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. 2012 Jun 8;419(3-4):211-22.
doi: 10.1016/j.jmb.2012.03.012. Epub 2012 Mar 23.

Fragmentation-tree density representation for crystallographic modelling of bound ligands

Gerrit G Langer  1 , Guillaume X Evrard, Ciaran G Carolan, Victor S Lamzin
Affiliations

Fragmentation-tree density representation for crystallographic modelling of bound ligands

Gerrit G Langer et al. J Mol Biol. .

Abstract

The identification and modelling of ligands into macromolecular models is important for understanding molecule's function and for designing inhibitors to modulate its activities. We describe new algorithms for the automated building of ligands into electron density maps in crystal structure determination. Location of the ligand-binding site is achieved by matching numerical shape features describing the ligand to those of density clusters using a "fragmentation-tree" density representation. The ligand molecule is built using two distinct algorithms exploiting free atoms with inter-atomic connectivity and Metropolis-based optimisation of the conformational state of the ligand, producing an ensemble of structures from which the final model is derived. The method was validated on several thousand entries from the Protein Data Bank. In the majority of cases, the ligand-binding site could be correctly located and the ligand model built with a coordinate accuracy of better than 1 Å. We anticipate that the method will be of routine use to anyone modelling ligands, lead compounds or even compound fragments as part of protein functional analyses or drug design efforts.

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Figures

Fig. 1
Fig. 1
Schematic representation of the three-step procedure of crystallographic ligand building in ARP/wARP.
Fig. 2
Fig. 2
Fragmentation tree of the difference electron density of the oxyreductase structure 1ed5. The clusters for a protoporphyrin IX ring (HEM), N-omega-nitro-l-argi-nine (NRG) and a zinc ion (ZN) ion are shown in red, green and blue, respectively. Other features in the density map, including water molecules, are coloured black.
Fig. 3
Fig. 3
Construction of the FAD molecule (PDB code 2gf3) using the label-swapping algorithm: (a) the search ligand, (b) its sparse density cluster and (c) one high-scored subgraph-matching solution. The arrow in (a) points to the pivot ligand atom, which is assigned to each node. The arrow in (b) points to the group of nodes (marked as balls) from which the expansion leads to a complete model of the ligand (see also Fig. 4).
Fig. 4
Fig. 4
Matching of the FAD molecule (Fig. 3a) to the sparse grid shown in Fig. 3b. The evolution of the model building process for each starting node (of 98 nodes in total) is plotted on the horizontal axis. The number of ligand atoms assigned to a sub-cluster is on the vertical axis. The number of candidate models is delineated as follows: green stands for 0 models in a “stack” and red indicates that only the top 100 models are kept for further expansion; grey scale colours indicate intermediates between both extremes. Only the few labelled starting grid nodes allow for complete assignment of all ligand atoms.
Fig. 5
Fig. 5
Characteristics of the ligand-building test set.
Fig. 6
Fig. 6
The overall performance of the ligand-building procedure. Green areas denote successful building with an r.m.s.d. from the PDB model of less than 1.0 Å, red indicates building at a correctly identified binding site but with an r.m.s.d. higher than 1.0 Å, blue areas correspond to ligand models built in the wrong place, (a) 9389 cases with seven or more non-hydrogen atoms and (b) 2773 cases with ligand well-pronounced in the density (real-space map correlation of 0.8 or higher) and sizes from 20 to 40 atoms.
Fig. 7
Fig. 7
Examples of ligands of diverse sizes built in maps at various resolutions—deposited ligands are shown in atom colour, built ligands in yellow; the maps are contoured at a level of 1.5 sigma above the mean. (a) A sulphate ion in bovine pancreatic ribonuclease A built at 1.6 Å resolution with an r.m.s.d. of 0.45 Å to the reference structure (PDB code 1a5p); (b) the anti-cholesterol agent atorvastatin, bound to its biological target, HMG coenzyme A reductase (1hwk), at 2.2 Å with an r.m.s.d. of 0.23 Å; (c) a hexasaccharide ligand with 65 non-hydrogen atoms bound to a bacterial α-amylase (1qho) rebuilt with an r.m.s.d. of 0.31 Å at 1.7 Å resolution; (d) a transition-state analogue of a plant enzyme, myrosinase, (1e6q) built with a coordinate accuracy of 0.22 Å in a map at 1.35 Å; (e) 17-–-estradiol built with an r.m.s.d. of 0.31 Å in the map derived from a complex with the human estrogen receptor at 3.1 Å resolution (1ere).
Fig. 8
Fig. 8
(a) A plant lumazine synthase inhibitor built into a 3. 1-Å protein structure (PDB 1c41), to an r.m.s.d. of 1.9 Å from the deposited ligand. (b) S-Adenosyl methionine modelled in a tRNA methyltransferase enzyme (PDB 1v2x); a long flexible aliphatic chain was apparently disordered, leading to little density to guide its placement. The variation in atom placement (the deposited model is shown in grey, and the built model is shown in yellow) results and explains the r.m.s.d. of 2.5 Å observed.
Fig. 9
Fig. 9
Sparse representation (magenta balls and sticks) of an electron density cluster (blue wire) for the FAD ligand in the structure 2gf3 of sarcosine oxidase at 1.3 Å resolution.
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