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

PeerJ ◽  
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.

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

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.

Review and comparative assessment of similarity-based methods for prediction of drug–protein interactions in the druggable human proteome

2018 ◽  
Vol 20 (6) ◽  
pp. 2066-2087 ◽  
Author(s):  
Chen Wang ◽  
Lukasz Kurgan
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
Characteristic Curve ◽  
Protein Structures ◽  
Predictive Performance ◽  
Computational Prediction ◽  
Comprehensive Analysis ◽  
Model Combining ◽  
Benchmark Database ◽  
Key Aspects

AbstractDrug–protein interactions (DPIs) underlie the desired therapeutic actions and the adverse side effects of a significant majority of drugs. Computational prediction of DPIs facilitates research in drug discovery, characterization and repurposing. Similarity-based methods that do not require knowledge of protein structures are particularly suitable for druggable genome-wide predictions of DPIs. We review 35 high-impact similarity-based predictors that were published in the past decade. We group them based on three types of similarities and their combinations that they use. We discuss and compare key aspects of these methods including source databases, internal databases and their predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually and all possible combinations of similarities. We assess predictive quality at the database-wide DPI level and we are the first to also include evaluation over individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures area under the receiver operating characteristic curve of 0.93. We offer a comprehensive analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets. The benchmark database and a webserver for the seven predictors are freely available at http://biomine.cs.vcu.edu/servers/CONNECTOR/.

scholarly journals Computational prediction of Carica papaya extracts as potential drug agents against RNA polymerase and Spike proteins of SARS-nCoV2 

2020 ◽  
Author(s):  
Rashid Saif ◽  
Muhammad Osama Zafar ◽  
Muhammad Hassan Raza ◽  
Talha Rehman ◽  
Saeeda Zia ◽  
...  
Keyword(s):  
Rna Polymerase ◽  
Protein Interactions ◽  
Carica Papaya ◽  
Computational Prediction ◽  
Docking Study ◽  
Mass Scale ◽  
Binding Energies ◽  
Physiochemical Parameters ◽  
Novel Coronavirus ◽  
Major Drug

Abstract The emergence of COVID-19 outbreak caused by SARS-nCoV2 (Severe Acute Respiratory Syndrome novel coronavirus 2), lead to the mass-scale mortalities around the world within a short span of time. The hour of the need is to develop the strategies and designing drugs/vaccines to control the spread of this contagion. In this paper, we predict the promising drug agents from the Carica papaya compounds by docking them with two major drug target proteins of SARS-nCoV2, spike (7BZ5) and RNA-dependent RNA polymerase (7BW4). For this purpose, we used Molecular Operating Environment Software (MOE) for ligand-protein interactions and docking scores. Furthermore, we used PubChem, PDB and SwissADME web portals to retrieve ligands structures, proteins structures and to check Lipinski’s physiochemical parameters respectively. Cumulatively, this docking study has shown significant binding energies that (-4.2034 to -8.9013 Kcal/mol) indicates their potential against COVID-19 treatment. This study needs further evaluation on experimental basis.

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

2020 ◽  
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.

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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Carme Zambrana ◽  
Alexandros Xenos ◽  
René Böttcher ◽  
Noël Malod-Dognin ◽  
Nataša Pržulj
Keyword(s):  
Protein Interactions ◽  
Drug Target ◽  
Differentially Expressed ◽  
Human Interactome ◽  
Nitric Oxide Signaling ◽  
Host Interactions ◽  
New Drug ◽  
Human Proteins ◽  
Infection Mechanisms ◽  
Approved Drugs

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.

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

2020 ◽  
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.

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

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.

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

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.

scholarly journals Assessment of community efforts to advance computational prediction of protein-protein interactions

2021 ◽  
Author(s):  
Xu-Wen Wang ◽  
Lorenzo Madeddu ◽  
Kerstin Spirohn ◽  
Leonardo Martini ◽  
Adriano Fazzone ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Computational Prediction ◽  
Superior Performance ◽  
Systematic Evaluation ◽  
Human Interactome ◽  
C Elegans ◽  
Protein Protein Interaction ◽  
Human Ppis ◽  
Underlying Network

AbstractComprehensive insights from the human protein-protein interaction (PPI) network, known as the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of new PPIs. Many such approaches have been proposed. However, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 24 representative network-based methods to predict PPIs across five different interactomes, including a synthetic interactome generated by the duplication-mutation-complementation model, and the interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. We selected the top-seven methods through a computational validation on the human interactome. We next experimentally validated their top-500 predicted PPIs (in total 3,276 predicted PPIs) using the yeast two-hybrid assay, finding 1,177 new human PPIs (involving 633 proteins). Our results indicate that task-tailored similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods. Through experimental validation, we confirmed that the top-ranking methods show promising performance externally. For example, from the top 500 PPIs predicted by an advanced similarity-base method [MPS(B&T)], 430 were successfully tested by Y2H with 376 testing positive, yielding a precision of 87.4%. These results establish advanced similarity-based methods as powerful tools for the prediction of human PPIs.

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

2020 ◽  
Vol 48 (20) ◽  
pp. 11270-11283 ◽  
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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