DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors

TitleDNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors
Publication TypeJournal Article
Year of Publication2022
AuthorsBarissi, Sandro, Sala Alba, Wieczór Miłosz, Battistini Federica, and Orozco Modesto
JournalNucleic Acids Res
Volume50
Start Page9105
Issue16
Date Published09/2022
ISBN Number0305-1048
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.

URLhttps://doi.org/10.1093/nar/gkac708
Short TitleNucleic Acids Research
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