Crystallographic structure refinement is a complex procedure that combines a large number of very diverse steps, where each step may be very complex itself. Each refinement run requires selection of a model parameterization, a refinement target and an optimization method. These decisions are often dictated by the experimental data quality (completeness and resolution) and the current model quality (how complete the model is and the level of error in the atomic parameters). The diversity of data qualities (from ultrahigh to very low resolution) and model qualities (from crude molecular-replacement results to well refined near-final structures) generates the need for a large variety of possible model parameterizations, refinement targets and optimization methods.
Model parameters are variables used to describe the crystal content and its properties. Model parameters can be broken down into two categories: (i) those that describe the atomic model (atomic model parameters), such as atomic coordinates, atomic displacement parameters (ADPs), atomic occupancies and anomalous scattering terms (f′ and f′′), and (ii) non-atomic model parameters that describe bulk solvent, twinning, crystal anisotropy and so on. The parameters that describe the crystal are combined and expressed through the total model structure factors F
model, which are expected to match the corresponding observed values F
obs and other experimentally derived data (e.g. experimental phase information).
A refinement target is a mathematical function that quantifies the fit of the model parameters (expressed through F
model) and the experimental data (amplitudes, F
obs, or intensities, I
obs, and experimental phases if available). Typically, target functions are defined such that their value decreases as the model improves. This in turn formulates the goal of a crystallographic structure refinement as an optimization problem in which the model parameters are modified in order to achieve the lowest possible value of the target function or, in other words, minimization of the refinement target.
Algorithms to optimize the refinement target range from gradient-driven minimization, simulated-annealing-based methods and grid searches to interactive model building in a graphical environment. These methods vary in speed, scalability, convergence radius and applicability to current model parameters. The type of parameters to be optimized, the number of refinable parameters and the current model quality may all dictate the choice of optimization (target-minimization) method.
Below, we describe how crystallographic structure refinement is implemented in phenix.refine.