  Accurate in-silico prediction of conformational B-cell epitopes would lead to
major improvements in disease diagnostics, drug design and vaccine development.
A variety of computational methods, mainly based on machine learning
approaches, have been developed in the last decades to tackle this challenging
problem. Here, we rigorously benchmarked nine state-of-the-art conformational
B-cell epitope prediction webservers, including generic and antibody-specific
methods, on a dataset of over 250 antibody-antigen structures. The results of
our assessment and statistical analyses show that all the methods achieve very
low performances, and some do not perform better than randomly generated
patches of surface residues. In addition, we also found that commonly used
consensus strategies that combine the results from multiple webservers are at
best only marginally better than random. Finally, we applied all the predictors
to the SARS-CoV-2 spike protein as an independent case study, and showed that
they perform poorly in general, which largely recapitulates our benchmarking
conclusions. We hope that these results will lead to greater caution when using
these tools until the biases and issues that limit current methods have been
addressed, promote the use of state-of-the-art evaluation methodologies in
future publications, and suggest new strategies to improve the performance of
conformational B-cell epitope prediction methods.

Biomolecules