Volker Hähnke

Reaction-driven de novo design, synthesis and testing of potential type II kinase inhibitors

BACKGROUND De novo design of drug-like compounds with a desired pharmacological activity profile has become feasible through innovative computer algorithms. Fragment-based design and simulated chemical reactions allow for the rapid generation of candidate compounds as blueprints for organic synthesis. METHODS We used a combination of complementary virtual-screening tools for the analysis of de novo designed compounds that were generated with the aim to inhibit inactive polo-like kinase 1 (Plk1), a target for the development of cancer therapeutics. A homology model of the inactive state of Plk1 was constructed and the nucleotide binding pocket conformations in the DFG-in and DFG-out state were compared. The de novo-designed compounds were analyzed using pharmacophore matching, structure-activity landscape analysis, and automated ligand docking. One compound was synthesized and tested in vitro. RESULTS The majority of the designed compounds possess a generic architecture present in known kinase inhibitors. Predictions favor kinases as targets of these compounds but also suggest potential off-target effects. Several bioisosteric replacements were suggested, and de novo designed compounds were assessed by automated docking for potential binding preference toward the inactive (type II inhibitors) over the active conformation (type I inhibitors) of the kinase ATP binding site. One selected compound was successfully synthesized as suggested by the software. The de novo-designed compound exhibited inhibitory activity against inactive Plk1 in vitro, but did not show significant inhibition of active Plk1 and 38 other kinases tested. CONCLUSIONS Computer-based de novo design of screening candidates in combination with ligand- and receptor-based virtual screening generates motivated suggestions for focused library design in hit and lead discovery. Attractive, synthetically accessible compounds can be obtained together with predicted on- and off-target profiles and desired activities.

PhAST: pharmacophore alignment search tool.

We present a ligand‐based virtual screening technique (PhAST) for rapid hit and lead structure searching in large compound databases. Molecules are represented as strings encoding the distribution of pharmacophoric features on the molecular graph. In contrast to other text‐based methods using SMILES strings, we introduce a new form of text representation that describes the pharmacophore of molecules. This string representation opens the opportunity for revealing functional similarity between molecules by sequence alignment techniques in analogy to homology searching in protein or nucleic acid sequence databases. We favorably compared PhAST with other current ligand‐based virtual screening methods in a retrospective analysis using the BEDROC metric. In a prospective application, PhAST identified two novel inhibitors of 5‐lipoxygenase product formation with minimal experimental effort. This outcome demonstrates the applicability of PhAST to drug discovery projects and provides an innovative concept of sequence‐based compound screening with substantial scaffold hopping potential.

Virtual screening for compounds that mimic protein–protein interface epitopes

Modulation of protein–protein interactions (PPI) has emerged as a new concept in rational drug design. Here, we present a computational protocol for identifying potential PPI inhibitors. Relevant regions of interfaces (epitopes) are predicted for three‐dimensional protein models and serve as queries for virtual compound screening. We present a computational screening protocol that incorporates two different pharmacophore models. One model is based on the mathematical concept of autocorrelation vectors and the other utilizes fuzzy labeled graphs. In a proof‐of‐concept study, we were able to identify serine protease inhibitors using a predicted trypsin epitope as query. Our virtual screening framework may be suited for rapid identification of PPI inhibitors and suggesting bioactive tool compounds. Copyright for JCC Journal:

Multistep virtual screening for rapid and efficient identification of non-nucleoside bacterial thymidine kinase inhibitors.

Antimicrobial activity of trimethoprim/sulfamethoxazole (SXT) against Staphylococcus aureus (S. aureus) is antagonized by thymidine, which is abundant in infected or inflamed human tissue. To restore the antimicrobial activity of SXT in the presence of thymidine, we screened for small-molecule inhibitors of S. aureus thymidine kinase with non-nucleoside scaffolds. We present the successful application of an adaptive virtual screening protocol for novel antibiotics using a combination of ligand- and structure-based approaches. Two consecutive rounds of virtual screening and in vitro testing were performed that resulted in several non-nucleoside hits. The most potent compound exhibits substantial antimicrobial activity against both methicillin-resistant S. aureus strain ATCC 700699 and nonresistant strain ATCC 29213, when combined with SXT in the presence of thymidine. This study demonstrates how virtual screening can be used to guide hit finding in antibacterial screening campaigns with minimal experimental effort.

From Virtual Screening to Bioactive Compounds by Visualizing and Clustering of Chemical Space

Identification and visualization of ‘activity islands’ in chemical space is presented as a straightforward method for rapid automated identification of bioactive compounds and drug target profiling. We successfully applied this computational technique to finding inhibitors of Helicobacter pylori protease HtrA with new molecular scaffolds, and to deorphanizing of a compound from a combinatorial oxadiazole library. Bioactive molecules were discovered with minimal experimental effort. The results demonstrate that visualization of ‘chemical space’ provides an intuitive approach to molecular design and virtual screening in drug discovery, even in the absence of a three-dimensional receptor structure. Visualization of chemical data can help understand the structure of compound distributions in chemical space and guide molecular design experiments. [1–5] Commonly applied visualization techniques in chemistry are principal component analysis (PCA) [6] and self-organizing maps (SOMs, Kohonen networks). [7–9] Both methods have proven their value for visualization of compound libraries and virtual screening. Still, they suffer from several drawbacks. For example, a SOM’s quality to separate data depends on the chosen map size, i.e. the number of ‘neurons’ (local clusters, Voronoi fields), and determining the actual quality of a computed SOM projection is nontrivial. A perceived disadvantage is that SOMs lack immediate interpretability due to nonlinear projection. While for low-dimensional data linear projection by PCA seems to be preferable, SOMs have shown to produce more robust projections of high-dimensional data. [10] Here, we present a method for visualizing and interpreting high-dimensional chemical data, which is complementary to SOM and PCA projection and overcomes some of their disadvantages and limitations. The projection is based on stochastic proximity embedding (SPE). [11] SPE embeds data in a low-dimensional space in such a way that pairwise distances between compounds are preserved. As a consequence, patterns in the original high-dimensional data distribution become accessible to visual inspection. Making such patterns visible supports our intuitive interpretation how a molecular representation might distinguish between sets of compounds (e.g., active vs inactive) and create some kind of order in data space. Once activity islands are identified in the visualization, the compounds that form such local clusters can be extracted and subjected to biochemical tests. [12–15]

Pharmacophore alignment search tool: Influence of canonical atom labeling on similarity searching

Previously, (Hähnke et al., J Comput Chem 2009, 30, 761) we presented the Pharmacophore Alignment Search Tool (PhAST), a ligand‐based virtual screening technique representing molecules as strings coding pharmacophoric features and comparing them by global pairwise sequence alignment. To guarantee unambiguity during the reduction of two‐dimensional molecular graphs to one‐dimensional strings, PhAST employs a graph canonization step. Here, we present the results of the comparison of 11 different algorithms for graph canonization with respect to their impact on virtual screening. Retrospective screenings of a drug‐like data set were evaluated using the BEDROC metric, which yielded averaged values between 0.4 and 0.14 for the best‐performing and worst‐performing canonization technique. We compared five scoring schemes for the alignments and found preferred combinations of canonization algorithms and scoring functions. Finally, we introduce a performance index that helps prioritize canonization approaches without the need for extensive retrospective evaluation.

Pharmacophore alignment search tool: Influence of scoring systems on text‐based similarity searching

The text‐based similarity searching method Pharmacophore Alignment Search Tool is grounded on pairwise comparisons of potential pharmacophoric points between a query and screening compounds. The underlying scoring matrix is of critical importance for successful virtual screening and hit retrieval from large compound libraries. Here, we compare three conceptually different computational methods for systematic deduction of scoring matrices: assignment‐based, alignment‐based, and stochastic optimization. All three methods resulted in optimized pharmacophore scoring matrices with significantly superior retrospective performance in comparison with simplistic scoring schemes. Computer‐generated similarity matrices of pharmacophoric features turned out to agree well with a manually constructed matrix. We introduce the concept of position‐specific scoring to text‐based similarity searching so that knowledge about specific ligand‐receptor binding patterns can be included and demonstrate its benefit for hit retrieval. The approach was also used for automated pharmacophore elucidation in agonists of peroxisome proliferator activated receptor gamma, successfully identifying key interactions for receptor activation.

Significance estimation for sequence-based chemical similarity searching (PhAST) and application to AuroraA kinase inhibitors

BACKGROUND Chemical similarity searching allows the retrieval of preferred screening molecules from a compound database. Candidates are ranked according to their similarity to a reference compound (query). Assessing the statistical significance of chemical similarity scores helps prioritizing significant hits, and identifying cases where the database does not contain any promising compounds. METHOD Our text-based similarity measure, Pharmacophore Alignment Search Tool (PhAST), employs pair-wise sequence alignment. We adapted the concept of E-values as significance estimates and employed a sampling technique that incorporates the principle of importance sampling in a Markov chain Monte Carlo simulation to generate distributions of random alignment scores. These distributions were used to compute significance estimates for similarity scores in a preliminary prospective virtual screen for inhibitors of Aurora A kinase. CONCLUSION Assessing the significance of compound similarity computed with PhAST allows for a statistically motivated identification of candidate screening compounds. Inhibitors of Aurora A kinase were retrieved from a large compound library.

Pharmacophore alignment search tool: influence of the third dimension on text-based similarity searching.

Previously (Hähnke et al., J Comput Chem 2010, 31, 2810) we introduced the concept of nonlinear dimensionality reduction for canonization of two‐dimensional layouts of molecular graphs as foundation for text‐based similarity searching using our Pharmacophore Alignment Search Tool (PhAST), a ligand‐based virtual screening method. Here we apply these methods to three‐dimensional molecular conformations and investigate the impact of these additional degrees of freedom on virtual screening performance and assess differences in ranking behavior. Best‐performing variants of PhAST are compared with 16 state‐of‐the‐art screening methods with respect to significance estimates for differences in screening performance. We show that PhAST sorts new chemotypes on early ranks without sacrificing overall screening performance. We succeeded in combining PhAST with other virtual screening techniques by rank‐based data fusion, significantly improving screening capabilities. We also present a parameterization of double dynamic programming for the problem of small molecule comparison, which allows for the calculation of structural similarity between compounds based on one‐dimensional representations, opening the door to a holistic approach to molecule comparison based on textual representations.

Pharmacophore Alignment Search Tool (PhAST): Significance Assessment of Chemical Similarity

Previously, we proposed a ligand‐based virtual screening technique (PhAST) based on global alignment of linearized interaction patterns. Here, we applied techniques developed for similarity assessment in local sequence alignments to our method resulting in p‐values for chemical similarity. We compared two sampling strategies, a simple sampling strategy and a Markov Chain Monte Carlo (MCMC) method, and investigated the similarity of sampled distributions to Gaussian, Gumbel, modified Gumbel, and Gamma distributions. The Gumbel distribution with a Gaussian correction term was identified as the most similar to the observed empirical distributions. These techniques were applied in retrospective screenings on a drug‐like dataset. Obtained p‐values were adjusted to the size of the screening library with four different methods. Evaluation of E‐value thresholds corroborated the Bonferroni correction as a preferred means to identify significant chemical similarity with PhAST. An online version of PhAST with significance estimation is available at http://modlab‐cadd.ethz.ch/.