Designing a multi-neoantigen vaccine for melanoma: Integrating immunoinformatics and biophysics methods

Cancer usually evolves through the accumulation of several genetic alterations. In this context, somatic mutations create tumor-specific neoepitopes, termed neoantigens. These neoantigens are recognized by T cells as non-self, rendering them prime candidates for cancer vaccine design. Such vaccines train the human defense system to identify and eliminate cancer cells effectively Therefore, neoantigen-based vaccines can be a viable strategy for cancer immunotherapy. Their distinctive capacity to trigger a specific immune response against cancer cells highlights their importance as a promising cancer immunotherapy approach. The objective of the current study is to use various computer-aided design tools to hypothesize a multi-neoepitope vaccine construct (MNVC) to target melanoma. In building this multi-neoantigen-based vaccine, we used experimentally verified neoantigens from the cancer epitope database and analytical resources (CEDAR), ensuring the relevance of our approach. A collection of 700 neoantigens from the CEDAR database was subjected to immunoinformatics analysis, shortlisting them to 08 neoantigens. These were linked together using GPGPG linkers to create an MNVC, subsequently conjugated to a β-defensin adjuvant through an EAAAK linker to enhance immune response. The construct was predicted to be highly antigenic, with an antigenic score of 0.8335. Molecular docking revealed binding affinity with immune receptors such as MHC-I, MHC-II, and TLR-9 with estimated energy scores of -1045.5, -1517.9, and -1020.1 kcal/mol, respectively. This study suggestes that the designed vaccine candidate might exhibit potential as a treatment for melanoma cancer. Further experimental testing is essential to confirm its effectiveness and safety in elicting an immune response.