{"title":"利用前馈和递归神经网络匹配蛋白质β -薄片伴侣。","authors":"P Baldi, G Pollastri, C A Andersen, S Brunak","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>Predicting the secondary structure (alpha-helices, beta-sheets, coils) of proteins is an important step towards understanding their three dimensional conformations. Unlike alpha-helices that are built up from one contiguous region of the polypeptide chain, beta-sheets are more complex resulting from a combination of two or more disjoint regions. The exact nature of these long distance interactions remains unclear. Here we introduce two neural-network based methods for the prediction of amino acid partners in parallel as well as anti-parallel beta-sheets. The neural architectures predict whether two residues located at the center of two distant windows are paired or not in a beta-sheet structure. Variations on these architecture, including also profiles and ensembles, are trained and tested via five-fold cross validation using a large corpus of curated data. Prediction on both coupled and non-coupled residues currently approaches 84% accuracy, better than any previously reported method.</p>","PeriodicalId":79420,"journal":{"name":"Proceedings. International Conference on Intelligent Systems for Molecular Biology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2000-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Matching protein beta-sheet partners by feedforward and recurrent neural networks.\",\"authors\":\"P Baldi, G Pollastri, C A Andersen, S Brunak\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Predicting the secondary structure (alpha-helices, beta-sheets, coils) of proteins is an important step towards understanding their three dimensional conformations. Unlike alpha-helices that are built up from one contiguous region of the polypeptide chain, beta-sheets are more complex resulting from a combination of two or more disjoint regions. The exact nature of these long distance interactions remains unclear. Here we introduce two neural-network based methods for the prediction of amino acid partners in parallel as well as anti-parallel beta-sheets. The neural architectures predict whether two residues located at the center of two distant windows are paired or not in a beta-sheet structure. Variations on these architecture, including also profiles and ensembles, are trained and tested via five-fold cross validation using a large corpus of curated data. Prediction on both coupled and non-coupled residues currently approaches 84% accuracy, better than any previously reported method.</p>\",\"PeriodicalId\":79420,\"journal\":{\"name\":\"Proceedings. International Conference on Intelligent Systems for Molecular Biology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. International Conference on Intelligent Systems for Molecular Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. International Conference on Intelligent Systems for Molecular Biology","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Matching protein beta-sheet partners by feedforward and recurrent neural networks.
Predicting the secondary structure (alpha-helices, beta-sheets, coils) of proteins is an important step towards understanding their three dimensional conformations. Unlike alpha-helices that are built up from one contiguous region of the polypeptide chain, beta-sheets are more complex resulting from a combination of two or more disjoint regions. The exact nature of these long distance interactions remains unclear. Here we introduce two neural-network based methods for the prediction of amino acid partners in parallel as well as anti-parallel beta-sheets. The neural architectures predict whether two residues located at the center of two distant windows are paired or not in a beta-sheet structure. Variations on these architecture, including also profiles and ensembles, are trained and tested via five-fold cross validation using a large corpus of curated data. Prediction on both coupled and non-coupled residues currently approaches 84% accuracy, better than any previously reported method.