Document Detail

First insight into the prediction of protein folding rate change upon point mutation.
MedLine Citation:
PMID:  20616385     Owner:  NLM     Status:  MEDLINE    
SUMMARY: The accurate prediction of protein folding rate change upon mutation is an important and challenging problem in protein folding kinetics and design. In this work, we have collected experimental data on protein folding rate change upon mutation from various sources and constructed a reliable and non-redundant dataset with 467 mutants. These mutants are widely distributed based on secondary structure, solvent accessibility, conservation score and long-range contacts. From systematic analysis of these parameters along with a set of 49 amino acid properties, we have selected a set of 12 features for discriminating the mutants that speed up or slow down the folding process. We have developed a method based on quadratic regression models for discriminating the accelerating and decelerating mutants, which showed an accuracy of 74% using the 10-fold cross-validation test. The sensitivity and specificity are 63% and 76%, respectively. The method can be improved with the inclusion of physical interactions and structure-based parameters.
Liang-Tsung Huang; M Michael Gromiha
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Publication Detail:
Type:  Journal Article     Date:  2010-07-08
Journal Detail:
Title:  Bioinformatics (Oxford, England)     Volume:  26     ISSN:  1367-4811     ISO Abbreviation:  Bioinformatics     Publication Date:  2010 Sep 
Date Detail:
Created Date:  2010-08-18     Completed Date:  2010-09-30     Revised Date:  2013-05-20    
Medline Journal Info:
Nlm Unique ID:  9808944     Medline TA:  Bioinformatics     Country:  England    
Other Details:
Languages:  eng     Pagination:  2121-7     Citation Subset:  IM    
Department of Information Communication, Mingdao University, Changhua 523, Taiwan.
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MeSH Terms
Computational Biology / methods*
Point Mutation*
Protein Folding*
Protein Structure, Secondary / genetics
Regression Analysis
Sensitivity and Specificity

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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