Issue #52: A New Insight into the Study of Neural Cell Adhesion Molecule (NCAM) Polysialylation Inhibition Incorporated the Molecular Docking Models into the NMR Spectroscopy of a Crucial Peptide-Ligand Interaction.

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Signal of the Day

A New Insight into the Study of Neural Cell Adhesion Molecule (NCAM) Polysialylation Inhibition Incorporated the Molecular Docking Models into the NMR Spectroscopy of a Crucial Peptide-Ligand Interaction.

The expression of polysialic acid (polySia) on the neuronal cell adhesion molecule (NCAM) is called NCAM-polysialylation, which is strongly related to the migration and invasion of tumor cells and aggressive clinical status. During the NCAM polysialylation process, polysialyltransferases (polySTs), such as polysialyltransferase IV (ST8SIA4) or polysialyltransferase II (ST8SIA2), can catalyze the addition of CMP-sialic acid (CMP-Sia) to the NCAM to form polysialic acid (polySia). In this study, the docking models of polysialyltransferase IV (ST8Sia4) protein and different ligands were predicted using Alphafold 3 and DiffDock servers, and the prediction accuracy was further verified using the NMR experimental spectra of the interactions between polysialyltransferase domain (PSTD), a crucial peptide domain in ST8Sia4, and a different ligand. This combination strategy provides new insights into a quick and effective screening for inhibitors of tumor cell migration.

Why this matters: Enhances small-molecule or peptide docking accuracy for targeted drug discovery.

Also Worth Reading

Investigation of the potential mechanism by which methylparaben induces psoriasis: an integrated study using network toxicology, molecular docking, molecular dynamics simulation, and eight machine learning algorithms.

Psoriasis is a chronic inflammatory skin disease with limited safe and effective treatments. Methylparaben, a widely used preservative in cosmetics, pharmaceuticals, and food, is an emerging environmental pollutant linked to immune-related skin disorders, but its role and mechanism in psoriasis remain unclear. This study explored its potential mechanism using network toxicology, molecular docking, molecular dynamics simulation, and eight machine learning algorithms. Methylparaben targets were retrieved from GeneCards and TCMSP, and psoriasis-related targets from CTD and GeneCards. Overlapping targets were screened with Venny 2.1.0. A PPI network was constructed via STRING, and core targets identified using Cytoscape 3.10.2. GO and KEGG enrichment analyses were performed on DAVID. Molecular docking evaluated the binding affinity of methylparaben with key targets. A total of 138 compound-related and 5,592 psoriasis-related targets were identified. Core targets such as INS, HIF1A, and PPARG are involved in regulating immune-inflammatory responses, keratinocyte proliferation and differentiation, and oxidative stress. GO analysis revealed enrichment in xenobiotic metabolism, lipopolysaccharide response, and metal ion binding. KEGG analysis highlighted pathways related to cancer, chemical carcinogenesis from reactive oxygen species, and drug metabolism via cytochrome P450 enzymes. Molecular docking showed stable binding of methylparaben to INS (-4.5 kcal/mol), HIF1A (-5.9 kcal/mol), and PPARG (-5.5 kcal/mol), primarily through hydrogen bonds and hydrophobic interactions. Methylparaben may exert its effects on psoriasis via multi-target and multi-pathway mechanisms, influencing inflammation, oxidative stress, and cellular regulation. These findings provide valuable insight into its toxicological mechanism and potential therapeutic application.

scDock: Streamlining drug discovery targeting cell-cell communication via scRNA-seq analysis and molecular docking

Summary Identifying drugs that target intercellular communication networks represents a promising therapeutic strategy, yet linking single-cell RNA sequencing (scRNA-seq) analysis to structure-based drug screening remains technically challenging and requires substantial bioinformatics expertise. We present scDock, an integrated and user-friendly pipeline that seamlessly connects scRNA-seq data processing, cell–cell communication inference, and molecular docking-based drug discovery. Through a single configuration file, users can execute the complete workflow, from raw scRNA-seq data to ranked drug candidates, without programming skills. scDock automates the identification of disease-relevant ligand–receptor interactions from scRNA-seq data and perfoms structure-based virtual screening against these communication targets using Protein Data Bank (PDB) or AlphaFold-predicted protein structures. The pipeline generates comprehensive outputs at each stage, enabling users to explore intercellular signaling alterations and discover therapeutic compounds targeting specific cell–cell communications. scDock addresses a critical gap by providing an accessible end-to-end solution for communication-targeted drug discovery from single-cell data. Availability and Implementation scDock is freely available at https://github.com/Andrewneteye4343/scDock . It is implemented in R, Python, shell scripts, and supports Linux systems, including Ubuntu and Debian.

Docking and Persistent Operations for a Resident Underwater Vehicle

Our understanding of the oceans remains limited by sparse and infrequent observations, primarily because current methods are constrained by the high cost and logistical effort of underwater monitoring, relying either on sporadic surveys across broad areas or on long-term measurements at fixed locations. To overcome these limitations, monitoring systems must enable persistent and autonomous operations without the need for continuous surface support. Despite recent advances, resident underwater vehicles remain uncommon due to persistent challenges in autonomy, robotic resilience, and mechanical robustness, particularly under long-term deployment in harsh and remote environments. This work addresses these problems by presenting the development, deployment, and operation of a resident infrastructure using a docking station with a mini-class Remotely Operated Vehicle (ROV) at 90m depth. The ROVis equipped with enhanced onboard processing and perception, allowing it to autonomously navigate using USBL signals, dock via ArUco marker-based visual localisation fused through an Extended Kalman Filter, and carry out local inspection routines. The system demonstrated a 90% autonomous docking success rate and completed full inspection missions within four minutes, validating the integration of acoustic and visual navigation in real-world conditions. These results show that reliable, untethered operations at depth are feasible, highlighting the potential of resident ROV systems for scalable, cost-effective underwater monitoring.

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Quick Reads

Deciphering the role of gut microbiota metabolites in bronchiectasis through the perspectives of network pharmacology, Mendelian randomization, and molecular dynamics.

The role of gut microbiota and their metabolites in respiratory diseases via the “gut-lung axis” has garnered increasing attention, yet their specific mechanisms in bronchiectasis (BIS) remain unclear. Read more →

GSTM4 in oral cancer therapy: genome-wide Mendelian randomization and network pharmacology unveil mechanistic and therapeutic insights.

Oral cancer (OC) is a prevalent malignancy influenced by environmental factors such as smoking, alcohol consumption, poor oral hygiene, and HPV infection, as well as genetic susceptibility. Read more →

Background Pulmonary fibrosis (PF) is a progressive and fatal interstitial lung disease with limited treatment options. Read more →

Phytosterols from Rosaceae Species as Dual Modulators of Neuroinflammation and Induced Pluripotency.

Neuroinflammation is a central pathological hallmark of numerous neurodegenerative disorders, often linked to oxidative stress, immune dysregulation, and neuronal dysfunction. Read more →

Antifreeze Peptides Derived from Silver Carp Parvalbumin: Dual Mechanisms of Ice Inhibition and Membrane Stabilization for Superior Yeast Cryopreservation.

This study investigated the cryoprotective effects and mechanisms of antifreeze peptides derived from silver carp parvalbumins (PV-AFPs) in a yeast model, employing a combined experimental and computational approach. Read more →

Repurposing statins as potential antifungal agents for Candida albicans: in-vitro and in-silico approach.

Candida is a devastating fungal pathogen causing infection, exhibiting biofilm formation and leading to significant mortality and morbidity. Read more →

Valosin-Containing Protein as a therapeutic target in CAG repeat-driven Spinocerebellar ataxias: Integrative transcriptomic and computational insights.

Spinocerebellar ataxias (SCAs) are progressive neurodegenerative disorders caused by abnormal CAG repeat expansions in genes such as ATXN1, ATXN2, and ATXN3, with no effective therapeutic options currently available. Read more →

Unravelling the therapeutic potential of chrysin against ischemic stroke and post-stroke cognitive impairment: a network pharmacology and in-silico perspective.

Ischemic stroke (IS) is a cerebrovascular disorder that, if left untreated, can lead to secondary neurodegeneration and Post Stroke Cognitive Impairment (PSCI). Read more →

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