Network neighbors of viral targets and differentially expressed genes in COVID-19 are drug target candidates

AbstractThe COVID-19 pandemic is raging. It revealed the importance of rapid scientific advancement towards understanding and treating new diseases. To address this challenge, we adapt an explainable artificial intelligence algorithm for data fusion and utilize it on new omics data on viral–host interactions, human protein interactions, and drugs to better understand SARS-CoV-2 infection mechanisms and predict new drug–target interactions for COVID-19. We discover that in the human interactome, the human proteins targeted by SARS-CoV-2 proteins and the genes that are differentially expressed after the infection have common neighbors central in the interactome that may be key to the disease mechanisms. We uncover 185 new drug–target interactions targeting 49 of these key genes and suggest re-purposing of 149 FDA-approved drugs, including drugs targeting VEGF and nitric oxide signaling, whose pathways coincide with the observed COVID-19 symptoms. Our integrative methodology is universal and can enable insight into this and other serious diseases.

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Between viral targets and differentially expressed genes in COVID-19: the sweet spot for therapeutic intervention.

10.21203/rs.3.rs-152622/v2 ◽

2021 ◽

Author(s):

Carme Zambrana ◽

Alexandros Xenos ◽

René Böttcher ◽

Noel Malod-Dognin ◽

Natasa Przulj

Keyword(s):

Protein Interactions ◽

Drug Target ◽

Differentially Expressed ◽

Human Interactome ◽

Nitric Oxide Signaling ◽

Host Interactions ◽

New Drug ◽

Human Proteins ◽

Infection Mechanisms ◽

Approved Drugs

Abstract The COVID-19 pandemic is raging. It revealed the importance of rapid scientific advancement towards understanding and treating new diseases. To address this challenge, we adapt an explainable artificial intelligence algorithm for data fusion and utilize it on new omics data on viral-host interactions, human protein interactions, and drugs to better understand SARS-CoV-2 infection mechanisms and predict new drug-target interactions for COVID-19. We discover that in the human interactome, the human proteins targeted by SARS-CoV-2 proteins and the genes that are differentially expressed after the infection have common neighbors central in the interactome that may be key to the disease mechanisms. We uncover 185 new drug-target interactions targeting 49 of these key genes and suggest re-purposing of 149 FDA-approved drugs, including drugs targeting VEGF and nitric oxide signaling, whose pathways coincide with the observed COVID-19 symptoms. Our integrative methodology is universal and can enable insight into this and other serious diseases.

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Between viral targets and differentially expressed genes in COVID-19: the sweet spot for therapeutic intervention.

10.21203/rs.3.rs-152622/v1 ◽

2021 ◽

Author(s):

Carme Zambrana ◽

Alexandros Xenos ◽

René Böttcher ◽

Noel Malod-Dognin ◽

Natasa Przulj

Keyword(s):

Protein Interactions ◽

Drug Target ◽

Differentially Expressed ◽

Human Interactome ◽

Nitric Oxide Signaling ◽

Host Interactions ◽

New Drug ◽

Human Proteins ◽

Infection Mechanisms ◽

Approved Drugs

Abstract The COVID-19 pandemic is raging. It revealed the importance of rapid scientific advancement towards understanding and treating new diseases. To address this challenge, we adapt an explainable artificial intelligence algorithm for data fusion and utilize it on new omics data on viral-host interactions, human protein interactions, and drugs to better understand SARS-CoV-2 infection mechanisms and predict new drug-target interactions for COVID-19. We discover that in the human interactome, the human proteins targeted by SARS-CoV-2 proteins and the genes that are differentially expressed after the infection have common neighbors central in the interactome that may be key to the disease mechanisms. We uncover 185 new drug-target interactions targeting 49 of these key genes and suggest re-purposing of 149 FDA-approved drugs, including drugs targeting VEGF and nitric oxide signaling, whose pathways coincide with the observed COVID-19 symptoms. Our integrative methodology is universal and can enable insight into this and other serious diseases.

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H2V, a Database for Human Proteins and Genes in Response to SARS-CoV-2, SARS-CoV, and MERS-CoV Infection

10.21203/rs.3.rs-61338/v1 ◽

2020 ◽

Author(s):

Nan Zhou ◽

Jinku Bao ◽

Yuping Ning

Keyword(s):

Differentially Expressed Genes ◽

Protein Interactions ◽

Viral Infections ◽

Antiviral Agents ◽

Differentially Expressed ◽

Easy Access ◽

Differentially Expressed Proteins ◽

Protein Protein Interactions ◽

Associated Proteins ◽

Human Proteins

Abstract The ongoing COVID-19 pandemic in the world is caused by SARS-CoV-2, a new coronavirus firstly discovered in the end of 2019. It has led to more than 10 million confirmed cases and more than 500,000 confirmed deaths across 216 countries by 1 July 2020, according to WHO statistics. SARS-CoV-2, SARS-CoV, and MERS-CoV are alike, killing people, impairing economy, and inflicting long-term impacts on the society. However, no specific drug or vaccine has been approved as a cure for these viruses. The efforts to develop antiviral measures are hampered by insufficient understanding of molecular responses of human to viral infections. In this study, we collected experimentally validated human proteins that interact with SARS-CoV-2 proteins, human proteins whose expression, translation and phosphorylation levels experience significantly changes after SARS-CoV-2 or SARS-CoV infection, human proteins that correlate with COVID-19 severity, and human genes whose expression levels significantly changed upon SARS-CoV-2 or MERS-CoV infection. A database, H2V, was then developed for easy access to these data. Currently H2V includes: 332 human-SARS-CoV-2 protein-protein interactions; 65 differentially expressed proteins, 232 differentially translated proteins, 1298 differentially phosphorylated proteins, 204 severity associated proteins, and 4012 differentially expressed genes responding to SARS-CoV-2 infection; 66 differentially expressed proteins responding to SARS-CoV infection; and 6981 differentially expressed genes responding to MERS-CoV infection. H2V can help to understand the cellular responses associated with SARS-CoV-2, SARS-CoV and MERS-CoV infection. It is expected to speed up the development of antiviral agents and shed light on the preparation for potential coronavirus emergency in the future.Database url: http://www.zhounan.org/h2v

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Multi-schema computational prediction of the comprehensive SARS-CoV-2 vs. human interactome

PeerJ ◽

10.7717/peerj.11117 ◽

2021 ◽

Vol 9 ◽

pp. e11117

Author(s):

Kevin Dick ◽

Anand Chopra ◽

Kyle K. Biggar ◽

James R. Green

Keyword(s):

Protein Interactions ◽

Scientific Community ◽

Predictive Performance ◽

Computational Prediction ◽

Viral Disease ◽

High Confidence ◽

Human Interactome ◽

Holistic Understanding ◽

Human Proteins ◽

Novel Coronavirus

Background Understanding the disease pathogenesis of the novel coronavirus, denoted SARS-CoV-2, is critical to the development of anti-SARS-CoV-2 therapeutics. The global propagation of the viral disease, denoted COVID-19 (“coronavirus disease 2019”), has unified the scientific community in searching for possible inhibitory small molecules or polypeptides. A holistic understanding of the SARS-CoV-2 vs. human inter-species interactome promises to identify putative protein-protein interactions (PPI) that may be considered targets for the development of inhibitory therapeutics. Methods We leverage two state-of-the-art, sequence-based PPI predictors (PIPE4 & SPRINT) capable of generating the comprehensive SARS-CoV-2 vs. human interactome, comprising approximately 285,000 pairwise predictions. Three prediction schemas (all, proximal, RP-PPI) are leveraged to obtain our highest-confidence subset of PPIs and human proteins predicted to interact with each of the 14 SARS-CoV-2 proteins considered in this study. Notably, the use of the Reciprocal Perspective (RP) framework demonstrates improved predictive performance in multiple cross-validation experiments. Results The all schema identified 279 high-confidence putative interactions involving 225 human proteins, the proximal schema identified 129 high-confidence putative interactions involving 126 human proteins, and the RP-PPI schema identified 539 high-confidence putative interactions involving 494 human proteins. The intersection of the three sets of predictions comprise the seven highest-confidence PPIs. Notably, the Spike-ACE2 interaction was the highest ranked for both the PIPE4 and SPRINT predictors with the all and proximal schemas, corroborating existing evidence for this PPI. Several other predicted PPIs are biologically relevant within the context of the original SARS-CoV virus. Furthermore, the PIPE-Sites algorithm was used to identify the putative subsequence that might mediate each interaction and thereby inform the design of inhibitory polypeptides intended to disrupt the corresponding host-pathogen interactions. Conclusion We publicly released the comprehensive sets of PPI predictions and their corresponding PIPE-Sites landscapes in the following DataVerse repository: https://www.doi.org/10.5683/SP2/JZ77XA. The information provided represents theoretical modeling only and caution should be exercised in its use. It is intended as a resource for the scientific community at large in furthering our understanding of SARS-CoV-2.

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Predicting protein targets for drug-like compounds using transcriptomics

10.1101/254367 ◽

2018 ◽

Cited By ~ 1

Author(s):

Nicolas A. Pabon ◽

Yan Xia ◽

Samuel K. Estabrooks ◽

Zhaofeng Ye ◽

Amanda K. Herbrand ◽

...

Keyword(s):

Protein Interactions ◽

Network Effects ◽

Chemical Space ◽

Target Prediction ◽

Cell Types ◽

Supervised Machine Learning ◽

Human Interactome ◽

Protein Targets ◽

Drug Treatments ◽

Approved Drugs

AbstractThe development of an expanded chemical space for screening is an essential step in the challenge of identifying chemical probes for new, genomic-era protein targets. However, the difficulty of identifying targets for novel compounds leads to the prioritization of synthesis linked to known active scaffolds that bind familiar protein families, slowing the exploration of available chemical space. To change this paradigm, we validated a new pipeline capable of identifying compound-protein interactions even for compounds with no similarity to known drugs. Based on differential mRNA profiles from drug treatments and gene knockdowns across multiple cell types, we show that drugs cause gene regulatory network effects that correlate with those produced by silencing their target protein-coding gene. Applying supervised machine learning to exploit compound-knockdown signature correlations and enriching our predictions using an orthogonal structure-based screen, we achieved top-10/top-100 target prediction accuracies of 26%/41%, respectively, on a validation set 152 FDA-approved drugs and 3104 potential targets. We further predicted targets for 1680 compounds and validated a total of seven novel interactions with four difficult targets, including non-covalent modulators of HRAS and KRAS. We found that drug-target interactions manifest as gene expression correlations between drug treatment and both target gene knockdown and up/down-stream knockdowns. These correlations provide biologically relevant insight on the cell-level impact of disrupting protein interactions, highlighting the complex genetic phenotypes of drug treatments. Our pipeline can accelerate the identification and development of novel chemistries with potential to become drugs by screening for compound-target interactions in the full human interactome.

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Mesothelioma Interactome with 367 Novel Protein-Protein Interactions

10.1101/459065 ◽

2018 ◽

Author(s):

Kalyani B. Karunakaran ◽

Naveena Yanamala ◽

Gregory Boyce ◽

Madhavi K. Ganapathiraju

Keyword(s):

Protein Interactions ◽

Cellular Localization ◽

Expression Profiles ◽

Differentially Expressed ◽

Protein Protein Interactions ◽

Advanced Search ◽

Genomic Location ◽

One Year ◽

Approved Drugs ◽

Novel Protein

AbstractMalignant pleural mesothelioma (MPM) is an aggressive cancer of the thorax with a median survival of one year. We constructed an ‘MPM interactome’ with over 300 computationally predicted PPIs and over 1300 known PPIs of 62 literature-curated genes whose activity affects MPM. Known PPIs of the 62 MPM associated genes were derived from BioGRID and HPRD databases. Novel PPIs were predicted by applying the HiPPIP algorithm, which computes features of protein pairs such as cellular localization, molecular function, biological process membership, genomic location of the gene, gene expression in microarray experiments, protein domains and tissue membership, and classifies the pairwise features asinteractingornon-interactingbased on a random forest model. To our satisfaction, the interactome is significantly enriched with genes differentially expressed in MPM tumors compared with normal pleura, and with other thoracic tumors. The interactome is also significantly enriched with genes whose high expression has been correlated with unfavorable prognosis in lung cancer, and with genes differentially expressed on crocidolite exposure. 28 of the interactors of MPM proteins are targets of 147 FDA-approved drugs. By comparing differential expression profiles induced by drug to profiles induced by MPM, potentially repurposable drugs are identified from this drug list. Development of PPIs of disease-specific set of genes is a powerful approach with high translational impact – the interactome is a vehicle to piece together an integrated view on how genes associated with MPM through various high throughput studies are functionally linked, leading to clinically translatable results such as clinical trials with repurposed drugs. The PPIs are made available on a webserver, calledWiki-Pi MPMathttp://severus.dbmi.pitt.edu/wiki-MPMwith advanced search capabilities.One Sentence SummaryMesothelioma Interactome with 367 novel protein-protein interactions may shed light on the mechanisms of cancer genesis and progression

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Structural analysis of SARS-CoV-2 genome and predictions of the human interactome

10.1101/2020.03.28.013789 ◽

2020 ◽

Cited By ~ 7

Author(s):

Andrea Vandelli ◽

Michele Monti ◽

Edoardo Milanetti ◽

Alexandros Armaos ◽

Jakob Rupert ◽

...

Keyword(s):

Protein Interactions ◽

Viral Entry ◽

Viral Infections ◽

Host Cells ◽

Structural Level ◽

Rna Helicases ◽

Human Interactome ◽

Viral Genomes ◽

Human Proteins ◽

Genomic Regions

ABSTRACTSpecific elements of viral genomes regulate interactions within host cells. Here, we calculated the secondary structure content of >2000 coronaviruses and computed >100000 human protein interactions with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The genomic regions display different degrees of conservation. SARS-CoV-2 domain encompassing nucleotides 22500 – 23000 is conserved both at the sequence and structural level. The regions upstream and downstream, however, vary significantly. This part codes for the Spike S protein that interacts with the human receptor angiotensin-converting enzyme 2 (ACE2). Thus, variability of Spike S may be connected to different levels of viral entry in human cells within the population.Our predictions indicate that the 5’ end of SARS-CoV-2 is highly structured and interacts with several human proteins. The binding proteins are involved in viral RNA processing such as double-stranded RNA specific editases and ATP-dependent RNA-helicases and have strong propensity to form stress granules and phase-separated complexes. We propose that these proteins, also implicated in viral infections such as HIV, are selectively recruited by SARS-CoV-2 genome to alter transcriptional and post-transcriptional regulation of host cells and to promote viral replication.

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A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing

10.1101/2020.03.22.002386 ◽

2020 ◽

Cited By ~ 73

Author(s):

David E. Gordon ◽

Gwendolyn M. Jang ◽

Mehdi Bouhaddou ◽

Jiewei Xu ◽

Kirsten Obernier ◽

...

Keyword(s):

Protein Interactions ◽

Drug Targets ◽

Affinity Purification ◽

Drug Repurposing ◽

Human Protein ◽

Protein Protein Interaction ◽

Human Proteins ◽

Interaction Map ◽

Novel Coronavirus ◽

Approved Drugs

ABSTRACTAn outbreak of the novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 290,000 people since the end of 2019, killed over 12,000, and caused worldwide social and economic disruption1,2. There are currently no antiviral drugs with proven efficacy nor are there vaccines for its prevention. Unfortunately, the scientific community has little knowledge of the molecular details of SARS-CoV-2 infection. To illuminate this, we cloned, tagged and expressed 26 of the 29 viral proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), which identified 332 high confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 existing FDA-approved drugs, drugs in clinical trials and/or preclinical compounds, that we are currently evaluating for efficacy in live SARS-CoV-2 infection assays. The identification of host dependency factors mediating virus infection may provide key insights into effective molecular targets for developing broadly acting antiviral therapeutics against SARS-CoV-2 and other deadly coronavirus strains.

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Structural analysis of SARS-CoV-2 genome and predictions of the human interactome

Nucleic Acids Research ◽

10.1093/nar/gkaa864 ◽

2020 ◽

Vol 48 (20) ◽

pp. 11270-11283 ◽

Cited By ~ 3

Author(s):

Andrea Vandelli ◽

Michele Monti ◽

Edoardo Milanetti ◽

Alexandros Armaos ◽

Jakob Rupert ◽

...

Keyword(s):

Protein Interactions ◽

Viral Entry ◽

Viral Infections ◽

Host Cells ◽

Structural Level ◽

Rna Helicases ◽

Human Interactome ◽

Viral Genomes ◽

Human Proteins ◽

Genomic Regions

Abstract Specific elements of viral genomes regulate interactions within host cells. Here, we calculated the secondary structure content of >2000 coronaviruses and computed >100 000 human protein interactions with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The genomic regions display different degrees of conservation. SARS-CoV-2 domain encompassing nucleotides 22 500–23 000 is conserved both at the sequence and structural level. The regions upstream and downstream, however, vary significantly. This part of the viral sequence codes for the Spike S protein that interacts with the human receptor angiotensin-converting enzyme 2 (ACE2). Thus, variability of Spike S is connected to different levels of viral entry in human cells within the population. Our predictions indicate that the 5′ end of SARS-CoV-2 is highly structured and interacts with several human proteins. The binding proteins are involved in viral RNA processing, include double-stranded RNA specific editases and ATP-dependent RNA-helicases and have strong propensity to form stress granules and phase-separated assemblies. We propose that these proteins, also implicated in viral infections such as HIV, are selectively recruited by SARS-CoV-2 genome to alter transcriptional and post-transcriptional regulation of host cells and to promote viral replication.

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Computational Prediction of the Comprehensive SARS-CoV-2 vs. Human Interactome to Guide the Design of Therapeutics

10.1101/2020.03.29.014381 ◽

2020 ◽

Author(s):

Kevin Dick ◽

Kyle K. Biggar ◽

James R. Green

Keyword(s):

Protein Interactions ◽

Scientific Community ◽

Computational Prediction ◽

Viral Disease ◽

Protein Protein Interactions ◽

Human Interactome ◽

Angiotensin Converting Enzyme 2 ◽

Holistic Understanding ◽

Human Proteins ◽

Novel Coronavirus

AbstractUnderstanding the disease pathogenesis of the novel coronavirus, denoted SARS-CoV-2, is critical to the development of anti-SARS-CoV-2 therapeutics. The global propagation of the viral disease, denoted COVID-19 (“coronavirus disease 2019”), has unified the scientific community in searching for possible inhibitory small molecules or polypeptides. Given the known interaction between the human ACE2 (“Angiotensin-converting enzyme 2”) protein and the SARS-CoV virus (responsible for the coronavirus outbreak circa. 2003), considerable focus has been directed towards the putative interaction between the SARS-CoV-2 Spike protein and ACE2. However, a more holistic understanding of the SARS-CoV-2 vs. human inter-species interactome promises additional putative protein-protein interactions (PPI) that may be considered targets for the development of inhibitory therapeutics.To that end, we leverage two state-of-the-art, sequence-based PPI predictors (PIPE4 & SPRINT) capable of generating the comprehensive SARS-CoV-2 vs. human interactome, comprising approximately 285,000 pairwise predictions. Of these, we identify the high-scoring subset of human proteins predicted to interact with each of the 14 SARS-CoV-2 proteins by both methods, comprising 279 high-confidence putative interactions involving 225 human proteins. Notably, the Spike-ACE2 interaction was the highest ranked for both the PIPE4 and SPRINT predictors, corroborating existing evidence for this PPI. Furthermore, the PIPE-Sites algorithm was used to predict the putative subsequence that might mediate each interaction and thereby inform the design of inhibitory polypeptides intended to disrupt the corresponding host-pathogen interactions.We hereby publicly release the comprehensive set of PPI predictions and their corresponding PIPE-Sites landscapes in the following DataVerse repository: 10.5683/SP2/JZ77XA. All data and metadata are released under a CC-BY 4.0 licence. The information provided represents theoretical modeling only and caution should be exercised in its use. It is intended as a resource for the scientific community at large in furthering our understanding of SARS-CoV-2.

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