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. 2022 Oct 17:3:e18.
doi: 10.1017/qrd.2022.18. eCollection 2022.

Performance evaluation of flexible macrocycle docking in AutoDock

Matthew Holcomb  1 , Diogo Santos-Martins  1 , Andreas F Tillack  1 , Stefano Forli  1
Affiliations

Performance evaluation of flexible macrocycle docking in AutoDock

Matthew Holcomb et al. QRB Discov. .

Abstract

Macrocycles represent an important class of ligands, both in natural products and designed drugs. In drug design, macrocyclizations can impart specific ligand conformations and contribute to passive permeation by encouraging intramolecular H-bonds. AutoDock-GPU and Vina can model macrocyclic ligands flexibly, without requiring the enumeration of macrocyclic conformers before docking. Here, we characterize the performance of the method for handling macrocyclic compounds, which is implemented and the default behaviour for ligand preparation with our ligand preparation pipeline, Meeko. A pseudoatom is used to encode bond geometry and produce an anisotropic closure force for macrocyclic rings. This method is evaluated on a diverse set of small molecule and peptide macrocycles, ranging from 7- to 33-membered rings, showing little accuracy loss compared to rigid redocking of the X-ray macrocycle conformers. This suggests that for conformationally flexible macrocycles with unknown binding modes, this method can be effectively used to predict the macrocycle conformation.

Keywords: autodock; docking; macrocycles.

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Figures

None
Graphical abstract
Fig. 1.
Fig. 1.
Schematic representation of the handling of flexible macrocyclic rings by Meeko and AutoDock-GPU.
Fig. 2.
Fig. 2.
Distributions of the largest ring size in the smallest set of smallest rings for all ligands deposited in the PDB, and the curated subset used in this work.
Fig. 3.
Fig. 3.
Distributions of Boettcher molecular complexity scores for (a) non-macrocyclic ligands in the PDB (blue), (b) macrocyclic ligands (orange) in the PDB, and (c) the curated subset used here (green).
Fig. 4.
Fig. 4.
Distribution of root-mean-squared deviations (RMSD) for the best scoring pose found while redocking the macrocycle flexibly versus redocking with the crystallographic macrocycle conformation.
Fig. 5.
Fig. 5.
Comparison of experimental (colour) versus flexibly docked (colour) poses for selected successful redockings.
Fig. 6.
Fig. 6.
Difference in scores between the best pose found using flexible and rigid redocking versus the flexible redocking RMSD.
Fig. 7.
Fig. 7.
Distribution of root-mean-squared deviations (RMSD) for the best scoring pose found while redocking with 100M evaluations versus redocking with default autostop and heuristic settings.
Fig. 8.
Fig. 8.
Comparison of experimental (blue) versus docked (orange) poses for complexes (a) 5ta4, (b) 5eqi, and (c) 1nm6.
Fig. 9.
Fig. 9.
Comparison of experimental (blue) versus docked (orange) poses for macrocyclic HIV protease inhibitors (a) 4cpw, (b) 1b6j and (c) 1b6p.
Fig. 10.
Fig. 10.
Comparison of experimental (blue) versus docked (orange) poses for antibiotics (a) arylomycin C (3s04), (b) darobactin (7nrf), and (c) vancomycin (1rrv).
See this image and copyright information in PMC

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