KaydedilenlerDerginizi ekleyin

Dergiler / Black Sea Journal of Engineering and Science / 2026 / Cilt: 9 Sayı: 2

A Systematic In-Silico Screening of Bacterial Αlpha Galactosidases: Integrating Structural, Functional, and Immunoinformatic Analyses to Identify Potential Therapeutic Candidates

Ozan Kılıçkaya(Afyonkarahisar Health Sciences University, Faculty of Pharmacy, Deparment of Pharmaceutical Biotechnology)0000-0003-2710-2149
Yunus Ensari(Kafkas University, Faculty of Engineering/Architecture, Department of Bioengineering)0000-0002-4757-4197
Sayfa
742–758
Yayın Tarihi
2026-03-15
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

This study identifies specific bacterial α-Gal homologs that combine high substrate affinity with low immunogenicity potential as promising candidates for further experimental validation as next-generation biotechnological and novel bacterial homologs for Fabry disease.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:This study aims to identify novel enzyme candidates from bacterial sources to enhance the therapeutic potential of human alpha-galactosidase A (α-Gal).
📊
Temel Bulgular & Veri:In this context, a BLASTp search using human α-Gal as a reference identified 100 potential bacterial homologs.
💡
Sonuç & Katkı:This study identifies specific bacterial α-Gal homologs that combine high substrate affinity with low immunogenicity potential as promising candidates for further experimental validation as next-generation biotechnological and novel bacterial homologs for Fabry disease.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alpha-galactosidase · Fabry disease · Bioinformatics

Özet

This study aims to identify novel enzyme candidates from bacterial sources to enhance the therapeutic potential of human alpha-galactosidase A (α-Gal). The limitations of current enzyme replacement therapies for Fabry disease, such as immunogenicity, necessitate the search for alternative homologs with superior properties. In this context, a BLASTp search using human α-Gal as a reference identified 100 potential bacterial homologs. The three-dimensional structural models of these homologs were subjected to a rigorous quality control process using the SAVES server, and candidates with inadequate structural integrity were eliminated. The immunogenic potential of the selected candidates was assessed by predicting B-cell epitopes via the ElliProt server. For functional analysis, molecular docking simulations were performed with the natural substrate, globotriaosylceramide (Gb3), and the artificial substrate, p-nitrophenyl α-galactopyranoside (pNP-Gal). The results highlighted proteins such as A0A1M5FVV3 and Q5LFG6, which showed the highest binding affinity for the Gb3 substrate, and proteins like R6DB23 and R5RG66, which exhibited the highest affinity for the pNP-Gal substrate. Furthermore, the interactions of conserved Aspartate residues, which play a key role in substrate binding and are critical for catalytic activity, were confirmed. This study identifies specific bacterial α-Gal homologs that combine high substrate affinity with low immunogenicity potential as promising candidates for further experimental validation as next-generation biotechnological and novel bacterial homologs for Fabry disease.

Abstract

This study aims to identify novel enzyme candidates from bacterial sources to enhance the therapeutic potential of human alpha-galactosidase A (α-Gal). The limitations of current enzyme replacement therapies for Fabry disease, such as immunogenicity, necessitate the search for alternative homologs with superior properties. In this context, a BLASTp search using human α-Gal as a reference identified 100 potential bacterial homologs. The three-dimensional structural models of these homologs were subjected to a rigorous quality control process using the SAVES server, and candidates with inadequate structural integrity were eliminated. The immunogenic potential of the selected candidates was assessed by predicting B-cell epitopes via the ElliProt server. For functional analysis, molecular docking simulations were performed with the natural substrate, globotriaosylceramide (Gb3), and the artificial substrate, p-nitrophenyl α-galactopyranoside (pNP-Gal). The results highlighted proteins such as A0A1M5FVV3 and Q5LFG6, which showed the highest binding affinity for the Gb3 substrate, and proteins like R6DB23 and R5RG66, which exhibited the highest affinity for the pNP-Gal substrate. Furthermore, the interactions of conserved Aspartate residues, which play a key role in substrate binding and are critical for catalytic activity, were confirmed. This study identifies specific bacterial α-Gal homologs that combine high substrate affinity with low immunogenicity potential as promising candidates for further experimental validation as next-generation biotechnological and novel bacterial homologs for Fabry disease.

🔖

Bu Makaleye Atıf Yap

Kılıçkaya, O., Ensari, Y. (2026). A Systematic In-Silico Screening of Bacterial Αlpha Galactosidases: Integrating Structural, Functional, and Immunoinformatic Analyses to Identify Potential Therapeutic Candidates. Black Sea Journal of Engineering and Science, Cilt: 9 Sayı: 2, 742–758. https://doi.org/10.34248/bsengineering.1779567

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (31)

  1. Altschul, S. F., Madden, T. L., Schaffer, A. A., Zhang, J., Zhang, Z., Miller, W., & Lipman, D. J. (1997). Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Research, 25(17), 3389–3402.
  2. Anisha, G. S. (2023). Biopharmaceutical applications of α-galactosidases. Biotechnology and Applied Biochemistry, 70(1), 257–267.
  3. Arends, M., Biegstraaten, M., Wanner, C., Sirrs, S., Mehta, A., Elliott, P. M., Oder, D., Watkinson, O. T., Bichet, D. G., Khan, A., Iwanochko, M., Vaz, F. M., van Kuilenburg, A. B. P., West, M. L., Hughes, D. A., & Hollak, C. E. M. (2018). Agalsidase alfa versus agalsidase beta for the treatment of Fabry disease: An international cohort study. Journal of Medical Genetics, 55(5), 351–358.
  4. Azevedo, O., Cordeiro, F., Gago, M. F., Miltenberger‐Miltenyi, G., Ferreira, C., Sousa, N., & Cunha, D. (2021). Fabry disease and the heart: A comprehensive review. International Journal of Molecular Sciences, 22(9), 4434.
  5. Benkert, P., Künzli, M., & Schwede, T. (2009). QMEAN server for protein model quality estimation. Nucleic Acids Research, 37(suppl_2), W510–W514.
  6. Benkert, P., Tosatto, S. C. E., & Schomburg, D. (2008). QMEAN: A comprehensive scoring function for model quality assessment. Proteins: Structure, Function, and Bioinformatics, 71(1), 261–277.
  7. Borzova, N. V., & Varbanets, L. D. (2024). Distribution, properties, and practical significance of α-galactosidase. Mikrobiolohichnyi Zhurnal, 86(1), 90–113.
  8. Cervera-Tison, M., Tailford, L. E., Fuell, C., Bruel, L., Sulzenbacher, G., Henrissat, B., Berrin, J. G., Fons, M., Giardina, T., & Juge, N. (2012). Functional analysis of family GH36 α-galactosidases from Ruminococcus gnavus E1: Insights into the metabolism of a plant oligosaccharide by a human gut symbiont. Applied and Environmental Microbiology, 78(21), 7720–7732.
  9. Forster, S. C., Kumar, N., Anonye, B. O., Almeida, A., Viciani, E., Stares, M. D., Dunn, M., Mkandawire, T. T., Zhu, A., Shao, Y., Pike, L. J., Louie, T., Browne, H. P., Mitchell, A. L., Neville, B. A., Finn, R. D., & Lawley, T. D. (2019). A human gut bacterial genome and culture collection for improved metagenomic analyses. Nature Biotechnology, 37(2), 186–192.
  10. Gaillard, T. (2018). Evaluation of AutoDock and AutoDock Vina on the CASF-2013 benchmark. Journal of Chemical Information and Modeling, 58(8), 1697–1706.
  11. Garman, S. C. (2007). Structure–function relationships in α‐galactosidase A. Acta Paediatrica, 96, 6–16.
  12. Gasteiger, E., Hoogland, C., Gattiker, A., Duvaud, S. E., Wilkins, M. R., Appel, R. D., & Bairoch, A. (2005). Protein identification and analysis tools on the ExPASy server. In The proteomics protocols handbook (pp. 571–607). Humana Press.
  13. Gordo, I. (2019). Evolutionary change in the human gut microbiome: From a static to a dynamic view. PLoS Biology, 17(1), e3000155.
  14. Høi, M. H., Gade, F. S., Johansen, J. M., Würtzen, C., Winther, O., Nielsen, M., & Marcatili, P. (2024). DiscoTope-3.0: Improved B-cell epitope prediction using inverse folding latent representations. Frontiers in Immunology, 15, 1322712.
  15. Hon, J., Marusiak, M., Martinek, T., Kunka, A., Zendulka, J., Bednar, D., & Damborsky, J. (2021). SoluProt: Prediction of soluble protein expression in Escherichia coli. Bioinformatics, 37(1), 23–28.
  16. Ju, L. K., Loman, A. A., & Mahfuzul Islam, S. M. (2019). α-Galactosidase and its applications in food processing. In L. Melton, F. Shahidi, & P. Varelis (Eds.), Encyclopedia of food chemistry (pp. 124–128). Academic Press.
  17. Katrolia, P., Rajashekhara, E., Yan, Q., & Jiang, Z. (2014). Biotechnological potential of microbial α-galactosidases. Critical Reviews in Biotechnology, 34(4), 307–317.
  18. Kumart, S., Stecher, G., Suleski, M., Sanderford, M., Sharma, S., & Tamura, K. (2024). MEGA12: Molecular Evolutionary Genetic Analysis Version 12 for Adaptive and Green Computing. Molecular Biology and Evolution, 41(5), msae086.
  19. Lafond, M., Tauzin, A. S., Bruel, L., Laville, E., Lombard, V., Esque, J., André, I., Vidal, N., Pompeo, F., Quinson, N., Perrier, J., Fons, M., Potocki-Veronese, G., & Giardina, T. (2020). α-Galactosidase and sucrose-kinase relationships in a bi-functional AgaSK enzyme produced by the human gut symbiont Ruminococcus gnavus E1. Frontiers in Microbiology, 11, 579521.
  20. Land, H., & Humble, M. S. (2018). YASARA: A tool to obtain structural guidance in biocatalytic investigations. Methods in Molecular Biology, 1685, 43–67.
  21. Laskowski, R. R., MacArthur, M. W., Moss, D. S., & Thornton, J. M. (1993). PROCHECK: A program to check the stereochemical quality of protein structures. Journal of Applied Crystallography, 26(2), 283–291.
  22. Lenders, M., & Brand, E. (2021). Fabry disease: The current treatment landscape. Drugs, 81(6), 635–645.
  23. Lenders, M., Menke, E. R., & Brand, E. (2025). Progress and challenges in the treatment of Fabry disease. BioDrugs, 39(4), 517–535.
  24. Lidove, O., West, M. L., Pintos-Morell, G., Reisin, R., Nicholls, K., Figuera, L. E., Parini, R., Carvalho, L. R., Kampmann, C., Pastores, G. M., & Mehta, A. (2010). Effects of enzyme replacement therapy in Fabry disease—A comprehensive review of the medical literature. Genetics in Medicine, 12(11), 668–679.
  25. Modrego, A., Amaranto, M., Godino, A., Mendoza, R., Barra, J. L., & Corchero, J. L. (2021). Human α-Galactosidase A mutants: Priceless tools to develop novel therapies for Fabry disease. International Journal of Molecular Sciences, 22(12), 6518.
  26. Ponomarenko, J., Bui, H. H., Li, W., Fusseder, N., Bourne, P. E., Sette, A., & Peters, B. (2008). ElliPro: A new structure-based tool for the prediction of antibody epitopes. BMC Bioinformatics, 9, 514.
  27. Shi, Z. D., Motabar, O., Goldin, E., Liu, K., Southall, N., Sidransky, E., Austin, C. P., Griffiths, G. L., & Zheng, W. (2009). Synthesis and characterization of a new fluorogenic substrate for alpha-galactosidase. Analytical and Bioanalytical Chemistry, 394(7), 1903–1909.
  28. Trott, O., & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461.
  29. van der Veen, S. J., Hollak, C. E. M., van Kuilenburg, A. B. P., & Langeveld, M. (2020). Developments in the treatment of Fabry disease. Journal of Inherited Metabolic Disease, 43(5), 908–921.
  30. Wang, J., Cao, X., Chen, W., Xu, J., & Wu, B. (2021). Identification and characterization of a thermostable GH36 α-galactosidase from Anoxybacillus vitaminiphilus WMF1 and its application in synthesizing isofloridoside by reverse hydrolysis. International Journal of Molecular Sciences, 22(19), 10778.
  31. Wiederstein, M., & Sippl, M. J. (2007). ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Research, 35(suppl_2), W407–W410.