Finding Prediction of Interaction Between SARS-CoV-2 and Human Protein: A Data-Driven Approach
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ABSTRACT:
SUBMITTER: Ghosh M
PROVIDER: S-EPMC8093916 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Protein-protein interaction is one of the ways viruses interact with their hosts. Therefore, identifying protein interactions between viruses and hosts helps explain how virus proteins work, how they replicate, and how they cause disease. SARS-CoV-2 is a new type of virus that emerged from the coronavirus family in 2019 and caused a worldwide pandemic. Detection of human proteins interacting with this novel virus strain plays an important role in monitoring the cellular process of virus-associated infection. Within the scope of the study, a natural language processing-based collective learning method is proposed for the prediction of potential SARS-CoV-2-human PPIs. Protein language models were obtained with the prediction-based word2Vec and doc2Vec embedding methods and the frequency-based tf-idf method. Known interactions were represented by proposed language models and traditional feature extraction methods (conjoint triad and repeat pattern), and their performances were compared. The interaction data were trained with support vector machine, artificial neural network (ANN), k-nearest neighbor (KNN), naive Bayes (NB), decision tree (DT), and ensemble algorithms. Experimental results show that protein language models are a promising protein representation method for protein-protein interaction prediction. The term frequency-inverse document frequency-based language model performed the SARS-CoV-2 protein-protein interaction estimation with an error of 1.4%. Additionally, the decisions of high-performing learning models for different feature extraction methods were combined with a collective voting approach to make new interaction predictions. For 10,000 human proteins, 285 new potential interactions were predicted, with models combining decisions. Graphical abstract
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Project description:Coronavirus disease 2019 (COVID-19) has spread out as a pandemic threat affecting over 2 million people. The infectious process initiates via binding of SARS-CoV-2 Spike (S) glycoprotein to host angiotensin-converting enzyme 2 (ACE2). The interaction is mediated by the receptor-binding domain (RBD) of S glycoprotein, promoting host receptor recognition and binding to ACE2 peptidase domain (PD), thus representing a promising target for therapeutic intervention. Herein, we present a computational study aimed at identifying small molecules potentially able to target RBD. Although targeting PPI remains a challenge in drug discovery, our investigation highlights that interaction between SARS-CoV-2 RBD and ACE2 PD might be prone to small molecule modulation, due to the hydrophilic nature of the bi-molecular recognition process and the presence of druggable hot spots. The fundamental objective is to identify, and provide to the international scientific community, hit molecules potentially suitable to enter the drug discovery process, preclinical validation and development.
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Project description:COVID-19, caused by SARS-CoV-2, is a primarily pulmonary disease that can affect several organs, directly or indirectly. To date, there are many questions about the different pathological mechanisms. Here, we generate an approach to identify the cellular-level tropism of SARS-CoV-2 using human proteomics, virus-host interactions, and enrichment analysis. Through a network-based approach, the molecular context was visualized and analyzed. This procedure was also performed for SARS-CoV-1. We obtained proteomes and interactomes from 145 different cells corresponding to 57 different tissues. We discarded the cells without the proteins known for interacting with the virus, such as ACE2 or TMPRSS2. Of the remaining cells, a gradient of susceptibility to infection was observed. In addition, we identified proteins associated with the coagulation cascade that can be directly or indirectly affected by viral proteins. As a whole we identified 55 cells that could be potentially controlled by the virus, with different susceptibilities, mainly being pneumocytes, heart, kidney, liver, or small intestine cells. These results help to explain the molecular context and provide elements for possible treatments in the current situation. This strategy may be useful for other viruses, especially those with limited reported PPI, such as a new virus.
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Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the novel coronavirus which caused the coronavirus disease 2019 pandemic and infected more than 12 million victims and resulted in over 560,000 deaths in 213 countries around the world. Having no symptoms in the first week of infection increases the rate of spreading the virus. The increasing rate of the number of infected individuals and its high mortality necessitates an immediate development of proper diagnostic methods and effective treatments. SARS-CoV-2, similar to other viruses, needs to interact with the host proteins to reach the host cells and replicate its genome. Consequently, virus-host protein-protein interaction (PPI) identification could be useful in predicting the behavior of the virus and the design of antiviral drugs. Identification of virus-host PPIs using experimental approaches are very time consuming and expensive. Computational approaches could be acceptable alternatives for many preliminary investigations. In this study, we developed a new method to predict SARS-CoV-2-human PPIs. Our model is a three-layer network in which the first layer contains the most similar Alphainfluenzavirus proteins to SARS-CoV-2 proteins. The second layer contains protein-protein interactions between Alphainfluenzavirus proteins and human proteins. The last layer reveals protein-protein interactions between SARS-CoV-2 proteins and human proteins by using the clustering coefficient network property on the first two layers. To further analyze the results of our prediction network, we investigated human proteins targeted by SARS-CoV-2 proteins and reported the most central human proteins in human PPI network. Moreover, differentially expressed genes of previous researches were investigated and PPIs of SARS-CoV-2-human network, the human proteins of which were related to upregulated genes, were reported.
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Project description:G3BP1/2 are paralogous proteins that promote stress granule formation in response to cellular stresses, including viral infection. G3BP1/2 are prominent interactors of the nucleocapsid (N) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the functional consequences of the G3BP1-N interaction in the context of viral infection remain unclear. Here we used structural and biochemical analyses to define the residues required for G3BP1-N interaction, followed by structure-guided mutagenesis of G3BP1 and N to selectively and reciprocally disrupt their interaction. We found that mutation of F17 within the N protein led to selective loss of interaction with G3BP1 and consequent failure of the N protein to disrupt stress granule assembly. Introduction of SARS-CoV-2 bearing an F17A mutation resulted in a significant decrease in viral replication and pathogenesis in vivo, indicating that the G3BP1-N interaction promotes infection by suppressing the ability of G3BP1 to form stress granules.
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Project description:The serologic diagnosis of coronavirus disease 2019 (COVID-19) and the evaluation of vaccination effectiveness are identified by the presence of antibodies specific to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this paper, we present the electrochemical-based biosensing technique for the detection of antibodies specific to the SARS-CoV-2 proteins. Recombinant SARS-CoV-2 spike proteins (rSpike) were immobilised on the surface of a gold electrode modified by a self-assembled monolayer (SAM). This modified electrode was used as a sensitive element for the detection of polyclonal mouse antibodies against the rSpike (anti-rSpike). Electrochemical impedance spectroscopy (EIS) was used to observe the formation of immunocomplexes while cyclic voltammetry (CV) was used for additional analysis of the surface modifications. It was revealed that the impedimetric method and the elaborate experimental conditions are appropriate for the further development of electrochemical biosensors for the serological diagnosis of COVID-19 and/or the confirmation of successful vaccination against SARS-CoV-2.
| S-EPMC9223850 | biostudies-literature
Project description:BackgroundThe novel coronavirus (SARS-CoV-2) caused lethal infections worldwide during an unprecedented pandemic. Identification of the candidate viral epitopes is the first step in the design of vaccines against the viral infection. Several immunoinformatic approaches were employed to identify the SARS-CoV-2 epitopes that bind specifically with the major histocompatibility molecules class I (MHC-I). We utilized immunoinformatic tools to analyze the whole viral protein sequences, to identify the SARS-CoV-2 epitopes responsible for binding to the most frequent human leukocyte antigen (HLA) alleles in the Egyptian population. These alleles were also found with high frequency in other populations worldwide.ResultsMolecular docking approach showed that using the co-crystallized MHC-I and T cell receptor (TCR) instead of using MHC-I structure only, significantly enhanced docking scores and stabilized the conformation, as well as the binding affinity of the identified SARS-CoV-2 epitopes. Our approach directly predicts 7 potential vaccine subunits from the available SARS-CoV-2 spike and ORF1ab protein sequence. This prediction has been confirmed by published experimentally validated and in silico predicted spike epitope. On the other hand, we predicted novel epitopes (RDLPQGFSA and FCLEASFNY) showing high docking scores and antigenicity response with both MHC-I and TCR. Moreover, antigenicity, allergenicity, toxicity, and physicochemical properties of the predicted SARS-CoV-2 epitopes were evaluated via state-of-the-art bioinformatic approaches, showing high efficacy of the proposed epitopes as a vaccine candidate.ConclusionOur predicted SARS-CoV-2 epitopes can facilitate vaccine development to enhance the immunogenicity against SARS-CoV-2 and provide supportive data for further experimental validation. Our proposed molecular docking approach of exploiting both MHC and TCR structures can be used to identify potential epitopes for most microbial pathogens, provided the crystal structure of MHC co-crystallized with TCR.
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Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a +sense single-strand RNA virus. The virus has four major surface proteins: spike (S), envelope (E), membrane (M), and nucleocapsid (N), respectively. The constitutive proteins present a high grade of symmetry. Identifying a binding site is difficult. The virion is approximately 50-200 nm in diameter. Angiotensin-converting enzyme 2 (ACE2) acts as the cell receptor for the virus. SARS-CoV-2 has an increased affinity to human ACE2 compared with the original SAR strain. Topological space, and its symmetry, is a critical component in molecular interactions. By exploring this space, a suitable ligand space can be characterized accordingly. A spike protein (S) computational model in a complex with ACE 2 was generated using silica methods. Topological spaces were probed using high computational throughput screening techniques to identify and characterize the topological space of both SARS and SARS-CoV-2 spike protein and its ligand space. In order to identify the symmetry clusters, computational analysis techniques, together with statistical analysis, were utilized. The computations are based on crystallographic protein data bank PDB-based models of constitutive proteins. Cartesian coordinates of component atoms and some cluster maps were generated and analyzed. Dihedral angles were used in order to compute a topological receptor space. This computational study uses a multimodal representation of spike protein interactions with some fragment proteins. The chemical space of the receptors (a dimensional volume) suggests the relevance of the receptor as a drug target. The spike protein S of SARS and SARS-CoV-2 is analyzed and compared. The results suggest a mirror symmetry of SARS and SARS-CoV-2 spike proteins. The results show thatSARS-CoV-2 space is variable and has a distinct topology. In conclusion, surface proteins grant virion variability and symmetry in interactions with a potential complementary target (protein, antibody, ligand). The mirror symmetry of dihedral angle clusters determines a high specificity of the receptor space.
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Project description:BackgroundThe coronavirus (CoV) spike (S) protein is critical for receptor binding, membrane fusion and internalization of the virus into the human cells. We have tried to search the epitopic component of the S-protein that might be served as crucial targets for the vaccine development and also tried to understand the molecular mechanism of epitopes and TLR4/MD-2 complex for adaptive immunity.Material and methodsHere we identified the antigenicity and the epitopic divergence of S-protein via immunoinformatics approach. The study was performed to identify the epitopes, composition of amino acids and its distribution in epitopic regions, composition of amino acid between the identified epitopes, secondary structure architecture of epitopes, physicochemical and biochemical parameters and molecular interaction between the identified epitope and TLR4/MD-2 complex. The SARS-CoV-2 can be possibly recognised by TLR4 of host immune cells that are responsible for the adaptive immune response.ResultsWe identified four SARS-CoV-2 S-protein 9mer antigenic epitopes and observed that they bind with the TLR4/MD-2 complex by varied stable molecular bonding interactions. Molecular interaction between these characterized epitopes with TLR4/MD-2 complex might be indicated the binding affinity and downstream signalling of adaptive immune response. Different physicochemical and biochemical parameters such as O-glycosylation and N-glycosylation, Hydrophobicity, GRAVY were identified within epitopic regions of S-protein. These parameters help to understand the protein-protein interaction between epitopes and TLR4/MD-2 complex. The study also revealed different epitopic binding pockets of TLR4/MD-2 complex.ConclusionsThe identified epitopes impart suitable prospects for the development of novel peptide-based epitopic vaccine for the control of COVID-19 infection.
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Project description:The rapid global spread and dissemination of SARS-CoV-2 has provided the virus with numerous opportunities to develop several variants. Thus, it is critical to determine the degree of the variations and in which part of the virus those variations occurred. Therefore, in this study, methods that could be used to vectorize the sequence data, perform clustering analysis, and visualize the results were proposed using machine learning methods. To conduct this study, a total of 224,073 cases of SARS-CoV-2 sequence data were collected through NCBI and GISAID, and the data were visualized using dimensionality reduction and clustering analysis models such as T-SNE and DBSCAN. The SARS-CoV-2 virus, which was first detected, was distinguished from different variations, including Omicron and Delta, in the cluster results. Furthermore, it was possible to examine which codon changes in the spike protein caused the variants to be distinguished using feature importance extraction models such as Random Forest or Shapely Value. The proposed method has the advantage of being able to analyse and visualize a large amount of data at once compared to the existing tree-based sequence data analysis. The proposed method was able to identify and visualize significant changes between the SARS-CoV-2 virus, which was first detected in Wuhan, China, in December 2019, and the newly formed mutant virus group. As a result of clustering analysis using sequence data, it was possible to confirm the formation of clusters among various variants in a two-dimensional graph, and by extracting the importance of variables, it was possible to confirm which codon changes played a major role in distinguishing variants. Furthermore, since the proposed method can handle a variety of data sequences, it can be used for all kinds of diseases, including influenza and SARS-CoV-2. Therefore, the proposed method has the potential to become widely used for the effective analysis of disease variations.
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