PconsD: Ultra rapid, accurate model quality assessment for protein structure prediction
2013 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 29, no 14, 1817-1818 p.Article in journal (Refereed) Published
Clustering methods are often needed for accurately assessing the quality of modeled protein structures. Recent blind evaluation of quality assessment methods in CASP10 showed that there is very little difference between many different methods as far as ranking models and selecting best model are concerned. When comparing many models the computational cost of the model comparison can become significant. Here, we present PconsD, a very fast, stream-computing method for distance-driven model quality assessment, that runs on consumer hardware. PconsD is at least one order of magnitude faster than other methods of comparable accuracy.
Place, publisher, year, edition, pages
2013. Vol. 29, no 14, 1817-1818 p.
protein structure prediction, quality assessment, stream computing, GPGPU, MQAP, OpenCL
Bioinformatics and Systems Biology
Research subject Biochemistry with Emphasis on Theoretical Chemistry
IdentifiersURN: urn:nbn:se:su:diva-89364DOI: 10.1093/bioinformatics/btt272ISI: 000321747800019OAI: oai:DiVA.org:su-89364DiVA: diva2:617418
FunderEU, FP7, Seventh Framework Programme, 215524Swedish Research Council, VR-NT 2009-5072Swedish Research Council, VR-M 2010-3555VINNOVAEU, FP7, Seventh Framework Programme, 201924