F. DiMaio, D. Kondrashov, E. Bitto, A. Soni, C. Bingman, G. Phillips & J. Shavlik (2007).
Creating Protein Models from Electron-Density Maps using Particle-Filtering Methods. Bioinformatics, 23, pp. 2851-2858. doi: 10.1093/bioinformatics/btm480 PMCID: PMC2567142
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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 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.
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