A composite score for predicting errors in protein structure models

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dc.catalogadoraba
dc.contributor.authorEramian, D.
dc.contributor.authorShen, M. Y.
dc.contributor.authorDevos D.
dc.contributor.authorMelo Ledermann, Francisco Javier
dc.contributor.authorSali, A.
dc.contributor.authorMarti Renom, M. A.
dc.date.accessioned2025-02-06T19:43:30Z
dc.date.available2025-02-06T19:43:30Z
dc.date.issued2006
dc.description.abstractReliable prediction of model accuracy is an important unsolved problem in protein structure modeling. To address this problem, we studied 24 individual assessment scores, including physics-based energy functions, statistical potentials, and machine learning–based scoring functions. Individual scores were also used to construct ∼85,000 composite scoring functions using support vector machine (SVM) regression. The scores were tested for their abilities to identify the most native-like models from a set of 6000 comparative models of 20 representative protein structures. Each of the 20 targets was modeled using a template of <30% sequence identity, corresponding to challenging comparative modeling cases. The best SVM score outperformed all individual scores by decreasing the average RMSD difference between the model identified as the best of the set and the model with the lowest RMSD (ΔRMSD) from 0.63 Å to 0.45 Å, while having a higher Pearson correlation coefficient to RMSD (r = 0.87) than any other tested score. The most accurate score is based on a combination of the DOPE non-hydrogen atom statistical potential; surface, contact, and combined statistical potentials from MODPIPE; and two PSIPRED/DSSP scores. It was implemented in the SVMod program, which can now be applied to select the final model in various modeling problems, including fold assignment, target–template alignment, and loop modeling.
dc.format.extent13 páginas
dc.fuente.origenSIPA
dc.identifier.doi10.1110/ps.062095806
dc.identifier.eissn1469-896X
dc.identifier.issn0961-8368
dc.identifier.pubmedid16751606
dc.identifier.pubmedidPMC2242555
dc.identifier.scopusid2-s2.0-33745726716
dc.identifier.urihttps://doi.org/10.1110/ps.062095806
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/102190
dc.identifier.wosidWOS:000238707200008
dc.information.autorucFacultad de Ciencias Biológicas; Melo Ledermann, Francisco Javier; 0000-0002-0424-5991; 82342
dc.issue.numero7
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final1666
dc.pagina.inicio1653
dc.revistaProtein science : a publication of the Protein Society
dc.rightsacceso restringido
dc.subjectModel assessment
dc.subjectComparative modeling
dc.subjectFold assignment
dc.subjectStatistical potentials
dc.subjectSupportvector machine
dc.subjectProtein structure prediction
dc.subject.ddc570
dc.subject.deweyBiologíaes_ES
dc.titleA composite score for predicting errors in protein structure models
dc.typeartículo
dc.volumen15
sipa.codpersvinculados82342
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