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Variety of biological processes such as embryonic development, tissue remodeling and tissue repair involve controlled degradation of extra cellular matrix (ECM). This feature is a fundamental part of growth, invasion, and metastasis of malignant tumors. Matrix metalloproteinases (MMPs), a family of extracellular zinc-dependent neutral endopeptidases, are capable of degrading essentially all ECM components. They are the prime factors indulged in breaking down the extracellular matrix contributing to disease states such as arthritis, atherosclerosis, tumor cell invasion and metastasis. Collagenases show interesting differences in the crystal structures, despite being highly homologous to one another. Therefore, specific inhibition of MMP-1, MMP-8 and MMP-13 are considered to be an attractive target in drug discovery research. This in turn would be able to provide useful knowledge for developing specific new and active drug candidates targeting collagenases (MMP-1, MMP-8 and MMP-13). Computational design has the potential to provide a general, complementary approach for small molecule recognition in which design features and selectivity can be rationally programmed. The development of robust computational methods for the design of small molecule-binding proteins with high affinity and selectivity would have wide-ranging applications. The goal of existing methods for computational enzyme-derived conserved motif like peptide mimetic pharmaco-ligand design is to promote catalysis by creating energetically favorable hydrogen bonding, van der Waals, and electrostatic interactions to a high-energy reaction transition state(s) and/or intermediate(s). Although these interactions are also important for stabilizing the bound ground-state conformations of protein-small nano-linked druggable active conserved molecule complexes, they are not the sole determinant of small molecule binding. In this research study we have for the first time in silico discovered novel collagenase inhibitors using pharmacophore and structure based studies. We finally generated pharmacophore models using combined chemical informatic software for a diverse set of the fragmentation of the existing collagenase inhibitors (MMP-1, MMP-8 and MMP-13) with an aim to Lead identify and computer-aided molecular optimized of novel collagenase inhibitors consisting of a recored VAAHE/PRCGNPD peptidomimic highthroughput screened pharmacophore features to matrix contributing to disease arthritis states.

Article Type

Research Article – Abstract

Publication history

Received: Sep 20, 2017
Accepted: Sep 25, 2017
Published: Oct 01, 2017


Grigoriadis Ioannis, Grigoriadis George, Grigoriadis Nikolaos, George Galazios (2017) Lead identification and computer-aided molecular optimization of novel collagenase inhibitors consisting of a recored VAAHE/PRCGNPD peptidomimic highthroughput screened pharmacophore features to matrix contributing of disease arthritis states.

Authors Info

Grigoriadis Nikolaos
Department of IT Computer Aided Personalized Myoncotherapy, Cartigenea-Cardiogenea, Neurogenea-Cellgenea, Cordigenea-HyperoligandorolTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

Grigoriadis Ioannis
Department of Computer Drug Discovery Science, BiogenetoligandorolTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

Grigoriadis George
Department of Stem Cell Bank and ViroGeneaTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

George Galazios
Professor of Obstetrics and Gynecology,
Democritus University of Thrace,
Komotini, Greece;


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