Nerve growth factor (NGF) monoclonal antibodies inhibit chronic pain, yet failed to gain approval due to worsened joint damage in osteoarthritis patients. We report that neuropilin-1 (NRP1) is a coreceptor for NGF and tropomyosin-related kinase A (TrkA) pain signaling. NRP1 was coexpressed with TrkA in human and mouse nociceptors. NRP1 inhibitors suppressed NGF-stimulated excitation of human and mouse nociceptors and NGF-evoked nociception in mice. NRP1 knockdown inhibited NGF/TrkA signaling, whereas NRP1 overexpression enhanced signaling. NGF bound NRP1 with high affinity and interacted with and chaperoned TrkA from the biosynthetic pathway to the plasma membrane and endosomes, enhancing TrkA signaling. Molecular modeling suggested that the C-terminal R/KXXR/K NGF motif interacts with the extracellular “b” NRP1 domain within a plasma membrane NGF/TrkA/NRP1 of 2:2:2 stoichiometry. G α interacting protein C-terminus 1 (GIPC1), which scaffolds NRP1 and TrkA to myosin VI, colocalized in nociceptors with NRP1/TrkA. GIPC1 knockdown abrogated NGF-evoked excitation of nociceptors and pain-like behavior. Thus, NRP1 is a nociceptor-enriched coreceptor that facilitates NGF/TrkA pain signaling. NRP binds NGF and chaperones TrkA to the plasma membrane and signaling endosomes via the GIPC1 adaptor. NRP1 and GIPC1 antagonism in nociceptors offers a long-awaited nonopioid alternative to systemic antibody NGF sequestration for the treatment of chronic pain.

Aminoacyl-tRNA synthetases (ARSs) play essential roles in protein biosynthesis as well as in other cellular processes, often using evolutionarily acquired domains. For possible cooperativity and synergistic effects, nine ARSs assemble into the multi-tRNA synthetase complex (MSC) with three scaffold proteins: aminoacyl-tRNA synthetase complex-interacting multifunctional proteins 1, 2 and 3 (AIMP1, AIMP2 and AIMP3). X-ray crystallographic methods were implemented in order to determine the structure of a ternary subcomplex of the MSC comprising aspartyl-tRNA synthetase (DRS) and two glutathione S-transferase (GST) domains from AIMP2 and glutamyl-prolyl-tRNA synthetase (AIMP2_GST and EPRS_GST, respectively). While AIMP2_GST and EPRS_GST interact via conventional GST heterodimerization, DRS strongly interacts with AIMP2_GST via hydrogen bonds between the α7–β9 loop of DRS and the β2–α2 loop of AIMP2_GST, where Ser156 of AIMP2_GST is essential for the assembly. Structural analyses of DRS–AIMP2_GST–EPRS_GST reveal its pivotal architecture in the MSC and provide valuable insights into the overall assembly and conditionally required disassembly of the MSC.

A series of novel 5′(7′)-substituted-2′-oxospiro[1,3]dioxolane-2,3′-indoline-based N-hydroxypropenamides were designed, synthesized and evaluated for histone deacetylase (HDAC) inhibition and cytotoxicity. It was found that the compounds in this series displayed potent inhibitory effects against HDAC2 with IC50 values as low as 0.284μM, almost comparable to that of SAHA (IC50, 0.265μM), a positive control. In Western blot analysis, these compounds also exhibited noted inhibition toward histone deacetylation and this inhibition was found to correlate well with the cytotoxicity of the compounds in three human cancer cell lines. Docking studies indicated the compounds in this series bound to HDAC2 with high binding affinities (∼−9.8kcal/mol) compared to SAHA (−7.4kcal/mol)

Understanding protein structures can facilitate the development of therapeutic drugs. Traditionally, protein structures have been determined through experimental approaches such as X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy. While these methods are effective and are considered the gold standard, they are very resource-intensive and time-consuming, ultimately limiting their scalability. However, with recent developments in computational biology and artificial intelligence (AI), the field of protein prediction has been revolutionized. Innovations like AlphaFold and RoseTTAFold enable protein structure predictions to be made directly from amino acid sequences with remarkable speed and accuracy. Despite the enormous enthusiasm associated with these newly developed AI-approaches, their true potential in structure-based drug discovery remains uncertain. In fact, although these algorithms generally predict overall protein structures well, essential details for computational ligand docking, such as the exact location of amino acid side chains within the binding pocket, are not predicted with the necessary accuracy. Additionally, docking methodologies are considered more as a hypothesis generator rather than a precise predictor of ligand–target interactions, and thus, usually identify many false-positive hits among only a few correctly predicted interactions. In this paper, we are reviewing the latest development in this cutting-edge field with emphasis on the GPCR target class to assess the potential role of AI approaches in structure-based drug discovery.

Given the functional attributes of Doublecortin-like kinase 1 (DCLK1) in tumor growth, invasion, metastasis, cell motility, and tumor stemness, it is emerging as a therapeutic target in gastrointestinal cancers. Although a series of specific or nonspecific ATP-competitive inhibitors were identified against DCLK1, different types of scaffolds that can be utilized for the development of highly selective inhibitors or structural understanding of binding specificities of the compounds remain limited. Here, we present our work to repurpose a Janus kinase 1 inhibitor, ruxolitinib as a DCLK1 inhibitor, showing micromolar binding affinity and inhibitory activity. Furthermore, to gain an insight into its interaction mode with DCLK1, a crystal structure of the ruxolitinib-complexed DCLK1 has been determined and analyzed. Ruxolitinib as a nonspecific DCLK1 inhibitor characterized in this work is anticipated to provide a starting point for the structure-guided discovery of selective DCLK1 inhibitors.