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


ABSTRACT: 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.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.Source code and experimental density maps available at http://ftp.cs.wisc.edu/machine-learning/shavlik-group/programs/acmi/

SUBMITTER: DiMaio F 

PROVIDER: S-EPMC2567142 | biostudies-literature | 2007 Nov

REPOSITORIES: biostudies-literature

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

DiMaio Frank F   Kondrashov Dmitry A DA   Bitto Eduard E   Soni Ameet A   Bingman Craig A CA   Phillips George N GN   Shavlik Jude W JW  

Bioinformatics (Oxford, England) 20071012 21


<h4>Motivation</h4>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 gui  ...[more]

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