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Macrocycle modeling in ICM: benchmarking and evaluation in D3R Grand Challenge 4

  • Polo C.-H. Lam
  • Ruben Abagyan
  • Maxim TotrovEmail author
Article

Abstract

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.

Keywords

D3R Docking Macrocycles ICM Internal coordinate mechanics LigBEnD 

Abbreviations

PDB

Protein data bank

APF

Atomic property field

Notes

Acknowledgements

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.

Author contributions

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.

Supplementary material

10822_2019_225_MOESM1_ESM.docx (1.6 mb)
Supplementary file1 (DOCX 1649 kb)
10822_2019_225_MOESM2_ESM.csv (36 kb)
Supplementary file2 (CSV 35 kb)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Molsoft L.L.C.San DiegoUSA
  2. 2.Skaggs School of Pharmacy and Pharmaceutical SciencesUniversity of California San DiegoLa JollaUSA

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