A uniquely efficacious type of CFTR corrector with complementary mode of action.

Three distinct pharmacological corrector types (I, II, III) with different binding sites and additive behavior only partially rescue the F508del-cystic fibrosis transmembrane conductance regulator (CFTR) folding and trafficking defect observed in cystic fibrosis. We describe uniquely effective, macrocyclic CFTR correctors that were additive to the known corrector types, exerting a complementary "type IV" corrector mechanism. Macrocycles achieved wild-type-like folding efficiency of F508del-CFTR at the endoplasmic reticulum and normalized CFTR currents in reconstituted patient-derived bronchial epithelium. Using photo-activatable macrocycles, docking studies and site-directed mutagenesis a highly probable binding site and pose for type IV correctors was identified in a cavity between lasso helix-1 (Lh1) and transmembrane helix-1 of membrane spanning domain (MSD)-1, distinct from the known corrector binding sites. Since only F508del-CFTR fragments spanning from Lh1 until MSD2 responded to type IV correctors, these likely promote cotranslational assembly of Lh1, MSD1, and MSD2. Previously corrector-resistant CFTR folding mutants were also robustly rescued, suggesting substantial therapeutic potential for type IV correctors.

Pose, duplicate, then elaborate: Steps towards increased affinity for inhibitors targeting the specificity surface of the Pim-1 kinase.

In this study, fragment-sized hits binding to Pim-1 kinase with initially modest affinity were further optimized by combining computational, synthetic and crystallographic expertise, eventually resulting in potent ligands with affinities in the nanomolar range that address rarely-targeted regions of Pim-1 kinase. Starting from a set of crystallographically validated, chemically distinct fragments that bind to Pim-1 kinase but lack typical nucleotide mimetic structures, a library of extended fragments was built by exhaustive in silico reactions. After docking, minimization, clustering, visual inspection of the top-ranked compounds, and evaluation of ease of synthetic accessibility, either the original compound or a close derivative was synthesized and tested against Pim-1. For compounds showing the highest degree of Pim-1 inhibition the binding mode was determined crystallographically. Following a structure-guided approach, these were further optimized in a subsequent design cycle improving the compound's initial affinity by several orders of magnitude while synthesizing only a comparatively modest number of derivatives. The combination of computational and experimental approaches resulted in the development of a reasonably potent, novel molecular scaffold for inhibition of Pim-1 that targets specific surface regions, such as the interaction with R122 and P123 of the hinge region, which has been less frequently investigated in similar studies.

Improving the selectivity of 3-amidinophenylalanine-derived matriptase inhibitors.

A rational structure-based approach was employed to develop novel 3-amidinophenylalanine-derived matriptase inhibitors with improved selectivity against thrombin and factor Xa. Of all 23 new derivatives, several monobasic inhibitors exhibit high matriptase affinities and strong selectivity against thrombin. Some inhibitors also possess selectivity against factor Xa, although less pronounced as found for thrombin. A crystal structure of a selective monobasic matriptase inhibitor in complex with matriptase and three crystal structures of related compounds in trypsin and thrombin have been determined. The structures offer an explanation for the different selectivity profiles of these inhibitors and contribute to a more detailed understanding of the observed structure-activity relationship. Selected compounds were tested in vitro against a matriptase-dependent H9N2 influenza virus strain and demonstrated a concentration-dependent inhibition of virus replication in MDCK(II) cells.

Fragment evolution for GPCRs: the role of secondary binding sites in optimization.

We developed a docking-based fragment evolution approach that extends orthosteric fragments towards a less conserved secondary binding pocket of GPCRs. Evaluating 13 000 extensions for the ß1- and ß2-adrenergic receptors we synthesized and tested 112 bitopic molecules. Our results confirmed the positive contribution of the secondary binding pocket to both potency and selectivity optimizations.

Interrogating dense ligand chemical space with a forward-synthetic library.

Forward-synthetic databases are an efficient way to enumerate chemical space. We explored here whether these databases are good sources of novel protein ligands and how many molecules are obtainable and in which time frame. Based on docking calculations, series of molecules were selected to gain insights into the ligand structure-activity relationship. To evaluate the novelty of compounds in a challenging way, we chose the ß2-adrenergic receptor, for which a large number of ligands is already known. Finding dissimilar ligands is thus the exception rather than the rule. Here we report on the results, the successful synthesis of 127/240 molecules in just 2 weeks, the discovery of previously unreported dissimilar ligands of the ß2-adrenergic receptor, and the optimization of one series to a K D of 519 nM in only one round. Moreover, the finding that only 3 of 240 molecules had ever been synthesized before indicates that large parts of chemical space are unexplored.

Virtual Compound Libraries in Computer-Assisted Drug Discovery.

The use of virtual compound libraries in computer-assisted drug discovery has gained in popularity and has already lead to numerous successes. Here, we examine key static and dynamic virtual library concepts that have been developed over the past decade. To facilitate the search for new drugs in the vastness of chemical space, there are still several hurdles to overcome, including the current difficulties in screening and parsing efficiency and the need for more reliable vendors and accurate synthesis prediction tools. These challenges should be tackled by both the developers of virtual libraries and by their users, in order for the exploration of chemical space to live up to its potential.

Nanobody-Enabled Reverse Pharmacology on G-Protein-Coupled Receptors.

The conformational complexity of transmembrane signaling of G-protein-coupled receptors (GPCRs) is a central hurdle for the design of screens for receptor agonists. In their basal states, GPCRs have lower affinities for agonists compared to their G-protein-bound active state conformations. Moreover, different agonists can stabilize distinct active receptor conformations and do not uniformly activate all cellular signaling pathways linked to a given receptor (agonist bias). Comparative fragment screens were performed on a ß2 -adrenoreceptor-nanobody fusion locked in its active-state conformation by a G-protein-mimicking nanobody, and the same receptor in its basal-state conformation. This simple biophysical assay allowed the identification and ranking of multiple novel agonists and permitted classification of the efficacy of each hit in agonist, antagonist, or inverse agonist categories, thereby opening doors to nanobody-enabled reverse pharmacology.

Binding-Site Compatible Fragment Growing Applied to the Design of ß2-Adrenergic Receptor Ligands.

Fragment-based drug discovery is intimately linked to fragment extension approaches that can be accelerated using software for de novo design. Although computers allow for the facile generation of millions of suggestions, synthetic feasibility is however often neglected. In this study we computationally extended, chemically synthesized, and experimentally assayed new ligands for the ß2-adrenergic receptor (ß2AR) by growing fragment-sized ligands. In order to address the synthetic tractability issue, our in silico workflow aims at derivatized products based on robust organic reactions. The study started from the predicted binding modes of five fragments. We suggested a total of eight diverse extensions that were easily synthesized, and further assays showed that four products had an improved affinity (up to 40-fold) compared to their respective initial fragment. The described workflow, which we call "growing via merging" and for which the key tools are available online, can improve early fragment-based drug discovery projects, making it a useful creative tool for medicinal chemists during structure-activity relationship (SAR) studies.

Integrated structure- and ligand-based in silico approach to predict inhibition of cytochrome P450 2D6.

MOTIVATION: Cytochrome P450 (CYP) is a superfamily of enzymes responsible for the metabolism of drugs, xenobiotics and endogenous compounds. CYP2D6 metabolizes about 30% of drugs and predicting potential CYP2D6 inhibition is important in early-stage drug discovery. RESULTS: We developed an original in silico approach for the prediction of CYP2D6 inhibition combining the knowledge of the protein structure and its dynamic behavior in response to the binding of various ligands and machine learning modeling. This approach includes structural information for CYP2D6 based on the available crystal structures and molecular dynamic simulations (MD) that we performed to take into account conformational changes of the binding site. We performed modeling using three learning algorithms--support vector machine, RandomForest and NaiveBayesian--and we constructed combined models based on topological information of known CYP2D6 inhibitors and predicted binding energies computed by docking on both X-ray and MD protein conformations. In addition, we identified three MD-derived structures that are capable all together to better discriminate inhibitors and non-inhibitors compared with individual CYP2D6 conformations, thus ensuring complementary ligand profiles. Inhibition models based on classical molecular descriptors and predicted binding energies were able to predict CYP2D6 inhibition with an accuracy of 78% on the training set and 75% on the external validation set.

In silico prediction of aqueous solubility: a multimodel protocol based on chemical similarity.

Aqueous solubility is one of the most important ADMET properties to assess and to optimize during the drug discovery process. At present, accurate prediction of solubility remains very challenging and there is an important need of independent benchmarking of the existing in silico models such as to suggest solutions for their improvement. In this study, we developed a new protocol for improved solubility prediction by combining several existing models available in commercial or free software packages. We first performed an evaluation of ten in silico models for aqueous solubility prediction on several data sets in order to assess the reliability of the methods, and we proposed a new diverse data set of 150 molecules as relevant test set, SolDiv150. We developed a random forest protocol to evaluate the performance of different fingerprints for aqueous solubility prediction based on molecular structure similarity. Our protocol, called a "multimodel protocol", allows selecting the most accurate model for a compound of interest among the employed models or software packages, achieving r(2) of 0.84 when applied to SolDiv150. We also found that all models assessed here performed better on druglike molecules than on real drugs, thus additional improvement is needed in this direction. Overall, our approach enlarges the applicability domain as demonstrated by the more accurate results for solubility prediction obtained using our protocol in comparison to using individual models.