creators_name: Ansari, Hifzur Rahman
creators_name: Raghava, G.P.S.
type: article
datestamp: 2019-09-05 11:33:35
lastmod: 2019-09-05 11:33:35
metadata_visibility: show
title: In silico models for B-cell epitope recognition and signaling.
ispublished: pub
subjects: QR
keywords: B-cell epitopes Linear Conformational Support vector machines Hidden Markov models Immunoinformatics 
note: Copyright of this article belongs to Springer.
abstract: Tremendous technological advances in peptide synthesis and modification in recent years have resolved the major limitations of peptide-based vaccines. B-cell epitopes are major components of these vaccines (besides having other biological applications). Researchers have been developing in silico or computational models for the prediction of both linear and conformational B-cell epitopes, enabling immunologists and clinicians to identify the most promising epitopes for characterization in the laboratory. Attempts are also ongoing in systems biology to delineate the signaling networks in immune cells. Here we present all possible in silico models developed thus far in these areas.
date: 2013
date_type: published
publication: Methods in molecular biology (Clifton, N.J.)
volume: 993
publisher: Springer
pagerange: 129-38
refereed: TRUE
issn: 1940-6029
official_url: https://link.springer.com/protocol/10.1007%2F978-1-62703-342-8_9
citation:   Ansari, Hifzur Rahman and Raghava, G.P.S.  (2013) In silico models for B-cell epitope recognition and signaling.  Methods in molecular biology (Clifton, N.J.), 993.  pp. 129-38.  ISSN 1940-6029