Parapred: antibody paratope prediction using convolutional and recurrent neural networks | Zendy

Edgar Liberis | Zendy; Petar Veličković | Zendy; Pietro Sormanni | Zendy; Michele Vendruscolo | Zendy; Píetro Lió | Zendy

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Parapred: antibody paratope prediction using convolutional and recurrent neural networks

Author(s) -

Edgar Liberis,

Petar Veličković,

Pietro Sormanni,

Michele Vendruscolo,

Píetro Lió

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty305

Subject(s) - paratope , hypervariable region , computer science , leverage (statistics) , convolutional neural network , artificial intelligence , probabilistic logic , amino acid residue , computational biology , peptide sequence , machine learning , antibody , biology , biochemistry , epitope , genetics , gene

Antibodies play essential roles in the immune system of vertebrates and are powerful tools in research and diagnostics. While hypervariable regions of antibodies, which are responsible for binding, can be readily identified from their amino acid sequence, it remains challenging to accurately pinpoint which amino acids will be in contact with the antigen (the paratope).

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