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Bioinformatics Advance Access originally published online on October 12, 2007
Bioinformatics 2007 23(21):2851-2858; doi:10.1093/bioinformatics/btm480
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Creating protein models from electron-density maps using particle-filtering methods

Frank DiMaio 1,2,*, Dmitry A. Kondrashov 3, Eduard Bitto 4, Ameet Soni 1,2, Craig A. Bingman 4, George N. Phillips, Jr 1,3,4 and Jude W. Shavlik 1,2

1Department of Computer Sciences, 2Department of Biostatistics and Medical Informatics, 3Department of Biochemistry and 4Center for Eukaryotic Structural Genomics, University of Wisconsin, Madison, WI 53706, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: 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{at}cs.wisc.edu

Associate Editor: Burkhard Rost

Received on August 31, 2007; accepted on September 20, 2007

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