{"title":"Bioinformatics tools to Predict N-myristoylation Site a Comparison Study","authors":"Muhammad Zainul Arifin, A. A. Parikesit","doi":"10.54250/ijls.v1i1.10","DOIUrl":null,"url":null,"abstract":"Protein N-myristoylation is the covalent attachment of myrstate, via an amide bond, to the N-terminal glycine residue of a nascent polypeptide assisted by myristol-CoA protein: N-myristoltransferases (NMT). PROSITE motif describes 5 amino acid after glycine site that would give rise to myristoylation site. However, applied to whole database extract, this motif give too many false postive results. Therefore 2 new tools were developed for N-myristoylation prediction.Taking physical properties into consideration inceases prediction scores greatly. However, these algorithms still cannot predict myristoylated site correctly 100% of the time due to limited understanding of the real mechanims underlying N-myristoylation","PeriodicalId":375737,"journal":{"name":"Indonesian Journal of Life Sciences | ISSN: 2656-0682 (online)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Indonesian Journal of Life Sciences | ISSN: 2656-0682 (online)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54250/ijls.v1i1.10","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Protein N-myristoylation is the covalent attachment of myrstate, via an amide bond, to the N-terminal glycine residue of a nascent polypeptide assisted by myristol-CoA protein: N-myristoltransferases (NMT). PROSITE motif describes 5 amino acid after glycine site that would give rise to myristoylation site. However, applied to whole database extract, this motif give too many false postive results. Therefore 2 new tools were developed for N-myristoylation prediction.Taking physical properties into consideration inceases prediction scores greatly. However, these algorithms still cannot predict myristoylated site correctly 100% of the time due to limited understanding of the real mechanims underlying N-myristoylation