Analysing the Functional and Structural Impact of Single Nucleotide Polymorphisms in Matrix Metalloproteinase 3 Gene: An In-silico Approach
GC01-GC05
Correspondence
Dr. Shajee S Nair,
Government Medical College Manjeri Malappuram, Manjeri-676121, Kerala, India.
E-mail: drno2007@gmail.com
Introduction: Matrix Metalloproteinase 3 (MMP3) is a vital member of the MMP family, known for its wide range of substrate specificity and proteolytic activity against Extracellular Matrix (ECM) components. The role of functional polymorphisms in the MMP genes has been previously investigated in relation to cancer susceptibility, particularly breast cancer. Several Single-Nucleotide Polymorphisms (SNPs) in the MMP3 gene have been linked to a number of clinical illnesses, such as Coronary Artery Disease (CAD); however, the results were not entirely conclusive.
Aim: To identify pathogenic missense SNPs in the human MMP3 gene and analyse their effects on structure and function.
Materials and Methods: This was a record-based cross-sectional study performed using data retrieved from online resources. The analysis was conducted using a series of different bioinformatic tools, for which ethical clearance was obtained from the institution. The online tools used included Sorting Intolerant from Tolerant (SIFT), PolyPhen-2, PhD-SNP, PANTHER, PROVEAN, and SNPs and GO to predict harmful non synonymous SNPs (nsSNPs). Further analysis was performed using I-Mutant 2.0, MutPred2, Consurf, and HOPE software. These tools were able to filter out damaging SNPs and predict the impact of deleterious SNPs on the structure and function of the MMP3 protein.
Results: This study predicted two potentially pathogenic SNPs (D175Y and Y116C) out of 443 missense SNPs from dbSNP, which is a database of SNPs available on the National Centre for Biotechnology Information website. Further analysis revealed that these SNPs were located in highly conserved regions and were predicted to decrease protein stability.
Conclusion: In this study, two potentially pathogenic SNPs (D175Y and Y116C) were identified. Characterisation of these SNPs can help us gain a better understanding of the molecular basis of clinical conditions. The results of this study can be further validated by designing population-based studies and wet lab experiments. This will help in augmenting research and personalised medicine.