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Identification and classification of ion-channels across the tree of life provide functional insights into understudied CALHM channels.

The ion channel (IC) genes encoded in the human genome play fundamental roles in cellular functions and disease and are one of the largest classes of druggable proteins. However, limited knowledge of the diverse molecular and cellular functions carried out by ICs presents a major bottleneck in developing selective chemical probes for modulating their functions in disease states. The wealth of sequence data available on ICs from diverse organisms provides a valuable source of untapped information for illuminating the unique modes of channel regulation and functional specialization. However, the extensive diversification of IC sequences and the lack of a unified resource present a challenge in effectively using existing data for IC research. Here, we perform integrative mining of available sequence, structure, and functional data on 419 human ICs across disparate sources, including extensive literature mining by leveraging advances in large language models to annotate and curate the full complement of the "channelome". We employ a well-established orthology inference approach to identify and extend the IC orthologs across diverse organisms to above 48,000. We show that the depth of conservation and taxonomic representation of IC sequences can further be translated to functional similarities by clustering them into functionally relevant groups, which can be used for downstream functional prediction on understudied members. We demonstrate this by delineating co-conserved patterns characteristic of the understudied family of the Calcium Homeostasis Modulator (CALHM) family of ICs. Through mutational analysis of co-conserved residues altered in human diseases and electrophysiological studies, we show that these evolutionarily-constrained residues play an important role in channel gating functions. Thus, by providing new tools and resources for performing large comparative analyses on ICs, this study addresses the unique needs of the IC community and provides the groundwork for accelerating the functional characterization of dark channels for therapeutic intervention.

CALHM1

Scorpion venom peptides: Novel therapeutic approaches for inflammatory and hepatic disorders.

Chronic hepatic disorders, such as metabolic dysfunction associated steatohepatitis (MASH), alcohol associated liver disease (ALD), and viral hepatitis (Hepatitis B virus [HBV]/Hepatitis C virus [HCV]), are primarily driven by persistent immune-mediated inflammation and hepatic stellate cell activation leading to fibrosis, yet conventional therapies lack tissue and molecular specificity. Scorpion venom peptides, refined through evolutionary selection, provide highly potent, target specific scaffolds capable of modulating intrahepatic inflammatory networks. Recent in vivo preclinical studies indicate that voltage gated potassium (Kv1.3) channel blocking peptides, such as BmKK2, significantly reduce macrophage activation and inhibit downstream cytokine production, effectively ameliorating diet-induced steatohepatitis and tissue scarring in murine models. Engineered hepatotropic candidates, such as Smp76 and Mucroporin-M1, demonstrate dual therapeutic functions: they neutralize extracellular Hepatitis C particles and suppress key host transcription factors necessary for Hepatitis B replication. This review systematically examines scorpion venom peptides organized by disease category, covering their historical development, structural classification into disulfide-bridged and non-disulfide-bridged families, ion channel specificity, hepatic anti-inflammatory and antiviral mechanisms, and translational challenges including nano-formulation delivery strategies and computational drug design. These target-specific peptides are ultimately positioned as promising molecular leads that may bridge targeted immunomodulation with the resolution of chronic, progressive liver injury.

Anti-inflammatory effects

Drug target ontology to classify and integrate drug discovery data.

BACKGROUND: One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. RESULTS: As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. CONCLUSIONS: DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Biological Ontologies