TEpiNom: A computational framework integrating population data to prioritize Plasmodium falciparum T cell epitopes

Developing a highly effective malaria vaccine remains challenging due to Plasmodium falciparum's antigenic diversity and human leukocyte antigen (HLA) polymorphisms, which complicate antigen selection and limit immune protection. The first recommended malaria vaccine, RTS,S, provides partial, allele-specific protection with waning immunity, and recently developed R21 vaccine will likely encounter the same hurdles. To address these challenges, we developed a computational decision-support framework that integrates P. falciparum sequence diversity, predicted T cell epitope-HLA binding, and population-specific HLA allele frequencies from sub-Saharan Africa to prioritize conserved T cell epitopes for experimental evaluation. We analyzed 42 P. falciparum proteins, previously identified as vaccine candidates, generated consensus sequences from 18 African countries, and incorporated HLA allele frequencies from 24 sub-Saharan populations. CD8+ and CD4+ T cell epitopes were predicted using NetMHCpan-4.1 and NetMHCIIpan-4.1. Our tool, T cell Epitope Nomination (TEpiNom), applies integer linear programming to prioritize epitopes based on sequence conservation (>95%), predicted HLA binding affinity (median rank <10%), and breadth of HLA locus coverage, while minimizing redundancy across antigen targets. Using this framework, we identified 2265 MHC I and 1992 MHC II conserved epitopes spanning pre-erythrocytic, erythrocytic, and sexual stage proteins. Prioritized MHC I epitopes from pre-erythrocytic antigens FabZ, FabG, p36, and PKG achieved 100% predicted inter-locus MHC I coverage, and MHC II epitopes from pre-erythrocytic, erythrocytic, or sexual antigens provided 100% coverage for a given parasite life stage. In parallel, our search for epitope-dense regions identified short, conserved protein segments across all parasite life stages that independently provided complete predicted inter-locus coverage, highlighting compact targets with high HLA-promiscuous potential. Together, this study presents TEpiNom for the systematic prioritization of T cell epitopes and epitope-dense regions, to streamline preclinical malaria vaccine development by refining computational predictions into experimentally tractable candidates. The framework is adaptable for vaccine development against other diverse and evasive pathogens.