Macrocycle modeling in ICM: benchmarking and evaluation in D3R Grand Challenge 4
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Macrocycles represent a potentially vast extension of drug chemical space still largely untapped by synthetic compounds. Sampling of flexible rings is incorporated in the ICM-dock protocol. We tested the ability of ICM-dock to reproduce macrocyclic ligand–protein receptor complexes, first in a large retrospective benchmark (246 complexes), and next, in context of the D3R Grand Challenge 4 (GC4), where we modeled bound complexes and predicted activities for a series of macrocyclic BACE inhibitors. Sub-angstrom accuracy was achieved in ligand pose prediction both in cross-docking (D3R Challenge Stage 1A) and cognate (Stage 1B) setup. Stage 1B submission was top ranked by mean and average RMSDs, even though no ligand knowledge was used in our simulations on this Stage. Furthermore, we demonstrate successful receptor conformational selection in Stage 1A, aided by the enhanced ‘4D’ multiple receptor conformation docking protocol with optimized scoring offsets. In the activity 3D QSAR modeling, predictivity of the BACE pKd model was modest, while for the second target (Cathepsin-S), leading performance was achieved. Difference in activity prediction performance between the targets is likely explained by the amount of available and relevant training data.
KeywordsD3R Docking Macrocycles ICM Internal coordinate mechanics LigBEnD
Protein data bank
Atomic property field
The authors thank D3R organizers for coordinating the challenge. We also thank Eugene Raush for technical assistance, and Andrew Orry for proofreading of this manuscript.
The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.
Compliance with ethical standards
Conflict of interest
The authors declare no competing financial interest.
- 1.Abagyan R (2017). ICM user manual. https://www.molsoft.com/
- 12.Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47(7):1739–1749CrossRefGoogle Scholar
- 36.Totrov M, Abagyan, R (1999) Derivation of sensitive discrimination potential for virtual ligand screening. In: Proceedings of the third annual international conference on computational molecular biology, pp. 312–320Google Scholar