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J Comput Aided Mol Des. 2019 Jun;33(6):531-558. doi: 10.1007/s10822-019-00203-1. Epub 2019 May 3.

Complex macrocycle exploration: parallel, heuristic, and constraint-based conformer generation using ForceGen.

Author information

1
Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, USA. ajain@jainlab.org.
2
Applied Science, BioPharmics LLC, Santa Rosa, CA, USA.
3
Process Research and Development, Merck & Co., Inc., Kenilworth, NJ, USA.
4
Analytical Research and Development, Pfizer Inc., Groton, CT, USA.
5
Modeling and Informatics, Merck & Co., Inc., Kenilworth, NJ, USA.
6
Process Research and Development, Merck & Co., Inc., Kenilworth, NJ, USA. mikhail_reibarkh@merck.com.

Abstract

ForceGen is a template-free, non-stochastic approach for 2D to 3D structure generation and conformational elaboration for small molecules, including both non-macrocycles and macrocycles. For conformational search of non-macrocycles, ForceGen is both faster and more accurate than the best of all tested methods on a very large, independently curated benchmark of 2859 PDB ligands. In this study, the primary results are on macrocycles, including results for 431 unique examples from four separate benchmarks. These include complex peptide and peptide-like cases that can form networks of internal hydrogen bonds. By making use of new physical movements ("flips" of near-linear sub-cycles and explicit formation of hydrogen bonds), ForceGen exhibited statistically significantly better performance for overall RMS deviation from experimental coordinates than all other approaches. The algorithmic approach offers natural parallelization across multiple computing-cores. On a modest multi-core workstation, for all but the most complex macrocycles, median wall-clock times were generally under a minute in fast search mode and under 2 min using thorough search. On the most complex cases (roughly cyclic decapeptides and larger) explicit exploration of likely hydrogen bonding networks yielded marked improvements, but with calculation times increasing to several minutes and in some cases to roughly an hour for fast search. In complex cases, utilization of NMR data to constrain conformational search produces accurate conformational ensembles representative of solution state macrocycle behavior. On macrocycles of typical complexity (up to 21 rotatable macrocyclic and exocyclic bonds), design-focused macrocycle optimization can be practically supported by computational chemistry at interactive time-scales, with conformational ensemble accuracy equaling what is seen with non-macrocyclic ligands. For more complex macrocycles, inclusion of sparse biophysical data is a helpful adjunct to computation.

KEYWORDS:

Conformer generation; ForceGen; Macrocycle; Multi-core; NMR; RDC; Surflex

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