Biology and medicine have a long-standing interest in computational structure prediction and modeling of proteins. There are often missing regions or regions that need to be remodeled in protein structures. The process of predicting particular missing regions in a protein structure is called loop modeling. In this paper, we propose a generative adversarial network (GAN) in deep learning for loop modeling using the idea of image inpainting. The generative network is to capture the context of the loop region and predict the missing area. The adversarial network is to make the prediction look real and provide gradients to the generative network. The proposed network was evaluated on a common benchmark for loop modeling. Experiments show that our method can successfully predict the loop region and has achieved better performance than the state-of-the-art tools. To our knowledge, this work represents the first attempt of using GAN for any bioinformatics studies.
This work was supported in part by National Institutes of Health grant R01-GM100701. The high-performance computing infrastructure is supported by the National Science Foundation under grant number CNS-1429294.