EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 348 (2026) EPJ Web Conf., 348 (2026) 01021 References
Open Access
Issue
EPJ Web Conf.
Volume 348, 2026
3rd International Conference on Innovations in Molecular Structure & Instrumental Approaches (ICMSI 2026)
Article Number 01021
Number of page(s) 15
Section Life Science
DOI https://doi.org/10.1051/epjconf/202634801021
Published online 21 January 2026
  1. Panthee, B., Gyawali, S., Panthee, P., & Techato, K. (2022). Environmental and Human Microbiome for Health. Life (Basel, Switzerland), 12(3), 456. https://doi.org/10.3390/life12030456 [Google Scholar]
  2. Shim, J. A., Ryu, J. H., Jo, Y., & Hong, C. (2023). The role of gut microbiota in T cell immunity and immune-mediated disorders. International journal of biological sciences, 19(4), 1178–1191. https://doi.org/10.7150/ijbs.79430 [Google Scholar]
  3. Bach, E. M., Ramirez, K. S., Fraser, T. D., & Wall, D. H. (2020). Soil biodiversity integrates solutions for a sustainable future. Sustainability, 12(7), 2662. https://doi.org/10.3390/su12072662 [Google Scholar]
  4. Roslund, M. I., Puhakka, R., Grönroos, M., Nurminen, N., Oikarinen, S., Gazali, A. M., Cinek, O., Kramná, L., Siter, N., Vari, H. K., Soininen, L., Parajuli, A., Rajaniemi, J., Kinnunen, T., Laitinen, O. H., Hyöty, H., Sinkkonen, A., & ADELE research group (2020). Biodiversity intervention enhances immune regulation and health-associated commensal microbiota among daycare children. Science advances, 6(42), eaba2578. https://doi.org/10.1126/sciadv.aba2578 [Google Scholar]
  5. Sun, X., Liddicoat, C., Tiunov, A., Wang, B., Zhang, Y., Lu, C., … & Zhu, Y. G. (2023). Harnessing soil biodiversity to promote human health in cities. npj Urban sustainability, 3(1), 5. https://doi.org/10.1038/s42949-023-00086-0 [Google Scholar]
  6. Pérez-Cobas, A. E., Gómez-Valero, L., & Buchrieser, C. (2020). Metagenomic approaches in microbial ecology: an update on whole-genome and marker gene sequencing analyses. Microbial genomics, 6(8), mgen000409. https://doi.org/10.1099/mgea0.000409 [Google Scholar]
  7. Purohit, H. V., & Chakraborty, J. (2025). Metagenomic approaches for studying ubiquitous yet diverse nucleoid-associated proteins in microbial communities: challenges and advances. World journal of microbiology & biotechnology, 41(10), 383. https://doi.org/10.1007/s11274-025-04564-8 [Google Scholar]
  8. Prawiningrum, A. F., Paramita, R. I., & Panigoro, S. S. (2022). Immunoinformatics Approach for Epitope-Based Vaccine Design: Key Steps for Breast Cancer Vaccine. Diagnostics (Basel, Switzerland), 12(12), 2981. https://doi.org/10.3390/diagnostics12122981 [Google Scholar]
  9. Leitào, J. H., & Rodriguez-Ortega, M. J. (2020). Omics and Bioinformatics Approaches to Identify Novel Antigens for Vaccine Investigation and Development. Vaccines, 8(4), 653. https://doi.org/10.3390/vaccines8040653 [Google Scholar]
  10. Purohit HV, Kanojia H, Pandya V, Nalla Y, Raval KY, Kapadiya KM, Kamdar JH (2023) Soil as a host to the biotic community. In: Gupta P, Shahnawaz M (eds) Soil Microbiome of the cold habitats: trends and applications. CRC, pp. 17–30. https://doi.org/10.1201/9781003354031-2 [Google Scholar]
  11. Ma, H., Cornadö, D., & Raaijmakers, J. M. (2025). The soil-plant-human gut microbiome axis into perspective. Nature Communications, 16(1), 7748. https://doi.org/10.1038/s41467-025-62989-z [Google Scholar]
  12. Blum, W. E. H., Zechmeister-Boltenstern, S., & Keiblinger, K. M. (2019). Does Soil Contribute to the Human Gut Microbiome?. Microorganisms, 7(9), 287. https://doi.org/10.3390/microorganisms7090287 [CrossRef] [PubMed] [Google Scholar]
  13. Banerjee, S., & van der Heijden, M. G. A. (2023). Soil microbiomes and one health. Nature Reviews. Microbiology, 21(1), 6–20. https://doi.org/10.1038/s41579-022-00779-w [Google Scholar]
  14. Zhang, K., Hamidian, A. H., Tubic, A., Zhang, Y., Fang, J.K.H., Wu, C., & Lam, P. K. S. (2021). Understanding plastic degradation and microplastic formation in the environment: A review. Environmental pollution (Barking, Essex: 1987), 274, 116554. https://doi.org/10.1016/j.envpol.2021.116554 [Google Scholar]
  15. Haahtela T. (2019). A biodiversity hypothesis. Allergy, 74(8), 1445–1456. https://doi.org/10.1111/all.13763 [Google Scholar]
  16. Kummola, L., Gonzalez-Rodriguez, M. I., Marnila, P., Nurminen, N., Salomaa, T., Hiihtola, L., Mäkelä, I., Laitinen, O. H., Hyöty, H., Sinkkonen, A., & Junttila, I. S. (2023). Comparison of the effect of autoclaved and non-autoclaved live soil exposure on the mouse immune system: Effect of soil exposure on the immune system. BMC immunology, 24(1), 29. https://doi.org/10.1186/s12865-023-00565-0 [Google Scholar]
  17. Dekeukeleire, M., Vandenheuvel, D., Khondee, T., Delanghe, L., Van Rillaer, T., Thys, S., Timmermans, J. P., Lebeer, S., & Spacova, I. (2025). Immunostimulatory activity of inactivated environmental Bacillus isolates and their endospores. Scientific reports, 15(1), 30604. https://doi.org/10.1038/s41598-025-12833-7 [Google Scholar]
  18. Wnuk, E., Wasko, A., Walkiewicz, A., Bartmihski, P., Bejger, R., Mielnik, L., & Bieganowski, A. (2020). The effects of humic substances on DNA isolation from soils. PeerJ, 8, e9378. https://doi.org/10.7717/peerj.9378 [Google Scholar]
  19. Bowers, R. M., Kyrpides, N. C., Stepanauskas, R., Harmon-Smith, M., Doud, D., Reddy, T. B. K., Schulz, F., Jarett, J., Rivers, A. R., Eloe-Fadrosh, E. A., Tringe, S. G., Ivanova, N. N., Copeland, A., Clum, A., Becraft, E. D., Malmstrom, R. R., Birren, B., Podar, M., Bork, P., Weinstock, G. M., … Woyke, T. (2017). Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nature biotechnology, 35(8), 725–731. https://doi.org/10.1038/nbt.3893 [Google Scholar]
  20. Hyatt, D., Chen, G. L., Locascio, P. F., Land, M. L., Larimer, F. W., & Hauser, L. J. (2010). Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics, 11, 119. https://doi.org/10.1186/1471-2105-11-119 [Google Scholar]
  21. Iqbal, H. A., Craig, J. W., & Brady, S. F. (2014). Antibacterial enzymes from the functional screening of metagenomic libraries hosted in Ralstonia metallidurans. FEMS microbiology letters, 354(1), 19–26. https://doi.org/10.1111/1574-6968.12431 [Google Scholar]
  22. Kanojia, H., Purohit, H., Joshi, M., Kamdar, J. H., & Chakraborty, J. (2024). Microplastics Accumulate Microbial Pathogens in the Terrestrial Environment. In Microplastic Pollution (pp. 351–362). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-8357-5_20 [Google Scholar]
  23. Zhu, D., Ma, J., Li, G., Rillig, M. C., & Zhu, Y. G. (2022). Soil plastispheres as hotspots of antibiotic resistance genes and potential pathogens. The ISME journal, 16(2), 521–532. https://doi.org/10.1038/s41396-021-01103-9 [Google Scholar]
  24. Zamyatina, A., & Heine, H. (2020). Lipopolysaccharide Recognition in the Crossroads of TLR4 and Caspase-4/11 Mediated Inflammatory Pathways. Frontiers in immunology, 11, 585146. https://doi.org/10.3389/fimmu.2020.585146 [Google Scholar]
  25. Fu, X., Ou, Z., & Sun, Y. (2022). Indoor microbiome and allergic diseases: From theoretical advances to prevention strategies. Eco-Environment & Health, 1(3), 133–146. https://doi.org/10.1016/j.eehl.2022.09.002 [Google Scholar]
  26. Reynisson, B., Alvarez, B., Paul, S., Peters, B., & Nielsen, M. (2020). NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic acids research, 48(W1), W449-W454. https://doi.org/10.1093/nar/gkaa379 [Google Scholar]
  27. Galanis, K. A., Nastou, K. C., Papandreou, N. C., Petichakis, G. N., Pigis, D. G., & Iconomidou, V. A. (2021). Linear B-Cell Epitope Prediction for In Silico Vaccine Design: A Performance Review of Methods Available via Command-Line Interface. International journal of molecular sciences, 22(6), 3210. https://doi.org/10.3390/ijms22063210 [Google Scholar]
  28. Doytchinova, I. A., & Flower, D. R. (2007). VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC bioinformatics, 8, 4. https://doi.org/10.1186/1471-2105-8-4 [Google Scholar]
  29. Bukhari, S. N. H., Jain, A., Haq, E., Mehbodniya, A., & Webber, J. (2022). Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review. Pathogens (Basel, Switzerland), 11(2), 146. https://doi.org/10.3390/pathogens11020146 [Google Scholar]
  30. Khalid, K., & Poh, C. L. (2023). The Promising Potential of Reverse Vaccinology-Based Next-Generation Vaccine Development over Conventional Vaccines against Antibiotic-Resistant Bacteria. Vaccines, 11(7), 1264. https://doi.org/10.3390/vaccines11071264 [Google Scholar]
  31. Ong, E., Cooke, M. F., Huffman, A., Xiang, Z., Wong, M. U., Wang, H., Seetharaman, M., Valdez, N., & He, Y. (2021). Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning. Nucleic acids research, 49(W1), W671-W678. https://doi.org/10.1093/nar/gkab279 [Google Scholar]
  32. Jaiswal, V., Chanumolu, S. K., Gupta, A., Chauhan, R. S., & Rout, C. (2013). Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions. BMC bioinformatics, 14, 211. https://doi.org/10.1186/1471-2105-14-211 [Google Scholar]
  33. Dalsass, M., Brozzi, A., Medini, D., & Rappuoli, R. (2019). Comparison of Open-Source Reverse Vaccinology Programs for Bacterial Vaccine Antigen Discovery. Frontiers in immunology, 10, 113. https://doi.org/10.3389/fimmu.2019.00113 [Google Scholar]
  34. Manvani, R., Purohit, H., Sahoo, C. R., Rajput, M., & Shah, S. (2024). Immunoinformatics: an interdisciplinary technique for designing and engineering vaccine antigen. In Reverse Vaccinology (pp. 87–99). Academic Press. https://doi.org/10.1016/B978-0-443-13395-4.00012-5 [Google Scholar]
  35. Sang, Y., Nahashon, S. N., & Webby, R. J. (2025). Microbiome-Immune Interaction and Harnessing for Next-Generation Vaccines Against Highly Pathogenic Avian Influenza in Poultry. Vaccines, 13(8), 837. https://doi.org/10.3390/vaccines13080837 [Google Scholar]
  36. Villanueva-Flores, F., Sanchez-Villamil, J. I., & Garcia-Atutxa, I. (2025). AI-driven epitope prediction: a system review, comparative analysis, and practical guide for vaccine development. NPJ vaccines, 10(1), 207. https://doi.org/10.1038/s41541-025-01258-y [Google Scholar]
  37. Spiga, O., Visibelli, A., Pettini, F., Roncaglia, B., & Santucci, A. (2025). SHASI-ML: a machine learning-based approach for immunogenicity prediction in Salmonella vaccine development. Frontiers in cellular and infection microbiology, 15, 1536156. https://doi.org/10.3389/fcimb.2025.1536156 [Google Scholar]
  38. Vita, R., Mahajan, S., Overton, J. A., Dhanda, S. K., Martini, S., Cantrell, J. R., Wheeler, D. K., Sette, A., & Peters, B. (2019). The Immune Epitope Database (IEDB): 2018 update. Nucleic acids research, 47(D1), D339-D343. https://doi.org/10.1093/nar/gky1006 [Google Scholar]
  39. O'Donnell, T. J., Rubinsteyn, A., & Laserson, U. (2020). MHCflurry 2.0: Improved Pan-Allele Prediction of MHC Class I-Presented Peptides by Incorporating Antigen Processing. Cell systems, 11(1), 42–48.e7. https://doi.org/10.1016Zj.cels.2020.06.010 [Google Scholar]
  40. Jespersen, M. C., Peters, B., Nielsen, M., & Marcatili, P. (2017). BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic acids research, 45(W1), W24-W29. https://doi.org/10.1093/nar/gkx346 [Google Scholar]
  41. Zaharieva, N., Dimitrov, I., Flower, D. R., & Doytchinova, I. (2019). VaxiJen Dataset of Bacterial Immunogens: An Update. Current computer-aided drug design, 15(5), 398–400. https://doi.org/10.2174/1573409915666190318121838 [Google Scholar]
  42. Blin, K., Shaw, S., Kloosterman, A. M., Charlop-Powers, Z., Van Wezel, G. P., Medema, M. H., & Weber, T. (2021). antiSMASH 6.0: improving cluster detection and comparison capabilities. Nucleic acids research, 49(W1), W29-W35. https://doi.org/10.1093/nar/gkab335 [Google Scholar]
  43. D'Mello, A., Ahearn, C. P., Murphy, T. F., & Tettelin, H. (2019). ReVac: a reverse vaccinology computational pipeline for prioritization of prokaryotic protein vaccine candidates. BMC genomics, 20(1), 981. https://doi.org/10.1186/s12864-019-6195-y [Google Scholar]
  44. Paul, S., Sidney, J., Sette, A., & Peters, B. (2016). TepiTool: a pipeline for computational prediction of T cell epitope candidates. Current protocols in immunology, 114(1), 18–19. https://doi.org/10.1002/cpim.12 [Google Scholar]
  45. Ong, E., Wang, H., Wong, M. U., Seetharaman, M., Valdez, N., & He, Y. (2020). Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens. Bioinformatics (Oxford, England), 36(10), 3185–3191. https://doi.org/10.1093/bioinformatics/btaa119 [Google Scholar]
  46. Zeng, L., Qian, Y., Cui, X., Zhao, J., Ning, Z., Cha, J., … & Jian, Z. (2025). Immunomodulatory role of gut microbial metabolites: mechanistic insights and therapeutic frontiers. Frontiers in Microbiology, 16, 1675065. https://doi.org/10.3389/fmicb.2025.1675065 [Google Scholar]
  47. Akira, S., Uematsu, S., & Takeuchi, O. (2006). Pathogen recognition and innate immunity. Cell, 124(4), 783–801. https://doi.org/10.1016/j.cell.2006.02.015 [Google Scholar]
  48. Yoon, S. I., Kurnasov, O., Natarajan, V., Hong, M., Gudkov, A. V., Osterman, A. L., & Wilson, I. A. (2012). Structural basis of TLR5-flagellin recognition and signaling. Science, 335(6070), 859–864. https://doi.org/10.1126/science.1215584 [Google Scholar]
  49. Sehgal, S. N. (2003, May). Sirolimus: its discovery, biological properties, and mechanism of action. In Transplantation proceedings (Vol. 35, no. 3, pp. S7-S14). Elsevier. https://doi.org/10.1016/S0041-1345(03)00211-2 [Google Scholar]
  50. Gupta, S., Mittal, P., Madhu, M. K., & Sharma, V. K. (2017). IL17eScan: a tool for the identification of peptides inducing IL-17 response. Frontiers in immunology, 8, 1430. https://doi.org/10.3389/fimmu.2017.01430 [Google Scholar]
  51. Leitào, J. H., & Rodriguez-Ortega, M. J. (2020). Omics and bioinformatics approaches to identify novel antigens for vaccine investigation and development. Vaccines, 8(4), 653. https://doi.org/10.3390/vaccines8040653 [Google Scholar]
  52. Shao, G., Zhu, X., Hua, R., Lu, Z., Chen, Y., Yang, A., & Yang, G. (2025). Cocktail vaccine induces immunoprotection and modulates the fecal microbiota in dogs against Echinococcus granulosus infection. npj Vaccines, 10(1), 214. https://doi.org/10.1038/s41541-025-01275-x [Google Scholar]
  53. Razzak, A., Ahmed, F., & Mahmud, M. T. (2025). Development of a multi-epitope vaccine against Helicobacter pylori using a novel saRNA technology through an immunoinformatics approach. Scientific Reports, 15(1), 33753. https://doi.org/10.1038/s41598-025-99512-9 [Google Scholar]
  54. Shen, J., McFarland, A. G., Blaustein, R. A., Rose, L. J., Perry-Dow, K. A., Moghadam, A. A., … & Hartmann, E. M. (2022). An improved workflow for accurate and robust healthcare environmental surveillance using metagenomics. Microbiome, 10(1), 206. https://doi.org/10.1186/s40168-022-01412-x [Google Scholar]
  55. Takeuchi, T., Nakanishi, Y., & Ohno, H. (2024). Microbial metabolites and gut immunology. Annual review of immunology, 42(1), 153–178. https://doi.org/10.1146/annurev-immunol-090222-102035 [Google Scholar]
  56. Fernández-Tomé, S., Marin, A. C., Ortega Moreno, L., Baldan-Martin, M., Mora-Gutierrez, I., Lanas-Gimeno, A., … & Bernardo, D. (2019). Immunomodulatory Effect of Gut Microbiota-Derived Bioactive Peptides on Human Immune System from Healthy Controls and Patients with Inflammatory Bowel Disease. https://doi.org/10.3390/nu11112605 [Google Scholar]
  57. Rehmani, M. B. I., Arshad, F., Khan, M. U., Ejaz, H., Nishan, U., Alotaibi, A., Ullah, R., Chen, K., Ojha, S. C., & Shah, M. (2025). Computational design of an mRNA vaccine targeting antifungal-resistant Lomentospora prolificans. Scientific reports, 15(1), 34157. https://doi.org/10.1038/s41598-025-14907-y [Google Scholar]
  58. Nguyen, M. N., Krutz, N. L., Limviphuvadh, V., Lopata, A. L., Gerberick, G. F., & Maurer-Stroh, S. (2022). AllerCatPro 2.0: a web server for predicting protein allergenicity potential. Nucleic acids research, 50(W1), W36-W43. https://doi.org/10.1093/nar/gkac446 [Google Scholar]
  59. Liao, W. W., & Arthur, J. W. (2011). Predicting peptide binding to Major Histocompatibility Complex molecules. Autoimmunity reviews, 10(8), 469–473. https://doi.org/10.1016/j.autrev.2011.02.003 [Google Scholar]
  60. Kamdar, J. H., Jeba Praba, J., & Georrge, J. J. (2020). Artificial intelligence in medical diagnosis: methods, algorithms and applications. In Machine Learning with Health Care Perspective: Machine Learning and Healthcare (pp. 27–37). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-40850-32 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.