Literature DB >> 16302796

Combining in silico tools and NMR data to validate protein-ligand structural models: application to matrix metalloproteinases.

Ivano Bertini1, Marco Fragai, Andrea Giachetti, Claudio Luchinat, Massimiliano Maletta, Giacomo Parigi, Kwon Joo Yeo.   

Abstract

A combination of in silico tools and experimental NMR data is proposed for relatively fast determination of protein-ligand structural models and demonstrated from known inhibitors of matrix metalloproteinases (MMP). The 15N 1H heteronuclear single quantum coherence (HSQC) spectral assignment and the 3D structure, either X-ray or NMR, are needed. In this method, the HSQC spectrum with or without the ligand is used to determine the interaction region of the ligand. Docking calculations are then performed to obtain a set of structural models. From the latter, the nuclear Overhauser effects (NOEs) between the ligand and the protein can be predicted. Guided by these predictions, a number of NOEs can be detected and assigned through a HSQC NOESY experiment. These data are used as structural restraints to reject/refine the initial structural models through further in silico work. For a test protein (MMP-12, human macrophage metalloelastase), a final structure of a protein-ligand adduct was obtained that matches well with the full structural determination. A number of structural predictions were then made for adducts of a similar protein (MMP-1, human fibroblast collagenase) with the same and different ligands. The quality of the final results depended on the type and number of experimental NOEs, but in all cases, a well-defined ligand conformation in the protein binding site was obtained. This protocol is proposed as a viable alternative to the many approaches described in the literature.

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Year:  2005        PMID: 16302796     DOI: 10.1021/jm050574k

Source DB:  PubMed          Journal:  J Med Chem        ISSN: 0022-2623            Impact factor:   7.446


  10 in total

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2.  Structure of trans-resveratrol in complex with the cardiac regulatory protein troponin C.

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3.  Pairwise binding competition experiments for sorting hub-protein/effector interaction hierarchy and simultaneous equilibria.

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Review 4.  Bioinformatics and variability in drug response: a protein structural perspective.

Authors:  Jennifer L Lahti; Grace W Tang; Emidio Capriotti; Tianyun Liu; Russ B Altman
Journal:  J R Soc Interface       Date:  2012-05-02       Impact factor: 4.118

Review 5.  Investigating metabolite-protein interactions: an overview of available techniques.

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Journal:  Methods       Date:  2012-06-28       Impact factor: 3.608

Review 6.  Studies of metabolite-protein interactions: a review.

Authors:  Ryan Matsuda; Cong Bi; Jeanethe Anguizola; Matthew Sobansky; Elliott Rodriguez; John Vargas Badilla; Xiwei Zheng; Benjamin Hage; David S Hage
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Review 7.  Perspectives on NMR in drug discovery: a technique comes of age.

Authors:  Maurizio Pellecchia; Ivano Bertini; David Cowburn; Claudio Dalvit; Ernest Giralt; Wolfgang Jahnke; Thomas L James; Steve W Homans; Horst Kessler; Claudio Luchinat; Bernd Meyer; Hartmut Oschkinat; Jeff Peng; Harald Schwalbe; Gregg Siegal
Journal:  Nat Rev Drug Discov       Date:  2008-09       Impact factor: 84.694

8.  Matrix metalloproteinase-inhibitor interaction: the solution structure of the catalytic domain of human matrix metalloproteinase-3 with different inhibitors.

Authors:  Luis A Alcaraz; Lucia Banci; Ivano Bertini; Francesca Cantini; Antonio Donaire; Leonardo Gonnelli
Journal:  J Biol Inorg Chem       Date:  2007-08-21       Impact factor: 3.358

Review 9.  Biomolecular NMR: a chaperone to drug discovery.

Authors:  Marco Betz; Krishna Saxena; Harald Schwalbe
Journal:  Curr Opin Chem Biol       Date:  2006-05-05       Impact factor: 8.822

10.  Functional relationship between matrix metalloproteinase-11 and matrix metalloproteinase-14.

Authors:  Emilie Buache; Robert Thai; Corinne Wendling; Fabien Alpy; Adeline Page; Marie-Pierre Chenard; Vincent Dive; Marc Ruff; Annick Dejaegere; Catherine Tomasetto; Marie-Christine Rio
Journal:  Cancer Med       Date:  2014-08-01       Impact factor: 4.452

  10 in total

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