{"title":"使用支持向量机改进大分子晶体结构中的元素离子识别。","authors":"Nader Morshed, Nathaniel Echols, Paul D Adams","doi":"10.1107/S1399004715004241","DOIUrl":null,"url":null,"abstract":"<p><p>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. </p>","PeriodicalId":7047,"journal":{"name":"Acta crystallographica. Section D, Biological crystallography","volume":"71 Pt 5","pages":"1147-58"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4427199/pdf/","citationCount":"0","resultStr":"{\"title\":\"Using support vector machines to improve elemental ion identification in macromolecular crystal structures.\",\"authors\":\"Nader Morshed, Nathaniel Echols, Paul D Adams\",\"doi\":\"10.1107/S1399004715004241\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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. </p>\",\"PeriodicalId\":7047,\"journal\":{\"name\":\"Acta crystallographica. Section D, Biological crystallography\",\"volume\":\"71 Pt 5\",\"pages\":\"1147-58\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4427199/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Acta crystallographica. Section D, Biological crystallography\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1107/S1399004715004241\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2015/4/25 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta crystallographica. Section D, Biological crystallography","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1107/S1399004715004241","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2015/4/25 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
在建立大分子模型的过程中,晶体学家必须检查孤立原子的电子密度,并区分含有结构溶剂分子的位点和含有元素离子的位点。这项工作需要有关金属结合化学和散射特性的特定知识,而且容易出错。以前曾介绍过一种方法,可根据人工选择的标准识别一些元素的离子。本文介绍了使用支持向量机(SVM)自动将孤立原子分类为溶剂或各种离子之一的方法。我们构建了两个蛋白质晶体结构数据集,一个数据集包含了用异常衍射数据沉积的人工整理结构,另一个数据集包含了自动过滤的高分辨率结构。在人工编辑的数据集上,SVM 分类器能够高度准确地将钙离子与锰、锌、铁和镍离子区分开来,并将所有这五种离子与水分子区分开来。此外,在自动策划的高分辨率结构集上训练的 SVM 能够成功地对独立验证测试集中的大多数常见元素离子进行分类。这种方法很容易扩展到其他元素离子,还可以与以前基于化学环境和 X 射线散射的先验预期的方法结合使用。
Using support vector machines to improve elemental ion identification in macromolecular crystal structures.
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.