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ZiF-Predict: A Web Tool for Predicting DNA-Binding Specificity in C2H2 Zinc Finger Proteins

Engineering zinc finger protein motifs for specific DNA targets in genomes is critical in the field of genome engineering. We have developed a computational method for predicting recognition helices for C2H2 zinc fingers that bind to specific target DNA sites. This prediction is based on artificial...

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Detalles Bibliográficos
Autores principales: Molparia, Bhuvan, Goyal, Kanav, Sarkar, Anita, Kumar, Sonu, Sundar, Durai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5054441/
https://www.ncbi.nlm.nih.gov/pubmed/20691397
http://dx.doi.org/10.1016/S1672-0229(10)60013-7
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author Molparia, Bhuvan
Goyal, Kanav
Sarkar, Anita
Kumar, Sonu
Sundar, Durai
author_facet Molparia, Bhuvan
Goyal, Kanav
Sarkar, Anita
Kumar, Sonu
Sundar, Durai
author_sort Molparia, Bhuvan
collection PubMed
description Engineering zinc finger protein motifs for specific DNA targets in genomes is critical in the field of genome engineering. We have developed a computational method for predicting recognition helices for C2H2 zinc fingers that bind to specific target DNA sites. This prediction is based on artificial neural network using an exhaustive dataset of zinc finger proteins and their target DNA triplets. Users can select the option for two or three zinc fingers to be predicted either in a modular or synergistic fashion for the input DNA sequence. This method would be valuable for researchers interested in designing specific zinc finger transcription factors and zinc finger nucleases for several biological and biomedical applications. The web tool ZiF-Predict is available online at http://web.iitd.ac.in/~sundar/zifpredict/.
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spelling pubmed-50544412016-10-14 ZiF-Predict: A Web Tool for Predicting DNA-Binding Specificity in C2H2 Zinc Finger Proteins Molparia, Bhuvan Goyal, Kanav Sarkar, Anita Kumar, Sonu Sundar, Durai Genomics Proteomics Bioinformatics Application Note Engineering zinc finger protein motifs for specific DNA targets in genomes is critical in the field of genome engineering. We have developed a computational method for predicting recognition helices for C2H2 zinc fingers that bind to specific target DNA sites. This prediction is based on artificial neural network using an exhaustive dataset of zinc finger proteins and their target DNA triplets. Users can select the option for two or three zinc fingers to be predicted either in a modular or synergistic fashion for the input DNA sequence. This method would be valuable for researchers interested in designing specific zinc finger transcription factors and zinc finger nucleases for several biological and biomedical applications. The web tool ZiF-Predict is available online at http://web.iitd.ac.in/~sundar/zifpredict/. Elsevier 2010-06 2010-08-04 /pmc/articles/PMC5054441/ /pubmed/20691397 http://dx.doi.org/10.1016/S1672-0229(10)60013-7 Text en © 2010 Beijing Institute of Genomics http://creativecommons.org/licenses/by-nc-sa/3.0/ This is an open access article under the CC BY-NC-SA license (http://creativecommons.org/licenses/by-nc-sa/3.0/).
spellingShingle Application Note
Molparia, Bhuvan
Goyal, Kanav
Sarkar, Anita
Kumar, Sonu
Sundar, Durai
ZiF-Predict: A Web Tool for Predicting DNA-Binding Specificity in C2H2 Zinc Finger Proteins
title ZiF-Predict: A Web Tool for Predicting DNA-Binding Specificity in C2H2 Zinc Finger Proteins
title_full ZiF-Predict: A Web Tool for Predicting DNA-Binding Specificity in C2H2 Zinc Finger Proteins
title_fullStr ZiF-Predict: A Web Tool for Predicting DNA-Binding Specificity in C2H2 Zinc Finger Proteins
title_full_unstemmed ZiF-Predict: A Web Tool for Predicting DNA-Binding Specificity in C2H2 Zinc Finger Proteins
title_short ZiF-Predict: A Web Tool for Predicting DNA-Binding Specificity in C2H2 Zinc Finger Proteins
title_sort zif-predict: a web tool for predicting dna-binding specificity in c2h2 zinc finger proteins
topic Application Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5054441/
https://www.ncbi.nlm.nih.gov/pubmed/20691397
http://dx.doi.org/10.1016/S1672-0229(10)60013-7
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