A deep learning solution for crystallographic structure determination
Tom Pan, Shikai Jin, Mitchell D. Miller, Anastasios Kyrillidis, G.N. Phillips
Abstract
The general de novo solution of the crystallographic phase problem is difficult and only possible under certain conditions. This paper develops an initial pathway to a deep learning neural network approach for the phase problem in protein crystallography, based on a synthetic dataset of small fragments derived from a large well curated subset of solved structures in the Protein Data Bank (PDB). In particular, electron-density estimates of simple artificial systems are produced directly from corresponding Patterson maps using a convolutional neural network architecture as a proof of concept.
Topics & Concepts
Computer scienceConvolutional neural networkSimple (philosophy)Protein Data Bank (RCSB PDB)Deep learningProtein Data BankArtificial neural networkArtificial intelligencePhase (matter)Protein structureAlgorithmTheoretical computer scienceCrystallographyChemistryStereochemistryBiochemistryOrganic chemistryPhilosophyEpistemologyEnzyme Structure and FunctionProtein Structure and DynamicsMachine Learning in Materials Science