Nonbonded terms extrapolated from nonlocal knowledge-based energy functions improve error detection in near-native protein structure models

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dc.catalogadoraba
dc.contributor.authorFerrada, E.
dc.contributor.authorMelo Ledermann, Francisco Javier
dc.date.accessioned2025-02-06T19:48:08Z
dc.date.available2025-02-06T19:48:08Z
dc.date.issued2007
dc.description.abstractThe accurate assessment of structural errors plays a key role in protein structure prediction, constitutes the first step of protein structure refinement, and has a major impact on subsequent functional inference from structural data. In this study, we assess and compare the ability of different full atom knowledge-based potentials to detect small and localized errors in comparative protein structure models of known accuracy. We have evaluated the effect of incorporating close nonbonded pairwise atom terms on the task of classifying residue modeling accuracy. Since the direct and unbiased derivation of close nonbonded terms from current experimental data is not possible, we extrapolated those terms from the corresponding pseudo-energy functions of a nonlocal knowledge-based potential. It is shown that this methodology clearly improves the detection of errors in protein models, suggesting that a proper description of close nonbonded terms is important to achieve a more complete and accurate description of native protein conformations. The use of close nonbonded terms directly derived from experimental data exhibited a poor performance, demonstrating that these terms cannot be accurately obtained by using the current data and methodology. Some external knowledge-based energy functions that are widely used in model assessment also performed poorly, which suggests that the benchmark of models and the specific error detection task tested in this study constituted a difficult challenge. The methodology presented here could be useful to detect localized structural errors not only in high-quality protein models, but also in experimental protein structures.
dc.fechaingreso.objetodigital2025-02-15
dc.format.extent12 páginas
dc.fuente.origenSIPA
dc.identifier.doi10.1110/ps.062735907
dc.identifier.eissn1469-896X
dc.identifier.issn0961-8368
dc.identifier.pubmedid17586774
dc.identifier.pubmedidPMC2206707
dc.identifier.scopusid2-s2.0-34250886494
dc.identifier.urihttps://doi.org/10.1110/ps.062735907
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/102193
dc.identifier.wosidWOS:000247465400018
dc.information.autorucFacultad de Ciencias Biológicas; Melo Ledermann, Francisco Javier; 0000-0002-0424-5991; 82342
dc.issue.numero7
dc.language.isoen
dc.nota.accesocontenido completo
dc.pagina.final1421
dc.pagina.inicio1410
dc.revistaProtein science : a publication of the Protein Society
dc.rightsacceso abierto
dc.subject.ddc570
dc.subject.deweyMatemática física y química
dc.subject.ods03 Good health and well-being
dc.subject.odspa03 Salud y bienestar
dc.titleNonbonded terms extrapolated from nonlocal knowledge-based energy functions improve error detection in near-native protein structure models
dc.typeartículo
dc.volumen16
sipa.codpersvinculados82342
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