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Crystallographic disorder is variation in atomic positions, orientations or chemical occupation that is represented by an average crystal structure rather than one perfectly repeated local arrangement. A split-site disorder model can describe alternative positions with fractional crystallographic site occupancies. The IUCr disorder-group convention distinguishes atoms present together in one configuration from mutually alternative configurations.^[ref]

For an original single-atom model, suppose an atom occupies position0 along an axis with probability0.6 and position $d$ with probability0.4. ThenAt $d=1\mathring{\mathrm{A}}$, the mean is $0.4\mathring{\mathrm{A}}$ and the variance is $0.24{\mathring{\mathrm{A}}}^2$. There need be no atom at the mean position in any individual configuration. Fitting only an average position with a large atomic displacement parameter can conceal this distinction.

The same average occupation could arise from different unit cells adopting different configurations, from motion among configurations during measurement, or from a mixture of these effects. A static average alone does not determine an interchange rate.

Experimental interpretation therefore combines the electron density map, the allowed chemical geometry, occupation constraints and other evidence. Weak or broadened density should not automatically be assigned one specific dynamic mechanism.

Crystallographic site occupancy is the fraction of a specified atom type present at a modeled site in a crystal structure. Occupancy1 represents full occupation by that type; a fractional value describes an average over the crystal. The IUCr core dictionary distinguishes this fraction from simply listing the site coordinates and constrains the summed occupancies of atom types sharing a physical site.^[ref]

A fractional occupancy does not mean that an individual atom has been divided. For an original population example, suppose a site is occupied by the specified atom in750 of1000 equivalent unit cells. Its average occupancy is $q=0.75$. More generally, let the crystallographic site multiplicity be $m=4$ equivalent positions per unit cell, each with occupancy0.75. The expected number of those atoms per unit cell iswhere $I_j$ is1 when position $j$ is occupied and0 otherwise. Linearity of expectation does not require the occupations to be independent. Correlations and local ordering can differ while the average count remains the same.

If two alternative atom types completely and mutually exclusively fill one site, occupancies0.40and0.60 sum to1. That describes a different model from occupancy0.40 of one type with the rest vacant. Structural alternatives of this kind are associated with crystallographic disorder; occupancy must be interpreted together with the model of what shares the site.

In an ideal structure factor calculation, the contribution of a site is multiplied by its occupancy. For fixed position and atomic displacement parameter, write $F_H=q{A}_H$, where $A_H$ is its full-occupancy complex contribution. A heavy-atom derivative then has $F_{PH}=F_P+q{A}_H$. With original values $F_P=3+4i$ and $A_H=2$, occupancy0.5 gives squared amplitude $|4+4i{|}^2=32$, whereas occupancy1 gives $|5+4i{|}^2=41$. The native value is25. Intensity changes are therefore not simply proportional to occupancy, because the added amplitude interferes with the parent amplitude.

The IUCr dictionary for multiple isomorphous replacement records occupancy separately for a heavy-atom site in each derivative.^[ref] A refined fraction concerns that structural site and scattering model; it is not by itself a general measurement of bulk chemical purity.

A difference electron density map is an electron density map calculated to reveal disagreement between measured diffraction amplitudes and those predicted by a crystal structure model. The common $F_o-F_c$ map combines observed-minus-calculated amplitude information with phases, usually derived from the model. In practice map coefficients include weighting rather than simply subtracting raw measured intensities.^[ref]

For one simplified convention, let $F_c(\mathbf{h})=|F_c(\mathbf{h})|e^{i{\varphi }_c(\mathbf{h})}$. A schematic unweighted difference map isThe crystallographic phases remain necessary even though the coefficients represent differences. Positive features can indicate density missing from the model; negative features can indicate excessive modeled density. Errors in positions, crystallographic site occupancies or atomic displacement parameters can all contribute.

An original one-dimensional idealization takes ${\rho }_o(x)=3+2\cos (2\pi x)$ and ${\rho }_c(x)=3+\cos (2\pi x)$, with matching phases. Their difference isIt is positive near0 and negative near $1/2$, although its integral over one period is zero. Positive and negative residuals therefore need not mean that the total electron count is wrong.

Experimentally the difference electron density map guides corrections to a fitted macromolecular structural model. Finite diffraction data, uncertain phases and model-derived coefficients affect its interpretation; a peak alone does not uniquely identify an atom or chemical species.

A macromolecular structural model is a representation of the three-dimensional organization of a biological macromolecule or complex. It can include atom identities, coordinates, crystallographic site occupancy, atomic displacement parameters and alternative conformations. A deposited coordinate set is an interpreted model supported by measurements, rather than the measurements themselves.^[ref]

For X-ray crystallography, an electron density map constrains where atoms can be placed, while chemical geometry and experimental uncertainty constrain interpretation. Poorly resolved regions can remain absent from the model or be represented less precisely. A numerical coordinate printed to several decimal places does not establish equally fine positional accuracy.

An original coordinate comparison illustrates the distinction. Suppose two models place the same atom at $(0,0,0)$ and $(0.2,0,0)$ in angstrom units. Their separation is $0.2\mathring{\mathrm{A}}$, but that number alone cannot say which model is better. One must examine agreement with the data and the relevant uncertainty, not merely the number of digits or the difference between the coordinates.

A concrete example is calmodulin structure1CLL in the Protein Data Bank. The record contains a refined model with four calcium ions, while its primary citation describes poorly defined terminal residues. Both the observed binding environment and the unresolved regions are part of interpreting the structure.^[ref]

Structural claims should therefore specify which components and conformations are modeled, what experiment supports them and what remains unresolved. A model useful for one question does not automatically establish the complete dynamics or every biochemical activity of the molecule.