TY - JOUR
T1 - Linear B-cell epitope prediction for in silico vaccine design
T2 - A performance review of methods available via command-line interface
AU - Galanis, Kosmas A.
AU - Nastou, Katerina C.
AU - Papandreou, Nikos C.
AU - Petichakis, Georgios N.
AU - Pigis, Diomidis G.
AU - Iconomidou, Vassiliki A.
PY - 2021
Y1 - 2021
N2 - Linear B-cell epitope prediction research has received a steadily growing interest ever since the first method was developed in 1981. B-cell epitope identification with the help of an ac-curate prediction method can lead to an overall faster and cheaper vaccine design process, a crucial necessity in the COVID-19 era. Consequently, several B-cell epitope prediction methods have been developed over the past few decades, but without significant success. In this study, we review the current performance and methodology of some of the most widely used linear B-cell epitope pre-dictors which are available via a command-line interface, namely, BcePred, BepiPred, ABCpred, COBEpro, SVMTriP, LBtope, and LBEEP. Additionally, we attempted to remedy performance issues of the individual methods by developing a consensus classifier, which combines the separate predictions of these methods into a single output, accelerating the epitope-based vaccine design. While the method comparison was performed with some necessary caveats and individual methods might perform much better for specialized datasets, we hope that this update in performance can aid researchers towards the choice of a predictor, for the development of biomedical applications such as designed vaccines, diagnostic kits, immunotherapeutics, immunodiagnostic tests, antibody production, and disease diagnosis and therapy.
AB - Linear B-cell epitope prediction research has received a steadily growing interest ever since the first method was developed in 1981. B-cell epitope identification with the help of an ac-curate prediction method can lead to an overall faster and cheaper vaccine design process, a crucial necessity in the COVID-19 era. Consequently, several B-cell epitope prediction methods have been developed over the past few decades, but without significant success. In this study, we review the current performance and methodology of some of the most widely used linear B-cell epitope pre-dictors which are available via a command-line interface, namely, BcePred, BepiPred, ABCpred, COBEpro, SVMTriP, LBtope, and LBEEP. Additionally, we attempted to remedy performance issues of the individual methods by developing a consensus classifier, which combines the separate predictions of these methods into a single output, accelerating the epitope-based vaccine design. While the method comparison was performed with some necessary caveats and individual methods might perform much better for specialized datasets, we hope that this update in performance can aid researchers towards the choice of a predictor, for the development of biomedical applications such as designed vaccines, diagnostic kits, immunotherapeutics, immunodiagnostic tests, antibody production, and disease diagnosis and therapy.
KW - B-cell epitope
KW - Consensus prediction method
KW - Immunotherapy
KW - Linear epitope
KW - Vaccine design
U2 - 10.3390/ijms22063210
DO - 10.3390/ijms22063210
M3 - Review
C2 - 33809918
AN - SCOPUS:85102872113
VL - 22
JO - International Journal of Molecular Sciences (CD-ROM)
JF - International Journal of Molecular Sciences (CD-ROM)
SN - 1424-6783
IS - 6
M1 - 3210
ER -