Analyzing Kinase Similarity in Small Molecule and Protein Structural Space to Explore the Limits of Multi-Target Screening.

While selective inhibition is one of the key assets for a small molecule drug, many diseases can only be tackled by simultaneous inhibition of several proteins. An example where achieving selectivity is especially challenging are ligands targeting human kinases. This difficulty arises from the high structural conservation of the kinase ATP binding sites, the area targeted by most inhibitors. We investigated the possibility to identify novel small molecule ligands with pre-defined binding profiles for a series of kinase targets and anti-targets by in silico docking. The candidate ligands originating from these calculations were assayed to determine their experimental binding profiles. Compared to previous studies, the acquired hit rates were low in this specific setup, which aimed at not only selecting multi-target kinase ligands, but also designing out binding to anti-targets. Specifically, only a single profiled substance could be verified as a sub-micromolar, dual-specific EGFR/ErbB2 ligand that indeed avoided its selected anti-target BRAF. We subsequently re-analyzed our target choice and in silico strategy based on these findings, with a particular emphasis on the hit rates that can be expected from a given target combination. To that end, we supplemented the structure-based docking calculations with bioinformatic considerations of binding pocket sequence and structure similarity as well as ligand-centric comparisons of kinases. Taken together, our results provide a multi-faceted picture of how pocket space can determine the success of docking in multi-target drug discovery efforts.

Surprising Non-Additivity of Methyl Groups in Drug-Kinase Interaction.

Drug optimization is guided by biophysical methods with increasing popularity. In the context of lead structure modifications, the introduction of methyl groups is a simple but potentially powerful approach. Hence, it is crucial to systematically investigate the influence of ligand methylation on biophysical characteristics such as thermodynamics. Here, we investigate the influence of ligand methylation in different positions and combinations on the drug-kinase interaction. Binding modes and complex structures were analyzed using protein crystallography. Thermodynamic signatures were measured via isothermal titration calorimetry (ITC). An extensive computational analysis supported the understanding of the underlying mechanisms. We found that not only position but also stereochemistry of the methyl group has an influence on binding potency as well as the thermodynamic signature of ligand binding to the protein. Strikingly, the combination of single methyl groups does not lead to additive effects. In our case, the merger of two methyl groups in one ligand leads to an entirely new alternative ligand binding mode in the protein ligand complex. Moreover, the combination of the two methyl groups also resulted in a nonadditive thermodynamic profile of ligand binding. Molecular dynamics (MD) simulations revealed distinguished characteristic motions of the ligands in solution explaining the pronounced thermodynamic changes. The unexpected drastic change in protein ligand interaction highlights the importance of crystallographic control even for minor modifications such as the introduction of a methyl group. For an in-depth understanding of ligand binding behavior, MD simulations have shown to be a powerful tool.

Cosolvent-Enhanced Sampling and Unbiased Identification of Cryptic Pockets Suitable for Structure-Based Drug Design.

Modulating protein activity with small-molecules binding to cryptic pockets offers great opportunities to overcome hurdles in drug design. Cryptic sites are atypical binding sites in proteins that are closed in the absence of a stabilizing ligand and are thus inherently difficult to identify. Many studies have proposed methods to predict cryptic sites. However, a general approach to prospectively sample open conformations of these sites and to identify cryptic pockets in an unbiased manner suitable for structure-based drug design remains elusive. Here, we describe an all-atom, explicit cosolvent, molecular dynamics (MD) simulations-based workflow to sample the open states of cryptic sites and identify opened pockets, in a manner that does not require a priori knowledge about these sites. Furthermore, the workflow relies on a target-independent parametrization that only distinguishes between binding pockets for peptides or small molecules. We validated our approach on a diverse test set of seven proteins with crystallographically determined cryptic sites. The known cryptic sites were found among the three highest-ranked predicted cryptic sites, and an open site conformation was sampled and selected for most of the systems. Crystallographic ligand poses were well reproduced by docking into these identified open conformations for five of the systems. When the fully open state could not be reproduced, we were still able to predict the location of the cryptic site, or identify other cryptic sites that could be retrospectively validated with knowledge of the protein target. These characteristics render our approach valuable for investigating novel protein targets without any prior information.

Converging a Knowledge-Based Scoring Function: DrugScore2018.

We present DrugScore2018, a new version of the knowledge-based scoring function DrugScore, which builds upon the same formalism used to derive DrugScore but exploits a training data set of nearly 40 000 X-ray complex structures, a highly diverse and the, by far, largest data set ever used for such an endeavor. About 2.5 times as many pair potentials than before now have a data basis required to yield smooth potentials, and pair potentials could now be derived for eight more atom types, including interactions involving halogen atoms and metal ions highly relevant for medicinal chemistry. Probing for dependence on training data set size and quality, we show that DrugScore2018 potentials are converged. We evaluated DrugScore2018 in comprehensive scoring, ranking, docking, and screening tests on the CASF-2013 data set, allowing for a comparison with >30 other scoring functions. There, DrugScore2018 showed similar or improved performance in all aspects when compared to either DrugScore, DrugScoreCSD, or DSX and was, overall, the scoring function showing the most consistently good performance in scoring, ranking, and docking tests. Applying DrugScore2018 as objective function in AutoDock3 in a large-scale docking trial, using 4056 protein-ligand complexes from PDBbind 2016, reproduced a docked pose to within 2 ŠRMSD to the crystal structure in >75% of all dockings. These results are remarkable as the DrugScore2018 potentials were derived from crystallographic information only, without any further adaptation using binding affinity or docking decoy data. DrugScore2018 should thus be a competitive scoring and objective function for structure-based ligand design purposes.

Similarity- and Substructure-Based Development of ß2-Adrenergic Receptor Ligands Based on Unusual Scaffolds.

The ß2-adrenergic receptor (ß2AR) is a G protein-coupled receptor (GPCR) and a well-explored target. Here, we report the discovery of 13 ligands, ten of which are novel, of this particular GPCR. They have been identified by similarity- and substructure-based searches using multiple ligands, which were described in an earlier study, as starting points. Of note, two of the molecules used as queries here distinguish themselves from other ß2AR antagonists by their unique scaffold. The molecules described in this work allow us to explore the ligand space around the previously reported molecules in greater detail, leading to insights into their structure-activity relationship. We also report experimental binding and selectivity data and putative binding modes for the novel molecules.

Drugging specific conformational states of GPCRs: challenges and opportunities for computational chemistry.

Current advances in structural biology for membrane proteins support the existence of multiple Gprotein-coupled receptor (GPCR) conformations. These conformations can be associated to particular receptor states with definite coupling and signaling capacities. Drugging such receptor states represents an opportunity to discover a new generation of GPCR drugs with unprecedented specificity. However, exploiting recently available structural information to develop these drugs is still challenging. In this context, computational structure-based approaches can inform such drug development. In this review, we examine the potential of these approaches and the challenges they will need to overcome to guide the rational discovery of drugs targeting specific GPCR states.

Identifying modulators of CXC receptors 3 and 4 with tailored selectivity using multi-target docking.

The G protein-coupled receptors of the C-X-C subfamily form a group among the chemokine receptors whose endogenous ligands are peptides with a common Cys-X-Cys motif. The CXC chemokine receptors 3 and 4 (CXCR3, CXCR4), which are investigated in this study, are linked to severe diseases such as cancer, multiple sclerosis, and HIV infections. Of particular interest, this receptor pair potentially forms a target for a polypharmacological drug treatment. Considering known ligands from public databases, such dual binders have not been identified yet. We therefore applied large-scale docking to the structure of CXCR4 and a homology model of CXCR3 with the goal to predict such dual binders, as well as compounds selective for either one of the receptors. Using signaling and biochemical assays, we showed that more than 50% of these predictions were correct in each category, yielding ligands with excellent binding efficiencies. These results highlight that docking is a suitable tool for the identification of ligands with tailored binding profiles to GPCRs, even when using homology models. More importantly, we present novel CXCR3-CXCR4 dual modulators that might pave the road to understanding the mechanisms of polypharmacological inhibition of these receptors.

Deep sequencing-based identification of small non-coding RNAs in Streptomyces coelicolor.

Streptomyces coelicolor is considered the model organism among Gram positive, GC rich bacteria. Its genome has been sequenced but little is known about the occurrence and distribution of small non-coding RNAs in this biotechnologically relevant organism. Using deep sequencing we analyzed the transcriptome at the end of exponential growth, which corresponds to the onset of secondary metabolism. We mapped 193 transcriptional start sites of mRNA genes and identified putative new and alternative open reading frames. We identified 63 non-coding RNAs including 29 cis encoded antisense RNAs, and confirmed expression for 11, most of them being growth-phase dependent. A comparison between the sequencing results and bioinformatic sRNA predictions using Dynalign and RNAz revealed only a small overlap between the different approaches.