Sona Saitou, J. Iijima, M. Fujimoto, Y. Mochizuki, Koji Okuwaki, H. Doi, Y. Komeiji
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引用次数: 4
摘要
我们应用b谷歌的TensorFlow深度学习工具包来识别片段分子轨道(FMO)计算的可视化结果。典型的α -螺旋和β -片的蛋白质结构在片段间相互作用能的二维图中提供了一些特征模式,称为IFIE-map (Kurisaki et al., Biophys。化学,130(2007)1)。使用18种蛋白质和3种非蛋白质系统制备了1000张IFIE-map图像,这些图像的标签取决于α -螺旋和β -sheet的存在,并接受了TensorFlow的训练。最后,向TensorFlow输入新数据以测试其识别结构模式的能力。结果表明,该方法能够很好地判断出IFIE-map测试图像中的特征结构。从而证明了TensorFlow对IFIE-map的模式识别能力。
Application of TensorFlow to recognition of visualized results of fragment molecular orbital (FMO) calculations
We have applied Google's TensorFlow deep learning toolkit to recognize the visualized results of the fragment molecular orbital (FMO) calculations. Typical protein structures of alpha-helix and beta-sheet provide some characteristic patterns in the two-dimensional map of inter-fragment interaction energy termed as IFIE-map (Kurisaki et al., Biophys. Chem. 130 (2007) 1). A thousand of IFIE-map images with labels depending on the existences of alpha-helix and beta-sheet were prepared by employing 18 proteins and 3 non-protein systems and were subjected to training by TensorFlow. Finally, TensorFlow was fed with new data to test its ability to recognize the structural patterns. We found that the characteristic structures in test IFIE-map images were judged successfully. Thus the ability of pattern recognition of IFIE-map by TensorFlow was proven.