Using support vector machines to improve elemental ion identification in macromolecular crystal structures
- University of California, Berkeley, CA 94720 (United States)
- Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States)
A method to automatically identify possible elemental ions in X-ray crystal structures has been extended to use support vector machine (SVM) classifiers trained on selected structures in the PDB, with significantly improved sensitivity over manually encoded heuristics. In the process of macromolecular model building, crystallographers must examine electron density for isolated atoms and differentiate sites containing structured solvent molecules from those containing elemental ions. This task requires specific knowledge of metal-binding chemistry and scattering properties and is prone to error. A method has previously been described to identify ions based on manually chosen criteria for a number of elements. Here, the use of support vector machines (SVMs) to automatically classify isolated atoms as either solvent or one of various ions is described. Two data sets of protein crystal structures, one containing manually curated structures deposited with anomalous diffraction data and another with automatically filtered, high-resolution structures, were constructed. On the manually curated data set, an SVM classifier was able to distinguish calcium from manganese, zinc, iron and nickel, as well as all five of these ions from water molecules, with a high degree of accuracy. Additionally, SVMs trained on the automatically curated set of high-resolution structures were able to successfully classify most common elemental ions in an independent validation test set. This method is readily extensible to other elemental ions and can also be used in conjunction with previous methods based on a priori expectations of the chemical environment and X-ray scattering.
- OSTI ID:
- 22351152
- Journal Information:
- Acta Crystallographica. Section D: Biological Crystallography, Vol. 71, Issue Pt 5; Other Information: PMCID: PMC4427199; PMID: 25945580; PUBLISHER-ID: tz5065; OAI: oai:pubmedcentral.nih.gov:4427199; Copyright (c) Morshed et al. 2015; This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.; Country of input: International Atomic Energy Agency (IAEA); ISSN 0907-4449
- Country of Publication:
- Denmark
- Language:
- English
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Automated identification of elemental ions in macromolecular crystal structures
Automated identification of elemental ions in macromolecular crystal structures