Single Shot Residue Localization and Classification in Crystallographic Electron Density Maps

Abstract

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.

Keywords

• Neural networks
• image segmentation
• X-ray diffraction
• proteins
• amino acids