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Media type:
E-Article
Title:
Creating protein models from electron-density maps using particle-filtering methods
Contributor:
DiMaio, Frank;
Kondrashov, Dmitry A.;
Bitto, Eduard;
Soni, Ameet;
Bingman, Craig A.;
Phillips, George N.;
Shavlik, Jude W.
Published:
Oxford University Press (OUP), 2007
Published in:
Bioinformatics, 23 (2007) 21, Seite 2851-2858
Language:
English
DOI:
10.1093/bioinformatics/btm480
ISSN:
1367-4811;
1367-4803
Origination:
Footnote:
Description:
AbstractMotivation: One bottleneck in high-throughput protein crystallography is interpreting an electron-density map, that is, fitting a molecular model to the 3D picture crystallography produces. Previously, we developed Acmi (Automatic Crystallographic Map Interpreter), an algorithm that uses a probabilistic model to infer an accurate protein backbone layout. Here, we use a sampling method known as particle filtering to produce a set of all-atom protein models. We use the output of Acmi to guide the particle filter's sampling, producing an accurate, physically feasible set of structures.Results: We test our algorithm on 10 poor-quality experimental density maps. We show that particle filtering produces accurate all-atom models, resulting in fewer chains, lower sidechain RMS error and reduced R factor, compared to simply placing the best-matching sidechains on Acmi's trace. We show that our approach produces a more accurate model than three leading methods—Textal, Resolve and ARP/WARP—in terms of main chain completeness, sidechain identification and crystallographic R factor.Availability: Source code and experimental density maps available at http://ftp.cs.wisc.edu/machine-learning/shavlik-group/programs/acmi/Contact: dimaio@cs.wisc.edu