Modeling and designing enhancers by introducing and harnessing transcription factor binding units
Jiaqi Li, Pengcheng Zhang, Xi Xi, Liyang Liu, Lei Wei, Xiaowo Wang
Abstract
Enhancers serve as pivotal regulators of gene expression throughout various biological processes by interacting with transcription factors (TFs). While transcription factor binding sites (TFBSs) are widely acknowledged as key determinants of TF binding and enhancer activity, the significant role of their surrounding context sequences remains to be quantitatively characterized. Here we propose the concept of transcription factor binding unit (TFBU) to modularly model enhancers by quantifying the impact of context sequences surrounding TFBSs using deep learning models. Based on this concept, we develop DeepTFBU, a comprehensive toolkit for enhancer design. We demonstrate that designing TFBS context sequences can significantly modulate enhancer activities and produce cell type-specific responses. DeepTFBU is also highly efficient in the de novo design of enhancers containing multiple TFBSs. Furthermore, DeepTFBU enables flexible decoupling and optimization of generalized enhancers. We prove that TFBU is a crucial concept, and DeepTFBU is highly effective for rational enhancer design. Quantifying the function of enhancer sequences is crucial for systems biology. Here, the authors introduce an AI-added strategy to quantify the function of sequences surrounding TFBSs, which successfully modularizes the enhancer, and further achieves the re-design of enhancers with aimed functions.