Sequence-based modeling of low-affinity transcription factor-DNA binding through deep learning.

Abstract

Multiple layers of molecular determinants and mechanisms affect binding specificity between transcription factors (TFs) and DNA. DNA sequence-based deep learning models using convolutional neural networks (CNNs) and self-attention (SA) transformers have improved modeling accuracy and advanced our understanding of TF-DNA binding specificity through network interpretation. However, the systematic evaluation of various strategies for handling DNA sequence orientations in deep learning models-and their interpretation-remains underexplored, especially in the context of learning low-affinity binding site specificity. Using SELEX-seq data for eight Exd-Hox heterodimers in Drosophila, we compared canonical models with data augmentation and reverse-complement weight-sharing models. We found that reverse-complement weight-sharing CNN models and SA models trained with augmented data with reverse complements outperformed other approaches in modeling binding specificity. In this work, we evaluated several interpretation methods, including Gradient*input, DeconvNet, DeepLIFT, and in silico mutagenesis (ISM). Compared to other interpretation methods, ISM was less sensitive to model hyperparameter settings. In this work, we identified Exd-Ubx binding at low-affinity sites and suggested possible biophysical mechanisms. The findings of this study will be relevant for studying the functional role of low-affinity TF binding in gene regulatory mechanisms with possible implications on TF-DNA binding specificity guided protein design.