{"title":"Rossmann-Fold蛋白的分类与识别","authors":"Yue Liu, Xiaoqin Li, H. Xu, Hui Qiao","doi":"10.1109/FBIE.2008.76","DOIUrl":null,"url":null,"abstract":"Fold recognition is an important issue in protein structure research. The Rossmann-fold protein that has typical structure is a common kind of alpha/beta protein. The training set, selected from 22 families, is constituted of 79 Rossmann-fold proteins which have less than 25% sequence identity with each other. The hierarchical clustering method according to RMSD is applied and a profile-HMM based on structure alignment is built for each cluster. Testing on 9505 proteins with less than 95% sequence identity from Astral1.65, the sensitivity, specificity and MCC are 93.9%, 82.1% and 0.876 respectively. The result shows that building profile-HMMs after classification could reach precise fold recognition while a unified one cannot be built due to there are too many members in training set.","PeriodicalId":415908,"journal":{"name":"2008 International Seminar on Future BioMedical Information Engineering","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Classification and Recognition of Rossmann-Fold Protein\",\"authors\":\"Yue Liu, Xiaoqin Li, H. Xu, Hui Qiao\",\"doi\":\"10.1109/FBIE.2008.76\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fold recognition is an important issue in protein structure research. The Rossmann-fold protein that has typical structure is a common kind of alpha/beta protein. The training set, selected from 22 families, is constituted of 79 Rossmann-fold proteins which have less than 25% sequence identity with each other. The hierarchical clustering method according to RMSD is applied and a profile-HMM based on structure alignment is built for each cluster. Testing on 9505 proteins with less than 95% sequence identity from Astral1.65, the sensitivity, specificity and MCC are 93.9%, 82.1% and 0.876 respectively. The result shows that building profile-HMMs after classification could reach precise fold recognition while a unified one cannot be built due to there are too many members in training set.\",\"PeriodicalId\":415908,\"journal\":{\"name\":\"2008 International Seminar on Future BioMedical Information Engineering\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Seminar on Future BioMedical Information Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FBIE.2008.76\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Seminar on Future BioMedical Information Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FBIE.2008.76","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classification and Recognition of Rossmann-Fold Protein
Fold recognition is an important issue in protein structure research. The Rossmann-fold protein that has typical structure is a common kind of alpha/beta protein. The training set, selected from 22 families, is constituted of 79 Rossmann-fold proteins which have less than 25% sequence identity with each other. The hierarchical clustering method according to RMSD is applied and a profile-HMM based on structure alignment is built for each cluster. Testing on 9505 proteins with less than 95% sequence identity from Astral1.65, the sensitivity, specificity and MCC are 93.9%, 82.1% and 0.876 respectively. The result shows that building profile-HMMs after classification could reach precise fold recognition while a unified one cannot be built due to there are too many members in training set.