Michael J. Hartshorn

Improved protein–ligand docking using GOLD

The Chemscore function was implemented as a scoring function for the protein–ligand docking program GOLD, and its performance compared to the original Goldscore function and two consensus docking protocols, “Goldscore‐CS” and “Chemscore‐GS,” in terms of docking accuracy, prediction of binding affinities, and speed. In the “Goldscore‐CS” protocol, dockings produced with the Goldscore function are scored and ranked with the Chemscore function; in the “Chemscore‐GS” protocol, dockings produced with the Chemscore function are scored and ranked with the Goldscore function. Comparisons were made for a “clean” set of 224 protein–ligand complexes, and for two subsets of this set, one for which the ligands are “drug‐like,” the other for which they are “fragment‐like.” For “drug‐like” and “fragment‐like” ligands, the docking accuracies obtained with Chemscore and Goldscore functions are similar. For larger ligands, Goldscore gives superior results. Docking with the Chemscore function is up to three times faster than docking with the Goldscore function. Both combined docking protocols give significant improvements in docking accuracy over the use of the Goldscore or Chemscore function alone. “Goldscore‐CS” gives success rates of up to 81% (top‐ranked GOLD solution within 2.0 Å of the experimental binding mode) for the “clean list,” but at the cost of long search times. For most virtual screening applications, “Chemscore‐GS” seems optimal; search settings that give docking speeds of around 0.25–1.3 min/compound have success rates of about 78% for “drug‐like” compounds and 85% for “fragment‐like” compounds. In terms of producing binding energy estimates, the Goldscore function appears to perform better than the Chemscore function and the two consensus protocols, particularly for faster search settings. Even at docking speeds of around 1–2 min/compound, the Goldscore function predicts binding energies with a standard deviation of ∼10.5 kJ/mol. Proteins 2003;52:609–623.

AstexViewerTM †: a visualisation aid for structure-based drug design

AstexViewer™ is a Java molecular graphics program that can be used for visualisation in many aspects of structure-based drug design. This paper describes its functionality, implementation and examples of its use. The program can run as an Applet in a web browser allowing structures to be displayed without installing additional software. Applications of its use are described for visualisation and as part of a structure based design platform. The software is being made freely available to the community and may be downloaded from http://www.astex-technology.com/AstexViewer.

Protein−Ligand Docking against Non-Native Protein Conformers

In the validation of protein-ligand docking protocols, performance is mostly measured against native protein conformers, i.e. each ligand is docked into the protein conformation from the structure that contained that ligand. In real-life applications, however, ligands are docked against non-native conformations of the protein, i.e. the apo structure or a structure of a different protein-ligand complex. Here, we have constructed an extensive test set for assessing docking performance against non-native protein conformations. This new test set is built on the Astex Diverse Set (which we recently constructed for assessing native docking performance) and contains 1112 non-native structures for 65 drug targets. Using the protein-ligand docking program GOLD, the Astex Diverse Set and the new Astex Non-native Set, we established that, whereas docking performance (top-ranked solution within 2 A rmsd of the experimental binding mode) is approximately 80% for native docking, this drops to 61% for non-native docking. A similar drop-off is observed for sampling performance (any solution within 2 A): 91% for native docking vs 72% for non-native docking. No significant differences were observed between docking performance against apo and nonapo structures. We found that, whereas small variations in protein conformation are generally tolerated by our rigid docking protocol, larger protein movements result in a catastrophic drop-off in performance. Some docking performance and nearly all sampling performance can be recovered by considering dockings produced against a small number of non-native structures simultaneously. Docking against non-native structures of complexes containing ligands that are similar to the docked ligand also significantly improves both docking performance and sampling performance.

Automated Protein–Ligand Crystallography for Structure‐Based Drug Design

An approach to automate protein–ligand crystallography is presented, with the aim of increasing the number of structures available to structure‐based drug design. The methods we propose deal with the automatic interpretation of diffraction data for targets with known protein structures, and provide easy access to the results. Central to the system is a novel procedure that fully automates the placement of ligands into electron density maps. Automation provides an objective way to structure solution, whereas manual placement can be rather subjective, especially for data of low to medium resolution. Ligands are placed by docking into electron density, whilst taking care of protein–ligand interactions. The ligand fitting procedure has been validated on both public domain and in‐house examples. Some of the latter deal with cocktails of low‐molecular weight compounds, as used in fragment‐based drug discovery by crystallography. For such library‐screening experiments we show that the method can automatically identify which of the compounds from a cocktail is bound.

A web-based platform for virtual screening

A fully integrated, web-based, virtual screening platform has been developed to allow rapid virtual screening of large numbers of compounds. ORACLE is used to store information at all stages of the process. The system includes a large database of historical compounds from high throughput screenings (HTS) chemical suppliers, ATLAS, containing over 3.1 million unique compounds with their associated physiochemical properties (ClogP, MW, etc.). The database can be screened using a web-based interface to produce compound subsets for virtual screening or virtual library (VL) enumeration. In order to carry out the latter task within ORACLE a reaction data cartridge has been developed. Virtual libraries can be enumerated rapidly using the web-based interface to the cartridge. The compound subsets can be seamlessly submitted for virtual screening experiments, and the results can be viewed via another web-based interface allowing ad hoc querying of the virtual screening data stored in ORACLE.