How are B-cell and T-cell epitopes predicted computationally?

Epitope prediction enables vaccine design and immunotherapy development. B-cell epitopes: 1) Linear - predict exposed, flexible, hydrophilic regions using propensity scales (Parker, Kolaskar-Tongaonkar) or machine learning (BepiPred, ABCpred). 2) Conformational (majority) - require 3D structure; predict surface accessibility, protrusion, electrostatics; tools: DiscoTope, ElliPro. T-cell epitopes: 1) MHC Class I (CD8+) - predict peptide-MHC binding using position-specific scoring matrices or neural networks (NetMHCpan, MHCflurry); allele-specific models. 2) MHC Class II (CD4+) - more challenging due to open-ended binding groove; NetMHCIIpan. 3) Processing prediction - proteasomal cleavage, TAP transport. 4) Immunogenicity - not all binders are immunogenic; consider T-cell recognition, self-tolerance. Challenges: polymorphic MHC molecules, rare alleles, validation requirements.