1. Academic Validation
  2. Modelling the binding mode of macrocycles: Docking and conformational sampling

Modelling the binding mode of macrocycles: Docking and conformational sampling

  • Bioorg Med Chem. 2020 Jan 1;28(1):115143. doi: 10.1016/j.bmc.2019.115143.
Sarah J Martin 1 I-Jen Chen 2 A W Edith Chan 1 Nicolas Foloppe 3
Affiliations

Affiliations

  • 1 Wolfson Institute for Biomedical Research, University College London, Gower Street, London WC1E 6BT, UK.
  • 2 Vernalis (R&D) Ltd., Granta Park, Abington, Cambridge CB21 6GB, UK.
  • 3 Vernalis (R&D) Ltd., Granta Park, Abington, Cambridge CB21 6GB, UK. Electronic address: n.foloppe@vernalis.com.
Abstract

Drug discovery is increasingly tackling challenging protein binding sites regarding molecular recognition and druggability, including shallow and solvent-exposed protein-protein interaction interfaces. Macrocycles are emerging as promising chemotypes to modulate such sites. Despite their chemical complexity, macrocycles comprise important drugs and offer advantages compared to non-cyclic analogs, hence the recent impetus in the medicinal chemistry of macrocycles. Elaboration of macrocycles, or constituent fragments, can strongly benefit from knowledge of their binding mode to a target. When such information from X-ray crystallography is elusive, computational docking can provide working models. However, few studies have explored docking protocols for macrocycles, since conventional docking methods struggle with the conformational complexity of macrocycles, and also potentially with the shallower topology of their binding sites. Indeed, macrocycle binding mode prediction with the mainstream docking software GOLD has hardly been explored. Here, we present an in-depth study of macrocycle docking with GOLD and the ChemPLP scores. First, we summarize the thorough curation of a test set of 41 protein-macrocycle X-ray structures, raising the issue of lattice contacts with such systems. Rigid docking of the known bioactive conformers was successful (three top ranked poses) for 92.7% of the systems, in absence of crystallographic waters. Thus, without conformational search issues, scoring performed well. However, docking success dropped to 29.3% with the GOLD built-in conformational search. Yet, the success rate doubled to 58.5% when GOLD was supplied with extensive conformer ensembles docked rigidly. The reasons for failure, sampling or scoring, were analyzed, exemplified with particular cases. Overall, binding mode prediction of macrocycles remains challenging, but can be much improved with tailored protocols. The analysis of the interplay between conformational sampling and docking will be relevant to the prospective modelling of macrocycles in general.

Keywords

Computational chemistry; Conformers; Docking; Drug discovery; Macrocycle; Molecular recognition.

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