HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article ACCEPTED PREPRINT Full Text (PDF) All Versions of this Article: ps.034660.108v1 ps.034660.108v2 17/7/1256    most recent Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Ouyang, Z. Articles by Liang, J. PubMed PubMed Citation Articles by Ouyang, Z. Articles by Liang, J. Social Bookmarking                   What's this?

Predicting protein folding rates from geometric contact and amino acid sequence

¹ University of Illinois at Chicago;
² University of Illinois at chicago

(RECEIVED January 22, 2008; ACCEPTED April 7, 2008)

Protein folding speeds are known to vary over more than 8 orders of magnitude. Plaxco, Simons, and Baker first showed a correlation of folding speed with the topology of the native protein. That and subsequent studies showed that if the native structure of a protein is known, it's folding speed can be predicted reasonably well through a correlation with the "localness" of the contacts in the protein. In the present work, we develop a related measure, the geometric contact number, n, which is the number of nonlocal contacts that are well-packed, by a Voronoi criterion. We find, first, that in 80 proteins, the largest such database of proteins yet studied, n is a consistently excellent predictor of folding speeds of both two-state fast-folders and more complex multi-state folders. Second, we show that folding rates can also be predicted from amino acid sequences directly, without the need to know the native topology or other structural properties.

Keywords: Protein Structure/Folding; Computational Analysis of Protein Structure; folding rate; geometric contact number; zippers model

³ E-mail: jliang{at}uic.edu

CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?