Validation of ligands in macromolecular structures determined by X-ray crystallography

Abstract

Crystallographic studies of ligands bound to biological macromolecules (proteins and nucleic acids) play a crucial role in structure-guided drug discovery and design, and also provide atomic level insights into the physical chemistry of complex formation between macromolecules and ligands. The quality with which small-molecule ligands have been modelled in Protein Data Bank (PDB) entries has been, and continues to be, a matter of concern for many investigators. Correctly interpreting whether electron density found in a binding site is compatible with the soaked or co-crystallized ligand or represents water or buffer molecules is often far from trivial. The Worldwide PDB validation report (VR) provides a mechanism to highlight any major issues concerning the quality of the data and the model at the time of deposition and annotation, so the depositors can fix issues, resulting in improved data quality. The ligand-validation methods used in the generation of the current VRs are described in detail, including an examination of the metrics to assess both geometry and electron-density fit. It is found that the LLDF score currently used to identify ligand electron-density fit outliers can give misleading results and that better ligand-validation metrics are required.

Keywords: PDB; Protein Data Bank; ligands; three-dimensional macromolecular structure; validation.

Figure 1

Plots showing how the RMSZ value from Mogul analysis of bond lengths for ligands in all PDB depositions solved by X-ray crystallography varies with deposition date and ligand size. The boxes show the upper and lower quartile range, with the thick line marking the median value. The whiskers mark the 10th and 90th percentile of the data, following Kleywegt & Jones (2002 ▸).

Figure 2

Visualization of two ligands from the PDB together with electron-density maps. The LiteMol viewer (Sehnal et al., 2017 ▸) on the PDBe website (Mir et al., 2018 ▸) has been used to visualize the ligands with EDS-style (Kleywegt et al., 2004 ▸) electron-density maps. In each case, the 2mF _o − DF _c map is shown as a blue mesh contoured at 0.39 e Å⁻³, whereas the mF _o − DF _c difference map is shown by solid green and red surfaces contoured at +0.25 and −0.25 e Å⁻³, respectively. (a) Model and electron density for the well placed ligand 468 from PDB entry 4tzt (He et al., 2006 ▸). (b) The ligand DIF in PDB entry 3ib0 (Mir et al., 2009 ▸).

Figure 3

LLDF is plotted versus RSCC for all ligands in the PDB for which both values are available (see Table 1 ▸). Box plots and whiskers are as in Fig. 1 ▸.

Figure 4

Visualization of two ligands from the PDB together with electron-density maps where LLDF values provide misleading indications. (a) Example of a false positive: the map for PDB entry 1kcc at atomic resolution (1.0 Å; Shimizu et al., 2002 ▸) shows electron density for a well placed ligand GTR where each atom is individually resolved. The high RSCC value reflects the good fit. In contrast, the LLDF value is high so that the ligand is incorrectly marked as an electron-density fit outlier in the current VR. (b) Example of a false negative: the ligand FER in PDB entry 1kyz (Zubieta et al., 2002 ▸) has a poor fit to the electron density, resulting in large amounts of difference density. The LLDF value of 1.3 results in the ligand not being marked as an electron-density fit outlier in the current VR, whereas the low RSCC value suggests a problem.

Figure 5

Distribution of (a) LLDF, (b) RSCC and (c) RSR for structures by data resolution for the set of ligands in Table 1 ▸. Box plots and whiskers are as in Fig. 1 ▸.