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Daily Signal February 19, 2026 · 8 min read

Issue #51: Structure-Guided Engineering of High-Affinity Antibodies Against Zika Virus Using Deep Learning and Molecular Dynamics.

Protein Design Digest - 2026-02-19 - 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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Structure-Guided Engineering of High-Affinity Antibodies Against Zika Virus Using Deep Learning and Molecular Dynamics.

Zika virus (ZIKV) remains a global health threat, for which no licensed antiviral treatment has been available. In this study, we employed in silico approaches to optimize monoclonal antibodies targeting the Zika virus envelope protein (ZIKV E) in the Domain III (DIII) region, which is crucial for receptor binding and virus entry. A high-resolution crystal structure of ZIKV E in complex with the neutralizing antibody ZV-64 was used as a template for designing a library of antibody variants through targeted double-point mutations. The variants were systematically evaluated for stability, binding affinity, solubility, and protein-protein interaction potential using FoldX, DeepPurpose, SoluProt, and molecular docking. Among all the mutants, Variants-213 and -206 were identified as the top candidates, exhibiting the most favorable predicted binding affinity and solubility compared to the control antibody. The molecular dynamics simulations further revealed the structural stability of the two mutant variants, in which Variant-206 showed a predicted binding energy (-76.90 kcal/mol) along with higher conformational flexibilities. The findings demonstrate the use of computational antibody engineering to identify potentially high-affinity therapeutics against ZIKV, providing a foundation for future experimental validation and therapeutic development against ZIKV.

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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.

Field-induced slow magnetic relaxation, molecular docking and antibacterial studies of quasi-isotropic copper(II) (S = ½) systems stabilised by tetradentate (ONNO) and tridentate (NNO)-donor ligands.

A series of penta-coordinate Cu(II) complexes were synthesised and structurally characterised to explore the relationship between coordinating environment, molecular magnetism, and antibacterial activities. A dinuclear complex, [Cu2(L1)2] (1), derived from an ONNO-coordinated tetradentate ligand (H2L1 = N,N’-bis[(3-methoxy-2-hydroxybenzylidene)]ethane-1,2-diamine), and three paramagnetic Cu(II) complexes, [Cu2(N3)2(L2)2] (2), [Cu(SCN)(L2)]n (3), and [Cu(CH3COO)(L2)]n (4), stabilised by a tridentate NNO-donor ligand (HL2 = (E)-1-(pyridin-2-yldiazenyl)naphthalen-2-ol), were isolated. Dinuclear complex 2 features an asymmetric end-on μ1,1-azido bridge, whereas 3 and 4 exhibit end-to-end μ1,3-thiocyanate/acetate bridges, forming one-dimensional polymeric architectures. Single-crystal X-ray diffraction confirmed their square-pyramidal molecular geometries. Complexes 1 and 4 exhibit field-induced single-molecule magnet (SMM) behaviour, consistent with quasi-isotropic S = ½ Cu(II) centres. All complexes show χMT ≈ 0.4 cm3 mol-1 K-1 with a slight decrease below 10 K. EPR parameters support the existence of mixed orbital character dz2/dx2-y2 (gx = 2.23, gy = 2.06, gz = 1.90) (1) and dx2-y2 (g∥ = 2.21, g⊥ = 2.06) (4) in the ground states. Molecular docking analyses demonstrated complex 3 has strong binding affinities against four biologically relevant targets: B-DNA (PDB ID: 1BNA), human DNA topoisomerase I (hTOPI, PDB ID: 1SC7), Escherichia coli MenB enzyme (EC-MenB, PDB ID: 3T88), and human serum albumin (HSA, PDB ID: 4LA0), indicating its potential for target-specific activity.

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Integrating network pharmacology to elucidate the protective mechanisms of syringin in sperm injury.

Aim Syringin demonstrates significant pharmacological effects in cancer and autoimmune disease models, primarily through its anti-apoptotic, anti-inflammatory, and immune-modulating properties. Read more →

Designing Novel NMDA Receptor Antagonists for Ischemic Stroke: A 3D-QSAR and Molecular Dynamics Simulation Approach.

Background Strokes represent a significant global health concern, with ischemic stroke being the most prevalent and deadly form. Read more →

Computational study of chalcone-cyanopyrimidine hybrids as LSD1 inhibitors: Assessing the influence of FAD on binding affinity and inhibition.

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In-silico identification of antimalarial compounds targeting PfMDR1 of <i>Plasmodium falciparum</i>.

PfMDR1, a key transporter protein in Plasmodium falciparum , contributes to antimalarial drug resistance by actively expelling drugs like chloroquine from the parasite’s digestive vacuole, lowering their intracellular efficacy. Read more →

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

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