首页|Predicting the DNA binding specificity of mutated transcription factors using family-level biophysically interpretable machine learning
Predicting the DNA binding specificity of mutated transcription factors using family-level biophysically interpretable machine learning
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
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.
NETL
NSTL
