Computational B-cell epitope identification and production of neutralizing murine antibodies against Atroxlysin-I.

ABSTRACT: Epitope identification is essential for developing effective antibodies that can detect and neutralize bioactive proteins. Computational prediction is a valuable and time-saving alternative for experimental identification. Current computational methods for epitope prediction are underused and undervalued due to their high false positive rate. In this work, we targeted common properties of linear B-cell epitopes identified in an individual protein class (metalloendopeptidases) and introduced an alternative method to reduce the false positive rate and increase accuracy, proposing to restrict predictive models to a single specific protein class. For this purpose, curated epitope sequences from metalloendopeptidases were transformed into frame-shifted Kmers (3 to 15 amino acid residues long).

SUBMITTER: Kozlova EEG 

PROVIDER: S-EPMC6175905 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature