Computational Viromics: Applications of the Computational Biology in Viromics Studies

Lu, Congyu; Peng, Yousong

doi:10.1007/s12250-021-00395-7

Cited by 4 publications

(3 citation statements)

References 45 publications

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“…Understanding the interaction network between viruses and hosts may help much in clarifying the mechanism of virus cross-species transmission and improving the prediction model of predicting virus cross-species transmission ( 35 , 36 ). Despite extensive efforts to uncover virus-host protein-protein interactions, the current knowledge remains incomplete ( 20 , 22 , 34 ).…”

Section: Discussionmentioning

confidence: 99%

Prediction of mammalian virus cross-species transmission based on host proteins

Zhang,

Lu,

et al. 2023

Microbiol Spectr

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Most emerging viruses are spilled over from mammals. Understanding the mechanism of virus cross-species transmission and identifying zoonotic viruses before their emergence are critical for the prevention and control of newly emerging viruses. This study systematically investigated the host proteins associated with the cross-species transmission of mammalian viruses based on 1,271 pairs of virus-mammal interactions including 382 viruses from 33 viral families and 73 mammal species from 11 orders. Numerous host proteins were found to contribute to the cross-species transmission of mammalian viruses. Host proteins potentially contributing to virus cross-species transmission are specific to viral families, and few overlaps of such host proteins are observed in different viral families. Based on these host proteins, the random-forest (RF) models were built to predict the cross-species transmission potential of mammalian viruses. Moderate performance was obtained when using all viruses together. However, when modeling by viral family, the performance of the RF models varied much among viral families. In 13 viral families such as Flaviviridae , Retroviridae, and Poxviridae , the AUC of the RF model was greater than 0.8. Finally, the contribution of virus receptors to cross-species transmission was evaluated, and the virus receptor was found to have a minor effect in predicting the cross-species transmission of mammalian viruses. The study deepens our understanding of the mechanism of virus cross-species transmission and provides a framework for predicting the cross-species transmission of mammalian viruses. IMPORTANCE Emerging viruses pose serious threats to humans. Understanding the mechanism of virus cross-species transmission and identifying zoonotic viruses before their emergence are critical for the prevention and control of emerging viruses. This study systematically identified host factors associated with cross-species transmission of mammalian viruses and further built machine-learning models for predicting cross-species transmission of the viruses based on host factors including virus receptors. The study not only deepens our understanding of the mechanism of virus cross-species transmission but also provides a framework for predicting the cross-species transmission of mammalian viruses based on host factors.

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Section: Discussionmentioning

confidence: 99%

Prediction of mammalian virus cross-species transmission based on host proteins

Zhang,

Lu,

et al. 2023

Microbiol Spectr

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“…An enormous number of viruses have been identified, thanks to the rapid development of Next-Generation-Sequencing (NGS) technology, in comparison to traditional virus identification methods based on virus isolation [6][7][8] . For example, the IMG/VR database, recognized as the largest virus sequence database, has amassed over 15 million viral genomic sequences 6 .…”

Section: Main Textmentioning

confidence: 99%

Exploring the genome and protein space of viruses

Mao

et al. 2022

Preprint

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Background: Recent metagenomic studies have identified a huge number of viruses. Yet, the systematic assessment of the true genetic diversity of the whole virus community in our planet remains to be investigated. Here, we explored the genome and protein spaces of viruses by simulating the process of virus discovery in viral metagenomic studies. Results: This study estimated that there would be at least 3.52e+06 viral Operational Taxonomic Units (vOTUs) and 2.16e+07 viral protein clusters on earth, while only about 30% of the viral genetic diversity has been identified so far. Considering the balance of costs and the detected number of novel viruses, an additional 3.89e+05 samples are required to capture 84% of the total genetic diversity. When analyzing the viral genetic diversity by taxonomy and ecosystem, the estimated viral genetic diversity were consistent with those mentioned above. Conclusions: This study for the first time explored the virus genome and protein space and estimated the total number of vOTUs and vPCs on earth when the virus genetic space was saturated. It provides a guide for future sequencing efforts of virus discovery and contributes to a better understanding of viral diversity in nature.

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“…Currently, viral infection is a major factor threatening human health and global economic development (Qiu et al, 2017;Rasul, 2020;Lu and Peng, 2021). For instance, the current pandemic disease of novel coronavirus pneumonia, induced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused nearly 280 million confirmed cases and more than 5 million deaths worldwide by the end of 2021.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Powered Prediction of Human-Virus Protein-Protein Interactions

Yang

Ren

et al. 2022

Front. Microbiol.

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Identifying human-virus protein-protein interactions (PPIs) is an essential step for understanding viral infection mechanisms and antiviral response of the human host. Recent advances in high-throughput experimental techniques enable the significant accumulation of human-virus PPI data, which have further fueled the development of machine learning-based human-virus PPI prediction methods. Emerging as a very promising method to predict human-virus PPIs, deep learning shows the powerful ability to integrate large-scale datasets, learn complex sequence-structure relationships of proteins and convert the learned patterns into final prediction models with high accuracy. Focusing on the recent progresses of deep learning-powered human-virus PPI predictions, we review technical details of these newly developed methods, including dataset preparation, deep learning architectures, feature engineering, and performance assessment. Moreover, we discuss the current challenges and potential solutions and provide future perspectives of human-virus PPI prediction in the coming post-AlphaFold2 era.

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Computational Viromics: Applications of the Computational Biology in Viromics Studies

Cited by 4 publications

References 45 publications

Prediction of mammalian virus cross-species transmission based on host proteins

Prediction of mammalian virus cross-species transmission based on host proteins

Exploring the genome and protein space of viruses

Deep Learning-Powered Prediction of Human-Virus Protein-Protein Interactions

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