{"title":"对无序结构预测的展望。","authors":"Charles S Bond","doi":"10.1107/s2059798325008599","DOIUrl":null,"url":null,"abstract":"Low-confidence regions in computational protein models have been identified to represent a largely untapped resource that may contain valuable structural information.","PeriodicalId":501686,"journal":{"name":"Acta Crystallographica Section D","volume":"27 1","pages":"584"},"PeriodicalIF":0.0000,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Perspective on structure predictions of disorder.\",\"authors\":\"Charles S Bond\",\"doi\":\"10.1107/s2059798325008599\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Low-confidence regions in computational protein models have been identified to represent a largely untapped resource that may contain valuable structural information.\",\"PeriodicalId\":501686,\"journal\":{\"name\":\"Acta Crystallographica Section D\",\"volume\":\"27 1\",\"pages\":\"584\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Acta Crystallographica Section D\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1107/s2059798325008599\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Crystallographica Section D","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1107/s2059798325008599","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Low-confidence regions in computational protein models have been identified to represent a largely untapped resource that may contain valuable structural information.
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