Thierry Langer

LigandScout: 3-D Pharmacophores Derived from Protein-Bound Ligands and Their Use as Virtual Screening Filters

From the historically grown archive of protein-ligand complexes in the Protein Data Bank small organic ligands are extracted and interpreted in terms of their chemical characteristics and features. Subsequently, pharmacophores representing ligand-receptor interaction are derived from each of these small molecules and its surrounding amino acids. Based on a defined set of only six types of chemical features and volume constraints, three-dimensional pharmacophore models are constructed, which are sufficiently selective to identify the described binding mode and are thus a useful tool for in-silico screening of large compound databases. The algorithms for ligand extraction and interpretation as well as the pharmacophore creation technique from the automatically interpreted data are presented and applied to a rhinovirus capsid complex as application example.

Why Drugs Fail - A Study on Side Effects in New Chemical Entities

Over 90% of the market withdrawals were caused by drug toxicity. Hepatotoxicity and cardiovascular toxicity proved to be the major causes for two out of three market withdrawals in the respective time period. In clinical phases I-III 43% of drug development project terminations were due to insufficient efficacy of the investigated compound. The second important issue, which caused one third of the projects to be closed, was toxicity. ADME parameters and economic and other reasons played a minor role. The results of our study indicate that compared with previous studies on this subject, no major improvements have been achieved in the last decade.

Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection--what can we learn from earlier mistakes?

Within the last few years a considerable amount of evaluative studies has been published that investigate the performance of 3D virtual screening approaches. Thereby, in particular assessments of protein–ligand docking are facing remarkable interest in the scientific community. However, comparing virtual screening approaches is a non-trivial task. Several publications, especially in the field of molecular docking, suffer from shortcomings that are likely to affect the significance of the results considerably. These quality issues often arise from poor study design, biasing, by using improper or inexpressive enrichment descriptors, and from errors in interpretation of the data output. In this review we analyze recent literature evaluating 3D virtual screening methods, with focus on molecular docking. We highlight problematic issues and provide guidelines on how to improve the quality of computational studies. Since 3D virtual screening protocols are in general assessed by their ability to discriminate between active and inactive compounds, we summarize the impact of the composition and preparation of test sets on the outcome of evaluations. Moreover, we investigate the significance of both classic enrichment parameters and advanced descriptors for the performance of 3D virtual screening methods. Furthermore, we review the significance and suitability of RMSD as a measure for the accuracy of protein–ligand docking algorithms and of conformational space sub sampling algorithms.

Recognizing Pitfalls in Virtual Screening: A Critical Review

The aim of virtual screening (VS) is to identify bioactive compounds through computational means, by employing knowledge about the protein target (structure-based VS) or known bioactive ligands (ligand-based VS). In VS, a large number of molecules are ranked according to their likelihood to be bioactive compounds, with the aim to enrich the top fraction of the resulting list (which can be tested in bioassays afterward). At its core, VS attempts to improve the odds of identifying bioactive molecules by maximizing the true positive rate, that is, by ranking the truly active molecules as high as possible (and, correspondingly, the truly inactive ones as low as possible). In choosing the right approach, the researcher is faced with many questions: where does the optimal balance between efficiency and accuracy lie when evaluating a particular algorithm; do some methods perform better than others and in what particular situations; and what do retrospective results tell us about the prospective utility of a particular method? Given the multitude of settings, parameters, and data sets the practitioner can choose from, there are many pitfalls that lurk along the way which might render VS less efficient or downright useless. This review attempts to catalogue published and unpublished problems, shortcomings, failures, and technical traps of VS methods with the aim to avoid pitfalls by making the user aware of them in the first place.

Molecule-pharmacophore superpositioning and pattern matching in computational drug design

Three-dimensional (3D) pharmacophore modeling is a technique for describing the interaction of a small molecule ligand with a macromolecular target. Since chemical features in a pharmacophore model are well known and highly transparent for medicinal chemists, these models are intuitively understandable and have been increasingly successful in computational drug discovery in the past few years. The performance and applicability of pharmacophore modeling depends on two main factors: the definition and placement of pharmacophoric features and the alignment techniques used to overlay 3D pharmacophore models and small molecules. An overview of key technologies and latest developments in the area of 3D pharmacophores is given and provides insight into different approaches as implemented by the 3D pharmacophore modeling packages like Catalyst, MOE, Phase and LigandScout.

Efficient overlay of small organic molecules using 3D pharmacophores

Aligning and overlaying two or more bio-active molecules is one of the key tasks in computational drug discovery and bio-activity prediction. Especially chemical-functional molecule characteristics from the view point of a macromolecular target represented as a 3D pharmacophore are the most interesting similarity measure when describing and analyzing macromolecule-ligand interaction. In this study, a novel approach for aligning rigid three-dimensional molecules according to their chemical-functional pharmacophoric features is presented and compared to the overlay of experimentally determined poses in a comparable macromolecule coordinate frame. The presented approach identifies optimal chemical feature pairs using distance and density characteristics obtained by correlating pharmacophoric geometries and thus proves to be faster than existing combinatorial alignment methods and creates more reasonable alignments than pure atom-based methods. Examples will be provided to demonstrate the feasibility, speed and intuitiveness of this method.

Comparative performance assessment of the conformational model generators omega and catalyst: a large-scale survey on the retrieval of protein-bound ligand conformations.

In continuation of our studies to evaluate the ability of various conformer generators to produce bioactive conformations, we present the extension of our work on the analysis of Catalysts conformational subsampling algorithm in a comparative evaluation with OpenEyes currently updated tool Omega 2.0. Our study is based on an enhanced test set of 778 drug molecules and pharmacologically relevant compounds extracted from the Protein Data Bank (PDB). We elaborated protocols for two common conformer generation use cases and applied them to both programs: (i) high-throughput settings for processing large databases and (ii) high-quality settings for binding site exploration or lead structure refinement. While Catalyst is faster in the first case, Omega 2.0 better reproduces the bound ligand conformations from the PDB in less time for the latter case.

Comparative analysis of protein-bound ligand conformations with respect to catalyst's conformational space subsampling algorithms.

We examined the quality of Catalysts conformational model generation algorithm via a large scale study based on the crystal structures of a sample of 510 pharmaceutically relevant protein-ligand complexes extracted from the Protein Data Bank (PDB). Our results show that the tested algorithms implemented within Catalyst are able to produce high quality conformers, which in most of the cases are well suited for in silico drug research. Catalyst-specific settings were analyzed, such as the method used for the conformational model generation (FAST vs BEST) and the maximum number of generated conformers. By setting these options for higher fitting quality, the average RMS values describing the similarity of experimental and simulated conformers were improved from an RMS of 1.06 with max. 50 FAST generated conformers to an RMS of 0.93 with max. 255 BEST generated conformers, which represents an improvement by 12%. Each method provides best fitting conformers with an RMS value<1.50 in more than 80% of all cases. We analyzed the computing time/quality ratio of various conformational model generation settings and examined ligands in high energy conformations. Furthermore, properties of the same ligands in various proteins were investigated, and the fitting qualities of experimental conformations from the PDB and the Cambridge Structural Database (CSD) were compared. One of the most important conclusions of former studies, the fact that bioactive conformers often have energy high above that of global minima, was confirmed.

In silico target fishing for rationalized ligand discovery exemplified on constituents of Ruta graveolens

The identification of targets whose interaction is likely to result in the successful treatment of a disease is of growing interest for natural product scientists. In the current study we performed an exemplary application of a virtual parallel screening approach to identify potential targets for 16 secondary metabolites isolated and identified from the aerial parts of the medicinal plant RUTA GRAVEOLENS L. Low energy conformers of the isolated constituents were simultaneously screened against a set of 2208 pharmacophore models generated in-house for the IN SILICO prediction of putative biological targets, i. e., target fishing. Based on the predicted ligand-target interactions, we focused on three biological targets, namely acetylcholinesterase (AChE), the human rhinovirus (HRV) coat protein and the cannabinoid receptor type-2 (CB (2)). For a critical evaluation of the applied parallel screening approach, virtual hits and non-hits were assayed on the respective targets. For AChE the highest scoring virtual hit, arborinine, showed the best inhibitory IN VITRO activity on AChE (IC (50) 34.7 muM). Determination of the anti-HRV-2 effect revealed 6,7,8-trimethoxycoumarin and arborinine to be the most active antiviral constituents with IC (50) values of 11.98 muM and 3.19 muM, respectively. Of these, arborinine was predicted virtually. Of all the molecules subjected to parallel screening, one virtual CB (2) ligand was obtained, i. e., rutamarin. Interestingly, in experimental studies only this compound showed a selective activity to the CB (2) receptor ( Ki of 7.4 muM) by using a radioligand displacement assay. The applied parallel screening paradigm with constituents of R. GRAVEOLENS on three different proteins has shown promise as an IN SILICO tool for rational target fishing and pharmacological profiling of extracts and single chemical entities in natural product research.

Discovery of High-Affinity Ligands of σ1 Receptor, ERG2, and Emopamil Binding Protein by Pharmacophore Modeling and Virtual Screening

ERG2, emopamil binding protein (EBP), and sigma-1 receptor (sigma(1)) are enzymes of sterol metabolism and an enzyme-related protein, respectively, that share high affinity for various structurally diverse compounds. To discover novel high-affinity ligands, pharmacophore models were built with Catalyst based upon a series of 23 structurally diverse chemicals exhibiting K(i) values from 10 pM to 100 microM for all three proteins. In virtual screening experiments, we retrieved drugs that were previously reported to bind to one or several of these proteins and also tested 11 new hits experimentally, of which three, among them raloxifene, had affinities for sigma(1) or EBP of <60 nM. When used to search a database of 3525 biochemicals of intermediary metabolism, a slightly modified ERG2 pharmacophore model successfully retrieved 10 substrate candidates among the top 28 hits. Our results indicate that inhibitor-based pharmacophore models for sigma(1), ERG2, and EBP can be used to screen drug and metabolite databases for chemically diverse compounds and putative endogenous ligands.