Using support vector machines to improve elemental ion identification in macromolecular crystal structures

Journal Article · Sat Apr 25 00:00:00 EDT 2015 · Acta Crystallographica. Section D: Biological Crystallography (Online)

DOI:https://doi.org/10.1107/S1399004715004241· OSTI ID:1213439

Morshed, Nader ^[1]; Echols, Nathaniel ^[2]; Adams, Paul D. ^[3]

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1. Univ. of California, Berkeley, CA (United States). College of Letters and Science; Lawrence Berkeley National Lab., Berkeley, CA (United States). Physical Biosciences Div.
2. Lawrence Berkeley National Lab., Berkeley, CA (United States). Physical Biosciences Div.
3. Lawrence Berkeley National Lab., Berkeley, CA (United States). Physical Biosciences Div.; Univ. of California, Berkeley, CA (United States). Dept. og Bioengineering.

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.

• Research Organization: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1213439
• Journal Information: Acta Crystallographica. Section D: Biological Crystallography (Online), Vol. 71, Issue 5; ISSN 1399-0047
• Publisher: International Union of Crystallography
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
elemental ion identification
support vector machines
model building