Structure Prediction of the Bombyx mori Sericin 4 Protein

Authors

  • Khushnudbek Eshchanov Department of Chemistry, Urgench State University, Urgench, Uzbekistan
  • Dono Babadjanova Department of Chemistry, Urgench State University, Urgench, Uzbekistan
  • Mukhabbat Baltaeva Department of Chemistry, Urgench State University, Urgench, Uzbekistan

DOI:

https://doi.org/10.53560/PPASA(62-4)693

Keywords:

Sericin 4, Silk, Ramachandran Z-Score, Minimum Energy, Solubility, Structure

Abstract

Natural silk (Bombyx mori) has been found to contain sericin 1, sericin 2, sericin 3, and sericin 4 proteins. The sequence of amino acid residues in them has also been well studied. However, there is little information on the molecular structure of sericin 4. We conducted studies on the prediction of the sericin 4 molecule's structure using the AlphaFold3 and YASARA computational servers. Molecular dynamics simulations were performed in aqueous solution to evaluate the stability and determine the most favourable conformation of the predicted sericin 4 structure. We mainly used the ProSA-web, Ramachandran Z and Molprobity score to evaluate the predicted structure of sericin 4, and the reliability of the predicted model was determined. The predicted molecular structure serves as a preliminary, yet robust, model of sericin 4.

References

1. R. Suryawanshi, J. Kanoujia, P. Parashar, and S. Saraf. Sericin. A versatile protein biopolymer with therapeutic significance. Current Pharmaceutical Design 26(42): 5414-5429 (2020).

2. G. Das, H.S. Shin, E.V.R. Campos, L.F. Fraceto, M.D.P. Rodriguez-Torres, K.C.F. Mariano, D.R. Araujo, F. Fernández-Luqueño, R. Grillo, and J.K. Patra. Sericin based nanoformulations: a comprehensive review on molecular mechanisms of interaction with organisms to biological applications. Journal of Nanobiotechnology 19: 30 (2021).

3. A.A. Sarymsakov, S.S. Yarmatov, and K.E. Yunusov. Extraction of Sericin from Cocoons of the Silkworm Bombyx Mori, Its Characteristics, and a Dietary Supplement on Its Basis to Prevent Diabetes Mellitus. Polymer Science Series B 66(1): 89-96 (2024).

4. M.N. Padamwar and A.P. Pawar. Silk sericin and its applications: A review. Journal of Scientific & Industrial Research 63(4): 323-329 (2004).

5. L. Lamboni, Y. Li, and Y. Zhang. Silk sericin-enhanced hydrogel for tissue engineering and wound healing. Biomaterials Science 7(11): 4567-4578 (2019).

6. Z. Wang, Y. Zhang, and Y. Yang. Sericin-based biomaterials for regenerative medicine: Current insights and future directions. Advanced Healthcare Materials 10(15): 2100456 (2021).

7. C.J. Park, J. Ryoo, C.S. Ki, J.W. Kim, I.S. Kim, D.G. Bae, and I.C. Um. Effect of molecular weight on the structure and mechanical properties of silk sericin gel, film, and sponge. International Journal of Biological Macromolecules 119: 821-832 (2018).

8. H. Yun, H. Oh, M.K. Kim, H.W. Kwak, J.Y. Lee, I.Ch. Um, S.K. Vootla, and K.H. Lee. Extraction conditions of Antheraea mylitta sericin with high yields and minimum molecular weight degradation. International Journal of Biological Macromolecules 52: 59-65 (2013).

9. H.Y. Kweon, J.H. Yeo, K.G. Lee, Y.W. Lee, Y.H. Park, J.H. Nahm, and C.S. Cho. Effects of poloxamer on the gelation of silk sericin. Macromolecular Rapid Communications 21(18): 1302-1305 (2000).

10. Y.N. Jo, B.D. Park, and I.C. Um. Effect of storage and drying temperature on the gelation behavior and structural characteristics of sericin. International Journal of Biological Macromolecules 81: 936-941 (2015).

11. R.I. Kunz, R.M.C. Brancalhão, L.D.F.C. Ribeiro, and M.R.M. Natali. Silkworm Sericin: Properties and Biomedical Applications. BioMed Research International 2016: 8175701 (2016).

12. R. Aad, I. Dragojlov, and S. Vesentini. Sericin Protein: Structure, Properties, and Applications. Journal of Functional Biomaterials 15(11): 322 (2024).

13. Q. Xia, Z. Zhou, C. Lu, D. Cheng, F. Dai, B. Li, P. Zhao, X. Zha, T. Cheng, C. Chai, et al. A draft sequence for the genome of the domesticated silkworm (Bombyx mori). Science 306(5703): 1937-1940 (2004).

14. H. Yun, M. K. Kim, and H.W. Kwak. Structural characterization and biological activities of sericin from different silkworm races. International Journal of Industrial Entomology 27(1): 135-140 (2013).

15. H. Okamoto, F. Ishikawa, and Y. Suzuki. Structural analysis of sericin genes. Homologies with fibroin gene in the 5'flanking nucleotide sequences. Journal of Biological Chemistry 257(24): 15192-15199 (1982).

16. B. Kludkiewicz, Y. Takasu, R. Fedic, T. Tamura, F. Sehnal, and M. Zurovec. Structure and expression of the silk adhesive protein Ser2 in Bombyx mori. Insect Biochemistry and Molecular Biology 39(12): 938-946 (2009).

17. K.I. Komatsu. Chemistry and structure of silk. Jarq-Japan Agricultural Research Quarterly 13(1): 64-72 (1979).

18. Y. Takasu, H. Yamada, and K. Tsubouchi. Isolation of three main sericin components from the cocoon of the silkworm, Bombyx mori. Bioscience, Biotechnology, and Biochemistry 66(12): 2715-2718 (2002).

19. Y. Takasu, H. Yamada, T. Tamura, H. Sezutsu, K. Mita, and K. Tsubouchi. Identification and characterization of a novel sericin gene expressed in the anterior middle silk gland of the silkworm Bombyx mori. Insect Biochemistry and Molecular Biology 37 (11): 1234-1240 (2007).

20. Z. Dong, K. Guo, X. Zhang, T. Zhang, Y. Zhang, S. Ma, H. Chang, M. Tang, L. An, Q. Xia, and P. Zhao. Identification of Bombyx mori sericin 4 protein as a new biological adhesive. International Journal of Biological Macromolecules 132: 1121-1130 (2019).

21. D.W. Mount (Ed.). Bioinformatics: Sequence and Genome Analysis (2nd Edition). Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York, United States of America (2004).

22. P. Chakrabarti and D. Pal. The interrelationships of side-chain and main-chain conformations in proteins. Progress in Biophysics and Molecular Biology 76(1-2): 1-102 (2001).

23. J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko, A. Bridgland, C. Meyer, S.A.A. Kohl, A. J. Ballard, A. Cowie, B. Romera-Paredes, S. Nikolov, R. Jain, J. Adler, T. Back, S. Petersen, D. Reiman, E. Clancy, M. Zielinski, M. Steinegger, M. Pacholska, T. Berghammer, S. Bodenstein, D. Silver, O. Vinyals, A.W. Senior, K. Kavukcuoglu, P. Kohli, and D. Hassabis. Highly accurate protein structure prediction with AlphaFold. Nature 596: 583 (2021).

24. R.H. Yousif, H.A. Wahab, K. Shameli, and N.B. Khairudin. Exploring the Molecular Interactions between Neoculin and the Human Sweet Taste Receptors Through Computational Approaches. Sains Malaysiana 49(3): 517-525 (2020).

25. R.F. Service. The game has changed. AI triumphs at protein folding. Science 370(6521): 1144-1145 (2020).

26. H.A. Mesrabadi, K. Faez, and J. Pirgazi. Drug-target interaction prediction based on protein features, using wrapper feature selection. Scientific Reports 13: 3594 (2023).

27. K.K. Barani, M. Mohammadi, M. Ghambarian, and Z. Azizi. Fe3O4/ZnO@ MWCNT promoted green synthesis of biological active of new azepinooxazepine derivatives: Combination of experimental and theoretical study. Polycyclic Aromatic Compounds 44(1): 528-554 (2024).

28. H.A. Guvenilir and T. Doğan. How to approach machine learning-based prediction of drug/compound–target interactions. Journal of Cheminformatics 15: 16 (2023).

29. L.N. David, M.C. Michael, and L.L. Albert (Eds.). Polypeptides Fold Rapidly by a Stepwise Process. In: Lehninger Principles of Biochemistry (7th Edition.). W.H. Freeman, New York, USA (2017).

30. P. Hunter. Into the fold. Advances in technology and algorithms facilitate great strides in protein structure prediction. EMBO Reports 7(3): 249-252 (2006).

31. J. Abramson, J. Adler, J. Dunger, R. Evans, T. Green, A. Pritzel, O. Ronneberger, L. Willmore, A.J. Ballard, J. Bambrick, S.W. Bodenstein, D.A. Evans, Ch. Hung, M. O’Neill, D. Reiman, K. Tunyasuvunakool, Z. Wu, A. Žemgulytė, E. Arvaniti, C. Beattie, O. Bertolli, A. Bridgland, A. Cherepanov, M. Congreve, A.I. Cowen-Rivers, A. Cowie, M. Figurnov, F.B. Fuchs, H. Gladman, R. Jain, Y.A. Khan, C.M.R. Low, K. Perlin, A. Potapenko, P. Savy, S. Singh, A. Stecula, A. Thillaisundaram, C. Tong, S. Yakneen, E.D. Zhong, M. Zielinski, A. Žídek, V. Bapst, P. Kohli, M. Jaderberg, D. Hassabis, and J.M. Jumper. Accurate structure prediction of biomolecular interactions with AlphaFold3. Nature 630: 493-500 (2024).

32. M. Wiederstein and M.J. Sippl. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Research 35: W407-W410 (2007).

33. M.J. Sippl. Recognition of Errors in Three-Dimensional Structures of Proteins. Proteins 17(4): 355-362 (1993).

34. O.V. Sobolev, P.V. Afonine, N.W. Moriarty, M.L. Hekkelman, R.P. Joosten, A. Perrakis, and P.D. Adams. A global Ramachandran score identifies protein structures with unlikely stereochemistry. Structure 28(11): 1249-1258 (2020).

35. E. Krieger, K. Joo, J. Lee, J. Lee, S. Raman, J. Thompson, M. Tyka, D. Baker, and K. Karplus. Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8. Proteins 77(9): 114-22 (2009).

36. L. Zhang and J. Skolnick. What should the Z-score of native protein structures be? Protein Science 7(5):1201-1207 (1998).

37. S. Lyskov, F.C. Chou, S.Ó. Conchúir, B.S. Der, K. Drew, D. Kuroda, J. Xu, B.D. Weitzner, P.D. Renfrew, P. Sripakdeevong, B. Borgo, J.J. Havranek, B. Kuhlman, T. Kortemme, R. Bonneau, J.J. Gray, and R. Das. Serverification of Molecular Modeling Applications: The Rosetta Online Server That Includes Everyone (ROSIE). PLoS One 8(5): e63906 (2013).

38. G. Bayarri, P. Andrio, A. Hospital, M. Orozco, and J.L. Gelpí. BioExcel Building Blocks Workflows (BioBB-Wfs), an integrated web-based platform for biomolecular simulations. Nucleic Acids Research 50(W1): W99–W107 (2022).

39. M.R. Wilkins, E. Gasteiger, A. Bairoch, J.C. Sanchez, K.L. Williams, R.D. Appel, and D.F. Hochstrasser. Protein identification and analysis tools in the ExPASy server. Methods in Molecular Biology 112: 531-552 (1999).

40. M. Naveed, K. Javed, T. Aziz, A. Zafar, M. Fatima, H.M. Rehman, A.A. Khan, A.S. Alamri, W.F. Alsanie, and M. Alhomrani. Innovative Approach of High-Throughput Screening in the Drug Discovery Quest for Chronic Bronchitis Treatment. Journal of Computational Biophysics and Chemistry 24(02): 173-187 (2025).

41. M. Naveed, I. Ali, T. Aziz, A. Saleem, Z. Rajpoot, S. Khaleel, A.A. Khan, M. Al-harbi and T.H. Albekairi. Computational and GC-MS screening of bioactive compounds from Thymus Vulgaris targeting mycolactone protein associated with Buruli ulcer. Scientific Reports 15(1): 131 (2025).

42. M. Oeller, R. Kang, R. Bell, H. Ausserwöger, P. Sormanni, and M. Vendruscolo. Sequence-based prediction of pH-dependent protein solubility using CamSol. Briefings in Bioinformatics 24(2): 1-7 (2023).

Published

2025-12-08

How to Cite

Eshchanov, K., Dono Babadjanova, & Mukhabbat Baltaeva. (2025). Structure Prediction of the Bombyx mori Sericin 4 Protein. Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, 62(4). https://doi.org/10.53560/PPASA(62-4)693

Issue

Section

Research Articles

Similar Articles

<< < 6 7 8 9 10 11 

You may also start an advanced similarity search for this article.