Predicting the catalytic sites of isopenicillin N synthase (IPNS) related non-haem iron-dependent oxygenases and oxidases (NHIDOX) through a structural superimposition and molecular docking approach

• Published in: African journal of biotechnology Vol. 11; no. 75; pp. 13947 - 13953
• Main Author: Soon Chin, Hong
• Format: Journal Article
• Language: English
• Published: 18.09.2012
• ISSN: 1684-5315; 1684-5315
• DOI: 10.5897/AJB12.803

Isopenicillin N synthase (IPNS) related Non-haem iron-dependent oxygenases and oxidases (NHIDOX) demonstrated a striking structural conservativeness, even with low protein sequence homology. It is evident that these enzymes have an architecturally similar catalytic centre with active ligands lining the reactive pocket. Deacetoxycephalosporin C synthase (DAOCS), isopenicillin N synthase (IPNS), deacetylcephalosporin C synthase (DACS), clavaminate synthase 1 and 2 (CAS1 and 2) are important bacterial enzymes that catalyze the formation of beta -lactam antibiotics belonging to this enzyme family. Most plant enzyme members within this subfamily namely flavonol synthase (FLS), leucoanthocyanidin dioxygenase (LDOX), anthocyanidin synthase (ANS), 1-aminocyclopropane-1-carboxylic acid oxidase (ACCO), gibberellin 20-oxidase (G sub(20)O), desacetoxyvindoline-4-hydroxylase (D4H), flavanone beta -hydroxylase (F3H), and hyoscyamine 6 beta -hydroxylase (H6H) are involved in catalyzing the biosyntheses of plant secondary metabolites. With the advancement of protein structural analysis software, it is possible to predict the catalytic sites of protein that shared a structural resemblance. By exploiting the superimposition model of DAOCS-IPNS, DAOCS-IPNS-CAS, G sub(20)O-LDOX, FLS-LDOX, ACCO-LDOX, D4HLDOX, F3H-LDOX and H6H-LDOX model; a computational protocol for predicting the catalytic sites of proteins is now made available. This study shows that without the crystallized or nuclear magnetic resonance (NMR) structures of most NHIDOX enzyme, the plausible catalytic sites of protein can be forecasted using this structural bioinformatics approach.