Deorphanizing Peptides Using Structure Prediction.

Many endogenous peptides rely on signaling pathways to exert their function, but identifying their cognate receptors remains a challenging problem. We investigate the use of AlphaFold-Multimer complex structure prediction together with transmembrane topology prediction for peptide deorphanization. We find that AlphaFold's confidence metrics have strong performance for prioritizing true peptide-receptor interactions. In a library of 1112 human receptors, the method ranks true receptors in the top percentile on average for 11 benchmark peptide-receptor pairs.

Modeling MEK4 Kinase Inhibitors through Perturbed Electrostatic Potential Charges.

MEK4, mitogen-activated protein kinase kinase 4, is overexpressed and induces metastasis in advanced prostate cancer lesions. However, the value of MEK4 as an oncology target has not been pharmacologically validated because selective chemical probes targeting MEK4 have not been developed. With advances in both computer and biological high-throughput screening, selective chemical entities can be discovered. Structure-based quantitative structure-activity relationship (QSAR) modeling often fails to generate accurate models due to poor alignment of training sets containing highly diverse compounds. Here we describe a highly predictive, nonalignment based robust QSAR model based on a data set of strikingly diverse MEK4 inhibitors. We computed the electrostatic potential (ESP) charges using a density functional theory (DFT) formalism of the donor and acceptor atoms of the ligands and hinge residues. Novel descriptors were then generated from the perturbation of the charge densities of the donor and acceptor atoms and were used to model a diverse set of 84 compounds, from which we built a robust predictive model.

Structure-Based Computational Scanning of Chemical Modification Sites in Biologics.

To address the challenges of short half-life, immunogenicity, and nonspecific distribution, chemical modifications of peptide and protein-based drugs have emerged as a versatile strategy for improving their therapeutic efficacy. One such modification involves the derivatization of peptides and proteins with fatty acids, which can protract their half-life, modify their biodistribution, and potentially enable targeted delivery to specific tissues or disease sites of interest. However, the present strategies for the synthesis of such synthetically modified biologics require numerous rounds of experimental testing and often yield unstable, inactive, or heterogeneous products. To address the inefficiencies in designing modified biologics, we developed a hybrid computational workflow that integrates RosettaMatch from the Rosetta suite of protein modeling tools with molecular dynamics (MD) simulations. This approach not only reduces the number of amino acid positions that need to be experimentally tested by targeting only the most promising candidates for modification but also expedites the design of chemically modified biologics with the desired properties, ensuring a rapid and cost-effective development cycle. Although we demonstrate the utility of our method on a peptide therapeutic, GLP-1, with different fatty acid derivatizations, this straightforward approach has the potential to streamline the design process of a diverse range of chemically modified therapeutics, enabling tailored enhancements to their pharmacokinetic properties.

Continuing issues with Lead: Recent Advances in Detection.

In the past Pb2+ has been used in many industries, including gasoline, piping, toys, paints, and more. The use of lead has led to a natural increase of lead concentration in the environment especially in air and water. According to the U.S. CDC "no level of lead in blood is considered safe." Exposure to very low amounts of lead can cause several health complications including developmental and neurological disorders. Over the past several years an emphasis has been placed in developing systems that can detect lead at a very low concentration. A great deal of work has been accomplished in the development of Pb2+ sensors that can not only detect but also quantify the amount and in some cases in the presence of other metal ions. Herein, we describe current regulations, mode of exposure and recent development of sensing techniques.

Rational Design of Highly Potent and Selective Covalent MAP2K7 Inhibitors.

The mitogen-activated protein kinase signaling cascade is conserved across eukaryotes, where it plays a critical role in the regulation of activities including proliferation, differentiation, and stress responses. This pathway propagates external stimuli through a series of phosphorylation events, which allows external signals to influence metabolic and transcriptional activities. Within the cascade, MEK, or MAP2K, enzymes occupy a molecular crossroads immediately upstream to significant signal divergence and cross-talk. One such kinase, MAP2K7, also known as MEK7 and MKK7, is a protein of great interest in the molecular pathophysiology underlying pediatric T cell acute lymphoblastic leukemia (T-ALL). Herein, we describe the rational design, synthesis, evaluation, and optimization of a novel class of irreversible MAP2K7 inhibitors. With a streamlined one-pot synthesis, favorable in vitro potency and selectivity, and promising cellular activity, this novel class of compounds wields promise as a powerful tool in the study of pediatric T-ALL.

Design and structural validation of peptide-drug conjugate ligands of the kappa-opioid receptor.

Despite the increasing number of GPCR structures and recent advances in peptide design, the development of efficient technologies allowing rational design of high-affinity peptide ligands for single GPCRs remains an unmet challenge. Here, we develop a computational approach for designing conjugates of lariat-shaped macrocyclized peptides and a small molecule opioid ligand. We demonstrate its feasibility by discovering chemical scaffolds for the kappa-opioid receptor (KOR) with desired pharmacological activities. The designed De Novo Cyclic Peptide (DNCP)-ß-naloxamine (NalA) exhibit in vitro potent mixed KOR agonism/mu-opioid receptor (MOR) antagonism, nanomolar binding affinity, selectivity, and efficacy bias at KOR. Proof-of-concept in vivo efficacy studies demonstrate that DNCP-ß-NalA(1) induces a potent KOR-mediated antinociception in male mice. The high-resolution cryo-EM structure (2.6 Å) of the DNCP-ß-NalA-KOR-Gi1 complex and molecular dynamics simulations are harnessed to validate the computational design model. This reveals a network of residues in ECL2/3 and TM6/7 controlling the intrinsic efficacy of KOR. In general, our computational de novo platform overcomes extensive lead optimization encountered in ultra-large library docking and virtual small molecule screening campaigns and offers innovation for GPCR ligand discovery. This may drive the development of next-generation therapeutics for medical applications such as pain conditions.

An integrated platform approach enables discovery of potent, selective and ligand-competitive cyclic peptides targeting the GIP receptor.

In any drug discovery effort, the identification of hits for further optimisation is of crucial importance. For peptide therapeutics, display technologies such as mRNA display have emerged as powerful methodologies to identify these desired de novo hit ligands against targets of interest. The diverse peptide libraries are genetically encoded in these technologies, allowing for next-generation sequencing to be used to efficiently identify the binding ligands. Despite the vast datasets that can be generated, current downstream methodologies, however, are limited by low throughput validation processes, including hit prioritisation, peptide synthesis, biochemical and biophysical assays. In this work we report a highly efficient strategy that combines bioinformatic analysis with state-of-the-art high throughput peptide synthesis to identify nanomolar cyclic peptide (CP) ligands of the human glucose-dependent insulinotropic peptide receptor (hGIP-R). Furthermore, our workflow is able to discriminate between functional and remote binding non-functional ligands. Efficient structure-activity relationship analysis (SAR) combined with advanced in silico structural studies allow deduction of a thorough and holistic binding model which informs further chemical optimisation, including efficient half-life extension. We report the identification and design of the first de novo, GIP-competitive, incretin receptor family-selective CPs, which exhibit an in vivo half-life up to 10.7 h in rats. The workflow should be generally applicable to any selection target, improving and accelerating hit identification, validation, characterisation, and prioritisation for therapeutic development.

Synthesis and Biological Evaluation of 3-Arylindazoles as Selective MEK4 Inhibitors.

Herein we report the discovery of a novel series of highly potent and selective mitogen-activated protein kinase kinase 4 (MEK4) inhibitors. MEK4 is an upstream kinase in MAPK signaling pathways that phosphorylates p38 MAPK and JNK in response to mitogenic and cellular stress queues. MEK4 is overexpressed and induces metastasis in advanced prostate cancer lesions. However, the value of MEK4 as an oncology target has not been pharmacologically validated because selective chemical probes targeting MEK4 have not been developed. Optimization of this series via structure-activity relationships and molecular modeling led to the identification of compound 6 ff (4-(6-fluoro-2H-indazol-3-yl)benzoic acid), a highly potent and selective MEK4 inhibitor. This series of inhibitors is the first of its kind in both activity and selectivity and will be useful in further defining the role of MEK4 in prostate and other cancers.

A Chemical Probe Strategy for Interrogating Inhibitor Selectivity Across the MEK Kinase Family.

MEK4 is an upstream kinase in MAPK signaling pathways where it phosphorylates p38 MAPK and JNK in response to mitogenic and cellular stress queues. MEK4 is overexpressed and induces metastasis in advanced prostate cancer lesions. However, the value of MEK4 as an oncology target has not been pharmacologically validated because selective chemical probes targeting MEK4 have not been developed. Despite a high level of sequence homology in the ATP-binding site, most reported MEK inhibitors are selective for MEK1/2 and display reduced potency toward other MEKs. Here, we present the first functional and binding selectivity-profiling platform of the MEK family. We applied the platform to profile a set of known kinase inhibitors and used the results to develop an in silico approach for small molecule docking against MEK proteins. The docking studies identified molecular features of the ligands and corresponding amino acids in MEK proteins responsible for high affinity binding versus those driving selectivity. WaterLOGSY and saturation transfer difference (STD) NMR spectroscopy techniques were utilized to understand the binding modes of active compounds. Further minor synthetic manipulations provide a proof of concept by showing how information gained through this platform can be utilized to perturb selectivity across the MEK family. This inhibitor-based approach pinpoints key features governing MEK family selectivity and clarifies empirical selectivity profiles for a set of kinase inhibitors. Going forward, the platform provides a rationale for facilitating the development of MEK-selective inhibitors, particularly MEK4 selective inhibitors, and repurposing of kinase inhibitors for probing the structural selectivity of isoforms.