Structural Bioinformatics Analysis of Disease-related Mutations.

Park,  Seong Jin; Oh,  Sangho; Park,  Daeui; Bhak,  Jong

doi:2008.6.3.142

Genomics Inform > Volume 6(3); 2008 > Article

Original Article

Genomics & Informatics 2008;6(3): 142-146.

DOI: https://doi.org/10.5808/gi.2008.6.3.142

Structural Bioinformatics Analysis of Disease-related Mutations.

Seong Jin Park, Sangho Oh, Daeui Park, Jong Bhak

Korean BioInformation Center (KOBIC), KRIBB, Daejeon 305-806, Korea.jongbhak@yahoo.com, daeui@kribb.re.kr

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 isease. 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 A (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.

Keywords: human gene mutation database (HGMD); structural property; physico-chemical property; structural mutatomics