DOGS: Reaction-Driven de novo Design of Bioactive Compounds

We present a computational method for the reaction-based de novo design of drug-like molecules. The software DOGS (Design of Genuine Structures) features a ligand-based strategy for automated ‘in silico’ assembly of potentially novel bioactive compounds. The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features. We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability, based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles. This enables the software to suggest a synthesis route for each designed compound. Two prospective case studies are presented together with details on the algorithm and its implementation. De novo designed ligand candidates for the human histamine H4 receptor and γ-secretase were synthesized as suggested by the software. The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties

Benchmarking of Multivariate Similarity Measures for High Content Screening Fingerprints in Phenotypic Drug Discovery

With the evolving technology during the past decade High Content Screening (HCS) has become a powerful tool in drug discovery as it is amenable to high throughput measuring of cellular responses to chemical disturbance while providing a highly multiplexed and quantitative phenotypic readout. These image-based readouts such as cell size, shape, intensity and texture characterize the corresponding cell phenotype and are thus defined as HCS fingerprints. Systematic analysis of HCS fingerprints allows for objective computational comparisons of cellular responses that enable the detection of phenotypic outcomes of large scale small molecule screens and consequently compound hit candidates. Feature selection methods and similarity metrics, as the basis for phenotype identification and clustering, are critical for the quality of such computational analyses. Here, we present a systematic evaluation of more than 15 different similarity measures, such as Mahalanobis distance, distance correlation, maximum information coefficient, or cosine similarity, in combination with unsupervised linear or non-linear feature selection methods. We evaluate their potential to capture biologically relevant image features and their applicability in HCS and drug discovery with data from a high-throughput HCS campaign. The results of the experiments highlight the benefits and drawbacks of the different vector comparison methods in respect to the application under consideration. We show that non-linear correlation based similarity measures such as Kendall’s τ and Spearman’s ρ perform well in most of the tested scenarios and outperform other non-parameterized measures as the Euclidian or Manhattan distance. Our results also demonstrate that measures based on comparing the correlations of selected up- and down regulated variables can be high-performing as well. In this context, we present four novel modifications of the frequently used connectivity map similarity measure which surpass the original version in our experiments. This study provides a basis for phenotypic analysis in future HCS campaigns

In Silico Adoption of an Orphan Nuclear Receptor NR4A1

A 4.1μs molecular dynamics simulation of the NR4A1 (hNur77) apo-protein has been undertaken and a previously undetected druggable pocket has become apparent that is located remotely from the ‘traditional’ nuclear receptor ligand-binding site. A NR4A1/bisindole ligand complex at this novel site has been found to be stable over 1 μs of simulation and to result in an interesting conformational transmission to a remote loop that has the capacity to communicate with a NBRE within a RXR-α/NR4A1 heterodimer. Several features of the simulations undertaken indicate how NR4A1 can be affected by alternate-site modulators

<i>In Silico</i> adoption of an orphan nuclear receptor NR4A1

A 4.1 μs molecular dynamics simulation of the NR4A1 (hNur77) apo-protein has been undertaken and a previously undetected druggable pocket has become apparent that is located remotely from the 'traditional' nuclear receptor ligand-binding site. A NR4A1/bis-indole ligand complex at this novel site has been found to be stable over 1 μs of simulation and to result in an interesting conformational transmission to a remote loop that has the capacity to communicate with a NBRE within a RXR-α/NR4A1 heterodimer. Several features of the simulations undertaken indicate how NR4A1 can be affected by alternate-site modulators

Linking phenotypes and modes of action through high-content screen fingerprints

High content screening (HCS) is a powerful technique for monitoring phenotypic responses to treatments on a cellular and sub-cellular level. Cellular phenotypes can be characterized by multivariate image readouts such as shape, intensity or texture. The corresponding feature vectors can thus be defined as HCS-fingerprints that serve as a powerful biological compound descriptor. Therefore clustering or classification of HCS-fingerprints across compound treatments allows for the identification of similarities in protein targets or pathways. We developed an HCS¬-based profiling panel that serves as basis for characterizing the mode of action of compounds. This panel measures phenotypic effects in six different compartments of U2-OS cells, namely the nucleus, the cytoplasm, the endoplasmic reticulum, the Golgi apparatus and the cytoskeleton. We profiled a set of 2’725 well-annotated compounds and clustered their corresponding HCS-fingerprints to establish links between predominant cellular phenotypes and cellular processes and protein targets. We found various different clusters enriched for individual targets (e.g. HDAC, HSP90, TOP1, HMGCR, TUB), signaling pathways (e.g. PIK3/AKT/mTOR), or gene sets associated with diseases (e.g. psoriasis, leukemia). Based on this clustering we were able to identify novel compound target associations for selected compounds such as a sub micromolar inhibitory activity of Silmitasertib (a Casein Kinase inhibitor) on PI3K and mTOR

Representative snapshots taken from the MD simulation of ligand 1 within the new binding pocket (cyan, after 100 ns; magenta, after 800ns), fitted onto the starting geometry of the docked complex (green).

<p>Representative snapshots taken from the MD simulation of ligand 1 within the new binding pocket (cyan, after 100 ns; magenta, after 800ns), fitted onto the starting geometry of the docked complex (green).</p

The predicted binding site (blue/blue grey spheres) is located close to the N-terminus of NR4A1.

<p>The flexible loop that is stabilized by ligand binding is indicated by the red ellipse.</p

Apo-NR4A1 (blue, PDB:2QW4) compared to a snapshot of NR4A1 after 45ns simulation in the presence of 1 (green).

<p>The blue double headed arrows indicate the axes of the helices 1 and 9 in the apo-structure. The regions of the protein that rearrange on ligand binding are denoted by the ellipses (Red; adjacent to the binding site. Violet; remote loop ²⁵F FQELVLPHFGKEDAGD-D⁴⁰).</p

Clustering (above) and DASH, [35] (below) analyses of the 4.1 μs simulation of NR4A1.

<p>The clustering results are color-coded to indicate the population of the cluster in the given time period. The blue vertical dashed lines indicate transitions detected by DASH. The red dashed boxes indicate the clusters/conformations in which the binding pocket discussed below was found.</p