AllerTOP--a server for in silico prediction of allergens
Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex aliment...
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Published in | BMC bioinformatics Vol. 14 Suppl 6; no. Suppl 6; p. S4 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
England
BioMed Central
17.04.2013
BioMed Central Ltd |
Subjects | |
Online Access | Get full text |
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Abstract | Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences.
A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z1, z2 and z3) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity.
AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin. |
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AbstractList | BACKGROUNDAllergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences. RESULTSA set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z1, z2 and z3) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity. CONCLUSIONSAllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin. Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences. A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z1, z2 and z3) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity. AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin. BACKGROUND: Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences. RESULTS: A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z1, z2 and z3) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity. CONCLUSIONS: AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin. Background: Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences. Results: A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z sub(1), z sub(2 )and z sub(3)) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naive Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop . AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity. Conclusions: AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin. Doc number: S4 Abstract Background: Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences. Results: A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z -descriptors (z1 , z2 and z3 ) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (k NN). The best performing model was derived by k NN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop . AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity. Conclusions: AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin. |
ArticleNumber | S4 |
Author | Flower, Darren R Dimitrov, Ivan Doytchinova, Irini |
AuthorAffiliation | 1 Faculty of Pharmacy, Medical University of Sofia, 2 Dunav st., 1000 Sofia, Bulgaria 2 Life and Health Sciences, Aston University, Aston Triangle, Birmingham, B4 7ET, UK |
AuthorAffiliation_xml | – name: 2 Life and Health Sciences, Aston University, Aston Triangle, Birmingham, B4 7ET, UK – name: 1 Faculty of Pharmacy, Medical University of Sofia, 2 Dunav st., 1000 Sofia, Bulgaria |
Author_xml | – sequence: 1 givenname: Ivan surname: Dimitrov fullname: Dimitrov, Ivan organization: Faculty of Pharmacy, Medical University of Sofia, 2 Dunav st,, Sofia, Bulgaria – sequence: 2 givenname: Darren R surname: Flower fullname: Flower, Darren R – sequence: 3 givenname: Irini surname: Doytchinova fullname: Doytchinova, Irini |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23735058$$D View this record in MEDLINE/PubMed |
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Cites_doi | 10.1002/1521-3838(200006)19:3<264::AID-QSAR264>3.0.CO;2-A 10.1016/j.molimm.2007.01.019 10.1093/bioinformatics/bth477 10.1186/1471-2105-8-4 10.1016/S0031-3203(96)00142-2 10.1371/journal.pone.0005861 10.1136/bmj.316.7132.686 10.1016/S0169-7439(98)00062-8 10.1186/1471-2105-5-133 10.1093/nar/gkl343 10.1021/jm00390a003 10.1093/nar/gkg010 10.1145/1656274.1656278 10.1096/fj.02-1052fje 10.1110/ps.2500102 10.1111/j.0141-9838.2004.00728.x 10.1093/bioinformatics/btp163 10.1093/bioinformatics/bth286 10.1093/bioinformatics/bti700 10.1093/toxsci/55.2.235 10.1038/nri1372 10.1093/bioinformatics/btl621 10.1034/j.1398-9995.2003.00224.x 10.1016/0003-2670(93)80437-P |
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References | 15373946 - BMC Bioinformatics. 2004 Sep 16;5:133 19516900 - PLoS One. 2009;4(6):e5861 19304878 - Bioinformatics. 2009 Jun 1;25(11):1422-3 17150996 - Bioinformatics. 2007 Feb 15;23(4):504-6 17382394 - Mol Immunol. 2007 May;44(12):3256-60 15319257 - Bioinformatics. 2005 Jan 1;21(1):39-50 16844994 - Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W202-9 16204345 - Bioinformatics. 2005 Dec 1;21(23):4201-4 15173835 - Nat Rev Immunol. 2004 Jun;4(6):469-78 12520022 - Nucleic Acids Res. 2003 Jan 1;31(1):359-62 17207271 - BMC Bioinformatics. 2007;8:4 15771681 - Parasite Immunol. 2004 Nov-Dec;26(11-12):455-67 12611632 - In Silico Biol. 2002;2(4):525-34 9522798 - BMJ. 1998 Feb 28;316(7132):686-9 11910023 - Protein Sci. 2002 Apr;11(4):795-805 12709401 - FASEB J. 2003 Jun;17(9):1141-3 10828254 - Toxicol Sci. 2000 Jun;55(2):235-46 3599020 - J Med Chem. 1987 Jul;30(7):1126-35 15117757 - Bioinformatics. 2004 Nov 1;20(16):2572-8 14616117 - Allergy. 2003 Nov;58(11):1083-92 S Hellberg (5802_CR23) 1987; 30 Å Nyström (5802_CR20) 2000; 19 5802_CR26 AP Bradley (5802_CR29) 1997; 30 ZH Zhang (5802_CR11) 2007; 23 KB Li (5802_CR13) 2004; 20 FAO/WHO Codex Alimentarius Commission (5802_CR7) 2003 V Brusic (5802_CR8) 2003; 58 HC Muh (5802_CR24) 2009; 4 CA Janeway (5802_CR2) 1999 O Ivanciuc (5802_CR9) 2003; 31 SY Seong (5802_CR17) 2004; 4 S Saha (5802_CR15) 2006; 34 C Emanuelsson (5802_CR5) 2007; 44 FAO/WHO Agriculture and Consumer Protection (5802_CR6) 2001 A Zorzet (5802_CR25) 2002; 2 MWEJ Fiers (5802_CR10) 2004; 5 C Rusznak (5802_CR3) 1998; 316 MB Stadler (5802_CR12) 2003; 17 PJ Cooper (5802_CR1) 2004; 26 PM Andersson (5802_CR19) 1998; 42 R Furmonaviciene (5802_CR16) 2005; 21 IA Doytchinova (5802_CR22) 2007; 8 PJ Cock (5802_CR27) 2009; 25 M Lapinsh (5802_CR21) 2002; 11 AK Björklund (5802_CR14) 2005; 21 RDJ Huby (5802_CR4) 2000; 55 M Hall (5802_CR28) 2009; 11 S Wold (5802_CR18) 1993; 277 |
References_xml | – volume: 19 start-page: 264 year: 2000 ident: 5802_CR20 publication-title: Quant Struct-Act Relat doi: 10.1002/1521-3838(200006)19:3<264::AID-QSAR264>3.0.CO;2-A contributor: fullname: Å Nyström – volume: 44 start-page: 3256 year: 2007 ident: 5802_CR5 publication-title: Mol Immunol doi: 10.1016/j.molimm.2007.01.019 contributor: fullname: C Emanuelsson – volume: 21 start-page: 39 year: 2005 ident: 5802_CR14 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bth477 contributor: fullname: AK Björklund – ident: 5802_CR26 – volume: 8 start-page: 4 year: 2007 ident: 5802_CR22 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-8-4 contributor: fullname: IA Doytchinova – volume: 30 start-page: 1145 year: 1997 ident: 5802_CR29 publication-title: Pattern Recognition doi: 10.1016/S0031-3203(96)00142-2 contributor: fullname: AP Bradley – volume: 4 start-page: e5861 year: 2009 ident: 5802_CR24 publication-title: PLoS ONE doi: 10.1371/journal.pone.0005861 contributor: fullname: HC Muh – volume: 316 start-page: 686 year: 1998 ident: 5802_CR3 publication-title: BMJ doi: 10.1136/bmj.316.7132.686 contributor: fullname: C Rusznak – volume: 42 start-page: 41 year: 1998 ident: 5802_CR19 publication-title: Chemometr Intell Lab doi: 10.1016/S0169-7439(98)00062-8 contributor: fullname: PM Andersson – volume: 5 start-page: 133 year: 2004 ident: 5802_CR10 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-5-133 contributor: fullname: MWEJ Fiers – volume: 34 start-page: W202 year: 2006 ident: 5802_CR15 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkl343 contributor: fullname: S Saha – volume-title: Joint FAO/WHO Food Standards Programme. Rome, Italy year: 2003 ident: 5802_CR7 contributor: fullname: FAO/WHO Codex Alimentarius Commission – volume: 30 start-page: 1126 year: 1987 ident: 5802_CR23 publication-title: J Med Chem doi: 10.1021/jm00390a003 contributor: fullname: S Hellberg – volume: 31 start-page: 359 year: 2003 ident: 5802_CR9 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkg010 contributor: fullname: O Ivanciuc – volume: 11 start-page: 10 year: 2009 ident: 5802_CR28 publication-title: SIGKDD Explorations doi: 10.1145/1656274.1656278 contributor: fullname: M Hall – volume-title: Immunobiology: the immune system in health and disease year: 1999 ident: 5802_CR2 contributor: fullname: CA Janeway – volume: 17 start-page: 1141 year: 2003 ident: 5802_CR12 publication-title: FASEB J doi: 10.1096/fj.02-1052fje contributor: fullname: MB Stadler – volume: 11 start-page: 795 year: 2002 ident: 5802_CR21 publication-title: Protein Sci doi: 10.1110/ps.2500102 contributor: fullname: M Lapinsh – volume: 26 start-page: 455 year: 2004 ident: 5802_CR1 publication-title: Parasite Immunol doi: 10.1111/j.0141-9838.2004.00728.x contributor: fullname: PJ Cooper – volume: 25 start-page: 1422 year: 2009 ident: 5802_CR27 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btp163 contributor: fullname: PJ Cock – volume: 20 start-page: 2572 year: 2004 ident: 5802_CR13 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bth286 contributor: fullname: KB Li – volume: 21 start-page: 4201 year: 2005 ident: 5802_CR16 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bti700 contributor: fullname: R Furmonaviciene – volume: 55 start-page: 235 year: 2000 ident: 5802_CR4 publication-title: Toxicological Sci doi: 10.1093/toxsci/55.2.235 contributor: fullname: RDJ Huby – volume-title: Report of a Joint FAO/WHO Expert Consultation on Allergenicity of Foods Derived from Biotechnology. Rome, Italy year: 2001 ident: 5802_CR6 contributor: fullname: FAO/WHO Agriculture and Consumer Protection – volume: 2 start-page: 525 year: 2002 ident: 5802_CR25 publication-title: In Silico Biol contributor: fullname: A Zorzet – volume: 4 start-page: 469 year: 2004 ident: 5802_CR17 publication-title: Nat Rev Immunol doi: 10.1038/nri1372 contributor: fullname: SY Seong – volume: 23 start-page: 504 year: 2007 ident: 5802_CR11 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btl621 contributor: fullname: ZH Zhang – volume: 58 start-page: 1083 year: 2003 ident: 5802_CR8 publication-title: Allergy doi: 10.1034/j.1398-9995.2003.00224.x contributor: fullname: V Brusic – volume: 277 start-page: 239 year: 1993 ident: 5802_CR18 publication-title: Anal Chim Acta doi: 10.1016/0003-2670(93)80437-P contributor: fullname: S Wold |
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Snippet | Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly... Doc number: S4 Abstract Background: Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are... Background: Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that... BACKGROUNDAllergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that... BACKGROUND: Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that... |
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SubjectTerms | Algorithms Allergens - chemistry Allergens - immunology Allergies Amino Acid Sequence Amino acids Artificial Intelligence Bayes Theorem Bioinformatics Computational Biology - methods Computer Simulation Databases, Protein Food Hypersensitivity Humans Hypersensitivity - immunology Proceedings Proteins Proteins - chemistry Proteins - immunology Studies Toxins, Biological - chemistry Toxins, Biological - immunology |
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Title | AllerTOP--a server for in silico prediction of allergens |
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