We are analyzing https://link.springer.com/article/10.1007/s00726-006-0485-9.

Title:
Prediction of linear B-cell epitopes using amino acid pair antigenicity scale | Amino Acids
Description:
Identification of antigenic sites on proteins is of vital importance for developing synthetic peptide vaccines, immunodiagnostic tests and antibody production. Currently, most of the prediction algorithms rely on amino acid propensity scales using a sliding window approach. These methods are oversimplified and yield poor predicted results in practice. In this paper, a novel scale, called the amino acid pair (AAP) antigenicity scale, is proposed that is based on the finding that B-cell epitopes favor particular AAPs. It is demonstrated that, using SVM (support vector machine) classifier, the AAP antigenicity scale approach has much better performance than the existing scales based on the single amino acid propensity. The AAP antigenicity scale can reflect some special sequence-coupled feature in the B-cell epitopes, which is the essence why the new approach is superior to the existing ones. It is anticipated that with the continuous increase of the known epitope data, the power of the AAP antigenicity scale approach will be further enhanced.
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Keywords {🔍}

article, google, scholar, cas, protein, prediction, pubmed, chou, amino, proteins, acid, acids, epitopes, scale, bcell, antigenicity, peptide, content, predicting, biochem, subcellular, location, liu, sites, aap, support, machine, zhang, structural, privacy, cookies, data, linear, vector, composition, chem, huang, publish, search, pair, chen, yang, approach, based, svm, feature, epitope, access, chapter, learning,

Topics {✒️}

month download article/chapter huang kc chou sequence-coupled vector-projection model liu kc chou virus-specific synthetic peptide pseudo-amino acid composition b-cell epitopes favor linear b-cell epitopes special sequence-coupled feature 3d zernike descriptors alternate-subsite-coupled model vectorized sequence-coupling model protein structural classes protein signal sequences gao sh shao amino acid pair full article pdf aap antigenicity scale bhasin gp raghava receiver operating characteristics machine learning classifiers support vector machine b-cell epitopes protein structural class cell epitope prediction protein subcellular location protein-coupled receptors hydrophilicity scale derived protein linear epitopes privacy choices/manage cookies dyak2mxms1knsr0%3d 10 dyak2sxisvojtrk%3d 10 subcellular location prediction guo rs hodges fast structure recognition related subjects article log machine learning approaches virus-neutralizing antibody improved feature extraction nuclear receptors based markov chain theory predicted surface residues article cite amino acids amino acids 33 beta-turn types article chen existing scales based protein power spectrum

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         description:Identification of antigenic sites on proteins is of vital importance for developing synthetic peptide vaccines, immunodiagnostic tests and antibody production. Currently, most of the prediction algorithms rely on amino acid propensity scales using a sliding window approach. These methods are oversimplified and yield poor predicted results in practice. In this paper, a novel scale, called the amino acid pair (AAP) antigenicity scale, is proposed that is based on the finding that B-cell epitopes favor particular AAPs. It is demonstrated that, using SVM (support vector machine) classifier, the AAP antigenicity scale approach has much better performance than the existing scales based on the single amino acid propensity. The AAP antigenicity scale can reflect some special sequence-coupled feature in the B-cell epitopes, which is the essence why the new approach is superior to the existing ones. It is anticipated that with the continuous increase of the known epitope data, the power of the AAP antigenicity scale approach will be further enhanced.
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      headline:Prediction of linear B-cell epitopes using amino acid pair antigenicity scale
      description:Identification of antigenic sites on proteins is of vital importance for developing synthetic peptide vaccines, immunodiagnostic tests and antibody production. Currently, most of the prediction algorithms rely on amino acid propensity scales using a sliding window approach. These methods are oversimplified and yield poor predicted results in practice. In this paper, a novel scale, called the amino acid pair (AAP) antigenicity scale, is proposed that is based on the finding that B-cell epitopes favor particular AAPs. It is demonstrated that, using SVM (support vector machine) classifier, the AAP antigenicity scale approach has much better performance than the existing scales based on the single amino acid propensity. The AAP antigenicity scale can reflect some special sequence-coupled feature in the B-cell epitopes, which is the essence why the new approach is superior to the existing ones. It is anticipated that with the continuous increase of the known epitope data, the power of the AAP antigenicity scale approach will be further enhanced.
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