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

• Published in: Scientific reports Vol. 8; no. 1; pp. 14904 - 13
• Main Authors: Kozlova, Edgar Ernesto Gonzalez, Cerf, Loïc, Schneider, Francisco Santos, Viart, Benjamin Thomas, NGuyen, Christophe, Steiner, Bethina Trevisol, de Almeida Lima, Sabrina, Molina, Franck, Duarte, Clara Guerra, Felicori, Liza, Chávez-Olórtegui, Carlos, Machado-de-Ávila, Ricardo Andrez
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 08.10.2018; Nature Publishing Group
• Subjects: 631/114/2164; 631/250/24; 64/60; 82/1; 82/75; 82/83; Amino acids; Animal models; Antibodies; Computer applications; Datasets; Decision trees; Epitope mapping; Humanities and Social Sciences; Hypotheses; Identification; Immunization; Life Sciences; Lymphocytes B; Methods; Mimicry; multidisciplinary; Proteins; Science; Science (multidisciplinary); Neutralizing Antibody Production; Decision Tree Model; Metalloendopeptidase; Epitope Identification; Epitope Prediction
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-018-33298-x

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). These Kmers were decomposed into a matrix of biochemical attributes and used to train a decision tree classifier. The resulting prediction model showed a lower false positive rate and greater area under the curve when compared to state-of-the-art methods. Our predictions were used for synthesizing peptides mimicking the predicted epitopes for immunization of mice. A predicted linear epitope that was previously undetected by an experimental immunoassay was able to induce neutralizing-antibody production in mice. Therefore, we present an improved prediction alternative and show that computationally identified epitopes can go undetected during experimental mapping.