Nagendra Singh1, Manoj Kumar2, Md. Imtaiyaz Hassan3, Punit Kaur2
1Department of Computer Science, Jamia Millia Islamia, Jamia Nagar, New Delhi 110025, India.
2Department of Biophysics, All India Institute of Medical Sciences, New Delhi-110029, India.
3Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi 110025, India.


The modeling studies have been performed on E. coli β-Glucuronidase (GUS), which is a member of family 2 glycosyl hydrolase, in quest of finding high affinity hits by virtual screening approach using Discovery Studio (DS) 2.0 modeling package. Modeling studies using molecular docking have been first computationally validated on available experimental data on E. coli GUS inhibitor. The high resolution crystal structure of E. coli GUS complexed with its inhibitor Z78 (PDB id: 3LPG) was the starting material for docking studies. The inhibitor, Z78, was extracted and docked back into the binding site of the enzyme using LigandFit docking protocol to verify the predictive power of binding geometry of Z78 as compared to its crystal geometry. Similarly, predictive power of binding affinity calculations using several scoring functions available in DS 2.0 was tested with respect to experimental K i . The positional root mean squared (r.m.s) deviation of best binding mode geometry/conformation generated by LigandFit docking program and crystal conformation was 1.75 Å. This shows that binding mode predictive power of LigandFit docking program is good with this set of protein and ligands as positional r.m.s deviation less than 2 Å is considered successful docking. Similarly, the calculated binding affinity using empirical scoring function, Ludi 3, was 1 μM as compared to experimental Ki value of 0.68 μM i.e. about ~2-fold different. Hence, binding affinity predictive power of Ludi 3 was reasonably good and within the limit of accuracy of scoring function. Therefore, using the LigandFit docking protocol in combination of Ludi 3 scoring function, database screening of LigCAP library of small molecules was performed to identify novel class of potential hits with high affinity for E. coli GUS. Using the in silico structure based design approach LigandFit docking protocol in combination with empirical scoring function, a ligand, LigCAP1125 with high affinity has been identified for E. coli GUS. This ligand has the calculated binding affinity of 1.29*10-12 as compared to 9.332×10-5 . That means identified ligand has 10 7 fold higher affinities as compared to the best reported inhibitor so far. The Molecular Dynamics simulations indicate the stability of binding. Thus the identified inhibitor could be the potential inhibitor and can be experimentally tested further to provide the lead compound for the development of novel therapeutic entities against E. coli GUS.  Read more…

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