MTB-ImmunogenKG: An LLM-assisted knowledge graph for antigen selection in tuberculosis vaccine research

an optimized information-extraction pipeline and integrated with a large language model (LLM) system to enable knowledge-augmented reasoning. From over 77,000 publications indexed in PubMed (as of July 2024), this graph consolidates 1.48 million sentence-level statements spanning 14 entity types. In practical applications, MTB-ImmunogenKG enables contradiction-aware antigen profiling for 3,154 proteins (representing about 77% of the total annotated proteins) and improves protective-efficacy prediction with a Matthews correlation coefficient (MCC) gain of 0.19 over sequence-based tools and 0.45 over an LLM-only baseline. By delivering a traceable synthesis of fragmented findings, MTB-ImmunogenKG facilitates the construction of antigen panels and the pairing of adjuvants, thereby streamlining the experimental design cycle for next-generation TB vaccines.