Document Detail


Rapid model quality assessment for protein structure predictions using the comparison of multiple models without structural alignments.
MedLine Citation:
PMID:  19897565     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
MOTIVATION: The accurate prediction of the quality of 3D models is a key component of successful protein tertiary structure prediction methods. Currently, clustering- or consensus-based Model Quality Assessment Programs (MQAPs) are the most accurate methods for predicting 3D model quality; however, they are often CPU intensive as they carry out multiple structural alignments in order to compare numerous models. In this study, we describe ModFOLDclustQ--a novel MQAP that compares 3D models of proteins without the need for CPU intensive structural alignments by utilizing the Q measure for model comparisons. The ModFOLDclustQ method is benchmarked against the top established methods in terms of both accuracy and speed. In addition, the ModFOLDclustQ scores are combined with those from our older ModFOLDclust method to form a new method, ModFOLDclust2, that aims to provide increased prediction accuracy with negligible computational overhead. RESULTS: The ModFOLDclustQ method is competitive with leading clustering-based MQAPs for the prediction of global model quality, yet it is up to 150 times faster than the previous version of the ModFOLDclust method at comparing models of small proteins (<60 residues) and over five times faster at comparing models of large proteins (>800 residues). Furthermore, a significant improvement in accuracy can be gained over the previous clustering-based MQAPs by combining the scores from ModFOLDclustQ and ModFOLDclust to form the new ModFOLDclust2 method, with little impact on the overall time taken for each prediction. AVAILABILITY: The ModFOLDclustQ and ModFOLDclust2 methods are available to download from http://www.reading.ac.uk/bioinf/downloads/.
Authors:
Liam J McGuffin; Daniel B Roche
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2009-11-06
Journal Detail:
Title:  Bioinformatics (Oxford, England)     Volume:  26     ISSN:  1367-4811     ISO Abbreviation:  Bioinformatics     Publication Date:  2010 Jan 
Date Detail:
Created Date:  2010-01-12     Completed Date:  2010-04-09     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9808944     Medline TA:  Bioinformatics     Country:  England    
Other Details:
Languages:  eng     Pagination:  182-8     Citation Subset:  IM    
Affiliation:
School of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6AS, UK. l.j.mcguffin@reading.ac.uk
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MeSH Terms
Descriptor/Qualifier:
Computational Biology / methods*
Databases, Protein
Models, Molecular
Pattern Recognition, Automated / methods*
Protein Conformation*
Protein Folding
Proteins / chemistry*
Chemical
Reg. No./Substance:
0/Proteins

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


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