Ligand identification using electron-density map correlations - PubMed (original) (raw)

Ligand identification using electron-density map correlations

Thomas C Terwilliger et al. Acta Crystallogr D Biol Crystallogr. 2007 Jan.

Abstract

A procedure for the identification of ligands bound in crystal structures of macromolecules is described. Two characteristics of the density corresponding to a ligand are used in the identification procedure. One is the correlation of the ligand density with each of a set of test ligands after optimization of the fit of that ligand to the density. The other is the correlation of a fingerprint of the density with the fingerprint of model density for each possible ligand. The fingerprints consist of an ordered list of correlations of each the test ligands with the density. The two characteristics are scored using a Z-score approach in which the correlations are normalized to the mean and standard deviation of correlations found for a variety of mismatched ligand-density pairs, so that the Z scores are related to the probability of observing a particular value of the correlation by chance. The procedure was tested with a set of 200 of the most commonly found ligands in the Protein Data Bank, collectively representing 57% of all ligands in the Protein Data Bank. Using a combination of these two characteristics of ligand density, ranked lists of ligand identifications were made for representative (F(o) - F(c))exp(i(phi)c) difference density from entries in the Protein Data Bank. In 48% of the 200 cases, the correct ligand was at the top of the ranked list of ligands. This approach may be useful in identification of unknown ligands in new macromolecular structures as well as in the identification of which ligands in a mixture have bound to a macromolecule.

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Figures

Figure 1

Figure 1

(a) ATP fitted into model 2.5 Å density for ATP. (b) ddATP fitted into model density for ATP. (c) GTP fitted into model density for ATP.

Figure 2

Figure 2

Histograms of rank position of correct ligands. (a) Scoring using correlation of density, considering 119 unique ligands. (b) Scoring using Z score derived from correlation of density. (c) Scoring using Z score derived from correlation of fingerprints of density and fingerprints of model density. (d) Scoring using sum of Z scores from correlation of density and correlation of fingerprints of density. (e) As in (d), but considering all 200 of the most common ligands in the PDB. (f) As in (d), but considering only 31 unique ligands.

Figure 3

Figure 3

(a) F o − F c difference density for bacteriochlorophyll a at 2.4 Å (PDB code

1ogv

; Katona et al., 2003 ▶), fitted with the same ligand from PDB entry

1dv6

(Axelrod et al., 2000 ▶). (b) Difference density for cyclohexyl-hexyl-β-

d

-maltoside at a resolution of 1.1 Å (PDB code

1ong

; Venkatesan et al., 2004 ▶), fitted with the same ligand from PDB entry

1q2p

(Nukaga et al., 2003 ▶).

Figure 4

Figure 4

Fitting of F o − F c difference density for tris-(hydroxylmethyl)-methane from PDB entry

1m6z

(A. Noergaard, P. Harris, S. Larsen & H. E. M. Christensen, unpublished) at a resolution of 1.4 Å. (a) Density fitted by the same ligand from a different PDB entry (

1s18

; Dai et al., 2004 ▶). (b) Density fitted with oxalate. (c) Density fitted with dioxane.

Figure 5

Figure 5

Fingerprints of difference density. (a) Correlation of each of 119 unique ligands after fitting to difference density for tris-(hydroxyamino)-methane from PDB entry

1m6z

(A. Noergaard, P. Harris, S. Larsen & H. E. M. Christensen, unpublished work) at a resolution of 1.4 Å. The ligands are sorted from left to right based on increasing numbers of non-H atoms. (b) As in (a), except fitting to difference density for ATP from PDB entry

1aq2

at a resolution of 1.9 Å (Tari et al., 1997 ▶). The correlations are all indicated by filled triangles, except for the correlation of the correct ligand, which is indicated by an open diamond.

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