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


BACKGROUND
Source code and experimental density maps available at http://ftp.cs.wisc.edu/machine-learning/shavlik-group/programs/acmi/
DiMaio
Frank
F
Department of Computer Sciences, University of Wisconsin, Madison, WI 53706, USA. dimaio@cs.wisc.edu
Kondrashov
Dmitry A
DA
Bitto
Eduard
E
Soni
Ameet
A
Bingman
Craig A
CA
Phillips
George N
GN
Shavlik
Jude W
JW
D015502:Absorptiometry, Photon / Q000379:methods*; D000465:Algorithms; D003198:Computer Simulation; D018360:Crystallography, X-Ray / Q000379:methods*; D005374:Filtration / Q000379:methods; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D010316:Particle Size; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000648:ultrastructure*
D011506:Proteins