Conformator: A Novel Method for the Generation of Conformer Ensembles.

Computer-aided drug design methods such as docking, pharmacophore searching, 3D database searching, and the creation of 3D-QSAR models need conformational ensembles to handle the flexibility of small molecules. Here, we present Conformator, an accurate and effective knowledge-based algorithm for generating conformer ensembles. With 99.9% of all test molecules processed, Conformator stands out by its robustness with respect to input formats, molecular geometries, and the handling of macrocycles. With an extended set of rules for sampling torsion angles, a novel algorithm for macrocycle conformer generation, and a new clustering algorithm for the assembly of conformer ensembles, Conformator reaches a median minimum root-mean-square deviation (measured between protein-bound ligand conformations and ensembles of a maximum of 250 conformers) of 0.47 Å with no significant difference to the highest-ranked commercial algorithm OMEGA and significantly higher accuracy than seven free algorithms, including the RDKit DG algorithm. Conformator is freely available for noncommercial use and academic research.

StructureProfiler: an all-in-one tool for 3D protein structure profiling.

MOTIVATION: Three-dimensional protein structures are important starting points for elucidating protein function and applications like drug design. Computational methods in this area rely on high quality validation datasets which are usually manually assembled. Due to the increase in published structures as well as the increasing demand for specially tailored validation datasets, automatic procedures should be adopted. RESULTS: StructureProfiler is a new tool for automatic, objective and customizable profiling of X-ray protein structures based on the most frequently applied selection criteria currently in use to assemble benchmark datasets. As examples, four dataset configurations (Astex, Iridium, Platinum, combined), all results of the combined tests and the list of all PDB Ids passing the combined criteria set are attached in the Supplementary Material. AVAILABILITY AND IMPLEMENTATION: StructureProfiler is available as part of the ProteinsPlus web service http://proteins.plus and as standalone tool in the NAOMI ChemBio Suite. Dataset updates together with the tool can be found on http://www.zbh.uni-hamburg.de/structureprofiler. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Characterization of the Chemical Space of Known and Readily Obtainable Natural Products.

Natural products remain one of the most productive sources of chemical inspiration for the development of new drugs. The structures of more than 250 000 natural products are available from public databases. At least 10% of these compounds are readily obtainable for experimental testing from commercial vendors and public research institutions. While the physicochemical properties of known natural products have been thoroughly studied and compared to those of drugs and other types of small molecules, the information available on the content, coverage, and relevance of individual virtual and physical natural product libraries is clearly limited. The aim of this study was the development of a detailed understanding of the coverage of chemical space by known and readily obtainable natural products and by individual natural product databases. For this purpose, we compiled comprehensive data sets of known and readily obtainable natural products from 18 virtual databases (including the Dictionary of Natural Products), nine physical libraries, and the Protein Data Bank (PDB). We also developed and employed an algorithm ("SugarBuster") for the removal of sugars and sugar-like moieties, which are generally not in the focus of interest for drug discovery, from natural products. In addition, we devised a rule-based approach for the automated classification of natural products into natural product classes (alkaloids, steroids, flavonoids, etc.). Among the most important results of this study is the finding that the readily obtainable natural products are highly diverse and populate regions of chemical space that are of high relevance to drug discovery. In some cases, substantial differences in the coverage of natural product classes and chemical space by the individual databases are observed. More than 2000 natural products are identified for which at least one X-ray crystal structure of the compound in complex with a biomacromolecule is available from the PDB.

How Diverse Are the Protein-Bound Conformations of Small-Molecule Drugs and Cofactors?

Knowledge of the bioactive conformations of small molecules or the ability to predict them with theoretical methods is of key importance to the design of bioactive compounds such as drugs, agrochemicals, and cosmetics. Using an elaborate cheminformatics pipeline, which also evaluates the support of individual atom coordinates by the measured electron density, we compiled a complete set ("Sperrylite Dataset") of high-quality structures of protein-bound ligand conformations from the PDB. The Sperrylite Dataset consists of a total of 10,936 high-quality structures of 4,548 unique ligands. Based on this dataset, we assessed the variability of the bioactive conformations of 91 small molecules-each represented by a minimum of ten structures-and found it to be largely independent of the number of rotatable bonds. Sixty-nine molecules had at least two distinct conformations (defined by an RMSD greater than 1 Å). For a representative subset of 17 approved drugs and cofactors we observed a clear trend for the formation of few clusters of highly similar conformers. Even for proteins that share a very low sequence identity, ligands were regularly found to adopt similar conformations. For cofactors, a clear trend for extended conformations was measured, although in few cases also coiled conformers were observed. The Sperrylite Dataset is available for download from http://www.zbh.uni-hamburg.de/sperrylite_dataset.

Hit Dexter: A Machine-Learning Model for the Prediction of Frequent Hitters.

False-positive assay readouts caused by badly behaving compounds-frequent hitters, pan-assay interference compounds (PAINS), aggregators, and others-continue to pose a major challenge to experimental screening. There are only a few in silico methods that allow the prediction of such problematic compounds. We report the development of Hit Dexter, two extremely randomized trees classifiers for the prediction of compounds likely to trigger positive assay readouts either by true promiscuity or by assay interference. The models were trained on a well-prepared dataset extracted from the PubChem Bioassay database, consisting of approximately 311 000 compounds tested for activity on at least 50 proteins. Hit Dexter reached MCC and AUC values of up to 0.67 and 0.96 on an independent test set, respectively. The models are expected to be of high value, in particular to medicinal chemists and biochemists who can use Hit Dexter to identify compounds for which extra caution should be exercised with positive assay readouts. Hit Dexter is available as a free web service at http://hitdexter.zbh. uni-hamburg.de.

Benchmarking Commercial Conformer Ensemble Generators.

We assess and compare the performance of eight commercial conformer ensemble generators (ConfGen, ConfGenX, cxcalc, iCon, MOE LowModeMD, MOE Stochastic, MOE Conformation Import, and OMEGA) and one leading free algorithm, the distance geometry algorithm implemented in RDKit. The comparative study is based on a new version of the Platinum Diverse Dataset, a high-quality benchmarking dataset of 2859 protein-bound ligand conformations extracted from the PDB. Differences in the performance of commercial algorithms are much smaller than those observed for free algorithms in our previous study (J. Chem. Inf. MODEL: 2017, 57, 529-539). For commercial algorithms, the median minimum root-mean-square deviations measured between protein-bound ligand conformations and ensembles of a maximum of 250 conformers are between 0.46 and 0.61 Å. Commercial conformer ensemble generators are characterized by their high robustness, with at least 99% of all input molecules successfully processed and few or even no substantial geometrical errors detectable in their output conformations. The RDKit distance geometry algorithm (with minimization enabled) appears to be a good free alternative since its performance is comparable to that of the midranked commercial algorithms. Based on a statistical analysis, we elaborate on which algorithms to use and how to parametrize them for best performance in different application scenarios.

Alignment-Based Prediction of Sites of Metabolism.

Prediction of metabolically labile atom positions in a molecule (sites of metabolism) is a key component of the simulation of xenobiotic metabolism as a whole, providing crucial information for the development of safe and effective drugs. In 2008, an exploratory study was published in which sites of metabolism were derived based on molecular shape- and chemical feature-based alignment to a molecule whose site of metabolism (SoM) had been determined by experiments. We present a detailed analysis of the breadth of applicability of alignment-based SoM prediction, including transfer of the approach from a structure- to ligand-based method and extension of the applicability of the models from cytochrome P450 2C9 to all cytochrome P450 isozymes involved in drug metabolism. We evaluate the effect of molecular similarity of the query and reference molecules on the ability of this approach to accurately predict SoMs. In addition, we combine the alignment-based method with a leading chemical reactivity model to take reactivity into account. The combined model yielded superior performance in comparison to the alignment-based approach and the reactivity models with an average area under the receiver operating characteristic curve of 0.85 in cross-validation experiments. In particular, early enrichment was improved, as evidenced by higher BEDROC scores (mean BEDROC = 0.59 for α = 20.0, mean BEDROC = 0.73 for α = 80.5).

High-Quality Dataset of Protein-Bound Ligand Conformations and Its Application to Benchmarking Conformer Ensemble Generators.

We developed a cheminformatics pipeline for the fully automated selection and extraction of high-quality protein-bound ligand conformations from X-ray structural data. The pipeline evaluates the validity and accuracy of the 3D structures of small molecules according to multiple criteria, including their fit to the electron density and their physicochemical and structural properties. Using this approach, we compiled two high-quality datasets from the Protein Data Bank (PDB): a comprehensive dataset and a diversified subset of 4626 and 2912 structures, respectively. The datasets were applied to benchmarking seven freely available conformer ensemble generators: Balloon (two different algorithms), the RDKit standard conformer ensemble generator, the Experimental-Torsion basic Knowledge Distance Geometry (ETKDG) algorithm, Confab, Frog2 and Multiconf-DOCK. Substantial differences in the performance of the individual algorithms were observed, with RDKit and ETKDG generally achieving a favorable balance of accuracy, ensemble size and runtime. The Platinum datasets are available for download from http://www.zbh.uni-hamburg.de/platinum_dataset .

UNICON: A Powerful and Easy-to-Use Compound Library Converter.

The accurate handling of different chemical file formats and the consistent conversion between them play important roles for calculations in complex cheminformatics workflows. Working with different cheminformatic tools often makes the conversion between file formats a mandatory step. Such a conversion might become a difficult task in cases where the information content substantially differs. This paper describes UNICON, an easy-to-use software tool for this task. The functionality of UNICON ranges from file conversion between standard formats SDF, MOL2, SMILES, PDB, and PDBx/mmCIF via the generation of 2D structure coordinates and 3D structures to the enumeration of tautomeric forms, protonation states, and conformer ensembles. For this purpose, UNICON bundles the key elements of the previously described NAOMI library in a single, easy-to-use command line tool.