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

Autor: Kozlova EEG; Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, 6627, Belo Horizonte, Brazil. eegonzalezk@gmail.com., Cerf L; Departamento de Ciências da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil., Schneider FS; Sys2Diag UMR 9005 CNRS/ALCEDIAG, Complex System Modeling and Engineering for Diagnosis, Montpellier, France., Viart BT; UMR 8030, CNRS, Université Évry-Val-d'Essonne, CEA, Institut de Génomique - Genoscope, Laboratoire d'Analyses Bioinformatiques pour la Génomique et le Métabolisme, F-91000, Évry, France., NGuyen C; Sys2Diag UMR 9005 CNRS/ALCEDIAG, Complex System Modeling and Engineering for Diagnosis, Montpellier, France., Steiner BT; Laboratório de Biologia Celular e Molecular, Programa de Pós-graduação em Ciências da Saúde, Universidade do Extremo Sul Catarinense, Criciúma, Brazil., de Almeida Lima S; Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, 6627, Belo Horizonte, Brazil., Molina F; Sys2Diag UMR 9005 CNRS/ALCEDIAG, Complex System Modeling and Engineering for Diagnosis, Montpellier, France., Duarte CG; Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, 6627, Belo Horizonte, Brazil., Felicori L; Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, 6627, Belo Horizonte, Brazil., Chávez-Olórtegui C; Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, 6627, Belo Horizonte, Brazil., Machado-de-Ávila RA; Laboratório de Biologia Celular e Molecular, Programa de Pós-graduação em Ciências da Saúde, Universidade do Extremo Sul Catarinense, Criciúma, Brazil. r_andrez@yahoo.com.br.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2018 Oct 08; Vol. 8 (1), pp. 14904. Date of Electronic Publication: 2018 Oct 08.
DOI: 10.1038/s41598-018-33298-x
Abstrakt: 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.
Databáze: MEDLINE
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