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Prediction of B cell epitopes in proteins using a novel sequence similarity-based method. | LitMetric

Prediction of B cell epitopes in proteins using a novel sequence similarity-based method.

Sci Rep

Laboratory of Immunomedicine, Department of Immunology, Faculty of Medicine, University Complutense of Madrid, Pza Ramón y Cajal, s/n, 28040, Madrid, Spain.

Published: August 2022

AI Article Synopsis

Article Abstract

Prediction of B cell epitopes that can replace the antigen for antibody production and detection is of great interest for research and the biotech industry. Here, we developed a novel BLAST-based method to predict linear B cell epitopes. To that end, we generated a BLAST-formatted database upon a dataset of 62,730 known linear B cell epitope sequences and considered as a B cell epitope any peptide sequence producing ungapped BLAST hits to this database with identity ≥ 80% and length ≥ 8. We examined B cell epitope predictions by this method in tenfold cross-validations in which we considered various types of non-B cell epitopes, including 62,730 peptide sequences with verified negative B cell assays. As a result, we obtained values of accuracy, specificity and sensitivity of 72.54 ± 0.27%, 81.59 ± 0.37% and 63.49 ± 0.43%, respectively. In an independent dataset incorporating 503 B cell epitopes, this method reached accuracy, specificity and sensitivity of 74.85%, 99.20% and 50.50%, respectively, outperforming state-of-the-art methods to predict linear B cell epitopes. We implemented this BLAST-based approach to predict B cell epitopes at http://imath.med.ucm.es/bepiblast .

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9374694PMC
http://dx.doi.org/10.1038/s41598-022-18021-1DOI Listing

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