SrasV1, PubMed, Corpus, bibRecord, 001B34

Predicting linear B-cell epitopes using string kernels.

Identifieur interne : 001B34 ( PubMed/Corpus ); précédent : 001B33; suivant : 001B35

Predicting linear B-cell epitopes using string kernels.

Auteurs : Yasser El-Manzalawy ; Drena Dobbs ; Vasant Honavar

Source :

Journal of molecular recognition : JMR [ 0952-3499 ]

RBID : pubmed:18496882

English descriptors

KwdEn :
- Amino Acid Sequence, Animals, Artificial Intelligence, Databases, Protein, Epitope Mapping, Epitopes, B-Lymphocyte (chemistry), Epitopes, B-Lymphocyte (classification), Epitopes, B-Lymphocyte (genetics), Humans, Membrane Glycoproteins (chemistry), Membrane Glycoproteins (genetics), Membrane Glycoproteins (immunology), Molecular Sequence Data, Peptides (chemistry), Peptides (genetics), Peptides (immunology), SARS Virus (chemistry), SARS Virus (genetics), SARS Virus (immunology), Spike Glycoprotein, Coronavirus, Viral Envelope Proteins (chemistry), Viral Envelope Proteins (genetics), Viral Envelope Proteins (immunology).
MESH :
- chemical , chemistry : Epitopes, B-Lymphocyte, Membrane Glycoproteins, Peptides, Viral Envelope Proteins.
- chemical , classification : Epitopes, B-Lymphocyte.
- chemical , genetics : Epitopes, B-Lymphocyte, Membrane Glycoproteins, Peptides, Viral Envelope Proteins.
- chemical , immunology : Membrane Glycoproteins, Peptides, Viral Envelope Proteins.
- chemistry : SARS Virus.
- genetics : SARS Virus.
- immunology : SARS Virus.
- Amino Acid Sequence, Animals, Artificial Intelligence, Databases, Protein, Epitope Mapping, Humans, Molecular Sequence Data, Spike Glycoprotein, Coronavirus.

Abstract

The identification and characterization of B-cell epitopes play an important role in vaccine design, immunodiagnostic tests, and antibody production. Therefore, computational tools for reliably predicting linear B-cell epitopes are highly desirable. We evaluated Support Vector Machine (SVM) classifiers trained utilizing five different kernel methods using fivefold cross-validation on a homology-reduced data set of 701 linear B-cell epitopes, extracted from Bcipep database, and 701 non-epitopes, randomly extracted from SwissProt sequences. Based on the results of our computational experiments, we propose BCPred, a novel method for predicting linear B-cell epitopes using the subsequence kernel. We show that the predictive performance of BCPred (AUC = 0.758) outperforms 11 SVM-based classifiers developed and evaluated in our experiments as well as our implementation of AAP (AUC = 0.7), a recently proposed method for predicting linear B-cell epitopes using amino acid pair antigenicity. Furthermore, we compared BCPred with AAP and ABCPred, a method that uses recurrent neural networks, using two data sets of unique B-cell epitopes that had been previously used to evaluate ABCPred. Analysis of the data sets used and the results of this comparison show that conclusions about the relative performance of different B-cell epitope prediction methods drawn on the basis of experiments using data sets of unique B-cell epitopes are likely to yield overly optimistic estimates of performance of evaluated methods. This argues for the use of carefully homology-reduced data sets in comparing B-cell epitope prediction methods to avoid misleading conclusions about how different methods compare to each other. Our homology-reduced data set and implementations of BCPred as well as the APP method are publicly available through our web-based server, BCPREDS, at: http://ailab.cs.iastate.edu/bcpreds/.

DOI: 10.1002/jmr.893
PubMed: 18496882

Links to Exploration step

pubmed:18496882

Le document en format XML

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<front><div type="abstract" xml:lang="en">The identification and characterization of B-cell epitopes play an important role in vaccine design, immunodiagnostic tests, and antibody production. Therefore, computational tools for reliably predicting linear B-cell epitopes are highly desirable. We evaluated Support Vector Machine (SVM) classifiers trained utilizing five different kernel methods using fivefold cross-validation on a homology-reduced data set of 701 linear B-cell epitopes, extracted from Bcipep database, and 701 non-epitopes, randomly extracted from SwissProt sequences. Based on the results of our computational experiments, we propose BCPred, a novel method for predicting linear B-cell epitopes using the subsequence kernel. We show that the predictive performance of BCPred (AUC = 0.758) outperforms 11 SVM-based classifiers developed and evaluated in our experiments as well as our implementation of AAP (AUC = 0.7), a recently proposed method for predicting linear B-cell epitopes using amino acid pair antigenicity. Furthermore, we compared BCPred with AAP and ABCPred, a method that uses recurrent neural networks, using two data sets of unique B-cell epitopes that had been previously used to evaluate ABCPred. Analysis of the data sets used and the results of this comparison show that conclusions about the relative performance of different B-cell epitope prediction methods drawn on the basis of experiments using data sets of unique B-cell epitopes are likely to yield overly optimistic estimates of performance of evaluated methods. This argues for the use of carefully homology-reduced data sets in comparing B-cell epitope prediction methods to avoid misleading conclusions about how different methods compare to each other. Our homology-reduced data set and implementations of BCPred as well as the APP method are publicly available through our web-based server, BCPREDS, at: http://ailab.cs.iastate.edu/bcpreds/.</div>
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{{Explor lien
   |wiki=    Sante
   |area=    SrasV1
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   |clé=     pubmed:18496882
   |texte=   Predicting linear B-cell epitopes using string kernels.
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Predicting linear B-cell epitopes using string kernels.

Predicting linear B-cell epitopes using string kernels.

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