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Accurate Prediction of Transcriptional Activity of Single Missense Variants in HIV Tat with Deep Learning
Tat is an essential gene for increasing the transcription of all HIV genes, and affects HIV replication, HIV exit from latency, and AIDS progression. The Tat gene frequently mutates in vivo and produces variants with diverse activities, contributing to HIV viral heterogeneity as well as drug-resista...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10093788/ https://www.ncbi.nlm.nih.gov/pubmed/37047108 http://dx.doi.org/10.3390/ijms24076138 |
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author | Derbel, Houssemeddine Giacoletto, Christopher J. Benjamin, Ronald Chen, Gordon Schiller, Martin R. Liu, Qian |
author_facet | Derbel, Houssemeddine Giacoletto, Christopher J. Benjamin, Ronald Chen, Gordon Schiller, Martin R. Liu, Qian |
author_sort | Derbel, Houssemeddine |
collection | PubMed |
description | Tat is an essential gene for increasing the transcription of all HIV genes, and affects HIV replication, HIV exit from latency, and AIDS progression. The Tat gene frequently mutates in vivo and produces variants with diverse activities, contributing to HIV viral heterogeneity as well as drug-resistant clones. Thus, identifying the transcriptional activities of Tat variants will help to better understand AIDS pathology and treatment. We recently reported the missense mutation landscape of all single amino acid Tat variants. In these experiments, a fraction of double missense alleles exhibited intragenic epistasis. However, it is too time-consuming and costly to determine the effect of the variants for all double mutant alleles through experiments. Therefore, we propose a combined GigaAssay/deep learning approach. As a first step to determine activity landscapes for complex variants, we evaluated a deep learning framework using previously reported GigaAssay experiments to predict how transcription activity is affected by Tat variants with single missense substitutions. Our approach achieved a 0.94 Pearson correlation coefficient when comparing the predicted to experimental activities. This hybrid approach can be extensible to more complex Tat alleles for a better understanding of the genetic control of HIV genome transcription. |
format | Online Article Text |
id | pubmed-10093788 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100937882023-04-13 Accurate Prediction of Transcriptional Activity of Single Missense Variants in HIV Tat with Deep Learning Derbel, Houssemeddine Giacoletto, Christopher J. Benjamin, Ronald Chen, Gordon Schiller, Martin R. Liu, Qian Int J Mol Sci Article Tat is an essential gene for increasing the transcription of all HIV genes, and affects HIV replication, HIV exit from latency, and AIDS progression. The Tat gene frequently mutates in vivo and produces variants with diverse activities, contributing to HIV viral heterogeneity as well as drug-resistant clones. Thus, identifying the transcriptional activities of Tat variants will help to better understand AIDS pathology and treatment. We recently reported the missense mutation landscape of all single amino acid Tat variants. In these experiments, a fraction of double missense alleles exhibited intragenic epistasis. However, it is too time-consuming and costly to determine the effect of the variants for all double mutant alleles through experiments. Therefore, we propose a combined GigaAssay/deep learning approach. As a first step to determine activity landscapes for complex variants, we evaluated a deep learning framework using previously reported GigaAssay experiments to predict how transcription activity is affected by Tat variants with single missense substitutions. Our approach achieved a 0.94 Pearson correlation coefficient when comparing the predicted to experimental activities. This hybrid approach can be extensible to more complex Tat alleles for a better understanding of the genetic control of HIV genome transcription. MDPI 2023-03-24 /pmc/articles/PMC10093788/ /pubmed/37047108 http://dx.doi.org/10.3390/ijms24076138 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Derbel, Houssemeddine Giacoletto, Christopher J. Benjamin, Ronald Chen, Gordon Schiller, Martin R. Liu, Qian Accurate Prediction of Transcriptional Activity of Single Missense Variants in HIV Tat with Deep Learning |
title | Accurate Prediction of Transcriptional Activity of Single Missense Variants in HIV Tat with Deep Learning |
title_full | Accurate Prediction of Transcriptional Activity of Single Missense Variants in HIV Tat with Deep Learning |
title_fullStr | Accurate Prediction of Transcriptional Activity of Single Missense Variants in HIV Tat with Deep Learning |
title_full_unstemmed | Accurate Prediction of Transcriptional Activity of Single Missense Variants in HIV Tat with Deep Learning |
title_short | Accurate Prediction of Transcriptional Activity of Single Missense Variants in HIV Tat with Deep Learning |
title_sort | accurate prediction of transcriptional activity of single missense variants in hiv tat with deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10093788/ https://www.ncbi.nlm.nih.gov/pubmed/37047108 http://dx.doi.org/10.3390/ijms24076138 |
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