Understanding Human-Virus Protein-Protein Interactions Using a Human Protein Complex-Based Analysis Framework

Comparative analysis of dynamic transcriptomes reveals specific COVID-19 features and pathogenesis of immunocompromised populations

A dysfunction of human host genes and proteins in coronavirus infectious disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a key factor impacting clinical symptoms and outcomes. Yet, a detailed understanding of human host immune responses is still incomplete. Here, we applied RNA sequencing to 94 samples of COVID-19 patients with and without hematological tumors as well as COVID-19 uninfected non-tumor individuals to obtain a comprehensive transcriptome landscape of both hematological tumor patients and non-tumor individuals. In our analysis, we further accounted for the human-SARS-CoV-2 protein interactome, human protein interactome, and human protein complex subnetworks to understand the mechanisms of SARS-CoV-2 infection and host immune responses. Our data sets enabled us to identify important SARS-CoV-2 (non-)targeted differentially expressed genes and complexes post-SARS-CoV-2 infection in both hematological tumor and non-tumor individuals. We found several unique differentially expressed genes, complexes, and functions/pathways such as blood coagulation (APOE, SERPINE1, SERPINE2, and TFPI), lipoprotein particle remodeling (APOC2, APOE, and CETP), and pro-B cell differentiation (IGHM, VPREB1, and IGLL1) during COVID-19 infection in patients with hematological tumors. In particular, APOE, a gene that is associated with both blood coagulation and lipoprotein particle remodeling, is not only upregulated in hematological tumor patients post-SARS-CoV-2 infection but also significantly expressed in acute dead patients with hematological tumors, providing clues for the design of future therapeutic strategies specifically targeting COVID-19 in patients with hematological tumors. Our data provide a rich resource for understanding the specific pathogenesis of COVID-19 in immunocompromised patients, such as those with hematological malignancies, and developing effective therapeutics for COVID-19.

IMPORTANCE

A majority of previous studies focused on the characterization of coronavirus infectious disease 2019 (COVID-19) disease severity in people with normal immunity, while the characterization of COVID-19 in immunocompromised populations is still limited. Our study profiles changes in the transcriptome landscape post-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in hematological tumor patients and non-tumor individuals. Furthermore, our integrative and comparative systems biology analysis of the interactome, complexome, and transcriptome provides new insights into the tumor-specific pathogenesis of COVID-19. Our findings confirm that SARS-CoV-2 potentially tends to target more non-functional host proteins to indirectly affect host immune responses in hematological tumor patients. The identified unique genes, complexes, functions/pathways, and expression patterns post-SARS-CoV-2 infection in patients with hematological tumors increase our understanding of how SARS-CoV-2 manipulates the host molecular mechanism. Our observed differential genes/complexes and clinical indicators of normal/long infection and deceased COVID-19 patients provide clues for understanding the mechanism of COVID-19 progression in hematological tumors. Finally, our study provides an important data resource that supports the increasing value of the application of publicly accessible data sets to public health.

Network-based approach for drug repurposing against mpox

The current outbreak of mpox presents a significant threat to the global community. However, the lack of mpox-specific drugs necessitates the identification of additional candidates for clinical trials. In this study, a network medicine framework was used to investigate poxviruses-human interactions to identify potential drugs effective against the mpox virus (MPXV). The results indicated that poxviruses preferentially target hubs on the human interactome, and that these virally-targeted proteins (VTPs) tend to aggregate together within specific modules. Comorbidity analysis revealed that mpox is closely related to immune system diseases. Based on predicted drug-target interactions, 268 drugs were identified using the network proximity approach, among which 23 drugs displaying the least side-effects and significant proximity to MPXV were selected as the final candidates. Lastly, specific drugs were explored based on VTPs, differentially expressed proteins, and intermediate nodes, corresponding to different categories. These findings provide novel insights that can contribute to a deeper understanding of the pathogenesis of MPXV and development of ready-to-use treatment strategies based on drug repurposing.

RVvictor: Virus RNA-directed molecular interactions for RNA virus infection

RNA viruses are major human pathogens that cause seasonal epidemics and occasional pandemic outbreaks. Due to the nature of their RNA genomes, it is anticipated that virus's RNA interacts with host protein (INTPRO), messenger RNA (INTmRNA), and non-coding RNA (INTncRNA) to perform their particular functions during their transcription and replication. In other words, thus, it is urgently needed to have such valuable data on virus RNA-directed molecular interactions (especially INTPROs), which are highly anticipated to attract broad research interests in the fields of RNA virus translation and replication. In this study, a new database was constructed to describe the virus RNA-directed interaction (INTPRO, INTmRNA, INTncRNA) for RNA virus (RVvictor). This database is unique in a) unambiguously characterizing the interactions between viruses RNAs and host proteins, b) providing, for the first time, the most systematic RNA-directed interaction data resources in providing clues to understand the molecular mechanisms of RNA viruses' translation, and replication, and c) in RVvictor, comprehensive enrichment analysis is conducted for each virus RNA based on its associated target genes/proteins, and the enrichment results were explicitly illustrated using various graphs. We found significant enrichment of a suite of pathways related to infection, translation, and replication, e.g., HIV infection, coronavirus disease, regulation of viral genome replication, and so on. Due to the devastating and persistent threat posed by the RNA virus, RVvictor constructed, for the first time, a possible network of cross-talk in RNA-directed interaction, which may ultimately explain the pathogenicity of RNA virus infection. The knowledge base might help develop new anti-viral therapeutic targets in the future. It's now free and publicly accessible at: https://idrblab.org/rvvictor/.

Machine learning-based models for the prediction of breast cancer recurrence risk

Breast cancer is the most common malignancy diagnosed in women worldwide. The prevalence and incidence of breast cancer is increasing every year; therefore, early diagnosis along with suitable relapse detection is an important strategy for prognosis improvement. This study aimed to compare different machine algorithms to select the best model for predicting breast cancer recurrence. The prediction model was developed by using eleven different machine learning (ML) algorithms, including logistic regression (LR), random forest (RF), support vector classification (SVC), extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), decision tree, multilayer perceptron (MLP), linear discriminant analysis (LDA), adaptive boosting (AdaBoost), Gaussian naive Bayes (GaussianNB), and light gradient boosting machine (LightGBM), to predict breast cancer recurrence. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score were used to evaluate the performance of the prognostic model. Based on performance, the optimal ML was selected, and feature importance was ranked by Shapley Additive Explanation (SHAP) values. Compared to the other 10 algorithms, the results showed that the AdaBoost algorithm had the best prediction performance for successfully predicting breast cancer recurrence and was adopted in the establishment of the prediction model. Moreover, CA125, CEA, Fbg, and tumor diameter were found to be the most important features in our dataset to predict breast cancer recurrence. More importantly, our study is the first to use the SHAP method to improve the interpretability of clinicians to predict the recurrence model of breast cancer based on the AdaBoost algorithm. The AdaBoost algorithm offers a clinical decision support model and successfully identifies the recurrence of breast cancer.

Determining human-coronavirus protein-protein interaction using machine intelligence

The Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) virus spread the novel CoronaVirus −19 (nCoV-19) pandemic, resulting in millions of fatalities globally. Recent research demonstrated that the Protein-Protein Interaction (PPI) between SARS-CoV-2 and human proteins is accountable for viral pathogenesis. However, many of these PPIs are poorly understood and unexplored, necessitating a more in-depth investigation to find latent yet critical interactions. This article elucidates the host-viral PPI through Machine Learning (ML) lenses and validates the biological significance of the same using web-based tools. ML classifiers are designed based on comprehensive datasets with five sequence-based features of human proteins, namely Amino Acid Composition, Pseudo Amino Acid Composition, Conjoint Triad, Dipeptide Composition, and Normalized Auto Correlation. A majority voting rule-based ensemble method composed of the Random Forest Model (RFM), AdaBoost, and Bagging technique is proposed that delivers encouraging statistical performance compared to other models employed in this work. The proposed ensemble model predicted a total of 111 possible SARS-CoV-2 human target proteins with a high likelihood factor ≥70%, validated by utilizing Gene Ontology (GO) and KEGG pathway enrichment analysis. Consequently, this research can aid in a deeper understanding of the molecular mechanisms underlying viral pathogenesis and provide clues for developing more efficient anti-COVID medications.

MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses

Viral-host protein-protein interaction (VHPPI) prediction is essential to decoding molecular mechanisms of viral pathogens and host immunity processes that eventually help to control the propagation of viral diseases and to design optimized therapeutics. Multiple AI-based predictors have been developed to predict diverse VHPPIs across a wide range of viruses and hosts, however, these predictors produce better performance only for specific types of hosts and viruses. The prime objective of this research is to develop a robust meta predictor (MP-VHPPI) capable of more accurately predicting VHPPI across multiple hosts and viruses. The proposed meta predictor makes use of two well-known encoding methods Amphiphilic Pseudo-Amino Acid Composition (APAAC) and Quasi-sequence (QS) Order that capture amino acids sequence order and distributional information to most effectively generate the numerical representation of complete viral-host raw protein sequences. Feature agglomeration method is utilized to transform the original feature space into a more informative feature space. Random forest (RF) and Extra tree (ET) classifiers are trained on optimized feature space of both APAAC and QS order separate encoders and by combining both encodings. Further predictions of both classifiers are utilized to feed the Support Vector Machine (SVM) classifier that makes final predictions. The proposed meta predictor is evaluated over 7 different benchmark datasets, where it outperforms existing VHPPI predictors with an average performance of 3.07, 6.07, 2.95, and 2.85% in terms of accuracy, Mathews correlation coefficient, precision, and sensitivity, respectively. To facilitate the scientific community, the MP-VHPPI web server is available at https://sds_genetic_analysis.opendfki.de/MP-VHPPI/.

The impact of the suppression of highly connected protein interactions on the corona virus infection

Several highly effective Covid-19 vaccines are in emergency use, although more-infectious coronavirus strains, could delay the end of the pandemic even further. Because of this, it is highly desirable to develop fast antiviral drug treatments to accelerate the lasting immunity against the virus. From a theoretical perspective, computational approaches are useful tools for antiviral drug development based on the data analysis of gene expression, chemical structure, molecular pathway, and protein interaction mapping. This work studies the structural stability of virus–host interactome networks based on the graphical representation of virus–host protein interactions as vertices or nodes connected by commonly shared proteins. These graphical network visualization methods are analogous to those use in the design of artificial neural networks in neuromorphic computing. In standard protein-node-based network representation, virus–host interaction merges with virus–protein and host–protein networks, introducing redundant links associated with the internal virus and host networks. On the contrary, our approach provides a direct geometrical representation of viral infection structure and allows the effective and fast detection of the structural robustness of the virus–host network through proteins removal. This method was validated by applying it to H1N1 and HIV viruses, in which we were able to pinpoint the changes in the Interactome Network produced by known vaccines. The application of this method to the SARS-CoV-2 virus–host protein interactome implies that nonstructural proteins nsp4, nsp12, nsp16, the nuclear pore membrane glycoprotein NUP210, and ubiquitin specific peptidase USP54 play a crucial role in the viral infection, and their removal may provide an efficient therapy. This method may be extended to any new mutations or other viruses for which the Interactome Network is experimentally determined. Since time is of the essence, because of the impact of more-infectious strains on controlling the spread of the virus, this method may be a useful tool for novel antiviral therapies.

Viral hijacking mechanism in humans through protein-protein interactions

Numerous viruses have evolved mechanisms to inhibit or alter the host cell's apoptotic response as part of their coevolution with their hosts. The analysis of virus-host protein interactions require an in-depth understanding of both the viral and host protein structures and repertoires, as well as evolutionary mechanisms and pertinent biological facts. Throughout the course of a viral infection, there is constant battle for binding between virus and cellular proteins. Exogenous interfaces facilitating viral-host interactions are well known for constantly targeting and suppressing endogenous interfaces mediating intraspecific interactions, such as viral-viral and host-host connections. In these interactions, the protein-protein interactions (PPIs), are mostly shown as networks (protein interaction networks, PINs), with proteins represented as nodes and their interactions represented as edges. Host proteins with a higher degree of connectivity are more likely to interact with viral proteins. Due to technical advancements, three-dimensional interactions may now be visualized computationally utilizing molecular modeling and cryo-EM approaches. The uniqueness of viral domain repertoires, their evolution, and their activities during viral infection make viruses fascinating models for research. This chapter aims to provide readers a complete picture of the viral hijacking mechanism in protein-protein interactions.

A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19

Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.

Aging whole blood transcriptome reveals candidate genes for SARS-CoV-2-related vascular and immune alterations

Abstract

The risk of severe COVID-19 increases with age as older patients are at highest risk. Thus, there is an urgent need to identify how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) interacts with blood components during aging. We investigated the whole blood transcriptome from the Genotype-Tissue Expression (GTEx) database to explore differentially expressed genes (DEGs) translated into proteins interacting with viral proteins during aging. From 22 DEGs in aged blood, FASLG, CTSW, CTSE, VCAM1, and BAG3 were associated with immune response, inflammation, cell component and adhesion, and platelet activation/aggregation. Males and females older than 50 years old overexpress FASLG, possibly inducing a hyperinflammatory cascade. The expression of cathepsins (CTSW and CTSE) and the anti-apoptotic co-chaperone molecule BAG3 also increased throughout aging in both genders. By exploring single-cell RNA-sequencing data from peripheral blood of SARS-CoV-2-infected patients, we found FASLG and CTSW expressed in natural killer cells and CD8 + T lymphocytes, whereas BAG3 was expressed mainly in CD4 + T cells, naive T cells, and CD14 + monocytes. In addition, T cell exhaustion was associated with increased expression of CCL4L2 and DUSP4 over blood aging. LAG3, PDCD1, TIGIT, VCAM1, HLA-DRA, and TOX also increased in individuals aged 60–69 years old; conversely, the RGS2 gene decreased with aging. We further identified a distinct gene expression profile associated with type I interferon signaling following blood aging. These results revealed changes in blood molecules potentially related to SARS-CoV-2 infection throughout aging, emphasizing them as therapeutic candidates for aggressive clinical manifestation of COVID-19.

Key messages

• Prediction of host-viral interactions in the whole blood transcriptome during aging.

• Expression levels of FASLG, CTSW, CTSE, VCAM1, and BAG3 increase in aged blood.

• Blood interactome reveals targets involved with immune response, inflammation, and blood clots.

• SARS-CoV-2-infected patients with high viral load showed FASLG overexpression.

• Gene expression profile associated with T cell exhaustion and type I interferon signaling were affected with blood aging.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00109-021-02161-4.

LSTM-PHV: prediction of human-virus protein-protein interactions by LSTM with word2vec

Viral infection involves a large number of protein-protein interactions (PPIs) between human and virus. The PPIs range from the initial binding of viral coat proteins to host membrane receptors to the hijacking of host transcription machinery. However, few interspecies PPIs have been identified, because experimental methods including mass spectrometry are time-consuming and expensive, and molecular dynamic simulation is limited only to the proteins whose 3D structures are solved. Sequence-based machine learning methods are expected to overcome these problems. We have first developed the LSTM model with word2vec to predict PPIs between human and virus, named LSTM-PHV, by using amino acid sequences alone. The LSTM-PHV effectively learnt the training data with a highly imbalanced ratio of positive to negative samples and achieved AUCs of 0.976 and 0.973 and accuracies of 0.984 and 0.985 on the training and independent datasets, respectively. In predicting PPIs between human and unknown or new virus, the LSTM-PHV learned greatly outperformed the existing state-of-the-art PPI predictors. Interestingly, learning of only sequence contexts as words is sufficient for PPI prediction. Use of uniform manifold approximation and projection demonstrated that the LSTM-PHV clearly distinguished the positive PPI samples from the negative ones. We presented the LSTM-PHV online web server and support data that are freely available at http://kurata35.bio.kyutech.ac.jp/LSTM-PHV.

Prediction of the Effects of Variants and Differential Expression of Key Host Genes ACE2, TMPRSS2, and FURIN in SARS-CoV-2 Pathogenesis: An In Silico Approach

A new strain of the beta coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is solely responsible for the ongoing coronavirus disease 2019 (COVID-19) pandemic. Although several studies suggest that the spike protein of this virus interacts with the cell surface receptor, angiotensin-converting enzyme 2 (ACE2), and is subsequently cleaved by TMPRSS2 and FURIN to enter into the host cell, conclusive insight about the interaction pattern of the variants of these proteins is still lacking. Thus, in this study, we analyzed the functional conjugation among the spike protein, ACE2, TMPRSS2, and FURIN in viral pathogenesis as well as the effects of the mutations of the proteins through the implementation of several bioinformatics approaches. Analysis of the intermolecular interactions revealed that T27A (ACE2), G476S (receptor-binding domain [RBD] of the spike protein), C297T (TMPRSS2), and P812S (cleavage site for TMPRSS2) coding variants may render resistance in viral infection, whereas Q493L (RBD), S477I (RBD), P681R (cleavage site for FURIN), and P683W (cleavage site for FURIN) may lead to increase viral infection. Genotype-specific expression analysis predicted several genetic variants of ACE2 (rs2158082, rs2106806, rs4830971, and rs4830972), TMPRSS2 (rs458213, rs468444, rs4290734, and rs6517666), and FURIN (rs78164913 and rs79742014) that significantly alter their normal expression which might affect the viral spread. Furthermore, we also found that ACE2, TMPRSS2, and FURIN proteins are functionally co-related with each other, and several genes are highly co-expressed with them, which might be involved in viral pathogenesis. This study will thus help in future genomics and proteomics studies of SARS-CoV-2 and will provide an opportunity to understand the underlying molecular mechanism during SARS-CoV-2 pathogenesis.

Rabies Infection: An Overview of Lyssavirus-Host Protein Interactions

Viruses are obligatory intracellular parasites that use cell proteins to take the control of the cell functions in order to accomplish their life cycle. Studying the viral-host interactions would increase our knowledge of the viral biology and mechanisms of pathogenesis. Studies on pathogenesis mechanisms of lyssaviruses, which are the causative agents of rabies, have revealed some important host protein partners for viral proteins, especially for most studied species, i.e. RABV. In this review article, the key physical lyssavirus-host protein interactions, their contributions to rabies infection, and their exploitation are discussed to improve the knowledge about rabies pathogenesis.

Evolutionary selectivity of amino acid is inspired from the enhanced structural stability and flexibility of the folded protein

AIM

The present study attempts to decipher the site-specific amino acid alterations at certain positions experiencing preferential selectivity and their effect on proteins' stability and flexibility. The study examines the selection preferences by considering pair-wise non-bonded interaction energies of adjacent and interacting amino acids present at the interacting site, along with their evolutionary history.

MATERIALS AND METHODS

For the study, variations in the interacting residues of spike protein (S-Protein) receptor-binding domain (RBD) of different coronaviruses were examined. The MD simulation trajectory analysis revealed that, though all the variants studied were structurally stable at their native and bound confirmations, the RBD of 2019-nCoV/SARS-CoV-2 was found to be more flexible and more dynamic. Furthermore, a noticeable change observed in the non-bonded interaction energies of the amino acids interacting with the receptor corroborated their selection at respective positions.

KEY FINDINGS

The conformational changes exerted by the altered amino acids could be the reason for a broader range of interacting receptors among the selected proteins.

SIGNIFICANCE

The results envisage a strong indication that the residue selection at certain positions is governed by a well-orchestrated feedback mechanism, which follows increased stability and flexibility in the folded structure compared to its evolutionary predecessor.

nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning

Lysine crotonylation (Kcr) is a newly discovered type of protein post-translational modification and has been reported to be involved in various pathophysiological processes. High-resolution mass spectrometry is the primary approach for identification of Kcr sites. However, experimental approaches for identifying Kcr sites are often time-consuming and expensive when compared with computational approaches. To date, several predictors for Kcr site prediction have been developed, most of which are capable of predicting crotonylation sites on either histones alone or mixed histone and nonhistone proteins together. These methods exhibit high diversity in their algorithms, encoding schemes, feature selection techniques and performance assessment strategies. However, none of them were designed for predicting Kcr sites on nonhistone proteins. Therefore, it is desirable to develop an effective predictor for identifying Kcr sites from the large amount of nonhistone sequence data. For this purpose, we first provide a comprehensive review on six methods for predicting crotonylation sites. Second, we develop a novel deep learning-based computational framework termed as CNNrgb for Kcr site prediction on nonhistone proteins by integrating different types of features. We benchmark its performance against multiple commonly used machine learning classifiers (including random forest, logitboost, naïve Bayes and logistic regression) by performing both 10-fold cross-validation and independent test. The results show that the proposed CNNrgb framework achieves the best performance with high computational efficiency on large datasets. Moreover, to facilitate users' efforts to investigate Kcr sites on human nonhistone proteins, we implement an online server called nhKcr and compare it with other existing tools to illustrate the utility and robustness of our method. The nhKcr web server and all the datasets utilized in this study are freely accessible at http://nhKcr.erc.monash.edu/.

Molecular scaffolds from mother nature as possible lead compounds in drug design and discovery against coronaviruses: A landscape analysis of published literature and molecular docking studies

The recent outbreak of viral infection and its transmission has highlighted the importance of its slowdown for the safeguard of public health, globally. The identification of novel drugs and efficient therapies against these infectious viruses is need of the hour. The eruption of COVID-19 is caused by a novel acute respiratory syndrome virus SARS-CoV-2 which has taken the whole world by storm as it has transformed into a global pandemic. This lethal syndrome is a global health threat to general public which has already affected millions of people. Despite the development of some potential vaccines and repurposed drugs by some Pharma companies, this health emergency needs more attention due to the less efficacy of these vaccines coupled with the emergence of novel and resistant strains of SARS-CoV-2. Due to enormous structural diversity and biological applications, natural products are considered as a wonderful source of drugs for such diseases. Natural product based drugs constitute a substantial proportion of the pharmaceutical market particularly in the therapeutic areas of infectious diseases and oncology. The naturally occurring bioactive antiviral phytochemicals including alkaloids, flavonoids and peptides have been subjected to virtual screening against COVID-19. Since there is no specific medicine available for the treatment of Covid-19, designing new drugs using in silico methods plays an all important role to find that magic bullet which can target this lethal virus. The in silico method is not only quick but economical also when compared to the other conventional methods which are hit and trial methods. Based on this in silico approach, various natural products have been recently identified which might have a potential to inhibit COVID-19 outbreak. These natural products have been shown by these docking studies to interact with the spike protein of the novel coronavirus. This spike protein has been shown to bind to a transmembrane protein called Angiotensin converting enzyme 2 (ACE2), this protein acts as a receptor for the viral spike protein. This comprehensive review article anticipates providing a summary of the authentic and peer reviewed published literature about the potential of natural metabolites that can be developed into possible lead compounds against this new threat of Covid-19. Main focus of the article will be to highlight natural sources of potential anti-coronavirus molecules, mechanism of action, docking studies and the target proteins as well as their toxicity profiles. This review article intends to provide a starting point for the research endeavors that are needed for the design and development of drugs based on pure natural products, their synthetic or semi-synthetic derivatives and standardized plant extracts. This review article will be highly helpful for scientists who are working or intend to work on antiviral drugs from natural sources.

Expanding Our Understanding of COVID-19 from Biomedical Literature Using Word Embedding

A better understanding of the clinical characteristics of coronavirus disease 2019 (COVID-19) is urgently required to address this health crisis. Numerous researchers and pharmaceutical companies are working on developing vaccines and treatments; however, a clear solution has yet to be found. The current study proposes the use of artificial intelligence methods to comprehend biomedical knowledge and infer the characteristics of COVID-19. A biomedical knowledge base was established via FastText, a word embedding technique, using PubMed literature from the past decade. Subsequently, a new knowledge base was created using recently published COVID-19 articles. Using this newly constructed knowledge base from the word embedding model, a list of anti-infective drugs and proteins of either human or coronavirus origin were inferred to be related, because they are located close to COVID-19 on the knowledge base. This study attempted to form a method to quickly infer related information about COVID-19 using the existing knowledge base, before sufficient knowledge about COVID-19 is accumulated. With COVID-19 not completely overcome, machine learning-based research in the PubMed literature will provide a broad guideline for researchers and pharmaceutical companies working on treatments for COVID-19.

Current status and future perspectives of computational studies on human-virus protein-protein interactions

The protein-protein interactions (PPIs) between human and viruses mediate viral infection and host immunity processes. Therefore, the study of human-virus PPIs can help us understand the principles of human-virus relationships and can thus guide the development of highly effective drugs to break the transmission of viral infectious diseases. Recent years have witnessed the rapid accumulation of experimentally identified human-virus PPI data, which provides an unprecedented opportunity for bioinformatics studies revolving around human-virus PPIs. In this article, we provide a comprehensive overview of computational studies on human-virus PPIs, especially focusing on the method development for human-virus PPI predictions. We briefly introduce the experimental detection methods and existing database resources of human-virus PPIs, and then discuss the research progress in the development of computational prediction methods. In particular, we elaborate the machine learning-based prediction methods and highlight the need to embrace state-of-the-art deep-learning algorithms and new feature engineering techniques (e.g. the protein embedding technique derived from natural language processing). To further advance the understanding in this research topic, we also outline the practical applications of the human-virus interactome in fundamental biological discovery and new antiviral therapy development.

SARSCOVIDB-A New Platform for the Analysis of the Molecular Impact of SARS-CoV-2 Viral Infection

The COVID-19 pandemic caused by the new coronavirus (SARS-CoV-2) has become a global emergency issue for public health. This threat has led to an acceleration in related research and, consequently, an unprecedented volume of clinical and experimental data that include changes in gene expression resulting from infection. The SARS-CoV-2 infection database (SARSCOVIDB: https://sarscovidb.org/) was created to mitigate the difficulties related to this scenario. The SARSCOVIDB is an online platform that aims to integrate all differential gene expression data, at messenger RNA and protein levels, helping to speed up analysis and research on the molecular impact of COVID-19. The database can be searched from different experimental perspectives and presents all related information from published data, such as viral strains, hosts, methodological approaches (proteomics or transcriptomics), genes/proteins, and samples (clinical or experimental). All information was taken from 24 articles related to analyses of differential gene expression out of 5,554 COVID-19/SARS-CoV-2-related articles published so far. The database features 12,535 genes whose expression has been identified as altered due to SARS-CoV-2 infection. Thus, the SARSCOVIDB is a new resource to support the health workers and the scientific community in understanding the pathogenesis and molecular impact caused by SARS-CoV-2.

Prediction of SARS-CoV Interaction with Host Proteins during Lung Aging Reveals a Potential Role for TRIB3 in COVID-19

COVID-19 is prevalent in the elderly. Old individuals are more likely to develop pneumonia and respiratory failure due to alveolar damage, suggesting that lung senescence may increase the susceptibility to SARS-CoV-2 infection and replication. Considering that human coronavirus (HCoVs; SARS-CoV-2 and SARS-CoV) require host cellular factors for infection and replication, we analyzed Genotype-Tissue Expression (GTEx) data to test whether lung aging is associated with transcriptional changes in human protein-coding genes that potentially interact with these viruses. We found decreased expression of the gene tribbles homolog 3 (TRIB3) during aging in male individuals, and its protein was predicted to interact with HCoVs nucleocapsid protein and RNA-dependent RNA polymerase. Using publicly available lung single-cell data, we found TRIB3 expressed mainly in alveolar epithelial cells that express SARS-CoV-2 receptor ACE2. Functional enrichment analysis of age-related genes, in common with SARS-CoV-induced perturbations, revealed genes associated with the mitotic cell cycle and surfactant metabolism. Given that TRIB3 was previously reported to decrease virus infection and replication, the decreased expression of TRIB3 in aged lungs may help explain why older male patients are related to more severe cases of the COVID-19. Thus, drugs that stimulate TRIB3 expression should be evaluated as a potential therapy for the disease.

Dysregulation of the RIG-I-like Receptor Pathway Signaling by Peste des Petits Ruminants Virus Phosphoprotein

Peste des petits ruminants virus (PPRV) is a Morbillivirus that causes highly contagious and severe disease in various ruminants. PPRV infection leads to a severe inhibition of host antiviral immune response. Our previous study demonstrated that PPRV V protein blocks IFN response by targeting STAT proteins. In the current study, we identified the phosphoprotein (P) as a novel antagonistic factor of PPRV to counteract host antiviral innate immune response. PPRV P protein significantly suppressed RIG-I-like receptor pathway signaling and impaired IFN-β and ISGs expression by targeting IFN regulatory factor (IRF)3 in both human embryonic kidney 293T cells and primary goat fibroblasts. The 1-102 region of P protein was critical for the antagonistic function of P protein. P protein interacted with IRF association domain (IAD) of IRF3 to block the interaction between TBK1 and IRF3. The interaction between TBK1 and the IAD of IRF3 is responsible for triggering the phosphorylation of IRF3. P protein competed with TBK1 to bind to the IAD of IRF3 that contributed to the decreased phosphorylation of IRF3, which, in turn, interfered with the dimerization of IRF3 and blocked IRF3 nuclear transportation. Besides, we also found that P protein interacted with IRF5 and IRF8. However, the involved mechanism remains unknown. Taken together, our results reveal a novel mechanism by which PPRV P protein antagonizes host antiviral innate immune response by interacting with the transcription factor IRF3, thereby inhibiting the type I IFN production and promoting viral replication.

Pathway enrichment analysis of virus-host interactome and prioritization of novel compounds targeting the spike glycoprotein receptor binding domain-human angiotensin-converting enzyme 2 interface to combat SARS-CoV-2

SARS-CoV-2 has become a pandemic causing a serious global health concern. The absence of effective drugs for treatment of the disease has caused its rapid spread on a global scale. Similarly to the SARS-CoV, the SARS-CoV-2 is also involved in a complex interplay with the host cells. This infection is characterized by a diffused alveolar damage consistent with the Acute Respiratory Disease Syndrome (ARDS). To explore the complex mechanisms of the disease at the system level, we used a network medicine tools approach. The protein-protein interactions (PPIs) between the SARS-CoV and the associated human cell proteins are crucial for the viral pathogenesis. Since the cellular entry of SARS-CoV-2 is accomplished by binding of the spike glycoprotein binding domain (RBD) to the human angiotensin-converting enzyme 2 (hACE2), a molecule that can bind to the spike RDB-hACE2 interface could block the virus entry. Here, we performed a virtual screening of 55 compounds to identify potential molecules that can bind to the spike glycoprotein and spike-ACE2 complex interface. It was found that the compound ethyl 1-{3-[(2,4-dichlorobenzyl) carbamoyl]-1-ethyl-6-fluoro-4-oxo-1,4-dihydro-7-quinolinyl}-4-piperidine carboxylate (the S54 ligand) and ethyl 1-{3-[(2,4-dichlorobenzyl) carbamoyl]-1-ethyl-6-fluoro-4-oxo-1,4-dihydro-7-quinolinyl}-4 piperazine carboxylate (the S55 ligand) forms hydrophobic interactions with Tyr41A, Tyr505B and Tyr553B, Leu29A, Phe495B, respectively of the spike glycoprotein, the hotspot residues in the spike glycoprotein RBD-hACE2 binding interface. Furthermore, molecular dynamics simulations and free energy calculations using the MM-GBSA method showed that the S54 ligand is a stronger binder than a known SARS-CoV spike inhibitor SSAA09E3 (N-(9,10-dioxo-9, 10-dihydroanthracen-2-yl) benzamide).

Communicated by Ramaswamy H. Sarma

Human Gene Functional Network-Informed Prediction of HIV-1 Host Dependency Factors

Identification of HIV-1 HDFs remains a crucial step to understand the complicated relationships between human and HIV-1. To complement the experimental identification of HDFs, we have implemented an existing network-based gene discovery strategy to predict HDFs from the human genome. The core idea of the proposed method is that the rich information deposited in host gene functional networks can be effectively utilized to infer the potential HDFs. We hope the proposed prediction method could further guide hypothesis-driven experimental efforts to interrogate human–HIV-1 relationships and provide new hints for the development of antiviral drugs to combat HIV-1 infection.

Human immunodeficiency virus type 1 (HIV-1) depends on a class of host proteins called host dependency factors (HDFs) to facilitate its infection. So far experimental efforts have detected a certain number of HDFs, but the gene inventory of HIV-1 HDFs remains incomplete. Here, we implemented an existing network-based gene discovery strategy to predict HIV-1 HDFs. First, an encoding scheme based on a publicly available human tissue-specific gene functional network (GIANT; http://giant.princeton.edu/) was designed to convert each human gene into a 25,825-dimensional feature vector. Then, a random forest-based predictive model was trained on a data set containing 868 known HDFs and 1,736 non-HDFs. Through 5-fold cross-validation, an independent test, and comparison with one existing method, the proposed prediction method consistently revealed accurate and competitive performance. The highlight of our method should be ascribed to the introduction of the GIANT encoding scheme, which contains rich information regarding gene interactions. By merging known HDFs and genome-wide HDF prediction results, network analysis was conducted to catch the common patterns of HDFs in the context of the GIANT network. Interestingly, HDFs reveal significantly lower betweenness than HIV-1-interacting human proteins (i.e., HIV targets). In the meantime, the functional roles of HDFs were also examined by mapping all the HDF candidates into human protein complexes. Especially, we observed the frequent co-occurrence of HDFs and HIV targets at the protein complex level. Collectively, we hope the proposed prediction method not only can accelerate the HDF identification and antiviral drug target discovery, but also can provide some mechanistic insights into human-virus relationships.

IMPORTANCE Identification of HIV-1 HDFs remains a crucial step to understand the complicated relationships between human and HIV-1. To complement the experimental identification of HDFs, we have implemented an existing network-based gene discovery strategy to predict HDFs from the human genome. The core idea of the proposed method is that the rich information deposited in host gene functional networks can be effectively utilized to infer the potential HDFs. We hope the proposed prediction method could further guide hypothesis-driven experimental efforts to interrogate human–HIV-1 relationships and provide new hints for the development of antiviral drugs to combat HIV-1 infection.

A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19

The global coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to unprecedented social and economic consequences. The risk of morbidity and mortality due to COVID-19 increases dramatically in the presence of coexisting medical conditions, while the underlying mechanisms remain unclear. Furthermore, there are no approved therapies for COVID-19. This study aims to identify SARS-CoV-2 pathogenesis, disease manifestations, and COVID-19 therapies using network medicine methodologies along with clinical and multi-omics observations. We incorporate SARS-CoV-2 virus-host protein-protein interactions, transcriptomics, and proteomics into the human interactome. Network proximity measurement revealed underlying pathogenesis for broad COVID-19-associated disease manifestations. Analyses of single-cell RNA sequencing data show that co-expression of ACE2 and TMPRSS2 is elevated in absorptive enterocytes from the inflamed ileal tissues of Crohn disease patients compared to uninflamed tissues, revealing shared pathobiology between COVID-19 and inflammatory bowel disease. Integrative analyses of metabolomics and transcriptomics (bulk and single-cell) data from asthma patients indicate that COVID-19 shares an intermediate inflammatory molecular profile with asthma (including IRAK3 and ADRB2). To prioritize potential treatments, we combined network-based prediction and a propensity score (PS) matching observational study of 26,779 individuals from a COVID-19 registry. We identified that melatonin usage (odds ratio [OR] = 0.72, 95% CI 0.56-0.91) is significantly associated with a 28% reduced likelihood of a positive laboratory test result for SARS-CoV-2 confirmed by reverse transcription-polymerase chain reaction assay. Using a PS matching user active comparator design, we determined that melatonin usage was associated with a reduced likelihood of SARS-CoV-2 positive test result compared to use of angiotensin II receptor blockers (OR = 0.70, 95% CI 0.54-0.92) or angiotensin-converting enzyme inhibitors (OR = 0.69, 95% CI 0.52-0.90). Importantly, melatonin usage (OR = 0.48, 95% CI 0.31-0.75) is associated with a 52% reduced likelihood of a positive laboratory test result for SARS-CoV-2 in African Americans after adjusting for age, sex, race, smoking history, and various disease comorbidities using PS matching. In summary, this study presents an integrative network medicine platform for predicting disease manifestations associated with COVID-19 and identifying melatonin for potential prevention and treatment of COVID-19.

Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction

Protein-protein interactions (PPIs) are the physical connections between two or more proteins via electrostatic forces or hydrophobic effects. Identification of the PPIs is pivotal, which contributes to many biological processes including protein function, disease incidence, and therapy design. The experimental identification of PPIs via high-throughput technology is time-consuming and expensive. Bioinformatics approaches are expected to solve such restrictions. In this review, our main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs. Initially, we briefly introduce the currently available PPI databases and then review the state-of-the-art bioinformatics approaches, working principles, and their performances. Finally, we discuss the caveats and future perspective of the next generation algorithms for the prediction of PPIs.

A Network Medicine Approach to Investigation and Population-based Validation of Disease Manifestations and Drug Repurposing for COVID-19

The global Coronavirus Disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to unprecedented social and economic consequences. The risk of morbidity and mortality due to COVID-19 increases dramatically in the presence of co-existing medical conditions while the underlying mechanisms remain unclear. Furthermore, there are no proven effective therapies for COVID-19. This study aims to identify SARS-CoV-2 pathogenesis, diseases manifestations, and COVID-19 therapies using network medicine methodologies along with clinical and multi-omics observations. We incorporate SARS-CoV-2 virus-host protein-protein interactions, transcriptomics, and proteomics into the human interactome. Network proximity measure revealed underlying pathogenesis for broad COVID-19-associated manifestations. Multi-modal analyses of single-cell RNA-sequencing data showed that co-expression of ACE2 and TMPRSS2 was elevated in absorptive enterocytes from the inflamed ileal tissues of Crohn's disease patients compared to uninflamed tissues, revealing shared pathobiology by COVID-19 and inflammatory bowel disease. Integrative analyses of metabolomics and transcriptomics (bulk and single-cell) data from asthma patients indicated that COVID-19 shared intermediate inflammatory endophenotypes with asthma (including IRAK3 and ADRB2). To prioritize potential treatment, we combined network-based prediction and propensity score (PS) matching observational study of 18,118 patients from a COVID-19 registry. We identified that melatonin (odds ratio (OR) = 0.36, 95% confidence interval (CI) 0.22-0.59) was associated with 64% reduced likelihood of a positive laboratory test result for SARS-CoV-2. Using PS-matching user active comparator design, melatonin was associated with 54% reduced likelihood of SARS-CoV-2 positive test result compared to angiotensin II receptor blockers or angiotensin-converting enzyme inhibitors (OR = 0.46, 95% CI 0.24-0.86).

Targeting hub genes and pathways of innate immune response in COVID-19: A network biology perspective

The current pandemic of 2019 novel coronavirus disease (COVID-19) caused by a novel virus strain, 2019-nCoV/SARS-CoV-2 have posed a serious threat to global public health and economy. It is largely unknown how the human immune system responds to this infection. A better understanding of the immune response to SARS-CoV-2 will be important to develop therapeutics against COVID-19. Here, we have used transcriptomic profile of human alveolar adenocarcinoma cells (A549) infected with SARS-CoV-2 and employed a network biology approach to generate human-virus interactome. Network topological analysis discovers 15 SARS-CoV-2 targets, which belongs to a subset of interferon (IFN) stimulated genes (ISGs). These ISGs (IFIT1, IFITM1, IRF7, ISG15, MX1, and OAS2) can be considered as potential candidates for drug targets in the treatments of COVID-19. We have identified significant interaction between ISGs and TLR3 agonists, like poly I: C, and imiquimod, and suggests that TLR3 agonists can be considered as a potential drug for drug repurposing in COVID-19. Our network centric analysis suggests that moderating the innate immune response is a valuable approach to target COVID-19.

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Differential gene expression analysis of SARS-CoV-2 infected transcriptome

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Network based Human-SRAS-CoV-2 interactome analysis

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Interferon (IFN) stimulated genes (ISGs) are the most important targets.

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TLR3 agonists, like poly I:C, and imiquimod are identified as potential drugs.

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Targeting the innate immune response is a valuable approach against COVID-19.

Network-Based Analysis of OMICs Data to Understand the HIV-Host Interaction

The interaction of human immunodeficiency virus with human cells is responsible for all stages of the viral life cycle, from the infection of CD4+ cells to reverse transcription, integration, and the assembly of new viral particles. To date, a large amount of OMICs data as well as information from functional genomics screenings regarding the HIV–host interaction has been accumulated in the literature and in public databases. We processed databases containing HIV–host interactions and found 2910 HIV-1-human protein-protein interactions, mostly related to viral group M subtype B, 137 interactions between human and HIV-1 coding and non-coding RNAs, essential for viral lifecycle and cell defense mechanisms, 232 transcriptomics, 27 proteomics, and 34 epigenomics HIV-related experiments. Numerous studies regarding network-based analysis of corresponding OMICs data have been published in recent years. We overview various types of molecular networks, which can be created using OMICs data, including HIV–human protein–protein interaction networks, co-expression networks, gene regulatory and signaling networks, and approaches for the analysis of their topology and dynamics. The network-based analysis can be used to determine the critical pathways and key proteins involved in the HIV life cycle, cellular and immune responses to infection, viral escape from host defense mechanisms, and mechanisms mediating different susceptibility of humans to infection. The proteins and pathways identified in these studies represent a basis for developing new anti-HIV therapeutic strategies such as new drugs preventing infection of CD4+ cells and viral replication, effective vaccines, “shock and kill” and “block and lock” approaches to cure latent infection.

Systematic evaluation of machine learning methods for identifying human-pathogen protein-protein interactions

In recent years, high-throughput experimental techniques have significantly enhanced the accuracy and coverage of protein-protein interaction identification, including human-pathogen protein-protein interactions (HP-PPIs). Despite this progress, experimental methods are, in general, expensive in terms of both time and labour costs, especially considering that there are enormous amounts of potential protein-interacting partners. Developing computational methods to predict interactions between human and bacteria pathogen has thus become critical and meaningful, in both facilitating the detection of interactions and mining incomplete interaction maps. In this paper, we present a systematic evaluation of machine learning-based computational methods for human-bacterium protein-protein interactions (HB-PPIs). We first reviewed a vast number of publicly available databases of HP-PPIs and then critically evaluate the availability of these databases. Benefitting from its well-structured nature, we subsequently preprocess the data and identified six bacterium pathogens that could be used to study bacterium subjects in which a human was the host. Additionally, we thoroughly reviewed the literature on 'host-pathogen interactions' whereby existing models were summarized that we used to jointly study the impact of different feature representation algorithms and evaluate the performance of existing machine learning computational models. Owing to the abundance of sequence information and the limited scale of other protein-related information, we adopted the primary protocol from the literature and dedicated our analysis to a comprehensive assessment of sequence information and machine learning models. A systematic evaluation of machine learning models and a wide range of feature representation algorithms based on sequence information are presented as a comparison survey towards the prediction performance evaluation of HB-PPIs.

Inferring Virus-Host relationship between HPV and its host Homo sapiens using protein interaction network

Human papilloma virus (HPV) is a serious threat to human life globally with over 100 genotypes including cancer causing high risk HPVs. Study on protein interaction maps of pathogens with their host is a recent trend in ‘omics’ era and has been practiced by researchers to find novel drug targets. In current study, we construct an integrated protein interaction map of HPV with its host human in Cytoscape and analyze it further by using various bioinformatics tools. We found out 2988 interactions between 12 HPV and 2061 human proteins among which we identified MYLK, CDK7, CDK1, CDK2, JAK1 and 6 other human proteins associated with multiple viral oncoproteins. The functional enrichment analysis of these top-notch key genes is performed using KEGG pathway and Gene Ontology analysis, which reveals that the gene set is enriched in cell cycle a crucial cellular process, and the second most important pathway in which the gene set is involved is viral carcinogenesis. Among the viral proteins, E7 has the highest number of associations in the network followed by E6, E2 and E5. We found out a group of genes which is not targeted by the existing drugs available for HPV infections. It can be concluded that the molecules found in this study could be potential targets and could be used by scientists in their drug design studies.

Prediction of antiviral drugs against African swine fever viruses based on protein-protein interaction analysis

The African swine fever virus (ASFV) has severely influenced the swine industry of the world. Unfortunately, there is currently no effective antiviral drug or vaccine against the virus. Identification of new anti-ASFV drugs is urgently needed. Here, an up-to-date set of protein-protein interactions between ASFV and swine were curated by integration of protein-protein interactions from multiple sources. Thirty-eight swine proteins were observed to interact with ASFVs and were defined as ASFV-interacting swine proteins. The ASFV-interacting swine proteins were found to play a central role in the swine protein-protein interaction network, with significant larger degree, betweenness and smaller shortest path length than other swine proteins. Some of ASFV-interacting swine proteins also interacted with several other viruses and could be taken as potential targets of drugs for broad-spectrum effect, such as HSP90AB1. Finally, the antiviral drugs which targeted ASFV-interacting swine proteins and ASFV proteins were predicted. Several drugs with either broad-spectrum effect or high specificity on ASFV-interacting swine proteins were identified, such as Polaprezinc and Geldanamycin. Structural modeling and molecular dynamics simulation showed that Geldanamycin could bind with swine HSP90AB1 stably. This work could not only deepen our understanding towards the ASFV-swine interactions, but also help for the development of effective antiviral drugs against the ASFVs.

Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2

Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and 2019 novel coronavirus (2019-nCoV, also known as SARS-CoV-2), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV/SARS-CoV-2. Drug repurposing, representing as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV-host interactome and drug targets in the human protein-protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV/SARS-CoV-2 shares the highest nucleotide sequence identity with SARS-CoV (79.7%). Specifically, the envelope and nucleocapsid proteins of 2019-nCoV/SARS-CoV-2 are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV. Using network proximity analyses of drug targets and HCoV-host interactions in the human interactome, we prioritize 16 potential anti-HCoV repurposable drugs (e.g., melatonin, mercaptopurine, and sirolimus) that are further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. We further identify three potential drug combinations (e.g., sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the "Complementary Exposure" pattern: the targets of the drugs both hit the HCoV-host subnetwork, but target separate neighborhoods in the human interactome network. In summary, this study offers powerful network-based methodologies for rapid identification of candidate repurposable drugs and potential drug combinations targeting 2019-nCoV/SARS-CoV-2.

Temporal dynamics of protein complex formation and dissociation during human cytomegalovirus infection

The co-evolution and co-existence of viral pathogens with their hosts for millions of years is reflected in dynamic virus-host protein-protein interactions (PPIs) that are intrinsic to the spread of infections. Here, we investigate the system-wide dynamics of protein complexes throughout infection with the herpesvirus, human cytomegalovirus (HCMV). Integrating thermal shift assays and mass spectrometry quantification with virology and microscopy, we monitor the temporal formation and dissociation of hundreds of functional protein complexes and the dynamics of host-host, virus-host, and virus-virus PPIs. We establish pro-viral roles for cellular protein complexes and translocating proteins. We show the HCMV receptor integrin beta 1 dissociates from extracellular matrix proteins, becoming internalized with CD63, which is necessary for virus production. Moreover, this approach facilitates characterization of essential viral proteins, such as pUL52. This study of temporal protein complex dynamics provides insights into mechanisms of HCMV infection and a resource for biological and therapeutic studies.

Here, Hashimoto et al. apply mass spectrometry-based thermal proximity coaggregation to characterize the temporal dynamics of virus-host protein-protein interactions during human cytomegalovirus (HCMV) infection, uncovering proviral functions including the internalization of the HCMV receptor integrin beta 1 with CD63.

Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method

The identification of human-virus protein-protein interactions (PPIs) is an essential and challenging research topic, potentially providing a mechanistic understanding of viral infection. Given that the experimental determination of human-virus PPIs is time-consuming and labor-intensive, computational methods are playing an important role in providing testable hypotheses, complementing the determination of large-scale interactome between species. In this work, we applied an unsupervised sequence embedding technique (doc2vec) to represent protein sequences as rich feature vectors of low dimensionality. Training a Random Forest (RF) classifier through a training dataset that covers known PPIs between human and all viruses, we obtained excellent predictive accuracy outperforming various combinations of machine learning algorithms and commonly-used sequence encoding schemes. Rigorous comparison with three existing human-virus PPI prediction methods, our proposed computational framework further provided very competitive and promising performance, suggesting that the doc2vec encoding scheme effectively captures context information of protein sequences, pertaining to corresponding protein-protein interactions. Our approach is freely accessible through our web server as part of our host-pathogen PPI prediction platform (http://zzdlab.com/InterSPPI/). Taken together, we hope the current work not only contributes a useful predictor to accelerate the exploration of human-virus PPIs, but also provides some meaningful insights into human-virus relationships.