{"title":"塑造未知:从生成模型的视角揭示蛋白质结构空间。","authors":"Luiz Felipe Piochi, Hamed Khakzad","doi":"10.1016/j.cels.2025.101369","DOIUrl":null,"url":null,"abstract":"<p><p>Generative models can now design a diverse set of protein backbones, yet the quantification of distributional similarities of protein structure embeddings revealed that current models fail to capture the full spectrum of structural elements at different hierarchical levels. SHAPES (structural and hierarchical assessment of proteins with embedding similarity) quantifies these gaps and delivers a benchmark to guide next-generation protein design.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":"16 8","pages":"101369"},"PeriodicalIF":7.7000,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Shaping the uncharted: Revealing the protein structure space from the perspective of generative models.\",\"authors\":\"Luiz Felipe Piochi, Hamed Khakzad\",\"doi\":\"10.1016/j.cels.2025.101369\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Generative models can now design a diverse set of protein backbones, yet the quantification of distributional similarities of protein structure embeddings revealed that current models fail to capture the full spectrum of structural elements at different hierarchical levels. SHAPES (structural and hierarchical assessment of proteins with embedding similarity) quantifies these gaps and delivers a benchmark to guide next-generation protein design.</p>\",\"PeriodicalId\":93929,\"journal\":{\"name\":\"Cell systems\",\"volume\":\"16 8\",\"pages\":\"101369\"},\"PeriodicalIF\":7.7000,\"publicationDate\":\"2025-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cell systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.cels.2025.101369\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.cels.2025.101369","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Shaping the uncharted: Revealing the protein structure space from the perspective of generative models.
Generative models can now design a diverse set of protein backbones, yet the quantification of distributional similarities of protein structure embeddings revealed that current models fail to capture the full spectrum of structural elements at different hierarchical levels. SHAPES (structural and hierarchical assessment of proteins with embedding similarity) quantifies these gaps and delivers a benchmark to guide next-generation protein design.