Genomics & Informatics

Korea Genome Organization (한국유전체학회)
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Structural Bioinformatics Analysis of Disease-related Mutations

  • Park, Seong-Jin (Korean BioInformation Center (KOBIC), KRIBB) ;
  • Oh, Sang-Ho (Korean BioInformation Center (KOBIC), KRIBB) ;
  • Park, Dae-Ui (Korean BioInformation Center (KOBIC), KRIBB) ;
  • Bhak, Jong (Korean BioInformation Center (KOBIC), KRIBB)
  • Published : 2008.09.30
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Abstract

In order to understand the protein functions that are related to disease, it is important to detect the correlation between amino acid mutations and disease. Many mutation studies about disease-related proteins have been carried out through molecular biology techniques, such as vector design, protein engineering, and protein crystallization. However, experimental protein mutation studies are time-consuming, be it in vivo or in vitro. We therefore performed a bioinformatic analysis of known disease-related mutations and their protein structure changes in order to analyze the correlation between mutation and disease. For this study, we selected 111 diseases that were related to 175 proteins from the PDB database and 710 mutations that were found in the protein structures. The mutations were acquired from the Human Gene Mutation Database (HGMD). We selected point mutations, excluding only insertions or deletions, for detecting structural changes. To detect a structural change by mutation, we analyzed not only the structural properties (distance of pocket and mutation, pocket size, surface size, and stability), but also the physico-chemical properties (weight, instability, isoelectric point (IEP), and GRAVY score) for the 710 mutations. We detected that the distance between the pocket and disease-related mutation lay within $20\;{\AA}$ (98.5%, 700 proteins). We found that there was no significant correlation between structural stability and disease-causing mutations or between hydrophobicity changes and critical mutations. For large-scale mutational analysis of disease-causing mutations, our bioinformatics approach, using 710 structural mutations, called "Structural Mutatomics," can help researchers to detect disease-specific mutations and to understand the biological functions of disease-related proteins.

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References

  1. Capriotti, E., Fariselli, P., and Casadio, R. (2005). I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res. 33, W306-W310 https://doi.org/10.1093/nar/gki375
  2. Chapman, B., and Chang, J. (2000). Biopython: python tools for computational biology. ACM SIGBIO Newsletter 20, 15-19 https://doi.org/10.1145/360262.360268
  3. Gabbianelli, R., Battistoni, A., Polticelli, F., Meier, B., Schmidt, M., Rotilio, G., and Desideri, A. (1997). Effect of Lys175 mutation on structure function properties of Propionibacterium shermanii superoxide dismutase. Protein Engineering 10, 1067-1070 https://doi.org/10.1093/protein/10.9.1067
  4. Grantz Saskova, K., Kozisek, M., Lepsik, M., Brynda, J., Rezacova, P., Vaclavikova, J., Kagan, R., Machala, L., and Konvalinka, J. (2008). Contributing to the Reduced Susceptibility to HIV Protease Enzymatic and Structural Analysis of the I47A Mutation Inhibitor Lopinavir. Protein Sci. 17, 1555-1564 https://doi.org/10.1110/ps.036079.108
  5. Hendlich, M., Rippmann, F., and Barnickel, G. (1997). LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. J. Mol. Graph. Model. 15, 359-363 https://doi.org/10.1016/S1093-3263(98)00002-3
  6. Hubbard, S.J., and Thornton, J.M. (1993). NACCESS Computer Program. Dept. of Biochem. and Mol. Biol., University College London
  7. Jackson, D.A., and Chen, Y. (2004). Robust principal component analysis and outlier detection with ecological data. Environmetrics 15, 129-139 https://doi.org/10.1002/env.628
  8. John, B., and Sali, A. (2003). Comparative protein structure modeling by iterative alignment, model building and model assessment. Nucleic Acids Res. 31, 3982-3992 https://doi.org/10.1093/nar/gkg460
  9. Ng, P.C., and Henikoff, S. (2003). SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812-3814 https://doi.org/10.1093/nar/gkg509
  10. Park, D.I., Kim, B.C., Cho, S.W., Park, S.J., Choi, J.S., Kim, S.I., Bhak, J., and Lee, S.H. (2008). MassNet: a functional annotation service for protein mass spectrometry data. Nucleic Acids Research 36, W491-W495 https://doi.org/10.1093/nar/gkn241
  11. Ramensky, V., Bor, P., and Sunyaev, S. (2002). Human non-synonymous SNPs: server and survey. Nucleic Acids Res. 30(17), 3894-3900 https://doi.org/10.1093/nar/gkf493
  12. Tosha, T., Yoshioka, S., Ishimori, K., and Morishima, I. (2004). L358P Mutation on Cytochrome P450cam Simulates Structural Changes upon Putidaredoxin Binding. JBC 279, 42836-42843 https://doi.org/10.1074/jbc.M404216200