The emplace_local tool docks the input model within a sphere of density centered on a defined point in the half-maps, returning the docked model, a local weighted map, and quality of fit statistics.
The chosen region of density is analysed for local signal and noise (both of which are resolution and direction-dependent) to define a likelihood target for rotation and translation searches, followed by rigid-body refinement. The quality of fit can be judged by three factors: 1) whether there is a unique solution; 2) the log-likelihood-gain (LLG) score, which should preferably be greater than 60; 3) the map correlation coefficient to the weighted map used for docking.
The basic inputs are:
model file two unmasked half-map files nominal resolution specification of center of search region
Note that the search center can be specified explicitly with the sphere_center keyword (the usual approach) or by translating the search model to the desired search region and not providing a sphere_center keyword.
phenix.voyager.emplace_local map1=half1.map map2=half2.map d_min=3.3 model_file=model.pdb sphere_center=100,140,170
Read RJ, Millán C, McCoy AJ and Terwilliger TC. Likelihood-based signal and noise analysis for docking of models into cryo-EM maps. To be published.
Millán C, McCoy AJ, Terwilliger TC and Read RJ. Likelihood-based docking of models into cryo-EM maps. To be published.