Published in

International Union of Crystallography, Acta Crystallographica. Section d, Structural Biology, 3(73), p. 256-266, 2017

DOI: 10.1107/s2059798317003412

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Proper modelling of ligand binding requires an ensemble of bound and unbound states

Journal article published in 2017 by Nicholas M. Pearce ORCID, Tobias Krojer, Frank von Delft ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Although noncovalent binding by small molecules cannot be assumeda priorito be stoichiometric in the crystal lattice, occupancy refinement of ligands is often avoided by convention. Occupancies tend to be set to unity, requiring the occupancy error to be modelled by theBfactors, and residual weak density around the ligand is necessarily attributed to `disorder'. Where occupancy refinementisperformed, the complementary, superposed unbound state is rarely modelled. Here, it is shown that superior accuracy is achieved by modelling the ligand as partially occupied and superposed on a ligand-free `ground-state' model. Explicit incorporation of this model of the crystal, obtained from a reference data set, allows constrained occupancy refinement with minimal fear of overfitting. Better representation of the crystal also leads to more meaningful refined atomic parameters such as theBfactor, allowing more insight into dynamics in the crystal. An outline of an approach for algorithmically generating ensemble models of crystals is presented, assuming that data sets representing the ground state are available. The applicability of various electron-density metrics to the validation of the resulting models is assessed, and it is concluded that ensemble models consistently score better than the corresponding single-state models. Furthermore, it appears that ignoring the superposed ground state becomes the dominant source of model error, locally, once the overall model is accurate enough; modelling the local ground state properly is then more meaningful than correcting all remaining model errors globally, especially for low-occupancy ligands. Implications for the simultaneous refinement ofBfactors and occupancies, and for future evaluation of the limits of the approach, in particular its behaviour at lower data resolution, are discussed.