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Title: DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors
Author: Barissi, Sandro
Sala, Alba
Wieczór, Miłosz
Battistini, Federica
Orozco López, Modesto
Keywords: ADN
Biologia computacional
Computational biology
Issue Date: 26-Aug-2022
Publisher: Oxford University Press
Abstract: We present a physics-based machine learning approach to predict in vitro transcription factor binding affinities from structural and mechanical DNA properties directly derived from atomistic molecular dynamics simulations. The method is able to predict affinities obtained with techniques as different as uPBM, gcPBM and HT-SELEX with an excellent performance, much better than existing algorithms. Due to its nature, the method can be extended to epigenetic variants, mismatches, mutations, or any non-coding nucleobases. When complemented with chromatin structure information, our in vitro trained method provides also good estimates of in vivo binding sites in yeast.
Note: Reproducció del document publicat a:
It is part of: Nucleic Acids Research, 2022, vol. 50, num. 16, p. 9105-9114
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ISSN: 0305-1048
Appears in Collections:Articles publicats en revistes (Bioquímica i Biomedicina Molecular)

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