Literature DB >> 7634078

Prediction of new serine proteinase inhibitors.

I V Kurinov1, R W Harrison.   

Abstract

We describe here the use of a rapid computational method to predict the relative binding strengths of a series of small-molecule ligands for the serine proteinase trypsin. Flexible molecular models of the ligands were docked to the proteinase using an all-atom potential set, without cutoff limits for the non-bonded and electrostatic energies. The binding-strength calculation is done directly in terms of a molecular mechanics potential. The binding of eighteen different compounds, including non-binding controls, has been successfully predicted. The measured Ki is correlated with the predicted energy. The correctness of the theoretical calculations is demonstrated with both kinetics measurements and X-ray structure determination of six enzyme-inhibitor complexes.

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Year:  1994        PMID: 7634078     DOI: 10.1038/nsb1094-735

Source DB:  PubMed          Journal:  Nat Struct Biol        ISSN: 1072-8368


  16 in total

1.  Computational design of D-peptide inhibitors of hepatitis delta antigen dimerization.

Authors:  C D Elkin; H J Zuccola; J M Hogle; D Joseph-McCarthy
Journal:  J Comput Aided Mol Des       Date:  2000-11       Impact factor: 3.686

2.  Reproducing the conformations of protein-bound ligands: a critical evaluation of several popular conformational searching tools.

Authors:  J Boström
Journal:  J Comput Aided Mol Des       Date:  2001-12       Impact factor: 3.686

3.  Trypsin specificity as elucidated by LIE calculations, X-ray structures, and association constant measurements.

Authors:  Hanna-Kirsti Schrøder Leiros; Bjørn Olav Brandsdal; Ole Andreas Andersen; Vibeke Os; Ingar Leiros; Ronny Helland; Jacek Otlewski; Nils Peder Willassen; Arne O Smalås
Journal:  Protein Sci       Date:  2004-04       Impact factor: 6.725

4.  Importance of accurate charges in molecular docking: quantum mechanical/molecular mechanical (QM/MM) approach.

Authors:  Art E Cho; Victor Guallar; Bruce J Berne; Richard Friesner
Journal:  J Comput Chem       Date:  2005-07-15       Impact factor: 3.376

5.  Processing multimode binding situations in simulation-based prediction of ligand-macromolecule affinities.

Authors:  Akash Khandelwal; Viera Lukacova; Daniel M Kroll; Soumyendu Raha; Dogan Comez; Stefan Balaz
Journal:  J Phys Chem A       Date:  2005-07-28       Impact factor: 2.781

6.  Statistical potential for modeling and ranking of protein-ligand interactions.

Authors:  Hao Fan; Dina Schneidman-Duhovny; John J Irwin; Guangqiang Dong; Brian K Shoichet; Andrej Sali
Journal:  J Chem Inf Model       Date:  2011-11-21       Impact factor: 4.956

Review 7.  Prediction of binding constants of protein ligands: a fast method for the prioritization of hits obtained from de novo design or 3D database search programs.

Authors:  H J Böhm
Journal:  J Comput Aided Mol Des       Date:  1998-07       Impact factor: 3.686

8.  Two crystal structures of the leupeptin-trypsin complex.

Authors:  I V Kurinov; R W Harrison
Journal:  Protein Sci       Date:  1996-04       Impact factor: 6.725

9.  Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes.

Authors:  M D Eldridge; C W Murray; T R Auton; G V Paolini; R P Mee
Journal:  J Comput Aided Mol Des       Date:  1997-09       Impact factor: 3.686

10.  Inclusion of solvation and entropy in the knowledge-based scoring function for protein-ligand interactions.

Authors:  Sheng-You Huang; Xiaoqin Zou
Journal:  J Chem Inf Model       Date:  2010-02-22       Impact factor: 4.956

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