How is computational immunogenicity prediction used in therapeutic protein development?

In silico immunogenicity tools inform protein design and risk assessment: 1) T-cell epitope prediction - identify peptide sequences binding MHC Class II; algorithms (NetMHCIIpan, IEDB) trained on binding data; predict CD4+ T-helper epitopes critical for antibody responses. 2) Deimmunization - identify and mutate immunogenic sequences while maintaining function; iterate design to reduce epitope content. 3) Population coverage - assess epitope binding across HLA allele frequencies in different populations. 4) Aggregation prediction - aggregates enhance immunogenicity; predict aggregation-prone regions (AGGRESCAN, Zyggregator). 5) Comparison to human sequences - identify non-human sequences that may be immunogenic. 6) Regulatory peptides (Tregitopes) - include sequences that may induce tolerance. Limitations: prediction accuracy ~70-80%; MHC binding necessary but not sufficient for immunogenicity; does not capture B-cell epitopes well; post-translational modifications not fully addressed. Best used as part of integrated assessment with ex vivo assays (DC-T cell assays, PBMC stimulation) and clinical immunogenicity data.