We present a physics-based machine learning approach to predict transcription factor binding affinities in vitro 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 a predictive power superior to existing algorithms and a full generality which can be used to in the future to accommodate non-coding nucleobases.
When complemented with chromatin structure information, our in vitro trained method provides estimates of in vivo binding sites in yeast with unprecedented accuracy.[maxbutton id=”4″ url=”https://mmb.irbbarcelona.org/bioexcel-docs/data/BioExcel2_UC3_WF4/pdf” text=”Preprint” ]
Sandro Barissi, Alba Sala, Milosz Wieczor, Federica Battistini, Modesto Orozco (2022):
DNAffinity: A machine-learning approach to predict DNA binding affinities of transcription factors.