Title
PconsD: ultra rapid, accurate model quality assessment for protein structure prediction.
Abstract
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 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 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.
Year
DOI
Venue
2013
10.1093/bioinformatics/btt272
BIOINFORMATICS
Field
DocType
Volume
Data mining,Protein structure prediction,Ranking,Source code,Computer science,Model quality,Bioinformatics,Cluster analysis,Benchmarking
Journal
29
Issue
ISSN
Citations 
14
1367-4803
7
PageRank 
References 
Authors
0.53
2
2
Name
Order
Citations
PageRank
Marcin J. Skwark11448.76
Arne Elofsson263356.98