CAESAR: A new conformer generation algorithm based on recursive buildup and local rotational symmetry consideration
ABSTRACT A highly efficient conformer search algorithm based on a divide-and-conquer and recursive conformer build-up approach is presented in this paper. This approach is combined with consideration of local rotational symmetry so that conformer duplicates due to topological symmetry in the systematic search can be efficiently eliminated. This new algorithm, termed CAESAR (Conformer Algorithm based on Energy Screening and Recursive Buildup), has been implemented in Discovery Studio 1.7 as part of the Catalyst Component Collection. CAESAR has been validated by comparing the conformer models generated by the new method and Catalyst/FAST. CAESAR is consistently 5-20 times faster than Catalyst/FAST for all data sets investigated. The speedup is even more dramatic for molecules with high topological symmetry or for molecules that require a large number of conformers to be sampled. The quality of the conformer models generated by CAESAR has been validated by assessing the ability to reproduce the receptor-bound X-ray conformation of ligands extracted for the Protein Data Bank (PDB) and assessing the ability to adequately cover the pharmacophore space. It is shown that CAESAR is able to reproduce the receptor-bound conformation slightly better than the Catalyst/FAST method for a data set of 918 ligands retrieved from the PDB. In addition, it is shown that CEASAR covers the pharmacophore space as well or better than Catalyst/FAST.
SourceAvailable from: Ramon Pouplana[Show abstract] [Hide abstract]
ABSTRACT: Predicting the conformational preferences of flexible compounds is a challenging problem in drug design, where the recognition between ligand and receptor is affected by the ability of the interacting partners to adopt a favorable conformation for the binding. In order to explore the conformational space of flexible ligands and to obtain the relative free energy of the conformation wells, we have recently reported a multilevel computational strategy that relies on the predominant-state approximation - where the conformational space is partitioned into distinct conformational wells - and combines a low-level method for sampling the conformational minima and high-level ab initio calculations for estimating their relative stability. In this study, we assess the performance of the multilevel strategy for predicting the conformational preferences of a series of structurally related phenylethylamines and streptomycin in aqueous solution. The charged nature of these compounds and the chemical complexity of streptomycin make them a challenging test for the multilevel approach. Further, we explore the suitability of using a molecular mechanics approach as a source of approximate ensembles in the first stage of the multilevel strategy. The results support the reliability of the multilevel approach for obtaining an accurate conformational ensemble of small (bio)organic molecules in aqueous solution.The Journal of Physical Chemistry B 10/2014; 119(3). DOI:10.1021/jp506779y · 3.38 Impact Factor
[Show abstract] [Hide abstract]
ABSTRACT: In this paper, we describe the first prospective application of the shape-comparison program, WEGA (weighted Gaussian algorithm), to find new scaffolds for anti-tumor agents. A series of sixteen carbazole alkaloids extracted from Clausena vestita D. D. Tao, which have anti-tumor activities at the cellular level, were used as query molecules. A compound library was screened by ranking molecules based upon their 3D shape and pharmacophore similarities to known inhibitors. The relationship between the structures and activities was also studied through comparative molecular field analysis (CoMFA). Twelve hits show comparable growth inhibition activity against HepG2 cells (a hit rate of 60%); eight of the hits have new scaffolds (in comparison with known inhibitors). These results indicate that a shape-based screening approach, such as WEGA, can be efficiently used for scaffold hopping in a lead identification process.Medicinal Chemistry Communication 06/2014; 5(6):737. DOI:10.1039/c3md00397c · 2.63 Impact Factor
[Show abstract] [Hide abstract]
ABSTRACT: Biological effects of small molecules in an organism result from favorable interactions between the molecules and their target proteins. These interactions depend on chemical functionalities, bonds, and their 3D-orientations towards each other. These 3D-arrangements of chemical functionalities that make a small molecule active towards its target can be described by pharmacophore models. In these models, chemical functionalities are represented as so-called features. Commonly, they are obtained either from a set of active compounds or directly from the observed protein-ligand interactions as present in X-ray crystal structures, NMR structures, or docking poses. In this review, we explain the basics of pharmacophore modeling including dataset generation, 3D-representations and conformational analysis of small molecules, pharmacophore model construction, model validation, and its benefits to virtual screening and other applications. Copyright © 2014. Published by Elsevier Inc.Methods 10/2014; 71. DOI:10.1016/j.ymeth.2014.10.013 · 3.22 Impact Factor