title: Tackling the conformational sampling of larger flexible compounds and macrocycles in pharmacology and drug discovery.
abstract: Computational conformational sampling underpins much of molecular modeling and design in pharmaceutical work. The sampling of smaller drug-like compounds has been an active area of research. However, few studies have tested in details the sampling of larger more flexible compounds, which are also relevant to drug discovery, including therapeutic peptides, macrocycles, and inhibitors of protein-protein interactions. Here, we investigate extensively mainstream conformational sampling methods on three carefully curated compound sets, namely the 'Drug-like', larger 'Flexible', and 'Macrocycle' compounds. These test molecules are chemically diverse with reliable X-ray protein-bound bioactive structures. The compared sampling methods include Stochastic Search and the recent LowModeMD from MOE, all the low-mode based approaches from MacroModel, and MD/LLMOD recently developed for macrocycles. In addition to default settings, key parameters of the sampling protocols were explored. The performance of the computational protocols was assessed via (i) the reproduction of the X-ray bioactive structures, (ii) the size, coverage and diversity of the output conformational ensembles, (iii) the compactness/extendedness of the conformers, and (iv) the ability to locate the global energy minimum. The influence of the stochastic nature of the searches on the results was also examined. Much better results were obtained by adopting search parameters enhanced over the default settings, while maintaining computational tractability. In MOE, the recent LowModeMD emerged as the method of choice. Mixed torsional/low-mode from MacroModel performed as well as LowModeMD, and MD/LLMOD performed well for macrocycles. The low-mode based approaches yielded very encouraging results with the flexible and macrocycle sets. Thus, one can productively tackle the computational conformational search of larger flexible compounds for drug discovery, including macrocycles.
authors.affiliation: Vernalis (R&D) Ltd, Granta Park, Abington, Cambridge CB21 6GB, UK. Electronic address: i.chen@vernalis.com.
authors.forename: I-Jen
authors.initials: IJ
authors.lastname: Chen
authors.forename: Nicolas
authors.initials: N
authors.lastname: Foloppe
mesh_terms: D055808:Drug Discovery / Q000379:methods*; D047028:Macrocyclic Compounds / Q000737:chemistry*; D008958:Models, Molecular; D008968:Molecular Conformation; D015394:Molecular Structure
chemical_list: D047028:Macrocyclic Compounds
keywords: %BioConf_Rep; %GlobMin_found; 3D; Bioactive structures; Computational chemistry; Conformational sampling; Diel; Drug discovery; Flexible compounds; GB; GUI; Global energy minimum; LLMOD; LMOD; Low-mode; LowModeMD; MD-based simulated annealing followed by large-scale low-mode; MD/LLMOD; MMFF; MOE; MT/LLMOD; MT/LMOD; MW; Macrocycles; Max-Iteration; Merck molecular force field; NRot; NbConfs; OPLS; Oprea number of rotatable bonds; PDB; Rgyr; RotSteps; distance-dependent dielectric; generalized born; graphical user interface; large-scale low-mode; low-mode; maximum number of search iterations per compound; mixed torsional/large-scale low-mode; mixed torsional/low-mode; molecular operating environment; molecular weight; number of conformers; number of moves per rotatable bond; number of rotatable bonds; opr_nrot; optimized potentials for liquid simulations force field; percentage of compounds for which the bioactive structure was reproduced; percentage of compounds for which the global energy minimum was found; protein data bank; radius of gyration; search method combining low-mode moves and molecular dynamics; three-dimensional