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Computational design of host transcription-factors sets whose misregulation mimics the transcriptomic effect of viral infections.

ABSTRACT: The molecular mechanisms underlying viral pathogenesis are yet poorly understood owed to the large number of factors involved and the complexity of their interactions. Could we identify a minimal set of host transcription factors (TF) whose misregulation would result in the transcriptional profile characteristic of infected cells in absence of the virus? How many of such sets exist? Are all orthogonal or share critical TFs involved in specific biological functions? We have developed a computational methodology that uses a quantitative model of the transcriptional regulatory network (TRN) of Arabidopsis thaliana to explore the landscape of all possible re-engineered TRNs whose transcriptomic profiles mimic those observed in infected plants. We found core sets containing between six and 34 TFs, depending on the virus, whose in silico knockout or overexpression in the TRN resulted in transcriptional profiles that minimally deviate from those observed in infected plants.

SUBMITTER: Carrera J

PROVIDER: S-EPMC3525979 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Computational design of host transcription-factors sets whose misregulation mimics the transcriptomic effect of viral infections.

Carrera Javier J Elena Santiago F SF

Scientific reports 20121219

The molecular mechanisms underlying viral pathogenesis are yet poorly understood owed to the large number of factors involved and the complexity of their interactions. Could we identify a minimal set of host transcription factors (TF) whose misregulation would result in the transcriptional profile characteristic of infected cells in absence of the virus? How many of such sets exist? Are all orthogonal or share critical TFs involved in specific biological functions? We have developed a computatio ...[more]

PMID: 23256040

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Project description:Experimental approaches are currently used to determine viral-host interactions, but these approaches are both time-consuming and costly. For these reasons, computational-based approaches are recommended. In this study, using computational-based approaches, viral-host interactions of SARS-CoV-2 virus and human proteins were predicted. The study consists of four different stages; in the first stage viral and host protein sequences were obtained. In the second stage, protein sequences were converted into numerical expressions by various protein mapping methods. These methods are entropy-based, AVL-tree, FIBHASH, binary encoding, CPNR, PAM250, BLOSUM62, Atchley factors, Meiler parameters, EIIP, AESNN1, Miyazawa energies, Micheletti potentials, Z-scale, and hydrophobicity. In the third stage, a deep learning model was designed and BiLSTM was used for this. In the last stage, the protein sequences were classified, and the viral-host interactions were predicted. The performances of protein mapping methods were determined by accuracy, F1-score, specificity, sensitivity, and AUC scores. According to the classification results, the best classification process was obtained by the entropy-based method. With this method, 94.74% accuracy, and 0.95 AUC score were calculated. Then, the most successful classification process was performed with the Z-scale and 91.23% accuracy, and 0.96 AUC score were obtained. Although other protein mapping methods are not as efficient as Z-scale and entropy-based methods, they have achieved successful classification. AVL-tree, FIBHASH, binary encoding, CPNR, PAM250, BLOSUM62, Atchley factors, Meiler parameters and AESNN1 methods showed over 80% accuracy, F1-score, and AUC score. Accuracy scores of EIIP, Miyazawa energies, Micheletti potentials and hydrophobicity methods remained below 80%. When the results were examined in general, it was observed that the computational approaches were successful in predicting viral-host interactions between SARS-CoV-2 virus and human proteins.

Dataset Information

Computational design of host transcription-factors sets whose misregulation mimics the transcriptomic effect of viral infections.

Publications

Computational design of host transcription-factors sets whose misregulation mimics the transcriptomic effect of viral infections.

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