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An Integrated Computational Approach to Rationalize the Activity of Non-Zinc-Binding MMP-2 Inhibitors

Overview of attention for article published in PLOS ONE, November 2012
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Title
An Integrated Computational Approach to Rationalize the Activity of Non-Zinc-Binding MMP-2 Inhibitors
Published in
PLOS ONE, November 2012
DOI 10.1371/journal.pone.0047774
Pubmed ID
Authors

Antonella Di Pizio, Mariangela Agamennone, Massimiliano Aschi

Abstract

Matrix metalloproteinases are a family of Zn-proteases involved in tissue remodeling and in many pathological conditions. Among them MMP-2 is one of the most relevant target in anticancer therapy. Commonly, MMP inhibitors contain a functional group able to bind the zinc ion and responsible for undesired side effects. The discovery of potent and selective MMP inhibitors not bearing a zinc-binding group is arising for some MMP family members and represents a new opportunity to find selective and non toxic inhibitors.In this work we attempted to get more insight on the inhibition process of MMP-2 by two non-zinc-binding inhibitors, applying a general protocol that combines several computational tools (docking, Molecular Dynamics and Quantum Chemical calculations), that all together contribute to rationalize experimental inhibition data. Molecular Dynamics studies showed both structural and mechanical-dynamical effects produced by the ligands not disclosed by docking analysis. Thermodynamic Integration provided relative binding free energies consistent with experimentally observed activity data. Quantum Chemical calculations of the tautomeric equilibrium involving the most active ligand completed the picture of the binding process. Our study highlights the crucial role of the specificity loop and suggests that enthalpic effect predominates over the entropic one.

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The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 25%
Lecturer 2 13%
Student > Master 2 13%
Researcher 2 13%
Professor 1 6%
Other 2 13%
Unknown 3 19%
Readers by discipline Count As %
Chemistry 4 25%
Biochemistry, Genetics and Molecular Biology 4 25%
Agricultural and Biological Sciences 3 19%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Nursing and Health Professions 1 6%
Other 0 0%
Unknown 3 19%