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Prediction of DNA binding motifs from 3D models of transcription factors; identifying TLX3 regulated genes

Proper cell functioning depends on the precise spatio-temporal expression of its genetic material. Gene expression is controlled to a great extent by sequence-specific transcription factors (TFs). Our current knowledge on where and how TFs bind and associate to regulate gene expression is incomplete...

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Autores principales: Pujato, Mario, Kieken, Fabien, Skiles, Amanda A., Tapinos, Nikos, Fiser, Andras
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4267649/
https://www.ncbi.nlm.nih.gov/pubmed/25428367
http://dx.doi.org/10.1093/nar/gku1228
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author Pujato, Mario
Kieken, Fabien
Skiles, Amanda A.
Tapinos, Nikos
Fiser, Andras
author_facet Pujato, Mario
Kieken, Fabien
Skiles, Amanda A.
Tapinos, Nikos
Fiser, Andras
author_sort Pujato, Mario
collection PubMed
description Proper cell functioning depends on the precise spatio-temporal expression of its genetic material. Gene expression is controlled to a great extent by sequence-specific transcription factors (TFs). Our current knowledge on where and how TFs bind and associate to regulate gene expression is incomplete. A structure-based computational algorithm (TF2DNA) is developed to identify binding specificities of TFs. The method constructs homology models of TFs bound to DNA and assesses the relative binding affinity for all possible DNA sequences using a knowledge-based potential, after optimization in a molecular mechanics force field. TF2DNA predictions were benchmarked against experimentally determined binding motifs. Success rates range from 45% to 81% and primarily depend on the sequence identity of aligned target sequences and template structures, TF2DNA was used to predict 1321 motifs for 1825 putative human TF proteins, facilitating the reconstruction of most of the human gene regulatory network. As an illustration, the predicted DNA binding site for the poorly characterized T-cell leukemia homeobox 3 (TLX3) TF was confirmed with gel shift assay experiments. TLX3 motif searches in human promoter regions identified a group of genes enriched in functions relating to hematopoiesis, tissue morphology, endocrine system and connective tissue development and function.
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spelling pubmed-42676492014-12-23 Prediction of DNA binding motifs from 3D models of transcription factors; identifying TLX3 regulated genes Pujato, Mario Kieken, Fabien Skiles, Amanda A. Tapinos, Nikos Fiser, Andras Nucleic Acids Res Computational Biology Proper cell functioning depends on the precise spatio-temporal expression of its genetic material. Gene expression is controlled to a great extent by sequence-specific transcription factors (TFs). Our current knowledge on where and how TFs bind and associate to regulate gene expression is incomplete. A structure-based computational algorithm (TF2DNA) is developed to identify binding specificities of TFs. The method constructs homology models of TFs bound to DNA and assesses the relative binding affinity for all possible DNA sequences using a knowledge-based potential, after optimization in a molecular mechanics force field. TF2DNA predictions were benchmarked against experimentally determined binding motifs. Success rates range from 45% to 81% and primarily depend on the sequence identity of aligned target sequences and template structures, TF2DNA was used to predict 1321 motifs for 1825 putative human TF proteins, facilitating the reconstruction of most of the human gene regulatory network. As an illustration, the predicted DNA binding site for the poorly characterized T-cell leukemia homeobox 3 (TLX3) TF was confirmed with gel shift assay experiments. TLX3 motif searches in human promoter regions identified a group of genes enriched in functions relating to hematopoiesis, tissue morphology, endocrine system and connective tissue development and function. Oxford University Press 2014-12-16 2014-11-26 /pmc/articles/PMC4267649/ /pubmed/25428367 http://dx.doi.org/10.1093/nar/gku1228 Text en © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Computational Biology
Pujato, Mario
Kieken, Fabien
Skiles, Amanda A.
Tapinos, Nikos
Fiser, Andras
Prediction of DNA binding motifs from 3D models of transcription factors; identifying TLX3 regulated genes
title Prediction of DNA binding motifs from 3D models of transcription factors; identifying TLX3 regulated genes
title_full Prediction of DNA binding motifs from 3D models of transcription factors; identifying TLX3 regulated genes
title_fullStr Prediction of DNA binding motifs from 3D models of transcription factors; identifying TLX3 regulated genes
title_full_unstemmed Prediction of DNA binding motifs from 3D models of transcription factors; identifying TLX3 regulated genes
title_short Prediction of DNA binding motifs from 3D models of transcription factors; identifying TLX3 regulated genes
title_sort prediction of dna binding motifs from 3d models of transcription factors; identifying tlx3 regulated genes
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4267649/
https://www.ncbi.nlm.nih.gov/pubmed/25428367
http://dx.doi.org/10.1093/nar/gku1228
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