Graphic Encoding of Macromolecules for Efficient High-Throughput Analysis
Graphic Encoding of Macromolecules for Efficient High-Throughput Analysis
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DOI：
10.1145/3233547.3233607
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发表时间：
2018-08
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影响因子：
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通讯作者：
Trilce Estrada;Jeremy Benson;Hector Carrillo-Cabada;Asghar M. Razavi;M. Cuendet;H. Weinstein;E. Deelman;M. Taufer
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文献类型：
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作者：
Trilce Estrada;Jeremy Benson;Hector Carrillo-Cabada;Asghar M. Razavi;M. Cuendet;H. Weinstein;E. Deelman;M. Taufer
蛋白质的功能取决于其三维结构。目前基于同源性预测给定蛋白质功能的方法在规模上效果不佳。在这项工作中，我们提出了一种表示蛋白质，明确编码二级和三级结构到固定大小的图像。此外，我们提出了一个神经网络架构，利用我们的数据表示来执行蛋白质功能预测。我们通过对大约6.3万张图像进行5重交叉验证，验证了编码方法的有效性和神经网络架构的强度，在8个不同的类别中实现了80%的准确率。我们编码和分类蛋白质的新方法适合实时处理，从而实现高通量分析。
The function of a protein depends on its three-dimensional structure. Current approaches based on homology for predicting a given protein's function do not work well at scale. In this work, we propose a representation of proteins that explicitly encodes secondary and tertiary structure into fix-sized images. In addition, we present a neural network architecture that exploits our data representation to perform protein function prediction. We validate the effectiveness of our encoding method and the strength of our neural network architecture through a 5-fold cross validation over roughly 63 thousand images, achieving an accuracy of 80% across 8 distinct classes. Our novel approach of encoding and classifying proteins is suitable for real-time processing, leading to high-throughput analysis.