Sarah E Boyd, Maria Garcia de la Banda, Robert N Pike, James C Whisstock, George B Rudy
{"title":"持久性有机污染物:建模和预测蛋白酶特异性的计算工具。","authors":"Sarah E Boyd, Maria Garcia de la Banda, Robert N Pike, James C Whisstock, George B Rudy","doi":"10.1109/csb.2004.1332450","DOIUrl":null,"url":null,"abstract":"<p><p>Proteases play a fundamental role in the control of intra- and extracellular processes by binding and cleaving specific amino acid sequences. Identifying these targets is extremely challenging. Current computational attempts to predict cleavage sites are limited, representing these amino acid sequences as patterns or frequency matrices. Here we present PoPS, a publicly accessible bioinformatics tool (http://pops.csse.monash.edu.au/) which provides a novel method for building computational models of protease specificity that, while still being based on these amino acid sequences, can be built from any experimental data or expert knowledge available to the user. PoPS specificity models can be used to predict and rank likely cleavages within a single substrate, and within entire proteomes. Other factors, such as the secondary or tertiary structure of the substrate, can be used to screen unlikely sites. Furthermore, the tool also provides facilities to infer, compare and test models, and to store them in a publicly accessible database.</p>","PeriodicalId":87417,"journal":{"name":"Proceedings. IEEE Computational Systems Bioinformatics Conference","volume":" ","pages":"372-81"},"PeriodicalIF":0.0000,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/csb.2004.1332450","citationCount":"55","resultStr":"{\"title\":\"PoPS: a computational tool for modeling and predicting protease specificity.\",\"authors\":\"Sarah E Boyd, Maria Garcia de la Banda, Robert N Pike, James C Whisstock, George B Rudy\",\"doi\":\"10.1109/csb.2004.1332450\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Proteases play a fundamental role in the control of intra- and extracellular processes by binding and cleaving specific amino acid sequences. Identifying these targets is extremely challenging. Current computational attempts to predict cleavage sites are limited, representing these amino acid sequences as patterns or frequency matrices. Here we present PoPS, a publicly accessible bioinformatics tool (http://pops.csse.monash.edu.au/) which provides a novel method for building computational models of protease specificity that, while still being based on these amino acid sequences, can be built from any experimental data or expert knowledge available to the user. PoPS specificity models can be used to predict and rank likely cleavages within a single substrate, and within entire proteomes. Other factors, such as the secondary or tertiary structure of the substrate, can be used to screen unlikely sites. Furthermore, the tool also provides facilities to infer, compare and test models, and to store them in a publicly accessible database.</p>\",\"PeriodicalId\":87417,\"journal\":{\"name\":\"Proceedings. IEEE Computational Systems Bioinformatics Conference\",\"volume\":\" \",\"pages\":\"372-81\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/csb.2004.1332450\",\"citationCount\":\"55\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. IEEE Computational Systems Bioinformatics Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/csb.2004.1332450\",\"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. IEEE Computational Systems Bioinformatics Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/csb.2004.1332450","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
PoPS: a computational tool for modeling and predicting protease specificity.
Proteases play a fundamental role in the control of intra- and extracellular processes by binding and cleaving specific amino acid sequences. Identifying these targets is extremely challenging. Current computational attempts to predict cleavage sites are limited, representing these amino acid sequences as patterns or frequency matrices. Here we present PoPS, a publicly accessible bioinformatics tool (http://pops.csse.monash.edu.au/) which provides a novel method for building computational models of protease specificity that, while still being based on these amino acid sequences, can be built from any experimental data or expert knowledge available to the user. PoPS specificity models can be used to predict and rank likely cleavages within a single substrate, and within entire proteomes. Other factors, such as the secondary or tertiary structure of the substrate, can be used to screen unlikely sites. Furthermore, the tool also provides facilities to infer, compare and test models, and to store them in a publicly accessible database.