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.
Despite constant advances in X-ray crystallography, the resolution of the acquired electron density maps still poses a serious limit on protein protein structural model building efforts. Furthermore, the currently available toolkits require hours or even days for model building. Methods capable of processing a large volume of samples in a short time which can also handle low resolution samples are needed. This work proposes a neural network-based approach to locate and classify residues in crystallographic electron density maps automatically in a single forward pass without relying on the protein's residue sequence. Our proposed method shows an average 23.53% increase in accuracy over our previous approach and also compares favorably to currently available toolkits. It can process protein samples in seconds on consumer-grade hardware saving significant time and resources.
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