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

Tags:

• Proteins
• Three-dimensional displays
• Neural networks
• Image resolution
• Buildings
• Amino acids
• Solid modeling

Godo, A., Aoki, K., Nakagawa, A., and Yagi, Y. (2022). Residue Assignment in Crystallographic Protein Electron Density Maps With 3D Convolutional Networks. IEEE Access, 10:28760-28772.

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.

[DOI]

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