Prediction of global and local model quality in CASP8 using Pcons and ProQ
2009 (English)In: Proteins: Structure, Function, and Genetics, ISSN 0887-3585, E-ISSN 1097-0134, Vol. 77, no Suppl.9, 167-172 p.Article in journal (Refereed) Published
Model Quality Assessment Programs (MQAPs) are programs developed to rank protein models. These methods can be trained to predict the overall global quality of a model or what local regions in a model that are likely to be incorrect. In CASP8, we participated with two predictors that predict both global and local quality using either consensus information, Pcons, or purely structural information, ProQ. Consistently with results in previous CASPs, the best performance in CASP8 was obtained using the Pcons method. Furthermore, the results show that the modification introduced into Pcons for CASP8 improved the predictions against GDT-TS and now a correlation coefficient above 0.9 is achieved, whereas the correlation for ProQ is about 0.7. The correlation is better for the easier than for the harder targets, but it is not below 0.5 for a single target and below 0.7 only for three targets. The correlation coefficient for the best local quality MQAP is 0.68 showing that there is still clear room for improvement within this area. We also detect that Pcons still is not always able to identify the best model. However, we show that using a linear combination of Pcons and ProQ it is possible to select models that are better than the models from the best single server. In particular, the average quality over the hard targets increases by about 6% compared with using Pcons alone.
Place, publisher, year, edition, pages
2009. Vol. 77, no Suppl.9, 167-172 p.
Consensus, MQAP, Quality assessment
IdentifiersURN: urn:nbn:se:kth:diva-77464DOI: 10.1002/prot.22476ISI: 000272244700019PubMedID: 19544566OAI: oai:DiVA.org:kth-77464DiVA: diva2:491648
QC 201202152012-02-072012-02-072012-02-15Bibliographically approved