{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T09:08:03Z","timestamp":1776416883238,"version":"3.51.2"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,3,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Allergenicity, like antigenicity and immunogenicity, is a property encoded linearly and non-linearly, and therefore the alignment-based approaches are not able to identify this property unambiguously. A novel alignment-free descriptor-based fingerprint approach is presented here and applied to identify allergens and non-allergens. The approach was implemented into a four step algorithm. Initially, the protein sequences are described by amino acid principal properties as hydrophobicity, size, relative abundance, helix and \u03b2-strand forming propensities. Then, the generated strings of different length are converted into vectors with equal length by auto- and cross-covariance (ACC). The vectors were transformed into binary fingerprints and compared in terms of Tanimoto coefficient.<\/jats:p><jats:p>Results: The approach was applied to a set of 2427 known allergens and 2427 non-allergens and identified correctly 88% of them with Matthews correlation coefficient of 0.759. The descriptor fingerprint approach presented here is universal. It could be applied for any classification problem in computational biology. The set of E-descriptors is able to capture the main structural and physicochemical properties of amino acids building the proteins. The ACC transformation overcomes the main problem in the alignment-based comparative studies arising from the different length of the aligned protein sequences. The conversion of protein ACC values into binary descriptor fingerprints allows similarity search and classification.<\/jats:p><jats:p>Availability and implementation:\u2003The algorithm described in the present study was implemented in a specially designed Web site, named AllergenFP (FP stands for FingerPrint). AllergenFP is written in Python, with GIU in HTML. It is freely accessible at http:\/\/ddg-pharmfac.net\/Allergen FP.<\/jats:p><jats:p>Contact: \u00a0idoytchinova@pharmfac.net or ivanbangov@shu-bg.net<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt619","type":"journal-article","created":{"date-parts":[[2013,10,29]],"date-time":"2013-10-29T00:39:39Z","timestamp":1383007179000},"page":"846-851","source":"Crossref","is-referenced-by-count":646,"title":["AllergenFP: allergenicity prediction by descriptor fingerprints"],"prefix":"10.1093","volume":"30","author":[{"given":"Ivan","family":"Dimitrov","sequence":"first","affiliation":[{"name":"1 Medical University of Sofia, Faculty of Pharmacy, 2 Dunav st., 1000 Sofia and 2Konstantin Preslavski Shumen University, Faculty of Natural Sciences, 115 Universitetska st., 9712 Shumen, Bulgaria"}]},{"given":"Lyudmila","family":"Naneva","sequence":"additional","affiliation":[{"name":"1 Medical University of Sofia, Faculty of Pharmacy, 2 Dunav st., 1000 Sofia and 2Konstantin Preslavski Shumen University, Faculty of Natural Sciences, 115 Universitetska st., 9712 Shumen, Bulgaria"}]},{"given":"Irini","family":"Doytchinova","sequence":"additional","affiliation":[{"name":"1 Medical University of Sofia, Faculty of Pharmacy, 2 Dunav st., 1000 Sofia and 2Konstantin Preslavski Shumen University, Faculty of Natural Sciences, 115 Universitetska st., 9712 Shumen, Bulgaria"}]},{"given":"Ivan","family":"Bangov","sequence":"additional","affiliation":[{"name":"1 Medical University of Sofia, Faculty of Pharmacy, 2 Dunav st., 1000 Sofia and 2Konstantin Preslavski Shumen University, Faculty of Natural Sciences, 115 Universitetska st., 9712 Shumen, Bulgaria"}]}],"member":"286","published-online":{"date-parts":[[2013,10,27]]},"reference":[{"key":"2023012710445402600_btt619-B1","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1002\/9783527618279.ch3","article-title":"Representation of molecular structures. overview","volume-title":"Handbook of Chemoinformatics","author":"Barnard","year":"2003"},{"key":"2023012710445402600_btt619-B2","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1093\/bioinformatics\/bth477","article-title":"Supervised identification of allergen-representative peptides for in silico detection of potentially allergenic proteins","volume":"21","author":"Bj\u00f6rklund","year":"2005","journal-title":"Bioinformatics"},{"key":"2023012710445402600_btt619-B3","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1111\/j.0141-9838.2004.00728.x","article-title":"Intestinal worms and human allergy","volume":"26","author":"Cooper","year":"2004","journal-title":"Parasite Immunol."},{"key":"2023012710445402600_btt619-B4","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.molimm.2006.02.010","article-title":"Computer prediction of allergen proteins from sequence-derived protein structural and physicochemical properties","volume":"44","author":"Cui","year":"2007","journal-title":"Mol. 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