Modeling and Designing Enhancers by Introducing and Harnessing Transcription Factor Binding Units

Overview

Journal Nat Commun

Date 2025 Feb 8

PMID 39922842

Authors

Jiaqi Li

Pengcheng Zhang

Xi Xi

Liyang Liu

Lei Wei

Xiaowo Wang

Affiliations

Soon will be listed here.

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.

Citing Articles

Modeling and designing enhancers by introducing and harnessing transcription factor binding units.

Li J, Zhang P, Xi X, Liu L, Wei L, Wang X Nat Commun. 2025; 16(1):1469.

PMID: 39922842 PMC: 11807178. DOI: 10.1038/s41467-025-56749-2.

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