Abstract
The interaction of a small molecule with a protein target depends on its ability to adopt a three-dimensional structure that is complementary. Therefore, complete and rapid prediction of the conformational space a small molecule can sample is critical for both structure- and ligand-based drug discovery algorithms such as small molecule docking or three-dimensional quantitative structure–activity relationships. Here we have derived a database of small molecule fragments frequently sampled in experimental structures within the Cambridge Structure Database and the Protein Data Bank. Likely conformations of these fragments are stored as ‘rotamers’ in analogy to amino acid side chain rotamer libraries used for rapid sampling of protein conformational space. Explicit fragments take into account correlations between multiple torsion bonds and effect of substituents on torsional profiles. A conformational ensemble for small molecules can then be generated by recombining fragment rotamers with a Monte Carlo search strategy. BCL::Conf was benchmarked against other conformer generator methods including Confgen, Moe, Omega and RDKit in its ability to recover experimentally determined protein bound conformations of small molecules, diversity of conformational ensembles, and sampling rate. BCL::Conf recovers at least one conformation with a root mean square deviation of 2 Å or better to the experimental structure for 99 % of the small molecules in the Vernalis benchmark dataset. The ‘rotamer’ approach will allow integration of BCL::Conf into respective computational biology programs such as Rosetta.Graphical abstract:Conformation sampling is carried out using explicit fragment conformations derived from crystallographic structure databases. Molecules from the database are decomposed into fragments and most likely conformations/rotamers are used to sample correspondng sub-structure of a molecule of interest.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0095-1) contains supplementary material, which is available to authorized users.
Highlights
The interactions between small molecules and proteins are important for receptors, transporters, or enzymes to recognize their substrates as well as for small molecule therapeutics to bind to their target protein
We assess the performance of BCL::Conf (BCL) with curated generic ligand dataset known as the Vernalis dataset [22], in comparison with Confgen [31], Moe (Confimport) [32], Omega [33] and RDKit [24, 34]
The method utilizes the conformational space seen in the structure databases, Cambridge Structure Database [8] (CSD) and Protein Data Bank [9] (PDB), to sample conformations of small-molecules
Summary
The interactions between small molecules and proteins are important for receptors, transporters, or enzymes to recognize their substrates as well as for small molecule therapeutics to bind to their target protein. The biologically active conformation may be a single conformation or a small subset from the conformations sampled in solution or a new conformation, induced by protein binding. A uniform sampling of all energetically accessible small molecule conformations is essential for the success of protein small molecule docking simulations [1] for example in structure-based computer-aided drug discovery/design (CADD) [1,2,3]. Ligandbased CADD applications such as three-dimensional quantitative structure activity relationships (3D-QSAR) predictions [4] or pharmacophore modeling [5] rely on the use of conformational ensembles of molecules that capture the bioactive conformation as one of a diverse set of energetically accessible conformations [6, 7]
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