Literature DB >> 9336839

Ligand binding to proteins: the binding landscape model.

D W Miller1, K A Dill.   

Abstract

Models of ligand binding are often based on four assumptions: (1) steric fit: that binding is determined mainly by shape complementarity; (2) native binding: that ligands mainly bind to native states; (3) locality: that ligands perturb protein structures mainly at the binding site; and (4) continuity: that small changes in ligand or protein structure lead to small changes in binding affinity. Using a generalization of the 2D HP lattice model, we study ligand binding and explore these assumptions. We first validate the model by showing that it reproduces typical binding behaviors. We observe ligand-induced denaturation, ANS and heme-like binding, and "lock-and-key" and "induced-fit" specific binding behaviors characterized by Michaelis-Menten or more cooperative types of binding isotherms. We then explore cases where the model predicts violations of the standard assumptions. For example, very different binding modes can result from two ligands of identical shape. Ligands can sometimes bind highly denatured states more tightly than native states and yet have Michaelis-Menten isotherms. Even low-population binding to denatured states can cause changes in global stability, hydrogen-exchange rates, and thermal B-factors, contrary to expectations, but in agreement with experiments. We conclude that ligand binding, similar to protein folding, may be better described in terms of energy landscapes than in terms of simpler mass-action models.

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Year:  1997        PMID: 9336839      PMCID: PMC2143563          DOI: 10.1002/pro.5560061011

Source DB:  PubMed          Journal:  Protein Sci        ISSN: 0961-8368            Impact factor:   6.725


  36 in total

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Journal:  FEBS Lett       Date:  1987-11-16       Impact factor: 4.124

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Authors:  S W Englander; N R Kallenbach
Journal:  Q Rev Biophys       Date:  1983-11       Impact factor: 5.318

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Authors:  A A Kossiakoff
Journal:  Nature       Date:  1982-04-22       Impact factor: 49.962

5.  Hydrogen exchange rates in pancreatic trypsin inhibitor are not correlated to thermal stability in urea.

Authors:  B D Hilton; K Trudeau; C K Woodward
Journal:  Biochemistry       Date:  1981-08-04       Impact factor: 3.162

6.  The tritium-hydrogen exchange of myosin and its proteolytic fragments.

Authors:  D M Segal; W F Harrington
Journal:  Biochemistry       Date:  1967-03       Impact factor: 3.162

7.  Origins of structure in globular proteins.

Authors:  H S Chan; K A Dill
Journal:  Proc Natl Acad Sci U S A       Date:  1990-08       Impact factor: 11.205

8.  An antibody binding site on cytochrome c defined by hydrogen exchange and two-dimensional NMR.

Authors:  Y Paterson; S W Englander; H Roder
Journal:  Science       Date:  1990-08-17       Impact factor: 47.728

9.  Hydrogen exchange in RNase A: neutron diffraction study.

Authors:  A Wlodawer; L Sjölin
Journal:  Proc Natl Acad Sci U S A       Date:  1982-03       Impact factor: 11.205

10.  Amide proton exchange in proteins by EX1 kinetics: studies of the basic pancreatic trypsin inhibitor at variable p2H and temperature.

Authors:  H Roder; G Wagner; K Wüthrich
Journal:  Biochemistry       Date:  1985-12-03       Impact factor: 3.162

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  50 in total

Review 1.  Folding funnels, binding funnels, and protein function.

Authors:  C J Tsai; S Kumar; B Ma; R Nussinov
Journal:  Protein Sci       Date:  1999-06       Impact factor: 6.725

2.  Functional concerted motions in the bovine serum retinol-binding protein.

Authors:  P L Chau; D M van Aalten; R P Bywater; J B Findlay
Journal:  J Comput Aided Mol Des       Date:  1999-01       Impact factor: 3.686

3.  Analysis methods for identifying coordinated movements during ligand unbinding.

Authors:  P L Chau; P W A Howe
Journal:  J Comput Aided Mol Des       Date:  2002-10       Impact factor: 3.686

4.  Stability and the evolvability of function in a model protein.

Authors:  Jesse D Bloom; Claus O Wilke; Frances H Arnold; Christoph Adami
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5.  Revealing conformational substates of lipidated N-Ras protein by pressure modulation.

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Journal:  Proc Natl Acad Sci U S A       Date:  2011-12-27       Impact factor: 11.205

Review 6.  Seven transmembrane receptors as shapeshifting proteins: the impact of allosteric modulation and functional selectivity on new drug discovery.

Authors:  Terry Kenakin; Laurence J Miller
Journal:  Pharmacol Rev       Date:  2010-04-14       Impact factor: 25.468

7.  Distributions of experimental protein structures on coarse-grained free energy landscapes.

Authors:  Kannan Sankar; Jie Liu; Yuan Wang; Robert L Jernigan
Journal:  J Chem Phys       Date:  2015-12-28       Impact factor: 3.488

8.  Incorporating specificity into optimization: evaluation of SPA using CSAR 2014 and CASF 2013 benchmarks.

Authors:  Zhiqiang Yan; Jin Wang
Journal:  J Comput Aided Mol Des       Date:  2016-02-15       Impact factor: 3.686

9.  The ruggedness of protein-protein energy landscape and the cutoff for 1/r(n) potentials.

Authors:  Anatoly M Ruvinsky; Ilya A Vakser
Journal:  Bioinformatics       Date:  2009-02-23       Impact factor: 6.937

10.  Peptide modulation of class I major histocompatibility complex protein molecular flexibility and the implications for immune recognition.

Authors:  William F Hawse; Brian E Gloor; Cory M Ayres; Kevin Kho; Elizabeth Nuter; Brian M Baker
Journal:  J Biol Chem       Date:  2013-07-08       Impact factor: 5.157

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