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首页> 外文会议>IEEE International Conference on Tools with Artificial Intelligence >Protein Loop Modeling Using Deep Generative Adversarial Network
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Protein Loop Modeling Using Deep Generative Adversarial Network

机译：使用深度生成对抗网络的蛋白质环建模

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摘要

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.
机译：生物学和医学对蛋白质的计算结构预测和建模具有长期的兴趣。通常缺少蛋白质区域或需要重塑的区域。预测蛋白质结构中特定缺失区域的过程称为环模型。在本文中，我们提出了一种基于图像修复的深度学习的生成式对抗网络（GAN），用于循环建模。生成网络将捕获环路区域的上下文并预测缺失区域。对抗网络将使预测看起来真实，并为生成网络提供梯度。所提议的网络在环路建模的通用基准上进行了评估。实验表明，我们的方法可以成功地预测环路区域，并且比最先进的工具具有更好的性能。据我们所知，这项工作代表了将GAN用于任何生物信息学研究的首次尝试。

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