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 accurate 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 predictors 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.

Kosmas A. Galanis has a BSc in Biology and has performed his undergrad thesis in Bioinformatics. He is interested in the development of computational methods for protein function prediction. Katerina C. Nastou is a Biologist with a PhD in Bioinformatics. Her research focuses on the study of biological networks, the computational prediction of protein function and biological database development. Nikos C. Papandreou has a PhD in Biophysics and works as Special Laboratory Teaching Staff in "

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Ligand-Gated Ion Channels (LGICs) are one of the largest groups of transmembrane proteins. Due to their major role in synaptic transmission, both in the nervous system and the somatic neuromuscular junction, LGICs present attractive therapeutic targets.During the last few years several computational methods for the detection of LGICs have been developed. These methods are based on machine learning approaches utilizing features extracted solely from amino acid composition. However, special topological characteristics of these proteins have not been utilized to date, which results in weaknesses regarding the correct class categorization of predicted proteins. Here we report the development of LiGIoNs, a profile Hidden Markov Model (pHMM) method for the prediction and ligand-based classification of LGICs, utilizing their special topological characteristics. The method consists of a library of 35 pHMMs, built from the alignment of transmembrane segments of representative LGIC sequences. In addition,14 Pfam pHMMs are used to further annotate and correctly classify unknown protein sequences into one of the 10 LGIC subfamilies. Evaluation of the method showed that it outperforms existent methods in the detection of LGICs. On top of that LiGIoNs is the only currently available method that classifies LGICs into subfamilies.The method is available online at http://bioinformatics.biol.uoa.gr/ligions/.