Pseudonatural Products Occur Frequently in Biologically Relevant Compounds.

A new methodology for classifying fragment combinations and characterizing pseudonatural products (PNPs) is described. The source code is based on open-source tools and is organized as a Python package. Tasks can be executed individually or within the context of scalable, robust workflows. First, structures are standardized and duplicate entries are filtered out. Then, molecules are probed for the presence of predefined fragments. For molecules with more than one match, fragment combinations are classified. The algorithm considers the pairwise relative position of fragments within the molecule (fused atoms, linkers, intermediary rings), resulting in 18 different possible fragment combination categories. Finally, all combinations for a given molecule are assembled into a fragment combination graph, with fragments as nodes and combination types as edges. This workflow was applied to characterize PNPs in the ChEMBL database via comparison of fragment combination graphs with natural product (NP) references, represented by the Dictionary of Natural Products. The Murcko fragments extracted from 2000 structures previously described were used to define NP fragments. The results indicate that ca. 23% of the biologically relevant compounds listed in ChEMBL comply to the PNP definition and that, therefore, PNPs occur frequently among known biologically relevant small molecules. The majority (>95%) of PNPs contain two to four fragments, mainly (>95%) distributed in five different combination types. These findings may provide guidance for the design of new PNPs.

Light-Activatable TET-Dioxygenases Reveal Dynamics of 5-Methylcytosine Oxidation and Transcriptome Reorganization.

Ten-eleven-translocation (TET) dioxygenases catalyze the oxidation of 5-methylcytosine (5mC), the central epigenetic regulator of mammalian DNA. This activity dynamically reshapes the epigenome and transcriptome by depositing oxidized 5mC derivatives and initiating active DNA demethylation. However, studying this dynamic is hampered by the inability to selectively activate individual TETs with temporal control in cells. We report activation of TETs in mammalian cells by incorporation of genetically encoded 4,5-dimethoxy-2-nitrobenzyl-l-serine as a transient active-site block, and its subsequent deprotection with light. Our approach enables precise insights into the impact of cancer-associated TET2 mutations on the kinetics of TET2 catalysis in vivo, and allows time-resolved monitoring of target gene activation and transcriptome reorganization. This sets a basis for dissecting the order and kinetics of chromatin-associated events triggered by TET catalysis, ranging from DNA demethylation to chromatin and transcription regulation.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects.

Accurate ranking of compounds with regards to their binding affinity to a protein using computational methods is of great interest to pharmaceutical research. Physics-based free energy calculations are regarded as the most rigorous way to estimate binding affinity. In recent years, many retrospective studies carried out both in academia and industry have demonstrated its potential. Here, we present the results of large-scale prospective application of the FEP+ method in active drug discovery projects in an industry setting at Merck KGaA, Darmstadt, Germany. We compare these prospective data to results obtained on a new diverse, public benchmark of eight pharmaceutically relevant targets. Our results offer insights into the challenges faced when using free energy calculations in real-life drug discovery projects and identify limitations that could be tackled by future method development. The new public data set we provide to the community can support further method development and comparative benchmarking of free energy calculations.

A selective chemical probe for exploring the role of CDK8 and CDK19 in human disease.

There is unmet need for chemical tools to explore the role of the Mediator complex in human pathologies ranging from cancer to cardiovascular disease. Here we determine that CCT251545, a small-molecule inhibitor of the WNT pathway discovered through cell-based screening, is a potent and selective chemical probe for the human Mediator complex-associated protein kinases CDK8 and CDK19 with >100-fold selectivity over 291 other kinases. X-ray crystallography demonstrates a type 1 binding mode involving insertion of the CDK8 C terminus into the ligand binding site. In contrast to type II inhibitors of CDK8 and CDK19, CCT251545 displays potent cell-based activity. We show that CCT251545 and close analogs alter WNT pathway-regulated gene expression and other on-target effects of modulating CDK8 and CDK19, including expression of genes regulated by STAT1. Consistent with this, we find that phosphorylation of STAT1(SER727) is a biomarker of CDK8 kinase activity in vitro and in vivo. Finally, we demonstrate in vivo activity of CCT251545 in WNT-dependent tumors.

Binding Pose Flip Explained via Enthalpic and Entropic Contributions.

The anomalous binding modes of five highly similar fragments of TIE2 inhibitors, showing three distinct binding poses, are investigated. We report a quantitative rationalization for the changes in binding pose based on molecular dynamics simulations. We investigated five fragments in complex with the transforming growth factor ß receptor type 1 kinase domain. Analyses of these simulations using Grid Inhomogeneous Solvation Theory (GIST), pKA calculations, and a tool to investigate enthalpic differences upon binding unraveled the various thermodynamic contributions to the different binding modes. While one binding mode flip can be rationalized by steric repulsion, the second binding pose flip revealed a different protonation state for one of the ligands, leading to different enthalpic and entropic contributions to the binding free energy. One binding pose is stabilized by the displacement of entropically unfavored water molecules (binding pose determined by solvation entropy), ligands in the other binding pose are stabilized by strong enthalpic interactions, overcompensating the unfavorable water entropy in this pose (binding pose determined by enthalpic interactions). This analysis elucidates unprecedented details determining the flipping of the binding modes, which can elegantly explain the experimental findings for this system.

Discovery of potent and selective CDK8 inhibitors from an HSP90 pharmacophore.

Here we describe the discovery and optimization of 3-benzylindazoles as potent and selective inhibitors of CDK8, also modulating CDK19, discovered from a high-throughput screening (HTS) campaign sampling the Merck compound collection. The primary hits with strong HSP90 affinity were subsequently optimized to potent and selective CDK8 inhibitors which demonstrate inhibition of WNT pathway activity in cell-based assays. X-ray crystallographic data demonstrated that 3-benzylindazoles occupy the ATP binding site of CDK8 and adopt a Type I binding mode. Medicinal chemistry optimization successfully led to improved potency, physicochemical properties and oral pharmacokinetics. Modulation of phospho-STAT1, a pharmacodynamic biomarker of CDK8, was demonstrated in an APC-mutant SW620 human colorectal carcinoma xenograft model following oral administration.

OCEAN: Optimized Cross rEActivity estimatioN.

The prediction of molecular targets is highly beneficial during the drug discovery process, be it for off-target elucidation or deconvolution of phenotypic screens. Here, we present OCEAN, a target prediction tool exclusively utilizing publically available ChEMBL data. OCEAN uses a heuristics approach based on a validation set containing almost 1000 drug ← → target relationships. New ChEMBL data (ChEMBL20 as well as ChEMBL21) released after the validation was used for a prospective OCEAN performance check. The success rates of OCEAN to predict correctly the targets within the TOP10 ranks are 77% for recently marketed drugs and 62% for all new ChEMBL20 compounds and 51% for all new ChEMBL21 compounds. OCEAN is also capable of identifying polypharmacological compounds; the success rate for molecules simultaneously hitting at least two targets is 64% to be correctly predicted within the TOP10 ranks. The source code of OCEAN can be found at http://www.github.com/rdkit/OCEAN.

Structure of the epimerization domain of tyrocidine synthetase A.

Tyrocidine, a macrocyclic decapeptide from Bacillus brevis, is nonribosomally assembled by a set of multimodular peptide synthetases, which condense two D-amino acids and eight L-amino acids to produce this membrane-disturbing antibiotic. D-Phenylalanine, the first amino acid incorporated into tyrocidine, is catalytically derived from enzyme-bound L-Phe by the C-terminal epimerization (E) domain of tyrocidine synthetase A (TycA). The 1.5 Å resolution structure of the cofactor-independent TycA E domain reveals an intimate relationship to the condensation (C) domains of peptide synthetases. In contrast to the latter, the TycA E domain uses an enlarged bridge region to plug the active-site canyon from the acceptor side, whereas at the donor side a latch-like floor loop is suitably extended to accommodate the αIII helix of the preceding peptide-carrier domain. Additionally, E domains exclusively harbour a conserved glutamate residue, Glu882, that is opposite the active-site residue His743. This active-site topology implies Glu882 as a candidate acid-base catalyst, whereas His743 stabilizes in the protonated state a transient enolate intermediate of the L↔D isomerization.

Structure-based optimization of non-peptidic Cathepsin D inhibitors.

We discovered a novel series of non-peptidic acylguanidine inhibitors of Cathepsin D as target for osteoarthritis. The initial HTS-hits were optimized by structure-based design using CatD X-ray structures resulting in single digit nanomolar potency in the biochemical CatD assay. However, the most potent analogues showed only micromolar activities in an ex vivo glycosaminoglycan (GAG) release assay in bovine cartilage together with low cellular permeability and suboptimal microsomal stability. This new scaffold can serve as a starting point for further optimization towards in vivo efficacy.

Count on kappa.

In the 1960s, the kappa statistic was introduced for the estimation of chance agreement in inter- and intra-rater reliability studies. The kappa statistic was strongly pushed by the medical field where it could be successfully applied via analyzing diagnoses of identical patient groups. Kappa is well suited for classification tasks where ranking is not considered. The main advantage of kappa is its simplicity and the general applicability to multi-class problems which is the major difference to receiver operating characteristic area under the curve. In this manuscript, I will outline the usage of kappa for classification tasks, and I will evaluate the role and uses of kappa in specifically machine learning and cheminformatics.

Identification of SARS-CoV-2 Mpro inhibitors through deep reinforcement learning for de novo drug design and computational chemistry approaches.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of coronavirus disease (COVID-19) since its emergence in December 2019. As of January 2024, there has been over 774 million reported cases and 7 million deaths worldwide. While vaccination efforts have been successful in reducing the severity of the disease and decreasing the transmission rate, the development of effective therapeutics against SARS-CoV-2 remains a critical need. The main protease (Mpro) of SARS-CoV-2 is an essential enzyme required for viral replication and has been identified as a promising target for drug development. In this study, we report the identification of novel Mpro inhibitors, using a combination of deep reinforcement learning for de novo drug design with 3D pharmacophore/shape-based alignment and privileged fragment match count scoring components followed by hit expansions and molecular docking approaches. Our experimentally validated results show that 3 novel series exhibit potent inhibitory activity against SARS-CoV-2 Mpro, with IC50 values ranging from 1.3 µM to 2.3 µM and a high degree of selectivity. These findings represent promising starting points for the development of new antiviral therapies against COVID-19.

hERG me out.

A detailed analysis of the hERG content inside the ChEMBL database is performed. The correlation between the outcome from binding assays and functional assays is probed. On the basis of descriptor distributions, design paradigms with respect to structural and physicochemical properties of hERG active and hERG inactive compounds are challenged. Finally, classification models with different data sets are trained. All source code is provided, which is based on the Python open source packages RDKit and scikit-learn to enable the community to rerun the experiments. The code is stored on github ( https://github.com/pzc/herg_chembl_jcim).

TRAPP: a tool for analysis of transient binding pockets in proteins.

We present TRAPP (TRAnsient Pockets in Proteins), a new automated software platform for tracking, analysis, and visualization of binding pocket variations along a protein motion trajectory or within an ensemble of protein structures that may encompass conformational changes ranging from local side chain fluctuations to global backbone motions. TRAPP performs accurate grid-based calculations of the shape and physicochemical characteristics of a binding pocket for each structure and detects the conserved and transient regions of the pocket in an ensemble of protein conformations. It also provides tools for tracing the opening of a particular subpocket and residues that contribute to the binding site. TRAPP thus enables an assessment of the druggability of a disease-related target protein taking its flexibility into account.

VSFlow: an open-source ligand-based virtual screening tool.

Ligand-based virtual screening is a widespread method in modern drug design. It allows for a rapid screening of large compound databases in order to identify similar structures. Here we report an open-source command line tool which includes a substructure-, fingerprint- and shape-based virtual screening. Most of the implemented features fully rely on the RDKit cheminformatics framework. VSFlow accepts a wide range of input file formats and is highly customizable. Additionally, a quick visualization of the screening results as pdf and/or pymol file is supported.

Targeting oncogenic KRasG13C with nucleotide-based covalent inhibitors.

Mutations within Ras proteins represent major drivers in human cancer. In this study, we report the structure-based design, synthesis, as well as biochemical and cellular evaluation of nucleotide-based covalent inhibitors for KRasG13C, an important oncogenic mutant of Ras that has not been successfully addressed in the past. Mass spectrometry experiments and kinetic studies reveal promising molecular properties of these covalent inhibitors, and X-ray crystallographic analysis has yielded the first reported crystal structures of KRasG13C covalently locked with these GDP analogues. Importantly, KRasG13C covalently modified with these inhibitors can no longer undergo SOS-catalysed nucleotide exchange. As a final proof-of-concept, we show that in contrast to KRasG13C, the covalently locked protein is unable to induce oncogenic signalling in cells, further highlighting the possibility of using nucleotide-based inhibitors with covalent warheads in KRasG13C-driven cancer.

Fragtory: Pharmacophore-Focused Design, Synthesis, and Evaluation of an sp3-Enriched Fragment Library.

Fragment-based drug discovery has played an important role in medicinal chemistry and pharmaceutical research. Despite numerous demonstrated successes, the limited diversity and overrepresentation of planar, sp2-rich structures in commercial libraries often hamper the full potential of this approach. Hence, the thorough design of screening libraries inevitably determines the probability for meaningful hits and subsequent structural elaboration. Against this background, we present the generation of an exclusive fragment library based on iterative entry nomination by a specifically designed computational workflow: "Fragtory". Following a pharmacophore diversity-driven approach, we used Fragtory in an interdisciplinary academic setting to guide both tailored synthesis efforts and the implementation of in-house compounds to build a curated 288-member library of sp3-enriched fragments. Subsequent NMR screens against a model protein and hit validation by protein crystallography led to the identification of structurally novel ligands that were further characterized by isothermal titration calorimetry, demonstrating the applicability of our experimental approach.

Who cares for the protons?

It is tempting to use standard protonation states for the analysis of protein-ligand interactions. Two different pK(a) calculation methods, PROPKA (protein pK(a)) and MCCE (multi conformation continuum electrostatics), were applied to challenge this convenient behavior. As data basis, we selected five recently approved drugs for which structural information of the protein-drug complex is available. We analyzed the pK(a) calculations in terms of a measure termed BIPS (binary protonation states) recently introduced by us. Both methods agree in detecting the majority of the sites with atypical BIPS values. However, when using only one method, some of the atypcial BIPS value would have been missed. Therefore, we recommend using both methods to set such an interpretation on a solid basis.

Blind, one-eyed, or eagle-eyed? pKa calculations during blind predictions with staphylococcal nuclease.

In the current contribution, the performance of Poisson-Boltzmann-based pK(a) calculations of SNase mutants as part of a blind prediction exercise facilitated by the pK(a) cooperative ("pK(a) _coop") is described. A one parameter setting ("quick&dirty" approach) is used to provide an industry perspective where strong time constraints are frequently encountered. On the one hand, results are analyzed in terms of root mean square deviation performance. Furthermore, the pK(a) calculations are assessed for their ability to properly assign protonation state. For this purpose, a new measure called BIPS (binary protonation state at physiological pH) is introduced. Significant differences were found with both comparison measures based on the class of residues examined. In addition, the performance of PROPKA3 as well as the NULL model is examined on the same data set. Finally, pK(a) calculations on SNase mutants with available structural information have been performed and provide support for our calculation methods. The performance on this subset is better than on the pK(a) cooperative mutation data. In the pK(a) _coop data, no structural information on the generated mutants is available. This suggests the occurrence of a substantial structural rearrangement on the insertion of additional charged groups into SNase, which leads to improved prediction quality.

Picomolar FKBP inhibitors enabled by a single water-displacing methyl group in bicyclic [4.3.1] aza-amides.

Methyl groups can have profound effects in drug discovery but the underlying mechanisms are diverse and incompletely understood. Here we report the stereospecific effect of a single, solvent-exposed methyl group in bicyclic [4.3.1] aza-amides, robustly leading to a 2 to 10-fold increase in binding affinity for FK506-binding proteins (FKBPs). This resulted in the most potent and efficient FKBP ligands known to date. By a combination of co-crystal structures, isothermal titration calorimetry (ITC), density-functional theory (DFT), and 3D reference interaction site model (3D-RISM) calculations we elucidated the origin of the observed affinity boost, which was purely entropically driven and relied on the displacement of a water molecule at the protein-ligand-bulk solvent interface. The best compounds potently occupied FKBPs in cells and enhanced bone morphogenic protein (BMP) signaling. Our results show how subtle manipulation of the solvent network can be used to design atom-efficient ligands for difficult, solvent-exposed binding pockets.

Resistance to Avapritinib in PDGFRA-Driven GIST Is Caused by Secondary Mutations in the PDGFRA Kinase Domain.

Gastrointestinal stromal tumors (GIST) harboring activating mutations of PDGFRA respond to imatinib, with the notable exception of the most common mutation, D842V. Avapritinib is a novel, potent KIT/PDGFRA inhibitor with substantial clinical activity in patients with the D842V genotype. To date, only a minority of PDGFRA-mutant patients treated with avapritinib have developed secondary resistance. Tumor and plasma biopsies in 6 of 7 patients with PDGFRA primary mutations who progressed on avapritinib or imatinib had secondary resistance mutations within PDGFRA exons 13, 14, and 15 that interfere with avapritinib binding. Secondary PDGFRA mutations causing V658A, N659K, Y676C, and G680R substitutions were found in 2 or more patients each, representing recurrent mechanisms of PDGFRA GIST drug resistance. Notably, most PDGFRA-mutant GISTs refractory to avapritinib remain dependent on the PDGFRA oncogenic signal. Inhibitors that target PDGFRA protein stability or inhibition of PDGFRA-dependent signaling pathways may overcome avapritinib resistance. SIGNIFICANCE: Here, we provide the first description of avapritinib resistance mechanisms in PDGFRA-mutant GIST.This article is highlighted in the In This Issue feature, p. 1.