Abstract
DNA-binding transcription factors (TFs) play a central role in the gene expression of all organisms, from viruses to humans, including bacteria and archaea. The role of these proteins is the fate of gene expression in the context of environmental challenges. Because thousands of genomes have been sequenced to date, predictions of the encoded proteins are validated through the use of bioinformatics tools to obtain the necessary experimental, posterior knowledge. In this chapter, we describe three approaches to identify TFs in protein sequences. The first approach integrates the results of sequence comparisons and PFAM assignments, using as reference a manually curated collection of TFs. The second approach considers the prediction of DNA-binding structures, such as the classical helix-turn-helix (HTH); and the third approach considers a deep learning model. We suggest that all approaches must be considered together to increase the possibility of identifying new TFs in bacterial and archaeal genomes.
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Acknowledgments
We thank Joaquin Morales, Sandra Sauza, and Israel Sanchez for their technical support. Leonardo Ledesma is a doctoral student from Programa de Doctorado en Ingeniería y Ciencias de la Computación at UNAM and received a fellowship from Consejo Nacional de Ciencia y Tecnología (CONACYT CVU 857463). This work was supported by Dirección General de Asuntos del Personal Académico-Universidad Nacional Autónoma de México (IN-209620),Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo (P918PTE0261), and CONACYT (320012). There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Ledesma, L., Hernandez-Guerrero, R., Perez-Rueda, E. (2022). Prediction of DNA-Binding Transcription Factors in Bacteria and Archaea Genomes. In: Peeters, E., Bervoets, I. (eds) Prokaryotic Gene Regulation. Methods in Molecular Biology, vol 2516. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2413-5_7
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DOI: https://doi.org/10.1007/978-1-0716-2413-5_7
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