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

This Article Full Text Full Text (PDF) All Versions of this Article: ps.072895107v1 16/11/2412    most recent Alert me when this article is cited Alert me if a correction is posted Citation Map 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 Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Melo, F. Articles by Sali, A. Search for Related Content PubMed PubMed Citation Articles by Melo, F. Articles by Sali, A. Social Bookmarking                   What's this?

Fold assessment for comparative protein structure modeling

¹ Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile
² Department of Biopharmaceutical Sciences, University of California, San Francisco, California 94143-2240, USA
³ Department of Pharmaceutical Chemistry, University of California, San Francisco, California 94143-2240, USA
⁴ California Institute for Quantitative Biomedical Research, University of California, San Francisco, California 94143-2240, USA

(RECEIVED April 5, 2007; FINAL REVISION July 16, 2007; ACCEPTED July 18, 2007)

Accurate and automated assessment of both geometrical errors and incompleteness of comparative protein structure models is necessary for an adequate use of the models. Here, we describe a composite score for discriminating between models with the correct and incorrect fold. To find an accurate composite score, we designed and applied a genetic algorithm method that searched for a most informative subset of 21 input model features as well as their optimized nonlinear transformation into the composite score. The 21 input features included various statistical potential scores, stereochemistry quality descriptors, sequence alignment scores, geometrical descriptors, and measures of protein packing. The optimized composite score was found to depend on (1) a statistical potential z-score for residue accessibilities and distances, (2) model compactness, and (3) percentage sequence identity of the alignment used to build the model. The accuracy of the composite score was compared with the accuracy of assessment by single and combined features as well as by other commonly used assessment methods. The testing set was representative of models produced by automated comparative modeling on a genomic scale. The composite score performed better than any other tested score in terms of the maximum correct classification rate (i.e., 3.3% false positives and 2.5% false negatives) as well as the sensitivity and specificity across the whole range of thresholds. The composite score was implemented in our program MODELLER-8 and was used to assess models in the MODBASE database that contains comparative models for domains in approximately 1.3 million protein sequences.

Keywords: fold assessment; model assessment; statistical potentials; protein structure modeling; protein structure prediction

CiteULike

Connotea

Del.icio.us

Digg

Reddit

Technorati