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Bioinformatics Advance Access published online on October 12, 2007
Bioinformatics, 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 a,b,*, Dmitry A. Kondrashov c, Eduard Bitto d, Ameet Soni a,b, Craig A. Bingman d, George N. Phillips, Jr. c,a,d and Jude W. Shavlik a,b

aComputer Sciences Dept., bBiostatistics and Medical Informatics Dept., cBiochemistry Dept., dCenter for Eukaryotic Structural Genomics University of Wisconsin, Madison, WI, 53706

*To whom correspondence should be addressed. Mr. Frank DiMaio, E-mail: dimaio{at}cs.wisc.edu


  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, 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 ten poor-quality experimental density maps. We show that particle filtering produces accurate allatom 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 ftp://ftp.cs.wisc.edu/machine-learning/shavlik-group/programs/acmi/.

Contact: dimaio{at}cs.wisc.edu

Associate Editor: Prof. Burkhard Rost

Received on August 1, 2007; revised on August 31, 2007; accepted on September 20, 2007

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