Literature DB >> 15617806

Folding rate prediction using n-order contact distance for proteins with two- and three-state folding kinetics.

Linxi Zhang1, Tingting Sun.   

Abstract

It is a challenging task to understand the relationship between sequences and folding rates of proteins. Previous studies are found that one of contact order (CO), long-range order (LRO), total contact distance (TCD), chain topology parameter (CTP), and effective length (Leff) has a significant correlation with folding rate of proteins. In this paper, we introduce a new parameter called n-order contact distance (nOCD) and use it to predict folding rate of proteins with two- and three-state folding kinetics. A good linear correlation between the folding rate logarithm lnkf and nOCD with n=1.2, alpha=0.6 is found for two-state folders (correlation coefficient is -0.809, P-value<0.0001) and n=2.8, alpha=1.5 for three-state folders (correlation coefficient is -0.816, P-value<0.0001). However, this correlation is completely absent for three-state folders with n=1.2, alpha=0.6 (correlation coefficient is 0.0943, P-value=0.661) and for two-state folders with n=2.8, alpha=1.5 (correlation coefficient is -0.235, P-value=0.2116). We also find that the average number of contacts per residue Pm in the interval of m for two-state folders is smaller than that for three-state folders. The probability distribution P(gamma) of residue having gamma pairs of contacts fits a Gaussian distribution for both two- and three-state folders. We observe that the correlations between square radius of gyration S2 and number of residues for two- and three-state folders are both good, and the correlation coefficient is 0.908 and 0.901, and the slope of the fitting line is 1.202 and 0.795, respectively. Maybe three-state folders are more compact than two-state folders. Comparisons with nTCD and nCTP are also made, and it is found that nOCD is the best one in folding rate prediction.

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Year:  2005        PMID: 15617806     DOI: 10.1016/j.bpc.2004.07.036

Source DB:  PubMed          Journal:  Biophys Chem        ISSN: 0301-4622            Impact factor:   2.352


  5 in total

1.  On the role of structural class of a protein with two-state folding kinetics in determining correlations between its size, topology, and folding rate.

Authors:  Andrei Y Istomin; Donald J Jacobs; Dennis R Livesay
Journal:  Protein Sci       Date:  2007-11       Impact factor: 6.725

2.  What have we learned from the studies of two-state folders, and what are the unanswered questions about two-state protein folding?

Authors:  Doug Barrick
Journal:  Phys Biol       Date:  2009-02-10       Impact factor: 2.583

3.  Protein Folding Database (PFD 2.0): an online environment for the International Foldeomics Consortium.

Authors:  Kate F Fulton; Mark A Bate; Noel G Faux; Khalid Mahmood; Chris Betts; Ashley M Buckle
Journal:  Nucleic Acids Res       Date:  2006-12-14       Impact factor: 16.971

4.  Machine Learning: How Much Does It Tell about Protein Folding Rates?

Authors:  Marc Corrales; Pol Cuscó; Dinara R Usmanova; Heng-Chang Chen; Natalya S Bogatyreva; Guillaume J Filion; Dmitry N Ivankov
Journal:  PLoS One       Date:  2015-11-25       Impact factor: 3.240

Review 5.  The Molten Globule, and Two-State vs. Non-Two-State Folding of Globular Proteins.

Authors:  Kunihiro Kuwajima
Journal:  Biomolecules       Date:  2020-03-06
  5 in total

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