Residue Assignment in Crystallographic Protein Electron Density Maps With 3D Convolutional Networks

被引：4

作者：

Godo, Akos ^{[1
]}

Aoki, Kota ^{[1
]}

Nakagawa, Atsushi ^{[2
]}

Yagi, Yasushi ^{[1
]}

机构：

[1] Osaka Univ, Inst Sci & Technol, Dept Intelligent Media, Suita, Osaka 5650871, Japan

[2] Osaka Univ, Inst Prot Res, Suita, Osaka 5650871, Japan

来源：

IEEE ACCESS | 2022年 / 10卷

关键词：

Proteins; Three-dimensional displays; Neural networks; Image resolution; Buildings; Amino acids; Solid modeling; image segmentation; X-ray diffraction; proteins; amino acids; NEURAL-NETWORK; REFINEMENT; SEQUENCE;

D O I：

10.1109/ACCESS.2022.3156108

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This work proposes a neural network architecture, called 3D FC-DenseNet, for assigning amino acid labels to X-ray crystallographic electron density maps without relying on the amino acid sequence of proteins. The 3DFC-DenseNet is able to treat the task as a 3D semantic segmentation problem, assigning amino acid labels directly to protein electron density maps. By creating dedicated data sets and models for high, medium and low resolution samples, our method matches the performance of crystallographic toolkits for primary structure assignment at high resolutions. Furthermore, it outperforms them at medium resolution and functions at low resolutions where current toolkits and human ability fails.

引用

页码：28760 / 28772

页数：13

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