Multiple molecular modelling studies on some derivatives and analogues of glutamic acid as matrix metalloproteinase-2 inhibitors^$

$ Presented at the 9th International Symposium on Computational Methods in Toxicology and Pharmacology Integrating Internet Resources, CMTPI-2017, 27–30 October 2017, Goa, India.

Abstract

Matrix metalloproteinase-2 (MMP-2) is a potential target in anticancer drug discovery due to its association with angiogenesis, metastasis and tumour progression. In this study, 67 glutamic acid derivatives, synthesized and evaluated as MMP-2 inhibitors, were taken into account for multi-QSAR modelling study (regression-based 2D-QSAR, classification-based LDA-QSAR, Bayesian classification QSAR, HQSAR, 3D-QSAR CoMFA and CoMSIA as well as Open3DQSAR). All these QSAR studies were statistically validated individually. Regarding the 3D-QSAR analysis, the Open3DQSAR results were better than CoMFA and CoMSIA, although all these 3D-QSAR models supported each other. The importance of biphenylsulphonyl moiety over phenylacetyl/naphthylacetyl moieties was established due to its association with favourable steric and hydrophobic characters. HQSAR, LDA-QSAR and Bayesian classification QSAR studies also suggested that the biphenylsulphonamido group was better than the phenylacetylcarboxamido function. Additionally, glutamines were proven to be far better inhibitors than isoglutamines. Observations obtained from the current study were revalidated and supported by the earlier reported molecular modelling studies. Depending on these observations, newer glutamic acid-based compounds may be designed further in future for potent MMP-2 inhibitory activity.

Keywords:

• MMP-2 inhibitor
• 2D-QSAR
• LDA-QSAR
• Bayesian classification QSAR, HQSAR, 3D-QSAR CoMFA, 3D-QSAR CoMSIA, Open3DQSAR

Acknowledgements

TJ is grateful to Universities with Potential for Excellence (UPE), Phase-II programme of UGC, New Delhi to Jadavpur University, Kolkata, India for financial assistance. NA thankfully acknowledges UGC, New Delhi for providing Rajiv Gandhi National Fellowship (Grant No. F1-17.1/2014-15/RGNF-2014-15-SC-WES-73725/SA-III/Website). The authors are thankful to the authority of Jadavpur University, Kolkata, India and University of Calcutta, Kolkata, India for providing the necessary research facilities required for conducting this work.

Notes

$ Presented at the 9th International Symposium on Computational Methods in Toxicology and Pharmacology Integrating Internet Resources, CMTPI-2017, 27–30 October 2017, Goa, India.