STRUCTURAL BIOINFORMATICS OF HAEMOPHILUS INFLUENZAE MACRODOMAIN TER PROTEIN (MATP): IMPLICATIONS IN BIOCHEMISTRY, HEMATOLOGY AND DRUG DEVELOPMENT
Introduction: Haemophilus influenzae is a significant pathogen implicated in respiratory and systemic infections, particularly among vulnerable populations. The MatP protein (HI1647 gene product) plays a critical role in chromosome segregation and bacterial viability, making it a potential therapeutic target. This study aimed to model the 3D structure of the MatP protein and correlate its relevance with clinical and hematological parameters in infected patients. Objective: To model the three-dimensional structure of the structurally conserved MatP protein (HI1647) of Haemophilus influenzae using homology modeling and to investigate its potential as a novel antimicrobial drug target by correlating its role in bacterial viability with clinical and hematological indicators of infection severity in affected patients. Methodology: The MatP amino acid sequence was retrieved from NCBI, and a BLASTp search was performed against the PDB. The E. coli MatP/MatS complex (PDB ID: 3VEA) was selected as a template for homology modeling using MODELLER 9.10. The best model was validated using PROCHECK. Clinical and laboratory data, including CRP, ALT, LDH, IL-6, WBC count, neutrophils, platelets, and hemoglobin levels, were collected from confirmed H. influenzae cases. Results: The modeled HI1647 MatP protein showed high structural quality with >90% residues in the most favored regions of the Ramachandran plot. Clinically, infected patients exhibited elevated CRP, IL-6, and neutrophil levels, along with altered platelet counts. Conclusion: The study confirms the structural conservation of the MatP protein and highlights its potential as a drug target. Hematological parameters provide valuable indicators of infection severity, supporting an integrated approach to diagnosis and therapeutic development in H. influenzae infections.
Authors
- Halima Sadi
Publication Details
- Published
- 2024-11-29
- DOI
- https://doi.org/10.53555/c51ncy49
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 0.00