Authors

• Randy J Read, Claudia Millán, Airlie J McCoy

Purpose

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.

How emplace_local works

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.

Standard run of emplace_local

phenix.voyager.emplace_local map1=half1.map map2=half2.map
    d_min=3.3 model_file=model.pdb sphere_center=100,140,170

Literature

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.