High-Activity Enhancer Generation based on Feedback GAN with Domain Constraint and Curriculum Learning

Abstract

Enhancers are important cis-regulatory elements, enhancing the transcription of target genes. De novo design of high-activity enhancers is one of long-standing goals in generated biology for both clinical purpose and artificial life, because of their vital roles on regulation of cell development, differentiation, and apoptosis. But designing the enhancers with specific properties remains challenging, primarily due to the unclear understanding of enhancer regulatory codes. Here, we propose an AI-driven enhancer design method, named Enhancer-GAN, to generate high-activity enhancer sequences. Enhancer-GAN is firstly pre-trained on a large enhancer dataset that contains both low-activity and high-activity enhancers, and then is optimized to generate high-activity enhancers with feedback-loop mechanism. Domain constraint and curriculum learning were introduced into Enhancer-GAN to alleviate the noise from feedback loop and accelerate the training convergence. Experimental results on benchmark datasets demonstrate that the activity of generated enhancers is significantly higher than ones in benchmark dataset. Besides, we find 10 new motifs from generated high-activity enhancers. These results demonstrate Enhancer-GAN is promising to generate and optimize bio-sequences with desired properties.