Literature DB >> 11231110

Computational modelling of inhibitor binding to human thrombin.

K B Ljungberg1, J Marelius, D Musil, P Svensson, B Norden, J Aqvist.   

Abstract

Thrombin is an essential protein involved in blood clot formation and an important clinical target, since disturbances of the coagulation process cause serious cardiovascular diseases such as thrombosis. Here we evaluate the performance of a molecular dynamics based method for predicting the binding affinities of different types of human thrombin inhibitors. For a series of eight ligands the method ranks their relative affinities reasonably well. The binding free energy difference between high and low affinity representatives in the test set is quantitatively reproduced, as well as the stereospecificity for a chiral inhibitor. The original parametrisation of this linear interaction energy method requires the addition of a constant energy term in the case of thrombin. This yields a mean unsigned error of 0.68 kcal/mol for the absolute binding free energies. This type of approach is also useful for elucidating three-dimensional structure-activity relationships in terms of microscopic interactions of the ligands with the solvated enzyme.

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Year:  2001        PMID: 11231110     DOI: 10.1016/s0928-0987(00)00185-8

Source DB:  PubMed          Journal:  Eur J Pharm Sci        ISSN: 0928-0987            Impact factor:   4.384


  10 in total

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Authors:  V Zoete; O Michielin; M Karplus
Journal:  J Comput Aided Mol Des       Date:  2003-12       Impact factor: 3.686

2.  A combination of docking, QM/MM methods, and MD simulation for binding affinity estimation of metalloprotein ligands.

Authors:  Akash Khandelwal; Viera Lukacova; Dogan Comez; Daniel M Kroll; Soumyendu Raha; Stefan Balaz
Journal:  J Med Chem       Date:  2005-08-25       Impact factor: 7.446

3.  Screening of benzamidine-based thrombin inhibitors via a linear interaction energy in continuum electrostatics model.

Authors:  Orazio Nicolotti; Ilenia Giangreco; Teresa Fabiola Miscioscia; Marino Convertino; Francesco Leonetti; Leonardo Pisani; Angelo Carotti
Journal:  J Comput Aided Mol Des       Date:  2010-02-11       Impact factor: 3.686

4.  Computational analysis of binding of P1 variants to trypsin.

Authors:  B O Brandsdal; J Aqvist; A O Smalås
Journal:  Protein Sci       Date:  2001-08       Impact factor: 6.725

5.  QM/MM linear response method distinguishes ligand affinities for closely related metalloproteins.

Authors:  Akash Khandelwal; Stefan Balaz
Journal:  Proteins       Date:  2007-11-01

6.  Flooding enzymes: quantifying the contributions of interstitial water and cavity shape to ligand binding using extended linear response free energy calculations.

Authors:  Katie L Whalen; M Ashley Spies
Journal:  J Chem Inf Model       Date:  2013-09-06       Impact factor: 4.956

7.  Computational perspectives into plasmepsins structure-function relationship: implications to inhibitors design.

Authors:  Alejandro Gil L; Pedro A Valiente; Pedro G Pascutti; Tirso Pons
Journal:  J Trop Med       Date:  2011-07-03

8.  Evaluating the Performance of a Non-Bonded Cu2+ Model Including Jahn-Teller Effect into the Binding of Tyrosinase Inhibitors.

Authors:  Lucas Sousa Martins; Jerônimo Lameira; Hendrik G Kruger; Cláudio Nahum Alves; José Rogério A Silva
Journal:  Int J Mol Sci       Date:  2020-07-06       Impact factor: 5.923

9.  Three dimensional model of severe acute respiratory syndrome coronavirus helicase ATPase catalytic domain and molecular design of severe acute respiratory syndrome coronavirus helicase inhibitors.

Authors:  Marcin Hoffmann; Krystian Eitner; Marcin von Grotthuss; Leszek Rychlewski; Ewa Banachowicz; Tomasz Grabarkiewicz; Tomasz Szkoda; Andrzej Kolinski
Journal:  J Comput Aided Mol Des       Date:  2006-09-14       Impact factor: 3.686

10.  How Good is Jarzynski's Equality for Computer-Aided Drug Design?

Authors:  Kiet Ho; Duc Toan Truong; Mai Suan Li
Journal:  J Phys Chem B       Date:  2020-06-22       Impact factor: 2.991

  10 in total

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