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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.
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Authors (5)
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Sandro Barissi
- IRB Barcelona
-
Alba Sala
- IRB Barcelona
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Federica Battistini
- IRB Barcelona
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Modesto Orozco
- IRB Barcelona
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- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1093/nar/gkac708
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
NUCLEIC ACIDS RESEARCH
no. 50,
pages 9105 - 9114,
ISSN: 0305-1048 - Language:
- English
- Publication year:
- 2022
- Bibliographic description:
- Barissi S., Sala A., Wieczór M., Battistini F., Orozco M.: DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors// NUCLEIC ACIDS RESEARCH -Vol. 50,iss. 16 (2022), s.9105-9114
- DOI:
- Digital Object Identifier (open in new tab) 10.1093/nar/gkac708
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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