Pharmacophore modeling coupled with molecular dynamic simulation approach to identify new leads for meprin-β metalloprotease

Human meprin beta metalloprotease, a small subgroup of the astacin family, is a potent drug target for the treatment of several disorders such as fibrosis, neurodegenerative disease in particular Alzheimer and inflammatory bowel diseases. In this study, a ligand-based pharmacophore approach has been used for the selection of potentially active compounds to understand the inhibitory activities of meprin-β by using the sulfonamide scaffold based inhibitors. Using this dataset, a pharmacophore model (Hypo1) was selected on the basis of a highest correlation coefficient (0.959), lowest total cost (105.89) and lowest root mean square deviation (1.31 Å) values. All the pharmacophore hypotheses generated from the candidate inhibitors comprised four features: two hydrogen-bond acceptor, one hydrogen-bond donor and one zinc binder feature. The best validated pharmacophore model (Hypo1) was used for virtual screening of compounds from several databases. The selective hit compounds were filtered by drug likeness property, acceptable ADMET profile, molecular docking and DFT study. Molecular dynamic simulations with the final 10 hit compounds revealed that a large number of non-covalent interactions were formed with the active site and specificity sub-pockets of the meprin beta metalloprotease. This study assists in the development of the new lead molecules as well as gives a better understanding of their interaction with meprin-β.

[1]  W. L. Jorgensen,et al.  Comparison of simple potential functions for simulating liquid water , 1983 .

[2]  T. Spicer,et al.  Development of high throughput screening assays and pilot screen for inhibitors of metalloproteases meprin α and β , 2014, Biopolymers.

[3]  Olivier Barré,et al.  Proteomic Analyses Reveal an Acidic Prime Side Specificity for the Astacin Metalloprotease Family Reflected by Physiological Substrates , 2011, Molecular & Cellular Proteomics.

[4]  Mutasem O. Taha,et al.  Combining molecular dynamics simulation and ligand-receptor contacts analysis as a new approach for pharmacophore modeling: beta-secretase 1 and check point kinase 1 as case studies , 2016, Journal of Computer-Aided Molecular Design.

[5]  David V. Hinkley,et al.  Randomization Analysis of Experimental Data: The Fisher Randomization Test Comment , 1980 .

[6]  Erol Eroglu,et al.  A DFT-based quantum theoretic QSAR study of aromatic and heterocyclic sulfonamides as carbonic anhydrase inhibitors against isozyme, CA-II. , 2007, Journal of molecular graphics & modelling.

[7]  J. S. Bond,et al.  Prointerleukin-18 Is Activated by Meprin β in Vitro and in Vivo in Intestinal Inflammation* , 2008, Journal of Biological Chemistry.

[8]  Stephen R. Johnson,et al.  Molecular properties that influence the oral bioavailability of drug candidates. , 2002, Journal of medicinal chemistry.

[9]  Charles L. Brooks,et al.  Detailed analysis of grid‐based molecular docking: A case study of CDOCKER—A CHARMm‐based MD docking algorithm , 2003, J. Comput. Chem..

[10]  C. Overall,et al.  Metalloproteases meprin α and meprin β are C- and N-procollagen proteinases important for collagen assembly and tensile strength , 2013, Proceedings of the National Academy of Sciences.

[11]  C. Becker-Pauly,et al.  The metalloproteases meprin α and meprin β: unique enzymes in inflammation, neurodegeneration, cancer and fibrosis , 2013, The Biochemical journal.

[12]  J. Briggs,et al.  Dynamic pharmacophore model optimization: identification of novel HIV-1 integrase inhibitors. , 2006, Journal of medicinal chemistry.

[13]  S. Schilling,et al.  First insight into structure-activity relationships of selective meprin β inhibitors. , 2017, Bioorganic & medicinal chemistry letters.

[14]  D. Baker,et al.  A simple physical model for binding energy hot spots in protein–protein complexes , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Hongwei Jin,et al.  Toll-Like Receptor 7 Agonists: Chemical Feature Based Pharmacophore Identification and Molecular Docking Studies , 2013, PloS one.

[16]  C. Overall,et al.  Metalloprotease Meprin β Generates Nontoxic N-terminal Amyloid Precursor Protein Fragments in Vivo* , 2011, The Journal of Biological Chemistry.

[17]  S. Schilling,et al.  Structure-Guided Design, Synthesis, and Characterization of Next-Generation Meprin β Inhibitors. , 2018, Journal of medicinal chemistry.

[18]  S. Keleş,et al.  The bone morphogenetic protein 1/Tolloid-like metalloproteinases. , 2007, Matrix biology : journal of the International Society for Matrix Biology.

[19]  Hans-Dieter Höltje,et al.  Pharmacophore definition and three-dimensional quantitative structure-activity relationship study on structurally diverse prostacyclin receptor agonists. , 2002, Molecular pharmacology.

[20]  Kenneth M Merz,et al.  Prediction of aqueous solubility of a diverse set of compounds using quantitative structure-property relationships. , 2003, Journal of medicinal chemistry.

[21]  G. Shah,et al.  Discovery of Novel Acyl Coenzyme A: Cholesterol Acyltransferase Inhibitors: Pharmacophore‐Based Virtual Screening, Synthesis and Pharmacology , 2012, Chemical biology & drug design.

[22]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings , 1997 .

[23]  W. Bode,et al.  Structural basis for the sheddase function of human meprin β metalloproteinase at the plasma membrane , 2012, Proceedings of the National Academy of Sciences.

[24]  J J Baldwin,et al.  Prediction of drug absorption using multivariate statistics. , 2000, Journal of medicinal chemistry.

[25]  Bernard R. Brooks,et al.  New spherical‐cutoff methods for long‐range forces in macromolecular simulation , 1994, J. Comput. Chem..

[26]  Alexander D. MacKerell,et al.  An Improved Empirical Potential Energy Function for Molecular Simulations of Phospholipids , 2000 .

[27]  Robert P Sheridan,et al.  Why do we need so many chemical similarity search methods? , 2002, Drug discovery today.

[28]  G. Ciccotti,et al.  Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes , 1977 .

[29]  Asim Kumar Debnath,et al.  Pharmacophore mapping of a series of 2,4-diamino-5-deazapteridine inhibitors of Mycobacterium avium complex dihydrofolate reductase. , 2002, Journal of medicinal chemistry.

[30]  Massoud Amanlou,et al.  Potent Human Telomerase Inhibitors: Molecular Dynamic Simulations, Multiple Pharmacophore-Based Virtual Screening, and Biochemical Assays , 2015, J. Chem. Inf. Model..

[31]  H. Krell,et al.  Human meprin alpha and beta homo-oligomers: cleavage of basement membrane proteins and sensitivity to metalloprotease inhibitors. , 2004, The Biochemical journal.

[32]  R. Beynon,et al.  The astacin family of metalloendopeptidases , 1991, The Journal of biological chemistry.

[33]  Kamal Kumar,et al.  Scaffold Diversity Synthesis and Its Application in Probe and Drug Discovery. , 2016, Angewandte Chemie.

[34]  Satya P. Gupta QSAR studies on hydroxamic acids: a fascinating family of chemicals with a wide spectrum of activities. , 2015, Chemical reviews.

[35]  Víctor Quesada,et al.  Identification and Characterization of Human and Mouse Ovastacin , 2004, Journal of Biological Chemistry.