Robotics & Machine Learning Daily News2024,Issue(Feb.12) :150-151.

Predicting the DNA binding specificity of mutated transcription factors using family-level biophysically interpretable machine learning

Robotics & Machine Learning Daily News2024,Issue(Feb.12) :150-151.

Predicting the DNA binding specificity of mutated transcription factors using family-level biophysically interpretable machine learning

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Abstract

According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from biorxiv.org: "Sequence-specific interactions of transcription factors (TFs) with genomic DNA underlie many cellular processes. High-throughput in vitro binding assays coupled with computational analysis have made it possible to accurately define such sequence recognition in a biophysically interpretable yet mechanismagonistic way for individual TFs. The fact that such sequence-to-affinity models are now available for hundreds of TFs provides new avenues for predicting how the DNA binding specificity of a TF changes when its protein sequence is mutated. "To this end, we developed an analytical framework based on a tetrahedron embedding that can be applied at the level of a given structural TF family.

Key words

Biophysics/Cyborgs/Emerging Technologies/Genetics/Machine Learning/Proteins/Transcription Factor/Transcription Factors

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
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