Literature DB >> 8369436

Transition modes in Ising networks: an approximate theory for macromolecular recognition.

S Keating1, E Di Cera.   

Abstract

For a statistical lattice, or Ising network, composed of N identical units existing in two possible states, 0 and 1, and interacting according to a given geometry, a set of values can be found for the mean free energy of the 0-->1 transition of a single unit. Each value defines a transition mode in an ensemble of nu N = 3N - 2N possible values and reflects the role played by intermediate states in shaping the energetics of the system as a whole. The distribution of transition modes has a number of intriguing properties. Some of them apply quite generally to any Ising network, regardless of its dimension, while others are specific for each interaction geometry and dimensional embedding and bear on fundamental aspects of analytical number theory. The landscape of transition modes encapsulates all of the important thermodynamic properties of the network. The free energy terms defining the partition function of the system can be derived from the modes by simple transformations. Classical mean-field expressions can be obtained from consideration of the properties of transition modes in a rather straightforward way. The results obtained in the analysis of the transition mode distributions have been used to develop an approximate treatment of the problem of macromolecular recognition. This phenomenon is modeled as a cooperative process that involves a number of recognition subsites across an interface generated by the binding of two macromolecular components. The distribution of allowed binding free energies for the system is shown to be a superposition of Gaussian terms with mean and variance determined a priori by the theory. Application to the analysis of the biologically interaction of thrombin with hirudin has provided some useful information on basic aspects of the interaction, such as the number of recognition subsites involved and the energy balance for binding and cooperative coupling among them. Our results agree quite well with information derived independently from analysis of the crystal structure of the thrombin-hirudin complex.

Entities:  

Mesh:

Substances:

Year:  1993        PMID: 8369436      PMCID: PMC1225721          DOI: 10.1016/S0006-3495(93)81034-8

Source DB:  PubMed          Journal:  Biophys J        ISSN: 0006-3495            Impact factor:   4.033


  15 in total

Review 1.  LINKED FUNCTIONS AND RECIPROCAL EFFECTS IN HEMOGLOBIN: A SECOND LOOK.

Authors:  J WYMAN
Journal:  Adv Protein Chem       Date:  1964

2.  Contribution of the N-terminal region of hirudin to its interaction with thrombin.

Authors:  A Wallace; S Dennis; J Hofsteenge; S R Stone
Journal:  Biochemistry       Date:  1989-12-26       Impact factor: 3.162

3.  Molecular code for cooperativity in hemoglobin.

Authors:  G K Ackers; M L Doyle; D Myers; M A Daugherty
Journal:  Science       Date:  1992-01-03       Impact factor: 47.728

4.  Modulation of thrombin-hirudin interaction by specific ion effects.

Authors:  R De Cristofaro; J W Fenton; E Di Cera
Journal:  J Mol Biol       Date:  1992-07-05       Impact factor: 5.469

5.  Molecular basis of co-operativity in protein folding.

Authors:  E Freire; K P Murphy
Journal:  J Mol Biol       Date:  1991-12-05       Impact factor: 5.469

6.  Calculation of electrostatic interactions in proteins.

Authors:  J B Matthew; F R Gurd
Journal:  Methods Enzymol       Date:  1986       Impact factor: 1.600

7.  Kinetics of the inhibition of thrombin by hirudin.

Authors:  S R Stone; J Hofsteenge
Journal:  Biochemistry       Date:  1986-08-12       Impact factor: 3.162

8.  Helix probability profiles of denatured proteins and their correlation with native structures.

Authors:  P N Lewis; N Go; M Go; D Kotelchuck; H A Scheraga
Journal:  Proc Natl Acad Sci U S A       Date:  1970-04       Impact factor: 11.205

9.  Energetics of charge-charge interactions in proteins.

Authors:  M K Gilson; B H Honig
Journal:  Proteins       Date:  1988

10.  The binding capacity is a probability density function.

Authors:  E Di Cera; Z Q Chen
Journal:  Biophys J       Date:  1993-07       Impact factor: 4.033

View more
  2 in total

1.  Systematic derivation of partition functions for ligand binding to two-dimensional lattices.

Authors:  L Wang; E Di Cera
Journal:  Proc Natl Acad Sci U S A       Date:  1996-11-12       Impact factor: 11.205

Review 2.  Stochastic dynamics of macromolecular-assembly networks.

Authors:  Leonor Saiz; Jose M G Vilar
Journal:  Mol Syst Biol       Date:  2006-05-16       Impact factor: 11.429

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.