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Bioinformatics. 2007 Nov 1;23(21):2851-8. Epub 2007 Oct 12.

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

Author information

1
Department of Computer Sciences, University of Wisconsin, Madison, WI 53706, USA. dimaio@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 (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/

PMID:
17933855
PMCID:
PMC2567142
DOI:
10.1093/bioinformatics/btm480
[Indexed for MEDLINE]
Free PMC Article

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