Development of a NanoBRET Assay Platform to Detect Intracellular Ligands for the Chemokine Receptors CCR6 and CXCR1.

A conserved intracellular allosteric binding site (IABS) was recently identified at several G protein-coupled receptors (GPCRs). This target site allows the binding of allosteric modulators and enables a new mode of GPCR inhibition. Herein, we report the development of a NanoBRET-based assay platform based on the fluorescent ligand LT221 (5), to detect intracellular binding to CCR6 and CXCR1, two chemokine receptors that have been pursued as promising drug targets in inflammation and immuno-oncology. Our assay platform enables cell-free as well as cellular NanoBRET-based binding studies in a nonisotopic and straightforward manner. By combining this screening platform with a previously reported CXCR2 assay, we investigated CXCR1/CXCR2/CCR6 selectivity profiles for both known and novel squaramide analogues derived from navarixin, a known intracellular CXCR1/CXCR2 antagonist and phase II clinical candidate for the treatment of pulmonary diseases. By means of these studies we identified compound 10, a previously reported tert-butyl analogue of navarixin, as a low nanomolar intracellular CCR6 antagonist. Further, our assay platform clearly indicated intracellular binding of the CCR6 antagonist PF-07054894, currently evaluated in phase I clinical trials for the treatment of ulcerative colitis, thereby providing profound evidence for the existence and the pharmacological relevance of a druggable IABS at CCR6.

Challenges in natural product-based drug discovery assisted with in silico-based methods.

The application of traditional medicine by humans for the treatment of ailments as well as improving the quality of life far outdates recorded history. To date, a significant percentage of humans, especially those living in developing/underprivileged communities still rely on traditional medicine for primary healthcare needs. In silico-based methods have been shown to play a pivotal role in modern pharmaceutical drug discovery processes. The application of these methods in identifying natural product (NP)-based hits has been successful. This is very much observed in many research set-ups that use rationally in silico-based methods in combination with experimental validation techniques. The combination has rendered the use of in silico-based approaches even more popular and successful in the investigation of NPs. However, identifying and proposing novel NP-based hits for experimental validation comes with several challenges such as the availability of compounds by suppliers, the huge task of separating pure compounds from complex mixtures, the quantity of samples available from the natural source to be tested, not to mention the potential ecological impact if the natural source is exhausted. Because most peer-reviewed publications are biased towards "positive results", these challenges are generally not discussed in publications. In this review, we highlight and discuss these challenges. The idea is to give interested scientists in this field of research an idea of what they can come across or should be expecting as well as prompting them on how to avoid or fix these issues.

BC-11 is a covalent TMPRSS2 fragment inhibitor that impedes SARS-CoV-2 host cell entry.

Host cell entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is facilitated via priming of its spike glycoprotein by the human transmembrane protease serine 2 (TMPRSS2). Although camostat and nafamostat are two highly potent covalent TMPRSS2 inhibitors, they nevertheless did not hold promise in COVID-19 clinical trials, presumably due to their short plasma half-lives. Herein, we report an integrative chemogenomics approach based on computational modeling and in vitro enzymatic assays, for repurposing serine-targeted covalent inhibitors. This led to the identification of BC-11 as a covalent TMPRSS2 inhibitor displaying a unique selectivity profile for serine proteases, ascribable to its boronic acid warhead. BC-11 showed modest inhibition of SARS-CoV-2 (omicron variant) spike pseudotyped particles in a cell-based entry assay, and a combination of BC-11 and AHN 1-055 (a spike glycoprotein inhibitor) demonstrated better viral entry inhibition than either compound alone. Given its low molecular weight and good activity against TMPRSS2, BC-11 qualifies as a good starting point for further structural optimizations.

Towards the prediction of non-peptidic epitopes.

In-silico methods for the prediction of epitopes can support and improve workflows for vaccine design, antibody production, and disease therapy. So far, the scope of B cell and T cell epitope prediction has been directed exclusively towards peptidic antigens. Nevertheless, various non-peptidic molecular classes can be recognized by immune cells. These compounds have not been systematically studied yet, and prediction approaches are lacking. The ability to predict the epitope activity of non-peptidic compounds could have vast implications; for example, for immunogenic risk assessment of the vast number of drugs and other xenobiotics. Here we present the first general attempt to predict the epitope activity of non-peptidic compounds using the Immune Epitope Database (IEDB) as a source for positive samples. The molecules stored in the Chemical Entities of Biological Interest (ChEBI) database were chosen as background samples. The molecules were clustered into eight homogeneous molecular groups, and classifiers were built for each cluster with the aim of separating the epitopes from the background. Different molecular feature encoding schemes and machine learning models were compared against each other. For those models where a high performance could be achieved based on simple decision rules, the molecular features were then further investigated. Additionally, the findings were used to build a web server that allows for the immunogenic investigation of non-peptidic molecules (http://tools-staging.iedb.org/np_epitope_predictor). The prediction quality was tested with samples from independent evaluation datasets, and the implemented method received noteworthy Receiver Operating Characteristic-Area Under Curve (ROC-AUC) values, ranging from 0.69-0.96 depending on the molecule cluster.

CovPDB: a high-resolution coverage of the covalent protein-ligand interactome.

In recent years, the drug discovery paradigm has shifted toward compounds that covalently modify disease-associated target proteins, because they tend to possess high potency, selectivity, and duration of action. The rational design of novel targeted covalent inhibitors (TCIs) typically starts from resolved macromolecular structures of target proteins in their apo or holo forms. However, the existing TCI databases contain only a paucity of covalent protein-ligand (cP-L) complexes. Herein, we report CovPDB, the first database solely dedicated to high-resolution cocrystal structures of biologically relevant cP-L complexes, curated from the Protein Data Bank. For these curated complexes, the chemical structures and warheads of pre-reactive electrophilic ligands as well as the covalent bonding mechanisms to their target proteins were expertly manually annotated. Totally, CovPDB contains 733 proteins and 1,501 ligands, relating to 2,294 cP-L complexes, 93 reactive warheads, 14 targetable residues, and 21 covalent mechanisms. Users are provided with an intuitive and interactive web interface that allows multiple search and browsing options to explore the covalent interactome at a molecular level in order to develop novel TCIs. CovPDB is freely accessible at http://www.pharmbioinf.uni-freiburg.de/covpdb/ and its contents are available for download as flat files of various formats.

AroCageDB: A Web-Based Resource for Aromatic Cage Binding Sites and Their Intrinsic Ligands.

While aromatic cages have extensively been investigated in the context of structural biology, molecular recognition, and drug discovery, there exist to date no comprehensive resource for proteins sharing this conserved structural motif. To this end, we parsed the Protein Data Bank and thus constructed the Aromatic Cage Database (AroCageDB), a database for investigating the binding pocket descriptors and ligand binding space of aromatic-cage-containing proteins (ACCPs). AroCageDB contains 487 unique ACCPs bound to 890 unique ligands, for a total of 1636 complexes. This web-accessible database provides a user-friendly interface for the interactive visualization of ligand-bound ACCP structures, with a variety of search options that will open up opportunities for structural analyses and drug discovery campaigns. AroCageDB is freely available at http://www.pharmbioinf.uni-freiburg.de/arocagedb/.

Structure of Escherichia coli cytochrome bd-II type oxidase with bound aurachin D.

Cytochrome bd quinol:O2 oxidoreductases are respiratory terminal oxidases so far only identified in prokaryotes, including several pathogenic bacteria. Escherichia coli contains two bd oxidases of which only the bd-I type is structurally characterized. Here, we report the structure of the Escherichia coli cytochrome bd-II type oxidase with the bound inhibitor aurachin D as obtained by electron cryo-microscopy at 3 Å resolution. The oxidase consists of subunits AppB, C and X that show an architecture similar to that of bd-I. The three heme cofactors are found in AppC, while AppB is stabilized by a structural ubiquinone-8 at the homologous positions. A fourth subunit present in bd-I is lacking in bd-II. Accordingly, heme b595 is exposed to the membrane but heme d embedded within the protein and showing an unexpectedly high redox potential is the catalytically active centre. The structure of the Q-loop is fully resolved, revealing the specific aurachin binding.

ePharmaLib: A Versatile Library of e-Pharmacophores to Address Small-Molecule (Poly-)Pharmacology.

Bioactive compounds oftentimes bind to several target proteins, thereby exhibiting polypharmacology. Experimentally determining these interactions is however laborious, and structure-based virtual screening (SBVS) of bioactive compounds could expedite drug discovery by prioritizing hits for experimental validation. Here, we present ePharmaLib, a library of 15,148 e-pharmacophores modeled from solved structures of pharmaceutically relevant protein-ligand complexes of the screening Protein Data Bank (sc-PDB). ePharmaLib can be used for target fishing of phenotypic hits, side effect predictions, drug repurposing, and scaffold hopping. In retrospective SBVS, a good balance was obtained between computational efficiency and predictive accuracy. As a proof of concept, we carried out prospective SBVS in conjunction with a photometric assay, which inferred that the mechanism of action of neopterin (an endogenous immunomodulator) putatively stems from its inhibition (IC50 = 18 µM) of the human purine nucleoside phosphorylase. This ready-to-use library is freely available at http://www.pharmbioinf.uni-freiburg.de/epharmalib.

StreptomeDB 3.0: an updated compendium of streptomycetes natural products.

Antimicrobial resistance is an emerging global health threat necessitating the rapid development of novel antimicrobials. Remarkably, the vast majority of currently available antibiotics are natural products (NPs) isolated from streptomycetes, soil-dwelling bacteria of the genus Streptomyces. However, there is still a huge reservoir of streptomycetes NPs which remains pharmaceutically untapped and a compendium thereof could serve as a source of inspiration for the rational design of novel antibiotics. Initially released in 2012, StreptomeDB (http://www.pharmbioinf.uni-freiburg.de/streptomedb) is the first and only public online database that enables the interactive phylogenetic exploration of streptomycetes and their isolated or mutasynthesized NPs. In this third release, there are substantial improvements over its forerunners, especially in terms of data content. For instance, about 2500 unique NPs were newly annotated through manual curation of about 1300 PubMed-indexed articles, published in the last five years since the second release. To increase interoperability, StreptomeDB entries were hyperlinked to several spectral, (bio)chemical and chemical vendor databases, and also to a genome-based NP prediction server. Moreover, predicted pharmacokinetic and toxicity profiles were added. Lastly, some recent real-world use cases of StreptomeDB are highlighted, to illustrate its applicability in life sciences.

Pharmacoinformatic Investigation of Medicinal Plants from East Africa.

Medicinal plants have widely been used in the traditional treatment of ailments and have been proven effective. Their contribution still holds an important place in modern drug discovery due to their chemical, and biological diversities. However, the poor documentation of traditional medicine, in developing African countries for instance, can lead to the loss of knowledge related to such practices. In this study, we present the Eastern Africa Natural Products Database (EANPDB) containing the structural and bioactivity information of 1870 unique molecules isolated from about 300 source species from the Eastern African region. This represents the largest collection of natural products (NPs) from this geographical region, covering literature data of the period from 1962 to 2019. The computed physicochemical properties and toxicity profiles of each compound have been included. A comparative analysis of some physico-chemical properties like molecular weight, H-bond donor/acceptor, logPo/w , etc. as well scaffold diversity analysis has been carried out with other published NP databases. EANPDB was combined with the previously published Northern African Natural Products Database (NANPDB), to form a merger African Natural Products Database (ANPDB), containing ∼6500 unique molecules isolated from about 1000 source species (freely available at http://african-compounds.org). As a case study, latrunculins A and B isolated from the sponge Negombata magnifica (Podospongiidae) with previously reported antitumour activities, were identified via substructure searching as molecules to be explored as putative binders of histone deacetylases (HDACs).

Exploring Cocrystallized Aromatic Cage Binders to Target Histone Methylation Reader Proteins.

Histone methylation reader proteins (HMRPs) regulate gene transcription by recognizing, at their "aromatic cage" domains, various Lys/Arg methylation states on histone tails. Because epigenetic dysregulation underlies a wide range of diseases, HMRPs have become attractive drug targets. However, structure-based efforts in targeting them are still in their infancy. Structural information from functionally unrelated aromatic-cage-containing proteins (ACCPs) and their cocrystallized ligands could be a good starting point. In this light, we mined the Protein Data Bank to retrieve the structures of ACCPs in complex with cationic peptidic/small-molecule ligands. Our analysis revealed that the vast majority of retrieved ACCPs belong to three classes: transcription regulators (chiefly HMRPs), signaling proteins, and hydrolases. Although acyclic (and monocyclic) amines and quats are the typical cation-binding functional groups found in HMRP small-molecule inhibitors, numerous atypical cationic groups were identified in non-HMRP inhibitors, which could serve as potential bioisosteres to methylated Lys/Arg on histone tails. Also, as HMRPs are involved in protein-protein interactions, they possess large binding sites, and thus, their selective inhibition might only be achieved by large and more flexible (beyond rule of five) ligands. Hence, the ligands of the collected dataset represent suitable versatile templates for further elaboration into potent and selective HMRP inhibitors.

Automated recognition of functional compound-protein relationships in literature.

MOTIVATION: Much effort has been invested in the identification of protein-protein interactions using text mining and machine learning methods. The extraction of functional relationships between chemical compounds and proteins from literature has received much less attention, and no ready-to-use open-source software is so far available for this task. METHOD: We created a new benchmark dataset of 2,613 sentences from abstracts containing annotations of proteins, small molecules, and their relationships. Two kernel methods were applied to classify these relationships as functional or non-functional, named shallow linguistic and all-paths graph kernel. Furthermore, the benefit of interaction verbs in sentences was evaluated. RESULTS: The cross-validation of the all-paths graph kernel (AUC value: 84.6%, F1 score: 79.0%) shows slightly better results than the shallow linguistic kernel (AUC value: 82.5%, F1 score: 77.2%) on our benchmark dataset. Both models achieve state-of-the-art performance in the research area of relation extraction. Furthermore, the combination of shallow linguistic and all-paths graph kernel could further increase the overall performance slightly. We used each of the two kernels to identify functional relationships in all PubMed abstracts (29 million) and provide the results, including recorded processing time. AVAILABILITY: The software for the tested kernels, the benchmark, the processed 29 million PubMed abstracts, all evaluation scripts, as well as the scripts for processing the complete PubMed database are freely available at https://github.com/KerstenDoering/CPI-Pipeline.

Current computational methods for predicting protein interactions of natural products.

Natural products (NPs) are an indispensable source of drugs and they have a better coverage of the pharmacological space than synthetic compounds, owing to their high structural diversity. The prediction of their interaction profiles with druggable protein targets remains a major challenge in modern drug discovery. Experimental (off-)target predictions of NPs are cost- and time-consuming, whereas computational methods, on the other hand, are much faster and cheaper. As a result, computational predictions are preferentially used in the first instance for NP profiling, prior to experimental validations. This review covers recent advances in computational approaches which have been developed to aid the annotation of unknown drug-target interactions (DTIs), by focusing on three broad classes, namely: ligand-based, target-based, and target-ligand-based (hybrid) approaches. Computational DTI prediction methods have the potential to significantly advance the discovery and development of novel selective drugs exhibiting minimal side effects. We highlight some inherent caveats of these methods which must be overcome to enable them to realize their full potential, and a future outlook is given.