NUCLEAR TECHNIQUES, Volume. 46, Issue 3, 030101(2023)
X-ray crystallography experimental data screening based on convolutional neural network algorithms
Figures & Tables(15)
Fig. 1. Comparison of LN83 diffraction pattern before (a) and after (b) gray value equalization
Fig. 2. Diffraction pattern of protein crystal after gray value equalization
Fig. 3. LN83 diffraction pattern image enhancement results (a) Original image, (b) Flip left and right, (c) Rotate 90° counterclockwise, (d) Rotate 25° counterclockwise and move 10 pixels to the right, (e) Rotate 110° clockwise, move 5 pixels to the right and 5 pixels to the down, (f) Rotate 60° clockwise
Fig. 4. Flow chart of convolutional neural network for training and prediction
Fig. 5. Accuracy and operation rate of verification set and test set based on different networks(a) Verification set accuracy, (b) Test set accuracy, (c) Verification set running rate, (d) Test setverification set running rate
Fig. 6. t-SNE dimensionality reduction results of six convolutional neural networks (the circle is the "maybe " sample, the cross is the "Miss" sample, and the pentagram is the "hit" sample)(a) MobileNets, (b) ResNet, (c) Inception-v1, (d) Inception-v3, (e) Vgg16, (f) AlexNet
Fig. 8. MobileNets hit /maybe (a) and miss sample (b) reliability distribution
Fig. 9. Sample selected by MobileNets (a) Hit, (b) Maybe, (c) Miss
Table 1. Experimental data
View tableView in ArticleTable 1. Experimental data
数据
Dataset
蛋白质
Protein
入射能量
Incident energy / keV
仪器
Instrument
探测器
Detector
LN83 氢化酶蛋白质晶体 Hydrogenase 9.498 MFX Rayonix LN84 光系统 II Photosystem II 9.516 MFX Rayonix LO19 辛环素 Cyclophilin A 9.442 MFX Rayonix L498 嗜热菌蛋白酶 Thermolysin 9.773 CXI CSPAD
Table 2. Data classification
View tableView in ArticleTable 2. Data classification
数据类型
Data type
布拉格点的数量
Number of Bragg points
有效信息含量
Effective information content
命中Hit X≥10 较多有效信息 More valid information 也许命中 Maybe 10＞X≥4 较少有效信息 Less valid information 未命中 Miss X≤3 缺失有效信息 Loss valid information
Table 3. Five convolutional neural networks
View tableView in ArticleTable 3. Five convolutional neural networks
网络
Net
网络深度 / 层
Depth / layer
特点
Characteristic
AlexNet 8 网络层数少，采用ReLu激活函数 Less layer, use ReLu activation function Vgg16 16 采用小卷积核，收敛速度加快 Small convolution kernels to speed up convergence Inception-V1 22 并行计算，去除全连接层 Parallel computing, remove the full connection layer Inception-V3 46 并行计算，将卷积拆分，减少数据规模 Parallel computing, split convolution ResNet101 101 采用残差网络优化学习目标 Optimize learning objectives using residual network MobileNets-V1 28 卷积可分离，引入全局超参数 Separate the convolution depth, use global hyperparameters
Table 4. Verification set and test set accuracy of each samples based on MobileNets
View tableView in ArticleTable 4. Verification set and test set accuracy of each samples based on MobileNets
样品 Samples 验证集准确度 Accurancy / % 测试集准确度 Accurancy L498-氢化酶蛋白质晶体 Thermolysin 62.2 7/10 LN84-光系统 II Photosystem II 82.3 8/10 LN83-嗜热菌蛋白酶Hydrogenase 81.8 8/10 LO19-辛环素Cyclophilin A 78.0 9/10
Table 5. Accuracy of verification set and test set using different networks based on LN83
View tableView in ArticleTable 5. Accuracy of verification set and test set using different networks based on LN83
网络 Nets 标签 Label LN83-氢化酶蛋白质晶体 Hydrogenase 命中 Hit 也许命中 Maybe 未命中 Miss MobileNets 命中 Hit 0.919 0.070 0.011 也许命中 Maybe 0.168 0.701 0.131 未命中 Miss 0.014 0.043 0.943 Inception-v1 命中 Hit 0.935 0.043 0.022 也许命中 Maybe 0.350 0.416 0.234 未命中 Miss 0.008 0.028 0.964 Inception-v3 命中 Hit 0.958 0.029 0.013 也许命中 Maybe 0.547 0.343 0.109 未命中 Miss 0.058 0.202 0.740 Vgg16 命中 Hit 0.893 0.086 0.021 也许命中 Maybe 0.073 0.876 0.051 未命中 Miss 0.020 0.141 0.840 ResNet 命中 Hit 0.854 0.084 0.063 也许命中 Maybe 0.015 0.518 0.467 未命中 Miss 0.001 0.004 0.995 AlexNet 命中 Hit 0.907 0.014 0.079 也许命中 Maybe 0.927 0.022 0.051 未命中 Miss 0.509 0.016 0.475
Table 6. Accuracy of two classification based on Ln83 sample
View tableView in ArticleTable 6. Accuracy of two classification based on Ln83 sample
网络Nets 命中/也许命中Hit/maybe 未命中Miss MobileNets 0.970 0.943 Inception-V1 0.944 0.964 Inception-V3 0.972 0.740 Vgg16 0.974 0.840 ResNet 0.873 0.955 AlexNet 0.925 0.475
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Zi HUI, Li YU, Huan ZHOU, Lin TANG, Jianhua HE. X-ray crystallography experimental data screening based on convolutional neural network algorithms[J]. NUCLEAR TECHNIQUES, 2023, 46(3): 030101
Paper Information
Category: Research Articles
Received: Oct. 28, 2022
Accepted: --
Published Online: Apr. 17, 2023
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