Literature DB >> 557155

Statistical mechanical treatment of protein conformation. 5. A multistate model for specific-sequence copolymers of amino acids.

S Tanaka, H A Scheraga.   

Abstract

One-dimensional short-range interaction models for specific-sequence copolymers of amino acids have been developed in this series of papers. In the present paper, a multistate model (involving right-handed helical (hR), extended (epsilon), chain-reversal (R and S), left-handed helical (hL), right-handed bridge-region (zota R), left-handed bridge-region (zota L), and coil (or other) (c) states) is developed for the prediction of protein backbone conformation. This model involves ten parameters (WhR, UPSILONHR, V epsilon, VR, VS, WhL, VhL, U zota R, U zota L, and Uc) and requires a 10X10 statistical weight matrix. Assuming that the left-handed helical sequence cannot occur in proteins, this 10X10 matrix can be reduced to a 9X9 matrix with nine parameters (WhR, VhR, V epsilon, VR, VS, VhL, U zota R, U zota L, and Uc). A nearest neighbor approximation of this multistate model is also formulated; with the omission of left-handed helical sequences, and the inclusion of the left-handed bridge region in the c state, this approximate model requires a 7X7 matrix with statistical weights WhR, VhR, VS, VhL, U zota R, and Uc, expressed as values relative to the statistical weight of the epsilon state. The statistical weights for the multistate model are evaluated from the atomic coordinates of the X-ray structures of 26 native proteins. These statistical weights and the multistate model are applied in the prediction of the backbone conformations of proteins. The conformational probabilities of finding a residue in hR, epsilon, R, S, hL, zota R, or c states, defined as relative values with respect to their average values over the whole molecule, are calculated for bovine pancreatic trypsin inhibitor and clostridial flavodoxin, in order to select the most probable conformation for each residue of these proteins. The predicted results are compared to experimental observations and are discussed together with the reliability of the statistical weights. In the Appendix, the property of asymmetric nucleation of helical sequences is introduced into the (nearest neighbor) multistate model.

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Year:  1977        PMID: 557155     DOI: 10.1021/ma60055a002

Source DB:  PubMed          Journal:  Macromolecules        ISSN: 0024-9297            Impact factor:   5.985


  10 in total

1.  Predicting protein crystallization propensity from protein sequence.

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2.  Model of protein folding: incorporation of a one-dimensional short-range (Ising) model into a three-dimensional model.

Authors:  S Tanaka; H A Scheraga
Journal:  Proc Natl Acad Sci U S A       Date:  1977-04       Impact factor: 11.205

3.  Intrinsic α helix propensities compact hydrodynamic radii in intrinsically disordered proteins.

Authors:  Lance R English; Erin C Tilton; Benjamin J Ricard; Steven T Whitten
Journal:  Proteins       Date:  2017-01-05

Review 4.  Exploring Nearest Neighbor Interactions and Their Influence on the Gibbs Energy Landscape of Unfolded Proteins and Peptides.

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5.  Dihedral angles of tripeptides in solution directly determined by polarized Raman and FTIR spectroscopy.

Authors:  Reinhard Schweitzer-Stenner
Journal:  Biophys J       Date:  2002-07       Impact factor: 4.033

6.  PON-P2: prediction method for fast and reliable identification of harmful variants.

Authors:  Abhishek Niroula; Siddhaling Urolagin; Mauno Vihinen
Journal:  PLoS One       Date:  2015-02-03       Impact factor: 3.240

7.  Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes.

Authors:  Yerukala Sathipati Srinivasulu; Jyun-Rong Wang; Kai-Ti Hsu; Ming-Ju Tsai; Phasit Charoenkwan; Wen-Lin Huang; Hui-Ling Huang; Shinn-Ying Ho
Journal:  BMC Bioinformatics       Date:  2015-12-09       Impact factor: 3.169

8.  Application of fourier transform and proteochemometrics principles to protein engineering.

Authors:  Frédéric Cadet; Nicolas Fontaine; Iyanar Vetrivel; Matthieu Ng Fuk Chong; Olivier Savriama; Xavier Cadet; Philippe Charton
Journal:  BMC Bioinformatics       Date:  2018-10-16       Impact factor: 3.169

9.  Exploiting structural and topological information to improve prediction of RNA-protein binding sites.

Authors:  Stefan R Maetschke; Zheng Yuan
Journal:  BMC Bioinformatics       Date:  2009-10-18       Impact factor: 3.169

10.  Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors.

Authors:  Meijian Sun; Xia Wang; Chuanxin Zou; Zenghui He; Wei Liu; Honglin Li
Journal:  BMC Bioinformatics       Date:  2016-06-07       Impact factor: 3.169

  10 in total

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