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

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@cs.wisc.edu