Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses.

Ligand docking at a protein site can be improved by prioritizing poses by similarity to validated binding modes found in the crystal structures of ligand/protein complexes. The interactions formed in the predicted model are searched in each of the reference 3D structures, taken individually. We propose to merge the information provided by all references, creating a single representation of all known binding modes. The method is called LID, an acronym for Local Interaction Density. LID was benchmarked in a pose prediction exercise on 19 proteins and 1382 ligands using PLANTS as docking software. It was also tested in a virtual screening challenge on eight proteins, with a dataset of 140,000 compounds from DUD-E and PubChem. LID significantly improved the performance of the docking program in both pose prediction and virtual screening. The gain is comparable to that obtained with a rescoring approach based on the individual comparison of reference binding modes (the GRIM method). Importantly, LID is effective with a small number of references. LID calculation time is negligible compared to the docking time.

Binding mode information improves fragment docking.

Docking is commonly used in drug discovery to predict how ligand binds to protein target. Best programs are generally able to generate a correct solution, yet often fail to identify it. In the case of drug-like molecules, the correct and incorrect poses can be sorted by similarity to the crystallographic structure of the protein in complex with reference ligands. Fragments are particularly sensitive to scoring problems because they are weak ligands which form few interactions with protein. In the present study, we assessed the utility of binding mode information in fragment pose prediction. We compared three approaches: interaction fingerprints, 3D-matching of interaction patterns and 3D-matching of shapes. We prepared a test set composed of high-quality structures of the Protein Data Bank. We generated and evaluated the docking poses of 586 fragment/protein complexes. We observed that the best approach is twice as accurate as the native scoring function, and that post-processing is less effective for smaller fragments. Interestingly, fragments and drug-like molecules both proved to be useful references. In the discussion, we suggest the best conditions for a successful pose prediction with the three approaches.

Structural Insights on Fragment Binding Mode Conservation.

Aiming at a deep understanding of fragment binding to ligandable targets, we performed a large scale analysis of the Protein Data Bank. Binding modes of 1832 drug-like ligands and 1079 fragments to 235 proteins were compared. We observed that the binding modes of fragments and their drug-like superstructures binding to the same protein are mostly conserved, thereby providing experimental evidence for the preservation of fragment binding modes during molecular growing. Furthermore, small chemical changes in the fragment are tolerated without alteration of the fragment binding mode. The exceptions to this observation generally involve conformational variability of the molecules. Our data analysis also suggests that, provided enough fragments have been crystallized within a protein, good interaction coverage of the binding pocket is achieved. Last, we extended our study to 126 crystallization additives and discuss in which cases they provide information relevant to structure-based drug design.

Multi-target Fragments Display Versatile Binding Modes.

Promiscuity is an interesting concept in fragment-based drug design as fragments with low specificity can be advantageous for finding many screening hits. We present a PDB-wide analysis of multi-target fragments and their binding mode conservation. Focussing on multi-target fragments, we found that the majority shows non-conserved binding modes, even if they bind in a similar conformation or similar protein targets. Surprisingly, fragment properties alone are not able to predict whether a fragment will exhibit a versatile or conserved binding mode, emphasizing the interplay between protein and fragment features during a binding event and the importance of structure-based modelling.

Computational prediction of immune cell cytotoxicity.

Immunotoxicity, defined as adverse effects of xenobiotics on the immune system, is gaining increasing attention in the approval process of industrial chemicals and drugs. In-vivo and ex-vivo experiments have been the gold standard in immunotoxicity assessment so far, so the development of in-vitro and in-silico alternatives is an important issue. In this paper we describe a widely applicable, easy-to use computational approach which can serve as an initial immunotoxicity screen of new chemical entities. Molecular fingerprints describing chemical structure were used as parameters in a machine-learning approach based on the Naïve-Bayes learning algorithm. The model was trained using blood-cell growth inhibition data from the NCI database and validated externally with several in-house and literature-derived data sets tested in cytotoxicity assays on different types on immune cells. Both cross-validations and external validations resulted in areas under the receiver operator curves (ROC/AUC) of 75% or higher. The classification of the validation data sets occurred with excellent specificities and fair to excellent selectivities, depending on the data set. This means that the probability of actual immunotoxicity is very high for compounds classified as immunotoxic, while the fraction of false negative predictions might vary. Thus, in a multistep immunotoxicity screening scheme, the classification as immunotoxic can be accepted without additional confirmation, while compounds classified as not immunotoxic will have to be subjected to further investigation.

Do Fragments and Crystallization Additives Bind Similarly to Drug-like Ligands?

The success of fragment-based drug design (FBDD) hinges upon the optimization of low-molecular-weight compounds (MW < 300 Da) with weak binding affinities to lead compounds with high affinity and selectivity. Usually, structural information from fragment-protein complexes is used to develop ideas about the binding mode of similar but drug-like molecules. In this regard, crystallization additives such as cryoprotectants or buffer components, which are highly abundant in crystal structures, are frequently ignored. Thus, the aim of this study was to investigate the information present in protein complexes with fragments as well as those with additives and how they relate to the binding modes of their drug-like counterparts. We present a thorough analysis of the binding modes of crystallographic additives, fragments, and drug-like ligands bound to four diverse targets of wide interest in drug discovery and highly represented in the Protein Data Bank: cyclin-dependent kinase 2, ß-secretase 1, carbonic anhydrase 2, and trypsin. We identified a total of 630 unique molecules bound to the catalytic binding sites, among them 31 additives, 222 fragments, and 377 drug-like ligands. In general, we observed that, independent of the target, protein-fragment interaction patterns are highly similar to those of drug-like ligands and mostly cover the residues crucial for binding. Crystallographic additives are also able to show conserved binding modes and recover the residues important for binding in some of the cases. Moreover, we show evidence that the information from fragments and drug-like ligands can be applied to rescore docking poses in order to improve the prediction of binding modes.

Inhibition of DNA-Topoisomerase I by Acylated Triterpene Saponins from Pittosporum angustifolium Lodd.

Previous phytochemical investigation of the leaves and seeds of Pittosporum angustifolium Lodd. led to the isolation and structural elucidation of polyphenols and triterpene saponins. Evaluation for cytotoxicity of isolated saponins revealed that the predominant structural feature for a cytotoxic activity are acyl substituents at the oleanane aglycon backbone. The present work reports the results of a screening of 10 selected acylated saponins for their potential to inhibit the human DNA-topoisomerase I, giving rise to IC50 values in a range of 2.8-46.5 µM. To clarify the mode of observed cytotoxic action and, moreover, to distinguish from a pure surfactant effect which is commonly accompanied with saponins, these results indicate an involvement of the topoisomerase I and its role as a possible target structure for a cytotoxic activity. In addition, computational predictions of the fitting of saponins to the topoisomerase I-DNA complex, indicate a similar binding mode to that of clinically used topoisomerase I inhibitors. Ten acylated triterpene saponins from Pittosporum angustifolium were investigated for their potential to inhibit the human DNA-topoisomerase I and computational predictions of the fitting of saponins to the topoisomerase I-DNA complex were carried out.

WITHDRAWN--a resource for withdrawn and discontinued drugs.

Post-marketing drug withdrawals can be associated with various events, ranging from safety issues such as reported deaths or severe side-effects, to a multitude of non-safety problems including lack of efficacy, manufacturing, regulatory or business issues. During the last century, the majority of drugs voluntarily withdrawn from the market or prohibited by regulatory agencies was reported to be related to adverse drug reactions. Understanding the underlying mechanisms of toxicity is of utmost importance for current and future drug discovery. Here, we present WITHDRAWN, a resource for withdrawn and discontinued drugs publicly accessible at http://cheminfo.charite.de/withdrawn. Today, the database comprises 578 withdrawn or discontinued drugs, their structures, important physico-chemical properties, protein targets and relevant signaling pathways. A special focus of the database lies on the drugs withdrawn due to adverse reactions and toxic effects. For approximately one half of the drugs in the database, safety issues were identified as the main reason for withdrawal. Withdrawal reasons were extracted from the literature and manually classified into toxicity types representing adverse effects on different organs. A special feature of the database is the presence of multiple search options which will allow systematic analyses of withdrawn drugs and their mechanisms of toxicity.

Computational methods for prediction of in vitro effects of new chemical structures.

BACKGROUND: With a constant increase in the number of new chemicals synthesized every year, it becomes important to employ the most reliable and fast in silico screening methods to predict their safety and activity profiles. In recent years, in silico prediction methods received great attention in an attempt to reduce animal experiments for the evaluation of various toxicological endpoints, complementing the theme of replace, reduce and refine. Various computational approaches have been proposed for the prediction of compound toxicity ranging from quantitative structure activity relationship modeling to molecular similarity-based methods and machine learning. Within the "Toxicology in the 21st Century" screening initiative, a crowd-sourcing platform was established for the development and validation of computational models to predict the interference of chemical compounds with nuclear receptor and stress response pathways based on a training set containing more than 10,000 compounds tested in high-throughput screening assays. RESULTS: Here, we present the results of various molecular similarity-based and machine-learning based methods over an independent evaluation set containing 647 compounds as provided by the Tox21 Data Challenge 2014. It was observed that the Random Forest approach based on MACCS molecular fingerprints and a subset of 13 molecular descriptors selected based on statistical and literature analysis performed best in terms of the area under the receiver operating characteristic curve values. Further, we compared the individual and combined performance of different methods. In retrospect, we also discuss the reasons behind the superior performance of an ensemble approach, combining a similarity search method with the Random Forest algorithm, compared to individual methods while explaining the intrinsic limitations of the latter. CONCLUSIONS: Our results suggest that, although prediction methods were optimized individually for each modelled target, an ensemble of similarity and machine-learning approaches provides promising performance indicating its broad applicability in toxicity prediction.

Novel DNA topoisomerase IIα inhibitors from combined ligand- and structure-based virtual screening.

DNA topoisomerases are enzymes responsible for the relaxation of DNA torsional strain, as well as for the untangling of DNA duplexes after replication, and are important cancer drug targets. One class of topoisomerase inhibitors, "poisons", binds to the transient enzyme-DNA complex which occurs during the mechanism of action, and inhibits the religation of DNA. This ultimately leads to the accumulation of DNA double strand breaks and cell death. Different types of topoisomerases occur in human cells and several poisons of topoisomerase I and II are widely used clinically. However, their use is compromised by a variety of side effects. Recent studies confirm that the inhibition of the α-isoform of topoisomerase II is responsible for the cytotoxic effect, whereas the inhibition of the ß-isoform leads to development of adverse drug reactions. Thus, the discovery of agents selective for topoisomerase IIα is an important strategy for the development of topoisomerase II poisons with improved clinical profiles. Here, we present a computer-aided drug design study leading to the identification of structurally novel topoisomerase IIα poisons. The study combines ligand- and structure-based drug design methods including pharmacophore models, homology modelling, docking, and virtual screening of the National Cancer Institute compound database. From the 8 compounds identified from the computational work, 6 were tested for their capacity to poison topoisomerase II in vitro: 4 showed selective inhibitory activity for the α- over the ß-isoform and 3 of these exhibited cytotoxic activity. Thus, our study confirms the applicability of computer-aided methods for the discovery of novel topoisomerase II poisons, and presents compounds which could be investigated further as selective topoisomerase IIα inhibitors.

ProTox: a web server for the in silico prediction of rodent oral toxicity.

Animal trials are currently the major method for determining the possible toxic effects of drug candidates and cosmetics. In silico prediction methods represent an alternative approach and aim to rationalize the preclinical drug development, thus enabling the reduction of the associated time, costs and animal experiments. Here, we present ProTox, a web server for the prediction of rodent oral toxicity. The prediction method is based on the analysis of the similarity of compounds with known median lethal doses (LD50) and incorporates the identification of toxic fragments, therefore representing a novel approach in toxicity prediction. In addition, the web server includes an indication of possible toxicity targets which is based on an in-house collection of protein-ligand-based pharmacophore models ('toxicophores') for targets associated with adverse drug reactions. The ProTox web server is open to all users and can be accessed without registration at: http://tox.charite.de/tox. The only requirement for the prediction is the two-dimensional structure of the input compounds. All ProTox methods have been evaluated based on a diverse external validation set and displayed strong performance (sensitivity, specificity and precision of 76, 95 and 75%, respectively) and superiority over other toxicity prediction tools, indicating their possible applicability for other compound classes.

Development of purely structure-based pharmacophores for the topoisomerase I-DNA-ligand binding pocket.

Purely structure-based pharmacophores (SBPs) are an alternative method to ligand-based approaches and have the advantage of describing the entire interaction capability of a binding pocket. Here, we present the development of SBPs for topoisomerase I, an anticancer target with an unusual ligand binding pocket consisting of protein and DNA atoms. Different approaches to cluster and select pharmacophore features are investigated, including hierarchical clustering and energy calculations. In addition, the performance of SBPs is evaluated retrospectively and compared to the performance of ligand- and complex-based pharmacophores. SBPs emerge as a valid method in virtual screening and a complementary approach to ligand-focussed methods. The study further reveals that the choice of pharmacophore feature clustering and selection methods has a large impact on the virtual screening hit lists. A prospective application of the SBPs in virtual screening reveals that they can be used successfully to identify novel topoisomerase inhibitors.

Combination of ligand- and structure-based methods in virtual screening.

The combination of ligand- and structure-based molecular modelling methods has become a common approach in virtual screening. This review describes different strategies for integration of ligand- and structure-based methods which can be divided into sequential, parallel or hybrid approaches. Although no thorough performance comparisons between combined approaches are available, examples of successful applications in prospective and retrospective virtual screening are discussed. Most published studies use a sequential approach, utilising well-documented single methods successfully.

Probing the pharmacological properties of distinct subunit interfaces within heteromeric glycine receptors reveals a functional ßß agonist-binding site.

Synaptic glycine receptors (GlyRs) are hetero-pentameric chloride channels composed of α and ß subunits, which are activated by agonist binding at subunit interfaces. To examine the pharmacological properties of each potential agonist-binding site, we substituted residues of the GlyR α(1) subunit by the corresponding residues of the ß subunit, as deduced from sequence alignment and homology modeling based on the recently published crystal structure of the glutamate-gated chloride channel GluCl. These exchange substitutions allowed us to reproduce the ßα, αß and ßß subunit interfaces present in synaptic heteromeric GlyRs by generating recombinant homomeric receptors. When the engineered α(1) GlyR mutants were expressed in Xenopus oocytes, all subunit interface combinations were found to form functional agonist-binding sites as revealed by voltage clamp recording. The ßß-binding site displayed the most distinct pharmacological profile towards a range of agonists and modulators tested, indicating that it might be selectively targeted to modulate the activity of synaptic GlyRs. The mutational approach described here should be generally applicable to heteromeric ligand-gated ion channels composed of homologous subunits and facilitate screening efforts aimed at targeting inter-subunit specific binding sites.

Exploring DNA topoisomerase I ligand space in search of novel anticancer agents.

DNA topoisomerase I (Top1) is over-expressed in tumour cells and is an important target in cancer chemotherapy. It relaxes DNA torsional strain generated during DNA processing by introducing transient single-strand breaks and allowing the broken strand to rotate around the intermediate Top1-DNA covalent complex. This complex can be trapped by a group of anticancer agents interacting with the DNA bases and the enzyme at the cleavage site, preventing further topoisomerase activity. Here we have identified novel Top1 inhibitors as potential anticancer agents by using a combination of structure- and ligand-based molecular modelling methods. Pharmacophore models have been developed based on the molecular characteristics of derivatives of the alkaloid camptothecin (CPT), which represent potent antitumour agents and the main group of Top1 inhibitors. The models generated were used for in silico screening of the National Cancer Institute (NCI, USA) compound database, leading to the identification of a set of structurally diverse molecules. The strategy is validated by the observation that amongst these molecules are several known Top1 inhibitors and agents cytotoxic against human tumour cell lines. The potential of the untested hits to inhibit Top1 activity was further evaluated by docking into the binding site of a Top1-DNA complex, resulting in a selection of 10 compounds for biological testing. Limited by the compound availability, 7 compounds have been tested in vitro for their Top1 inhibitory activity, 5 of which display mild to moderate Top1 inhibition. A further compound, found by similarity search to the active compounds, also shows mild activity. Although the tested compounds display only low in vitro antitumour activity, our approach has been successful in the identification of structurally novel Top1 inhibitors worthy of further investigation as potential anticancer agents.

Different binding modes of tropeines mediating inhibition and potentiation of alpha1 glycine receptors.

Tropeines are bidirectional modulators of native and recombinant glycine receptors (GlyRs) and promising leads for the development of novel modulatory agents. Tropisetron potentiates and inhibits agonist-triggered GlyR currents at femto- to nanomolar and micromolar concentrations respectively. Here, the potentiating and inhibitory effects of another tropeine, 3alpha-(3'-methoxy-benzoyloxy)nortropane (MBN) were examined by voltage-clamp electrophysiology at wild type and mutant alpha1 GlyRs expressed in Xenopus laevis oocytes. Several substitutions around the agonist-binding cavity of the alpha1 subunit interface (N46C, F63A, N102A, R119K, R131A, E157C, K200A, Y202L and F207A) were found to reduce or eliminate MBN inhibition of glycine activation. In contrast, the binding site mutations Q67A, R119A and S129A which did not affect MBN inhibition abolished the potentiation of chloride currents elicited by low concentrations of the partial agonist taurine following pre-incubation with MBN. Thus, potentiation and inhibition involve distinct binding modes of MBN in the inter-subunit agonist-binding pocket of alpha1 GlyRs. Homology modelling and molecular dynamics simulations disclosed two distinct docking modes for MBN, which are consistent with the differential effects of individual binding site substitutions on MBN inhibition and potentiation respectively. Together these results suggest that distinct binding modes at adjacent binding sites located within the agonist-binding pocket of the GlyR mediate the bidirectional modulatory effects of tropeines.