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Daily Signal May 14, 2026 · 10 min read

Issue #108: scDock: streamlining drug discovery targeting cell-cell communication via scRNA-seq analysis and molecular docking.

Protein Design Digest #108: scDock: streamlining drug discovery targeting cell-cell communication vi…

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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 performs 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://doi.org/10.6084/m9.figshare.31370368 and https://github.com/Andrewneteye4343/scDock. It is implemented in R, Python, shell scripts, and supports Linux systems, including Ubuntu and Debian.

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

Also Worth Reading

Unraveling the anti-neuroinflammatory mechanisms of Cervus cucumis polypeptide injection in Alzheimer’s disease: insights from network pharmacology, molecular docking, molecular dynamics simulation, and experimental validation.

Objective Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with increasing global prevalence, in which neuroinflammation serves as a critical pathological driver exacerbating cognitive decline. While current therapies offer limited symptomatic relief, multi-target strategies are urgently needed. Cervus cucumis polypeptide injection (CCPI), a traditional Chinese medicine (TCM) formulation, has demonstrated anti-inflammatory properties; however, its mechanisms of action against AD remain unclear. This study aimed to elucidate the anti-AD potential mechanisms of CCPI using an integrated approach combining network pharmacology, molecular docking, molecular dynamics (MD) simulation, and experimental validation. Methods Active components and corresponding targets of CCPI were retrieved from the TCMSP database, while AD-related targets were collected from Genecards, OMIM, and DrugBank. Potential therapeutic targets were identified by intersecting drug and disease targets, followed by protein-protein interaction (PPI) network construction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Molecular docking and MD simulations were performed to evaluate interactions between potential active components and key targets. In vitro experiments were conducted on Aβ 25-35 -induced BV2 microglial cells to assess cell viability (CCK-8 assay), inflammatory cytokine levels (ELISA), and protein expression (Western blot) related to the neuroinflammation pathway and microglial polarization. Results A total of 28 active components and 50 common targets of CCPI for AD treatment were identified. Linoleic acid (LA) was determined to be a potential active component, with IL-6 as the key target based on PPI network topology. Molecular docking and MD simulation confirmed a stable binding affinity between LA and IL-6. KEGG analysis revealed significant enrichment in the HIF-1 signaling pathway, particularly the IL-6/STAT3/VEGF signaling pathway. In vitro , CCPI treatment significantly enhanced cell viability and attenuated the pro-inflammatory response, as evidenced by reduced levels of IL-6, IL-1β, and TNF-α, decreased the expression of the pro-inflammatory marker iNOS. Concurrently, it elevated the expression of the anti-inflammatory/repair-associated marker CD206. Western blot analysis further verified that CCPI suppressed IL-6/STAT3 activation while upregulating VEGF expression. Additionally, LA alone significantly reduced IL-6 levels and STAT3 phosphorylation, decreased the expression of iNOS, and increased the expression of CD206, with therapeutic efficacy comparable to CCPI. Conclusion CCPI exerts neuroprotective effects in AD models by regulating the IL-6/STAT3/VEGF pathway, downregulating the expression of the inflammation-related iNOS protein, upregulating the expression of the CD206 protein associated with anti-inflammatory and reparative functions, remodeling the functional state of microglia, inhibiting their pro-inflammatory responses, and enhancing their reparative functions. Its potential active component, LA, likely mediates this effect by stably binding to and inhibiting IL-6, thus suppressing the downstream STAT3 phosphorylation that drives inflammatory activation.

A multimodal approach integrating spectroscopy, deep learning guided molecular docking, and molecular dynamics simulation for predictive assessment of pioglitazone to albumin binding for formulation development.

Binding affinity is a critical parameter that can influence the state of the drug in vivo and help to define the formulation strategy. The current study implements a multimodal approach to analyse the binding affinity between human serum albumin (HSA) and pioglitazone. Ultraviolet (UV) absorbance and fluorescence spectrometry analyses were performed on different combinations of HSA and pioglitazone complexes, and the absorbance and fluorescence intensities were mapped to calculate the binding constant. DynamicBind, a distinct deep-learning artificial intelligence tool, was implemented to perform in silico docking studies using a non-conventional approach. Furthermore, molecular dynamics simulation was also performed to generate root mean square deviation, radius of gyration, and root mean square fluctuation values, followed by principal component analysis, probability distribution function, and free energy landscape analysis. The simulation output was analysed to interpret the binding affinity and associated conformation of the protein-active pharmaceutical ingredient (API) complex. The binding constant calculated through UV analysis was 1.1 × 10 4 M -1 . Fluorescence spectroscopic analysis derived a value of 1.7 × 10 5 M -1 . At the same time, DynamicBind predicted the cLDDT score for the top predicted model to be 0.634, and a binding affinity value of greater than 5, indicating a relatively moderate binding between pioglitazone and HSA. The results from molecular dynamics simulations further complemented our earlier observations, indicating non-covalent binding interactions and a stable protein-API complex, which is desirable for developing a formulation using HSA as a carrier polymer. This orthogonal approach also provided critical information on the fate of the API and possible considerations that needed to be made during the design of the formulation process, highlighting the need for similar approaches that could provide multifaceted advantages and help in optimising R&D costs and timelines.

Natural compounds extracted from medicinal herbs in the treatment of Parkinson’s disease; Molecular Docking, Molecular Dynamics simulation, and Quantum Mechanical calculations.

The dopamine D3 receptor (D3R), which belongs to class A of G-protein-coupled receptors (GPCRs), is a promising target and is significantly involved in the pathology of Parkinson’s disease progression. This study examines the inhibitory effects of natural compounds on D3R, highlighting their potential therapeutic applications in mitigating disease progression. Molecular docking simulations were conducted using AutoDock4.2, MOE, ICM, and Vina to evaluate the binding affinity of selected phytochemicals relative to levodopa (compound 1), a standard dopaminergic drug. Compounds such as 5, 13, and 15 demonstrated superior docking scores compared to compound 1. The ADMET analyses revealed favorable bioavailability and drug-likeness profiles, especially for 5, 13, and 15 compounds. Additionally, the stability of the 5_D3R and 13_D3R complexes relative to the 1_D3R complex was confirmed through a molecular dynamics (MD) simulation, which supports the biological potential of polygoni and green tea. According to the binding free energy calculated using MMPB(GB)SA, 5_D3R and 13_D3R complexes exhibit greater stability, which is in agreement with the MD simulation. Finally, a rigorous three-layer ONIOM (M06-2X/6-31G∗:PM6:AMBER) analysis confirmed that compounds 5 and 13 effectively inhibit D3R, highlighting their potential as promising drug candidates.

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Network Pharmacology, Molecular Docking and Dynamics Simulation Reveal the Anti-Colitis Mechanism of <i>Smilax glabra</i> Roxb.

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Chemical Redirection of Protein Folding Landscapes via Lysine-Targeted Induction of Helix-to-Sheet Transitions.

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Pipeline Tip

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The protein structure is the language of life; design is its poetry. — Recep Adiyaman

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