N. Rahmani, E. Ebrahimie, A. Niazi, Najaf Allahyari Fard, B. Bambai, Z. Minuchehr, M. Ebrahimi
{"title":"Unravelling Over-Represented Amino Acids in Protein Structure of Allergen Proteins; A Large-Scale Study","authors":"N. Rahmani, E. Ebrahimie, A. Niazi, Najaf Allahyari Fard, B. Bambai, Z. Minuchehr, M. Ebrahimi","doi":"10.22067/jcmr.v0i0.60190","DOIUrl":null,"url":null,"abstract":"Allergens are proteins or glycoproteins which make widespread disorders that can lead to a systemic anaphylactic shock and even death within a short period of time. Understanding the protein features that are involved in allergenicity is important in developing future treatments as well as engineering proteins in genetic transformation projects. A big dataset of 1439 protein features from 761 plant allergens and 7815 non-allergen proteins was constructed. Thereafter, 10 different attribute weighting algorithms were utilized to find the key characteristics differentiating allergens and non-allergen proteins. The frequency of Leu, Arg and Gln selected by different attribute weighting algorithms with more than 50% confidence, including attribute weighting by Weight_Info Gain, Weight Chi Squared, Weight_Gini Index and Weight_Relief. High amount of Gln and low percentage of Leu and Arg discriminate plant allergens from non-allergens","PeriodicalId":30607,"journal":{"name":"Journal of Cell and Molecular Research","volume":"8 1","pages":"65-70"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cell and Molecular Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22067/jcmr.v0i0.60190","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Allergens are proteins or glycoproteins which make widespread disorders that can lead to a systemic anaphylactic shock and even death within a short period of time. Understanding the protein features that are involved in allergenicity is important in developing future treatments as well as engineering proteins in genetic transformation projects. A big dataset of 1439 protein features from 761 plant allergens and 7815 non-allergen proteins was constructed. Thereafter, 10 different attribute weighting algorithms were utilized to find the key characteristics differentiating allergens and non-allergen proteins. The frequency of Leu, Arg and Gln selected by different attribute weighting algorithms with more than 50% confidence, including attribute weighting by Weight_Info Gain, Weight Chi Squared, Weight_Gini Index and Weight_Relief. High amount of Gln and low percentage of Leu and Arg discriminate plant allergens from non-allergens