title: Using support vector machines to improve elemental ion identification in macromolecular crystal structures.
abstract: 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.
authors.affiliation: College of Letters and Science, University of California, Berkeley, CA 94720, USA.
authors.forename: Nader
authors.initials: N
authors.lastname: Morshed
authors.affiliation: Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
authors.forename: Nathaniel
authors.initials: N
authors.lastname: Echols
authors.affiliation: Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
authors.forename: Paul D
authors.initials: PD
authors.lastname: Adams
mesh_terms: D000465:Algorithms; D002118:Calcium / Q000032:analysis; D018360:Crystallography, X-Ray; D007501:Iron / Q000032:analysis; D046911:Macromolecular Substances / Q000737:chemistry*; D008345:Manganese / Q000032:analysis; D008670:Metals / Q000032:analysis*; D008958:Models, Molecular; D009532:Nickel / Q000032:analysis; D011487:Protein Conformation; D060388:Support Vector Machine; D015032:Zinc / Q000032:analysis
chemical_list: D046911:Macromolecular Substances; D008670:Metals; D008345:Manganese; D009532:Nickel; D007501:Iron; D015032:Zinc; D002118:Calcium
keywords: elemental ion identification; model building; support vector machines