B. Vishnepolsky, M. Grigolava, G. Zaalishvili, M. Karapetian, M. Pirtskhalava
{"title":"DBAASP作为一种特殊预测工具,用于预测对特定目标物种的抗菌效力","authors":"B. Vishnepolsky, M. Grigolava, G. Zaalishvili, M. Karapetian, M. Pirtskhalava","doi":"10.3390/ecmc-4-05608","DOIUrl":null,"url":null,"abstract":"Antimicrobial peptides (AMPs) have been identified as a potentially new class of antibiotics. There are a lot of computational methods of AMP prediction. Although most of them can predict antimicrobial potency against any microbe (microbe is not identified) with rather high accuracy, prediction quality of these tools against particular bacterial strains is low [1,2]. Special prediction is a tool for the prediction of antimicrobial potency of peptides against particular target species with high accuracy. This tool is included into the Database of Antimicrobial Activity and Structure of Peptides (DBAASP, https://dbaasp.org [3]). In this presentation we describe this tool and predictive models for some Gram positive bacterial strains (Staphylococcus aureus ATCC 25923 and Bacillus subtilis) and a model for the prediction of hemolytic activity. Predictive model for Gram negative Escherichia coli ATCC 25922 was presented earlier [2,4]. Special prediction tool can be used for the design of peptides being active against particular strain. To demonstrate the capability of the tool, peptides predicted as active against E-coli ATCC 25922 and Staphylococcus aureus ATCC 25923 have been synthesized, and tested in vitro. The results have shown the justification of using special prediction tool for the design of new AMPs","PeriodicalId":20450,"journal":{"name":"Proceedings of 4th International Electronic Conference on Medicinal Chemistry","volume":"22 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"DBAASP Special prediction as a tool for the prediction of antimicrobial potency against particular target species\",\"authors\":\"B. Vishnepolsky, M. Grigolava, G. Zaalishvili, M. Karapetian, M. Pirtskhalava\",\"doi\":\"10.3390/ecmc-4-05608\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Antimicrobial peptides (AMPs) have been identified as a potentially new class of antibiotics. There are a lot of computational methods of AMP prediction. Although most of them can predict antimicrobial potency against any microbe (microbe is not identified) with rather high accuracy, prediction quality of these tools against particular bacterial strains is low [1,2]. Special prediction is a tool for the prediction of antimicrobial potency of peptides against particular target species with high accuracy. This tool is included into the Database of Antimicrobial Activity and Structure of Peptides (DBAASP, https://dbaasp.org [3]). In this presentation we describe this tool and predictive models for some Gram positive bacterial strains (Staphylococcus aureus ATCC 25923 and Bacillus subtilis) and a model for the prediction of hemolytic activity. Predictive model for Gram negative Escherichia coli ATCC 25922 was presented earlier [2,4]. Special prediction tool can be used for the design of peptides being active against particular strain. To demonstrate the capability of the tool, peptides predicted as active against E-coli ATCC 25922 and Staphylococcus aureus ATCC 25923 have been synthesized, and tested in vitro. The results have shown the justification of using special prediction tool for the design of new AMPs\",\"PeriodicalId\":20450,\"journal\":{\"name\":\"Proceedings of 4th International Electronic Conference on Medicinal Chemistry\",\"volume\":\"22 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 4th International Electronic Conference on Medicinal Chemistry\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/ecmc-4-05608\",\"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 of 4th International Electronic Conference on Medicinal Chemistry","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/ecmc-4-05608","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
DBAASP Special prediction as a tool for the prediction of antimicrobial potency against particular target species
Antimicrobial peptides (AMPs) have been identified as a potentially new class of antibiotics. There are a lot of computational methods of AMP prediction. Although most of them can predict antimicrobial potency against any microbe (microbe is not identified) with rather high accuracy, prediction quality of these tools against particular bacterial strains is low [1,2]. Special prediction is a tool for the prediction of antimicrobial potency of peptides against particular target species with high accuracy. This tool is included into the Database of Antimicrobial Activity and Structure of Peptides (DBAASP, https://dbaasp.org [3]). In this presentation we describe this tool and predictive models for some Gram positive bacterial strains (Staphylococcus aureus ATCC 25923 and Bacillus subtilis) and a model for the prediction of hemolytic activity. Predictive model for Gram negative Escherichia coli ATCC 25922 was presented earlier [2,4]. Special prediction tool can be used for the design of peptides being active against particular strain. To demonstrate the capability of the tool, peptides predicted as active against E-coli ATCC 25922 and Staphylococcus aureus ATCC 25923 have been synthesized, and tested in vitro. The results have shown the justification of using special prediction tool for the design of new AMPs