The correct con ﬁguration and interaction of a TCR with a pMHC complex is essential for the T cell to become activated and initiate an i mmune response. Thus, to explore the binding of our designed peptide vaccines, we initiated TCR-pMHC peptide interaction modeling. We found the PDB ﬁles for the HLA alleles HLA-B*58:01 on the RCSB protein data bank https://www.rcsb.org/ (accessed on 23 September 2023). Using MDockPeP https://zougrouptoolkit.missour i.edu/mdockpep/ (accessed on 23 September 2023) and CABS-dock [25,26], we a ttached a top epitope to the binding grooves of the HLA allele. We created two models of the peptide-MHC binding complex (Figure 4). TCR binding models were created using the same method as Kim et al. [21]. We used TCRModel https://tcrmodel.ibbr.umd. edu/ (accessed on 23 September 2023) to create 3D models of a TCR complex binding to our peptide-MHC complexes. Subsequently, we used PyMOL version 2.5.5 to edit all of the 3D models. In Figure 4, yellow color represents HLA alleles, and red represents epitopes. The 3D models we obtained were KSFEDIHHY, a mutation of the KRAS gene, binding to the MHC Class I molecule HLA-B*58:01 as well as KTYQGSYGF, a mutation of the TP53 gene, binding to the MHC Class I molecule HLA- B*58:01. All pMHC-TCR 3D molecular model st ructures generated in this study can be found in Figure 5 and Supplementary Files S6–S9. (A) ( B) Figure 4. (A) The peptide KSFEDIHHY, a mutation of th e KRAS gene, bound to MHC Class I mol- ecule HLA-B*58:01. ( B) The peptide KTYQGSYGF, a mutation of the TP53 gene, bound to MHC Class I molecule HLA-B*58:01. Figure 4. (A) The peptide KSFEDIHHY, a mutation of the KRAS gene, bound to MHC Class I molecule HLA-B*58:01. ( B) The peptide KTYQGSYGF, a mutation of the TP53 gene, bound to MHC Class I molecule HLA-B*58:01. Diseases 2024 ,12, 149 10 of 14 Diseases 2024 , 12, x FOR PEER REVIEW 11 of 15 Figure 5. The peptides KSFEDIHHY and KTYQGSYGF boun d to HLA-B*58:01 and their respective TCR complex. 6. Discussion We developed a personalized peptide-based vaccine for two patients with pancreatic ductal adenocarcinoma (PDAC). This proc ess began with RNA sequencing (RNAseq) analysis, which enabled the identi ﬁcation of speci ﬁc genetic mutations driving the devel- opment of PDAC in the patients. Based on this analysis, we developed a personalized cancer vaccine using our previously published peptide vaccine development strategy. Our approach involved targeting 100 epitopes that were prevalent in the PDAC patient and identi ﬁed as viable candidates for peptide vaccine design. By focusing on the speci ﬁc gene targets present in each patient, we intended to improve the speci ﬁcity of the vaccine, en- suring that it e ﬀectively targeted the unique genetic alterations present in the patient’s tumor. This method not only enhances the potential e ﬃcacy of the vaccine by adapting it to the individual’s genetic landsc ape but also mini mizes potential o ﬀ-target eﬀects, thus optimizing the therapeutic outcome. The ﬁnal ﬁltered epitopes are predicted to be immunogenic and antigenic, have a high or normal binding a ﬃnity, and are non-toxic and non-allergenic. The binding a ﬃnity restriction used in this study di ﬀers from other previous in silico vaccine design method- ologies using the same NetMHC tool. Our pr evious methods of peptide vaccine design used quantitative ﬁlters on the percentile rank of the binding a ﬃnity value. However, the percentile rank compares the epitopes to a test set of data in IEDB and, therefore, is not an accurate nor absolute assessment of binding a ﬃnity necessary for this study. Using the IC50 value instead is an absolute measure of the binding a ﬃnity of the epitopes. We are also able to specify the strength of the binding a ﬃnity based on the IC 50 value, which pro- vides more qualitative measures for comparison when transitioning to murine studies. By tailoring the vaccine to each patient’s speci ﬁc genetic makeup, we expect to enhance its eﬀectiveness and improve clinical outcomes. This approach represents a signi ﬁcant step forward in the ﬁeld of immunotherapy for PDAC, o ﬀering a more targeted and personal- ized treatment option that has the potential to transform the management of this challeng- ing disease. Figure 5. The peptides KSFEDIHHY and KTYQGSYGF bound to HLA-B*58:01 and their respective TCR complex. 6. Discussion We developed a personalized peptide-based vaccine for two patients with pancre- atic ductal adenocarcinoma (PDAC). This process began with RNA sequencing (RNAseq) analysis, which enabled the identification of specific genetic mutations driving the devel- opment of PDAC in the patients. Based on this analysis, we developed a personalized cancer vaccine using our previously published peptide vaccine development strategy. Our approach involved targeting 100 epitopes that were prevalent in the PDAC patient and identified as viable candidates for peptide vaccine design. By focusing on the specific gene targets present in each patient, we intended to improve the specificity of the vaccine, ensuring that it effectively targeted the unique genetic alterations present in the patient’s tumor. This method not only enhances the potential efficacy of the vaccine by adapting it to the individual’s genetic landscape but also minimizes potential off-target effects, thus optimizing the therapeutic outcome. The final filtered epitopes are predicted to be immunogenic and antigenic, have a high or normal binding affinity, and are non-toxic and non-allergenic. The binding affinity restriction used in this study differs from other previous in silico vaccine design methodologies using the same NetMHC tool. Our previous methods of peptide vaccine design used quantitative filters on the percentile rank of the binding affinity value. However, the percentile rank compares the epitopes to a test set of data in IEDB and, therefore, is not an accurate nor absolute assessment of binding affinity necessary for this study. Using the IC 50value instead is an absolute measure of the binding affinity of the epitopes. We are also able to specify the strength of the binding affinity based on the IC 50value, which provides more qualitative measures for comparison when transitioning to murine studies. By tailoring the vaccine to each patient’s specific genetic makeup, we expect to enhance its effectiveness and improve clinical outcomes. This approach represents a significant step forward in the field of immunotherapy for PDAC, offering a more targeted and personalized treatment option that has the potential to transform the management of this challenging disease. Diseases 2024 ,12, 149 11 of 14 The top epitopes selected using our novel methodology are widely recognized in the literature as common drivers and tumor-suppressor genes in PDAC [ 9,27,28]. Addi- tionally, these specific epitopes have been identified in trials involving the sequencing of human tumor samples [ 29,30]. The consistent presence of our top epitopes in both our reference study and other clinical trials of PDAC patients serves as strong validation of our personalized cancer vaccine design methodology. Using RNA sequencing analysis by Partek Flow, along with our peptide cancer vaccine design processes, we created a peptide vaccine derived from the individual’s tumor tissue genetic data. This integrative approach not only emphasizes the relevance of our vaccine targets but also enhances the precision medicine framework by adapting the therapeutic strategy to the genetic individualities of each patient’s tumor. This could potentially lead to improved clinical outcomes by specifically targeting the molecular abnormalities driving the cancer. Previous studies on the development of peptide vaccines have primarily concen- trated on creating generalized vaccines that could be used for a large and broad popu- lation [ 15,31–33]. These generalized vaccines target a limited set of gene mutations to increase sensitivity but often at the expense of specificity. The development of effec- tive global peptide vaccines poses additional challenges. The vast global diversity of HLA alleles complicates the creation of a peptide vaccine that can effectively target a comprehensive population [ 31]. Each individual’s HLA type influences how well their immune system can recognize and respond to the peptides presented by the vaccine, making it difficult to design a universally effective vaccine. The development of person- alized peptide vaccines has historically been limited by the cost and time to produce the peptides [ 32]. However, implementing a novel design method described in this study offers a unique and innovative solution to quickly design neoantigen personalized peptide-based vaccines. Recently, with the advent of advanced sequencing technology, neoantigen peptide vaccines are becoming a more viable solution for patients [ 34]. How- ever, the design process has been complicated by a multitude of software required to design a personalized vaccine. Our methodology using Partek Flow provides a sim- ple and streamlined RNAseq analysis procedure to obtain the list of neoantigens. Our program, SCGeneID, is useful for identifying and confirming proper alignment and identification of genes from the RNAseq analysis process. Overall, our methodology employs only two tools throughout the entire design process, significantly simplifying the development of personalized cancer vaccines. This streamlined approach not only reduces the complexity and duration of vaccine design but also enhances the precision with which these vaccines can be personalized to individual genetic profiles. 7. Limitations While this study presents a promising personalized cancer vaccine strategy targeting neoantigens in pancreatic ductal adenocarcinoma (PDAC) patients, there are several limita- tions that should be acknowledged. Firstly, the pilot trial size of two patients is relatively small, which could limit the generalizability of the methodology. A larger sample size would provide more robust data and a better account for variations and accommodate the heterogeneity inherent in the genetic landscape of PDAC more effectively. The purpose of this paper was to demonstrate a successful method for designing personalized peptide cancer vaccines. However, in future outcome-oriented studies, the use of a larger sample size would be favorable. While the absence of experimental confirmation may appear as a limitation, the significance of this innovative methodological framework for personalized cancer vaccines, being the first of its kind, corroborates the importance of this work. This framework enables the efficient prioritization of the most promising personalized vaccine candidates, thus accelerating the vaccine design process, enhancing the probability of success in subsequent preclinical and clinical evaluations, and also helping to optimize re- sources by focusing on the candidates for further preclinical studies. Peptide vaccines have their weaknesses in functionality as well. If a patient’s cancer significantly downregulates MHC, the probability of a peptide binding to an MHC receptor significantly decreases. Diseases 2024 ,12, 149 12 of 14 8. Future Directions We have developed an automation of the peptide vaccine design process using web scraping and API tools [ 24,25]. Implementation of such software would further simplify the personalized cancer vaccine process. Additionally, the use of these programs would allow for the prediction of MHC class II epitopes as well. Furthermore, moving the RNAseq analysis process from a cloud-based solution using Partek Flow to a hardware process using Python or R would allow for complete automation of the personalized vaccine design process. Given such a scaled program and processes, the only limitation to the vaccine design process would be the time to sequence a patient’s tumor tissue. 9. Conclusions We developed a personalized cancer vaccine targeting specific gene mutations preva- lent among PDAC patients by implementing our novel personalized vaccine design work- flow. This study addresses the limitations of generalized vaccines specifically for pancreatic ductal adenocarcinoma (PDAC). By analyzing the genetic alterations driving PDAC in a patient’s tumor tissue, we identified 100 gene mutations as targets for our personalized vaccine strategy. The gene targets were identified and validated using our SCGeneID program, which used the chromosome number and nucleotide position data. By inte- grating SCGeneID into our workflow, we not only enhanced the precision of our gene annotations but also significantly improved the efficiency of our data analysis process. This development represents a significant step forward in the application of computational tools in personalized vaccine design, providing a robust method for accurate gene identification and the validity of complex genomic analyses. The top 50 epitopes consisted of only high-affinity binding epitopes, indicating the potential efficacy of the vaccine. The use of IC 50values as an absolute measure of binding affinity provided more accurate and quantitative comparisons. To visualize the interactions between epitopes and HLA alleles, 3D models of TCR-peptide-MHC complexes were created. The personalized cancer vaccine developed in this study may hold great promise for PDAC patients. By targeting the unique genetic alterations in each patient’s tumor, this approach offers a more specific and personalized treatment option. Further research is warranted to simplify the variant identification and epitope ranking process. Supplementary Materials: The following supporting information can be downloaded at https: //www.mdpi.com/article/10.3390/diseases12070149/s1, File S1: SCGeneID Python program; F ile S2 : Top 50 Epitopes for Patient 1; File S3: Top 100 Epitopes for Patient 1; File S4: Top 50 Epitopes for Patient 2; File S5: Top 100 Epitopes for Patient 2; File S6: 3-dimensional structure of pMHC complex in Figure 4a; File S7: 3-dimensional structure of pMHC complex in Figure 4b; File S8: 3-dimensional structure of pMHC-TCR complex in Figure 5a; File S9: 3-dimensional structure of pMHC-TCR complex in Figure 5b. Author Contributions: Conceptualization, S.D.; methodology, S.D.