Abstract
Refinement is a process that involves bringing into agreement the structural model, available prior knowledge and experimental data. To achieve this, the refinement procedure optimizes a posterior conditional probability distribution of model parameters, including atomic coordinates, atomic displacement parameters (B factors), scale factors, parameters of the solvent model and twin fractions in the case of twinned crystals, given observed data such as observed amplitudes or intensities of structure factors. A library of chemical restraints is typically used to ensure consistency between the model and the prior knowledge of stereochemistry. If the observation-to-parameter ratio is small, for example when diffraction data only extend to low resolution, the Bayesian framework implemented in REFMAC5 uses external restraints to inject additional information extracted from structures of homologous proteins, prior knowledge about secondary-structure formation and even data obtained using different experimental methods, for example NMR. The refinement procedure also generates the `best' weighted electron-density maps, which are useful for further model (re)building. Here, the refinement of macromolecular structures using REFMAC5 and related tools distributed as part of the CCP4 suite is discussed.
Highlights
Attempting to understand the three-dimensional structures of macromolecules is akin to opening a ‘black box’: structural models can provide some ideas, or at least hypotheses to test, regarding the function of a molecule of interest
All three methods result in an ‘atomic model’, experimental data obtained using these different methods are subjected to different procedures using different tools, reflecting differences in the physical processes underlying each method
We review the Bayesian framework as implemented in the macromolecular structure-refinement program REFMAC5 (Murshudov et al, 2011), noting that comparable technologies are employed by other refinement software
Summary
Attempting to understand the three-dimensional structures of macromolecules is akin to opening a ‘black box’: structural models can provide some ideas, or at least hypotheses to test, regarding the function of a molecule of interest. For the majority of cases, our interpretation of incomplete and noisy experimental observations can be improved by using additional sources of information: the stereochemistry of constituent blocks of macromolecules, typical secondary-structure patterns, structures of related macromolecular domains, structural data obtained using different experimental methods etc. Longer range information, such as torsion angles and information relating to secondary structures and the composition of domains, might be needed This reflects a general principle: when complementing data with prior knowledge that is not contained in the data, the less experimental evidence one has the more one must rely on prior knowledge, the more prone refinement is to suffer bias towards prior information. We will discuss how the formalism of restraints (chemical restraints and external restraints) can be used to incorporate prior knowledge (including information obtained using different experimental methods) into the refinement process, and even establish information flow between different but related structures
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