Skip to main content
SpringerLink
Log in

Comparative Analyses of the Relative Effects of Various Mutations in Major Histocompatibility Complex I—a Way to Predict Protein-Protein Interactions

  • Published:
Applied Biochemistry and Biotechnology Aims and scope Submit manuscript

Abstract

Protein-protein interactions (PPIs) play pivotal roles in most of the biological processes. PPI dysfunctions are therefore associated with disease situations. Mutations often lead to PPI dysfunctions, but there are certain other types of mutations which do not cause any appreciable abnormalities. This second type of mutations is called polymorphic mutations. So far, there are many studies that deal with the identification of PPI sites, but clear-cut analyses of the involvements of mutations in PPI dysfunctions are few and far between. We therefore made an attempt to link the appearances of mutations and PPI disruptions. We used major histocompatibility complex as our reference protein complex. We analyzed the mutations leading to the disease amyloidosis and also the other mutations that do not lead to disease conditions. We computed various biophysical parameters like relative solvent accessibility to discriminate between the two different types of mutations. Our analyses for the first time came up with a plausible explanation for the effects of different types of mutations in disease development. Our future plans are to build tools to detect the effects of mutations in disease developments by disrupting the PPIs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. A. Ali, S. Bhattacharjee, A. Bagchi, (2015) Information systems design and intelligent applications: structural analyses of the mode of binding between AANAT protein with 14-3-3 protein involved in human melatonin synthesis,: pp. 127–132. doi:10.1007/978-81-322-2247-7.

  2. Bagchi, A., et al. (2013). Analysis of features from protein-protein hetero-complex structures to predict protein interaction interfaces using machine learning. Procedia Technology, 10, 62–66. doi:10.1016/j.protcy.12.337.

    Article  Google Scholar 

  3. Huang, J. T., Wang, T., Huang, S. R., & Li, X. (2015). Prediction of protein folding rates from simplified secondary structure alphabet. Journal of Theoretical Biology, 383, 1–6. doi:10.1016/j.jtbi.2015.07.024.

    Article  CAS  Google Scholar 

  4. J. Trowsdale, (2002) The gentle art of gene arrangement: the meaning of gene clusters., Genome Biol. 3 COMMENT2002. doi:10.1186/gb-2002-3-3- comment2002.

  5. Wearsch, P. a., & Cresswell, P. (2008). The quality control of MHC class I peptide loading. Current Opinion in Cell Biology, 20, 624–631. doi:10.1016/j.ceb.2008.09.005.

    Article  CAS  Google Scholar 

  6. Purcell, A. W., & Elliott, T. (2008). Molecular machinations of the MHC-I peptide loading complex. Current Opinion in Immunology, 20, 75–81. doi:10.1016/j.coi.2007.12.005.

    Article  CAS  Google Scholar 

  7. Zhang, A., Yu, H., He, Y., Shen, Y., Zhang, Y., Liu, J., Fu, B., Lv, D., Miao, F., & Zhang, J. (2015). TYK2, a candidate gene for type 1 diabetes, modulates apoptosis and the innate immune response in human pancreatic β-cells. Diabetes, 64, 3808–3817. doi:10.2337/db15-0362.

    Article  Google Scholar 

  8. Rost, B., & Sander, C. (1994). Conservation and prediction of RSA.

    Google Scholar 

  9. Esposito, G., Ricagno, S., Corazza, A., Rennella, E., Gümral, D., & Mimmi, M. C. (2008). The controlling roles of Trp60 and Trp95 in β2-microglobulin function, folding and amyloid aggregation properties. Journal of Molecular Biology, 378, 885–895. doi:10.1016/j.jmb.2008.03.002.

    Article  CAS  Google Scholar 

  10. Ricagno, S., Colombo, M., De Rosa, M., Sangiovanni, E., Giorgetti, S., & Raimondi, S. (2008). DE loop mutations affect β2-microglobulin stability and amyloid aggregation. Biochemical and Biophysical Research Communications, 377, 146–150. doi:10.1016/j.bbrc.2008.09.108.

    Article  CAS  Google Scholar 

  11. Ricagno, S., Raimondi, S., Giorgetti, S., Bellotti, V., & Bolognesi, M. (2009). Human beta-2 microglobulin W60V mutant structure: implications for stability and amyloid aggregation. Biochemical and Biophysical Research Communications, 380, 543–547. doi:10.1016/j.bbrc.2009.01.116.

    Article  CAS  Google Scholar 

  12. Santambrogio, C., Ricagno, S., Colombo, M., Barbiroli, A., Bonomi, F., & Bellotti, V. (2010). DE-loop mutations affect β2 microglobulin stability, oligomerization, and the low-pH unfolded form. Protein Science, 19, 1386–1394. doi:10.1002/pro.419.

    Article  CAS  Google Scholar 

  13. Maury, C. P. J. (2015). Origin of life. Primordial genetics: information transfer in a pre-RNA world based on self-replicating beta-sheet amyloid conformers. Journal of Theoretical Biology, 382, 292–297. doi:10.1016/j.jtbi.2015.07.008.

    Article  CAS  Google Scholar 

  14. Eichner, T., & Radford, S. E. (2009). A generic mechanism of beta2-microglobulin amyloid assembly at neutral pH involving a specific proline switch. Journal of Molecular Biology, 386, 1312–1326. doi:10.1016/j.jmb.2009.01.013.

    Article  CAS  Google Scholar 

  15. Routledge, K. E., Tartaglia, G. G., Platt, G. W., Vendruscolo, M., & Radford, S. E. (2009). Competition between intramolecular and intermolecular interactions in an amyloid-forming protein. Journal of Molecular Biology, 389, 776–786. doi:10.1016/j.jmb.2009.04.042.

    Article  CAS  Google Scholar 

  16. Blancas-Mejíaa, L. M., Horna, T. J., Marin-Arganya, M., Autona, M., Tischera, A., & Ramirez-Alvarado, M. (2015). Thermodynamic and fibril formation studies of full length immunoglobulin light chain AL-09 and its germline protein using scan rate dependent thermal unfolding. Biophysical Chemistry, 207, 13–20.

    Article  Google Scholar 

  17. Leinonen, R., Garcia Diez, F., Binns, D., Fleischmann, W., Lopez, R., & Apweiler, R. (2004). UniProt archive. Bioinformatics, 20, 3236–3237.

    Article  CAS  Google Scholar 

  18. Berman, H. M. (2008). The Protein Data Bank: a historical perspective. Acta Crystallographica. Section A, 64(Pt 1), 88–95. doi:10.1107/S0108767307035623.

    CAS  Google Scholar 

  19. Capriotti, E., & Altman, R. B. (2011). Improving the prediction of disease-related variants using protein three-dimensional structure. BMC Bioinformatics, 12, S3. doi:10.1186/1471-2105-12-S4-S3.

    Article  CAS  Google Scholar 

  20. Lüthy, R., Bowie, J. U., & Eisenberg, D. (1992). Assessment of protein models with three-dimensional profiles. Nature, 356, 83–85. doi:10.1038/356083a0.

    Article  Google Scholar 

  21. Laskowski, R. A., Rullmannn, J. A., MacArthur, M. W., Kaptein, R., & Thornton, J. M. (1996). AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. Journal of Biomolecular NMR, 8, 477–486. doi:10.1007/BF00228148.

    Article  CAS  Google Scholar 

  22. Chung, J. L., Wang, W., & Bourne, P. E. (2006). Exploiting sequence and structure homologs to identify protein-protein binding sites. Proteins: Structure, Function, and Genetics, 62, 630–640. doi:10.1002/prot.20741.

    Article  CAS  Google Scholar 

  23. Ramachandran, G. N., & Sasiskharan, V. (1968). Conformation of polypeptides and proteins. Advances in Protein Chemistry 23, 283–437.

  24. Hess, B., Kutzner, C., Van Der Spoel, D., & Lindahl, E. (2008). GRGMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. Journal of Chemical Theory and Computation, 4, 435–447.

    Article  CAS  Google Scholar 

  25. Pierce, B. G., Wiehe, K., Hwang, H., Kim, B.-H., Vreven, T., & Weng, Z. (2014). ZDOCK server: interactive docking prediction of protein–protein complexes and symmetric multimers. Bioinformatics, 30(12), 1771–1773. doi:10.1093/bioinformatics/btu097.

    Article  CAS  Google Scholar 

  26. Van Der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A. E., & Berendsen, H. J. (2005). GROMACS: fast, flexible, and free. Journal of Computational Chemistry, 26(16):1701–1718.

Download references

Acknowledgments

The authors would like to thank the reviewers for the constructive comments for the betterment of the manuscript. The authors acknowledge the generous help of Bioinformatics Infrastructural Facility (BIF), University of Kalyani, for providing the necessary equipments and workstation to carry out the experiments.

Ali A. is also thankful to UGC, India, for his fellowship.

Author information

Authors and Affiliations

  1. Department of Biochemistry and Biophysics, University of Kalyani, Kalyani, 741235, Nadia, West Bengal, India

    Ananya Ali, Ria Biswas, Sanchari Bhattacharjee, Prabahan Nath, Sumanjit Pan & Angshuman Bagchi

Authors
  1. Ananya Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ria Biswas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sanchari Bhattacharjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Prabahan Nath
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sumanjit Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Angshuman Bagchi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Angshuman Bagchi.

Ethics declarations

Conflict of Interests

The authors declare that they have no competing interests.

Electronic supplementary material

ESM 1

(DOCX 98 kb)

ESM 2

(DOCX 272 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ali, A., Biswas, R., Bhattacharjee, S. et al. Comparative Analyses of the Relative Effects of Various Mutations in Major Histocompatibility Complex I—a Way to Predict Protein-Protein Interactions. Appl Biochem Biotechnol 180, 152–164 (2016). https://doi.org/10.1007/s12010-016-2090-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12010-016-2090-z

Keywords

Search

Navigation