Abstract
Computational generation of conformational ensembles is key to contemporary drug design. Selecting the members of the ensemble that will approximate the conformation most likely to bind to a desired target (the bioactive conformation) is difficult, given that the potential energy usually used to generate and rank the ensemble is a notoriously poor discriminator between bioactive and non-bioactive conformations. In this study an approach to generating a focused ensemble is proposed in which each conformation is assigned multiple rankings based not just on potential energy but also on solvation energy, hydrophobic or hydrophilic interaction energy, radius of gyration, and on a statistical potential derived from Cambridge Structural Database data. The best ranked structures derived from each system are then assembled into a new ensemble that is shown to be better focused on bioactive conformations. This pluralistic approach is tested on ensembles generated by the Molecular Operating Environment’s Low Mode Molecular Dynamics module, and by the Cambridge Crystallographic Data Centre’s conformation generator software.
Similar content being viewed by others
References
Schrodinger LLC, MacroModel, release 2017-4, San Diego, CA
Cappel D et al (2015) Exploring conformational search protocols for ligand-based virtual screening and 3-D QSAR modeling. J Comput Aided Mol Des 29(2):165–182
Chemical Computing Group, Inc., Molecular Operating Environment, version 2016.08
OpenEye Scientific Software, Inc., Omega, version 2.5.1.4.2013
Conformation search performance with XedeX. 2011; Available from http://www.cresset-group.com/2011/05/conformation-search-performance-with-xedex/
Macrae CF et al (2008) Mercury CSD 2.0—new features for the visualization and investigation of crystal structures. J Appl Crystallogr 41(1):466–470
RDKit: Open Source Cheminformatics Software. Available from http://www.rdkit.org
Miteva MA, Guyon F, Tuffery P (2010) Frog2: efficient 3D conformation ensemble generator for small compounds. Nucleic Acids Res 38:(Web Server):W622–W627
Vainio MJ, Johnson MS (2007) Generating conformer ensembles using a multiobjective genetic algorithm. J Chem Inf Model 47(6):2462–2474
O’Boyle NM et al (2011) Confab—systematic generation of diverse low energy conformers. J Cheminform. https://doi.org/10.1186/1758-2946-3-8
Klett J et al (2014) ALFA: Automatic ligand flexibility assignment. J Chem Inf Model 54(1):314–323
Watts KS et al (2014) Macrocycle conformational sampling with MacroModel. J Chem Inf Model 54(10):2680–2696
Pan L-L et al (2015) Free energy-based conformational search algorithm using the movable type sampling method. J Chem Theory Comput 11(12):5853–5864
Supady A, Blum V, Baldauf C (2015) First-principles molecular structure search with a genetic algorithm. J Chem Inf Model 55(11):2338–2348
Anighoro A, de la Vega A, de Leon, Bajorath J (2016) Predicting bioactive conformations and binding modes of macrocycles. J Comput Aided Mol Des 30(10):841–849
Kirchmair J et al (2006) Comparative performance assessment of the conformational model generators omega and catalyst: a large-scale survey on the retrieval of protein-bound ligand conformations. J Chem Inf Model 46(4):1848–1861
Agrafiotis DK et al (2007) Conformational sampling of bioactive molecules: a comparative study. J Chem Inf Model 47(3):1067–1086
Chen I-J, Foloppe N (2008) Conformational sampling of druglike molecules with MOE and catalyst: implications for pharmacophore modeling and virtual screening. J Chem Inf Model 48(9):1773–1791
Ebejer J-P, Morris GM, Deane CM (2012) Freely available conformer generation methods: how good are they?. J Chem Inf Model 52(5):1146–1158
Chen I-J, Foloppe N (2013) Tackling the conformational sampling of larger flexible compounds and macrocycles in pharmacology and drug discovery. Bioorg Med Chem 21(24):7898–7920
Watts KS et al (2010) ConfGen: a conformational search method for efficient generation of bioactive conformers. J Chem Inf Model 50(4):534–546
Labute P (2010) LowModeMD: implicit low-mode velocity filtering applied to conformational search of macrocycles and protein loops. J Chem Inf Model 50(5):792–800
Riniker S, Landrum GA (2015) Better informed distance geometry: using what we know to improve conformation generation. J Chem Inf Model 55(12):2562–2574
Taylor R et al (2014) Knowledge-based libraries for predicting the geometric preferences of druglike molecules. J Chem Inf Model 54(7):2500–2514
Cole J et al (2016) Knowledge-based optimization of molecular geometries using crystal structures. J Chem Inf Model 56(4):652–661
Hawkins PCD et al (2010) Conformer generation with OMEGA: algorithm and validation using high quality structures from the Protein Databank and Cambridge Structural Database. J Chem Inf Model 50(4):572–584
Hawkins PCD, Nicholls A (2012) Conformer generation with OMEGA: learning from the data set and the analysis of failures. J Chem Inf Model 52(11):2919–2936
Alzate-Morales JH et al (2007) A computational study of the protein-ligand interactions in CDK2 inhibitors: using quantum mechanics/molecular mechanics interaction energy as a predictor of the biological activity. Biophys J 92(2):430–439
Moraca F et al (2016) Computational evaluation of HIV‑1 gp120 conformations of soluble trimeric gp140 structures as targets for de novo docking of first and second-generation small-molecule CD4 mimics. J Chem Inf Model 56(10):2069–2079
Foloppe N, Chen I-J (2016) Towards understanding the unbound state of drug compounds: implications for the intramolecular reorganization energy upon binding. Bioorg Med Chem 24(10):2159–2189
Perola E, Charifson PS (2004) Conformational analysis of drug-like molecules bound to proteins: an extensive study of ligand reorganization upon binding. J Med Chem 47(10):2499–2510
Butler KT, Luque JF, Barril X (2009) Toward accurate relative energy predictions of the bioactive conformation of drugs. J Comput Chem 30(4):601–610
Avgy-David HH, Senderowitz H (2015) Toward focusing conformational ensembles on bioactive conformations: a molecular mechanics/quantum mechanics study. J Chem Inf Model 55(10):2154–2167
Juarez-Jiminez J et al (2015) Assessing the suitability of the multilevel strategy for the conformational analysis of small ligands. J Phys Chem B 119(3):1164–1172
Diller DJ, Merz KM Jr. (2002) Can we separate active from inactive conformations?. J Comput Aided Mol Des 16(2):105–112
Auer J, Bajorath J (2008) Distinguishing between bioactive and modeled compound conformations through mining of emerging chemical patterns. J Chem Inf Model 48(9):1747–1753
Musafia B, Senderowitz H (2009) Bioactive conformational biasing: a new method for focusing conformational ensembles on bioactive-like conformers. J Chem Inf Model 49(11):2469–2480
Charifson PS et al (1999) Consensus scoring: a method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J Med Chem 42(25):5100–5109
Molecular Networks GmbH, CORINA Classic, version 4.1.0.2017
Sadowski J, Gasteiger J (1993) From atoms and bonds to three-dimensional atomic coordinates: automatic model builders. Chem Rev 93(7):2567–2581
Veber DF et al (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45(12):2615–2623
Berman HM et al (2000) The protein data bank. Nucleic Acids Res 28(1):235–242
Wojciechowski M, Lesyng B (2004) Generalized Born model: analysis, refinement and applications to proteins. J Phys Chem B 108(47):18368–18376
ChemAxon. Available from http://www.chemaxon.com
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Habgood, M. Bioactive focus in conformational ensembles: a pluralistic approach. J Comput Aided Mol Des 31, 1073–1083 (2017). https://doi.org/10.1007/s10822-017-0089-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10822-017-0089-3