A Graphic Encoding Method for Quantitative Classification of Protein Structure and Representation of Conformational Changes.
A Graphic Encoding Method for Quantitative Classification of Protein Structure and Representation of Conformational Changes.
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DOI：
10.1109/tcbb.2019.2945291
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发表时间：
2021-07
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为了成功地预测蛋白质在其整个轨迹中的功能，除了揭示其构象状态的变化外，还需要采用在大规模执行时保持其3D信息的技术。我们将编码二级和三级结构的蛋白质表示扩展为固定大小的彩色图像，以及利用我们编码表示的神经网络架构（称为GEM-net）。我们在两个方面展示了我们的方法的适用性：(1)进行蛋白质功能预测，准确率在78%到83%之间；(2)在分子动力学模拟过程中可视化和检测蛋白质轨迹的构象变化。
In order to successfully predict a proteins function throughout its trajectory, in addition to uncovering changes in its conformational state, it is necessary to employ techniques that maintain its 3D information while performing at scale. We extend a protein representation that encodes secondary and tertiary structure into fix-sized, color images, and a neural network architecture (called GEM-net) that leverages our encoded representation. We show the applicability of our method in two ways: (1) performing protein function prediction, hitting accuracy between 78 and 83 percent, and (2) visualizing and detecting conformational changes in protein trajectories during molecular dynamics simulations.