2 impacts on innate immune signaling, ER-Golgi transport, and protein translation. It identified viral protein adjacency to specific host proteins whose regulatory variants are linked to COVID-19 severity, including the TRIM4 interferon signaling regulator which was found proximal to the SARS-CoV-2 M protein. Viral NSP1 protein adjacency to the EIF3 complex was associated with inhibited host protein translation whereas ORF6 localization with MAVS was associated with inhibited RIG-I 2CARD-mediated IFNB1 promoter activation. Quantitative proteomics identified candidate host targets for the NSP5 protease, with specific functional cleavage sequences in host proteins CWC22 and FANCD2. This data resource identifies host factors proximal to viral proteins in living human cells and nominates pathogenic mechanisms employed by SARS-CoV-2.Author SummarySARS-CoV-2 is the latest pathogenic coronavirus to emerge as a public health threat. We create a database of proximal host proteins to 17 SARS-CoV-2 viral proteins. We validate that NSP1 is proximal to the EIF3 translation initiation complex and is a potent inhibitor of translation. We also identify ORF6 antagonism of RNA-mediate innate immune signaling. We produce a database of potential host targets of the viral protease NSP5, and create a fluorescence-based assay to screen cleavage of peptide sequences. We believe that this data will be useful for identifying roles for many of the uncharacterized SARS-CoV-2 proteins and provide insights into the pathogenicity of new or emerging coronaviruses.

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Prediction of severe adverse events, modes of action and drug treatments for COVID-19’s complications

Scientific Reports ◽

10.1038/s41598-021-00368-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Courtney Astore ◽

Hongyi Zhou ◽

Joshy Jacob ◽

Jeffrey Skolnick

Keyword(s):

Adverse Events ◽

Molecular Mechanisms ◽

Clinical Manifestations ◽

Molecular Features ◽

Disease Protein ◽

Human Proteins ◽

Drug Treatments ◽

Severe Adverse Events ◽

Approved Drugs ◽

Fda Approved Drugs

AbstractFollowing SARS-CoV-2 infection, some COVID-19 patients experience severe host driven adverse events. To treat these complications, their underlying etiology and drug treatments must be identified. Thus, a novel AI methodology MOATAI-VIR, which predicts disease-protein-pathway relationships and repurposed FDA-approved drugs to treat COVID-19’s clinical manifestations was developed. SARS-CoV-2 interacting human proteins and GWAS identified respiratory failure genes provide the input from which the mode-of-action (MOA) proteins/pathways of the resulting disease comorbidities are predicted. These comorbidities are then mapped to their clinical manifestations. To assess each manifestation’s molecular basis, their prioritized shared proteins were subject to global pathway analysis. Next, the molecular features associated with hallmark COVID-19 phenotypes, e.g. unusual neurological symptoms, cytokine storms, and blood clots were explored. In practice, 24/26 of the major clinical manifestations are successfully predicted. Three major uncharacterized manifestation categories including neoplasms are also found. The prevalence of neoplasms suggests that SARS-CoV-2 might be an oncovirus due to shared molecular mechanisms between oncogenesis and viral replication. Then, repurposed FDA-approved drugs that might treat COVID-19’s clinical manifestations are predicted by virtual ligand screening of the most frequent comorbid protein targets. These drugs might help treat both COVID-19’s severe adverse events and lesser ones such as loss of taste/smell.

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The proximal proteome of 17 SARS-CoV-2 proteins links to disrupted antiviral signaling and host translation

PLoS Pathogens ◽

10.1371/journal.ppat.1009412 ◽

2021 ◽

Vol 17 (10) ◽

pp. e1009412

Author(s):

Jordan M. Meyers ◽

Muthukumar Ramanathan ◽

Ronald L. Shanderson ◽

Aimee Beck ◽

Laura Donohue ◽

...

Keyword(s):

Protein Translation ◽

Viral Proteins ◽

Host Protein ◽

Interferon Signaling ◽

Promoter Activation ◽

Host Proteins ◽

Data Resource ◽

Regulatory Variants ◽

Innate Immune Signaling ◽

Human Proteins

Viral proteins localize within subcellular compartments to subvert host machinery and promote pathogenesis. To study SARS-CoV-2 biology, we generated an atlas of 2422 human proteins vicinal to 17 SARS-CoV-2 viral proteins using proximity proteomics. This identified viral proteins at specific intracellular locations, such as association of accessary proteins with intracellular membranes, and projected SARS-CoV-2 impacts on innate immune signaling, ER-Golgi transport, and protein translation. It identified viral protein adjacency to specific host proteins whose regulatory variants are linked to COVID-19 severity, including the TRIM4 interferon signaling regulator which was found proximal to the SARS-CoV-2 M protein. Viral NSP1 protein adjacency to the EIF3 complex was associated with inhibited host protein translation whereas ORF6 localization with MAVS was associated with inhibited RIG-I 2CARD-mediated IFNB1 promoter activation. Quantitative proteomics identified candidate host targets for the NSP5 protease, with specific functional cleavage sequences in host proteins CWC22 and FANCD2. This data resource identifies host factors proximal to viral proteins in living human cells and nominates pathogenic mechanisms employed by SARS-CoV-2.

Download Full-text