Molecular evolution of a peptide GPCR ligand driven by artificial neural networks

Machine learning designs new GCGR/GLP-1R dual agonists with enhanced biological potency

Several peptide dual agonists of the human glucagon receptor (GCGR) and the glucagon-like peptide-1 receptor (GLP-1R) are in development for the treatment of type 2 diabetes, obesity and their associated complications. Candidates must have high potency at both receptors, but it is unclear whether the limited experimental data available can be used to train models that accurately predict the activity at both receptors of new peptide variants. Here we use peptide sequence data labelled with in vitro potency at human GCGR and GLP-1R to train several models, including a deep multi-task neural-network model using multiple loss optimization. Model-guided sequence optimization was used to design three groups of peptide variants, with distinct ranges of predicted dual activity. We found that three of the model-designed sequences are potent dual agonists with superior biological activity. With our designs we were able to achieve up to sevenfold potency improvement at both receptors simultaneously compared to the best dual-agonist in the training set.

Chemerin - exploring a versatile adipokine

Chemerin is a small chemotactic protein and a key player in initiating the early immune response. As an adipokine, chemerin is also involved in energy homeostasis and the regulation of reproductive functions. Secreted as inactive prochemerin, it relies on proteolytic activation by serine proteases to exert biological activity. Chemerin binds to three distinct G protein-coupled receptors (GPCR), namely chemokine-like receptor 1 (CMKLR1, recently named chemerin₁), G protein-coupled receptor 1 (GPR1, recently named chemerin₂), and CC-motif chemokine receptor-like 2 (CCRL2). Only CMKLR1 displays conventional G protein signaling, while GPR1 only recruits arrestin in response to ligand stimulation, and no CCRL2-mediated signaling events have been described to date. However, GPR1 undergoes constitutive endocytosis, making this receptor perfectly adapted as decoy receptor. Here, we discuss expression pattern, activation, and receptor binding of chemerin. Moreover, we review the current literature regarding the involvement of chemerin in cancer and several obesity-related diseases, as well as recent developments in therapeutic targeting of the chemerin system.

The Structural Basis of Peptide Binding at Class A G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) represent the largest membrane protein family and a significant target class for therapeutics. Receptors from GPCRs’ largest class, class A, influence virtually every aspect of human physiology. About 45% of the members of this family endogenously bind flexible peptides or peptides segments within larger protein ligands. While many of these peptides have been structurally characterized in their solution state, the few studies of peptides in their receptor-bound state suggest that these peptides interact with a shared set of residues and undergo significant conformational changes. For the purpose of understanding binding dynamics and the development of peptidomimetic drug compounds, further studies should investigate the peptide ligands that are complexed to their cognate receptor.

Cyclic Derivatives of the Chemerin C-Terminus as Metabolically Stable Agonists at the Chemokine-like Receptor 1 for Cancer Treatment

Chemerin is a small chemotactic protein and a modulator of the innate immune system. Its activity is mainly mediated by the chemokine-like receptor 1 (CMKLR1), a receptor expressed by natural killer cells, dendritic cells, and macrophages. Downregulation of chemerin is part of the immune evasion strategy exploited by several cancer types, including melanoma, breast cancer, and hepatocellular carcinoma. Administration of chemerin can potentially counteract these effects, but synthetically accessible, metabolically stable analogs are required. Other tumors display overexpression of CMKLR1, offering a potential entry point for targeted delivery of chemotherapeutics. Here, we present cyclic derivatives of the chemerin C-terminus (chemerin-9), the minimal activation sequence of chemerin. Chemerin-9 derivatives that were cyclized through positions four and nine retained activity while displaying full stability in blood plasma for more than 24 h. Therefore, these peptides could be used as a drug shuttle system to target cancer cells as demonstrated here by methotrexate conjugates.

Cyclic Analogues of the Chemerin C-Terminus Mimic a Loop Conformation Essential for Activating the Chemokine-like Receptor 1

The chemokine-like receptor 1 (CMKLR1) is a promising target for treating autoinflammatory diseases, cancer, and reproductive disorders. However, the interaction between CMKLR1 and its protein-ligand chemerin remains uncharacterized, and no drugs targeting this interaction have passed clinical trials. Here, we identify the binding mode of chemerin-9, the C-terminus of chemerin, at the receptor by combining complementary mutagenesis with structure-based modeling. Incorporating our experimental data, we present a detailed model of this binding site, including experimentally confirmed pairwise interactions for the most critical ligand residues: Chemerin-9 residue F⁸ binds to a hydrophobic pocket in CMKLR1 formed by the extracellular loop (ECL) 2, while F⁶ interacts with Y^2.68, suggesting a turn-like structure. On the basis of this model, we created the first cyclic peptide with nanomolar activity, confirming the overall binding conformation. This constrained agonist mimics the loop conformation adopted by the natural ligand and can serve as a lead compound for future drug design.

De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular de novo design and compound optimization. Herein, we report a generative model that bridges systems biology and molecular design, conditioning a generative adversarial network with transcriptomic data. By doing so, we can automatically design molecules that have a high probability to induce a desired transcriptomic profile. As long as the gene expression signature of the desired state is provided, this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds. Molecules designed by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures. Overall, this method represents an alternative approach to bridge chemistry and biology in the long and difficult road of drug discovery.

High quality hit identification remains a considerable challenge in de novo drug design. Here, the authors train a generative adversarial network with transcriptome profiles induced by a large set of compounds, enabling it to design molecules that are likely to induce desired expression profiles.

CMKLR1-targeting peptide tracers for PET/MR imaging of breast cancer

Background: Molecular targeting remains to be a promising approach in oncology. Overexpression of G protein-coupled receptors (GPCRs) in human cancer is offering a powerful opportunity for tumor-selective imaging and treatment employing nuclear medicine. We utilized novel chemerin-based peptide conjugates for chemokine-like receptor 1 (CMKLR1) targeting in a breast cancer xenograft model. Methods: By conjugation with the chelator 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA), we obtained a family of five highly specific, high-affinity tracers for hybrid positron emission tomography/magnetic resonance (PET/MR) imaging. A xenograft model with target-positive DU4475 and negative A549 tumors in immunodeficient nude mice enabled CMKLR1-specific imaging in vivo. We acquired small animal PET/MR images, assessed biodistribution by ex vivo measurements and investigated the tracer specificity by blocking experiments. Results: Five CMKLR1-targeting peptide tracers demonstrated high biological activity and affinity in vitro with EC₅₀ and IC₅₀ values below 2 nM. Our target-positive (DU4475) and target-negative (A549) xenograft model could be validated by ex vivo analysis of CMKLR1 expression and binding. After preliminary PET imaging, the three most promising tracers [⁶⁸Ga]Ga-DOTA-AHX-CG34, [⁶⁸Ga]Ga-DOTA-KCap-CG34 and [⁶⁸Ga]Ga-DOTA-ADX-CG34 with best tumor uptake were further analyzed. Hybrid PET/MR imaging along with concomitant biodistribution studies revealed distinct CMKLR1-specific uptake (5.1% IA/g, 3.3% IA/g and 6.2% IA/g 1 h post-injection) of our targeted tracers in DU4475 tumor tissue. In addition, tumor uptake was blocked by excess of unlabeled peptide (6.4-fold, 5.5-fold and 3.4-fold 1 h post-injection), further confirming CMKLR1 specificity. Out of five tracers, we identified these three tracers with moderate, balanced hydrophilicity to be the most potent in receptor-mediated tumor targeting. Conclusion: We demonstrated the applicability of ⁶⁸Ga-labeled peptide tracers by visualizing CMKLR1-positive breast cancer xenografts in PET/MR imaging, paving the way for developing them into theranostics for tumor treatment.

A desirability-based multi objective approach for the virtual screening discovery of broad-spectrum anti-gastric cancer agents

Gastric cancer is the third leading cause of cancer-related mortality worldwide and despite advances in prevention, diagnosis and therapy, it is still regarded as a global health concern. The efficacy of the therapies for gastric cancer is limited by a poor response to currently available therapeutic regimens. One of the reasons that may explain these poor clinical outcomes is the highly heterogeneous nature of this disease. In this sense, it is essential to discover new molecular agents capable of targeting various gastric cancer subtypes simultaneously. Here, we present a multi-objective approach for the ligand-based virtual screening discovery of chemical compounds simultaneously active against the gastric cancer cell lines AGS, NCI-N87 and SNU-1. The proposed approach relays in a novel methodology based on the development of ensemble models for the bioactivity prediction against each individual gastric cancer cell line. The methodology includes the aggregation of one ensemble per cell line using a desirability-based algorithm into virtual screening protocols. Our research leads to the proposal of a multi-targeted virtual screening protocol able to achieve high enrichment of known chemicals with anti-gastric cancer activity. Specifically, our results indicate that, using the proposed protocol, it is possible to retrieve almost 20 more times multi-targeted compounds in the first 1% of the ranked list than what is expected from a uniform distribution of the active ones in the virtual screening database. More importantly, the proposed protocol attains an outstanding initial enrichment of known multi-targeted anti-gastric cancer agents.

Epigenetic inactivation of galanin and GALR1/2 is associated with early recurrence in head and neck cancer

The aim of this study was to investigate the prognostic value of galanin (GAL) and galanin receptor (GALR) promoter hypermethylation in patients with head and neck squamous cell carcinoma (HNSCC). The methylation status of three genes-GAL, GALR1, and GALR2 was examined in HNSCC patient tumors using quantitative methylation-specific PCR (Q-MSP). To determine the prognostic value of GAL, GALR1 and GALR2 methylation status, their associations with various clinical characteristics and patient survival were assessed in HNSCC patient tumors (n = 142). Aberrant methylation of at least one gene was observed in 84 of the 142 (59.2 %) primary tumors analyzed. The methylation index, defined as the ratio between the number of methylated genes and the number of genes examined, was positively correlated with larger tumor size (P = 0.034) and disease recurrence (P < 0.001). In the multivariate logistic-regression analysis, methylation of both GAL and GALR1 exhibited the highest association with poor survival (hazard ratio, 6.83, P = 0.002). Moreover, among patients without lymph node metastasis, a multivariate analysis showed a significant trend for poor survival as the number of hypermethylated genes increased (log-rank test, P = 0.003). CpG hypermethylation is a likely mechanism of GAL and GALR1/2 gene inactivation, indicating that GAL and its receptors play a role in HNSCC tumorigenesis. As such, GAL and GALR1/2 methylation status may serve as an important biomarker for clinical outcome.