A new computational study published in Discover Chemistry by Zara Javaid, Hafiza Salaha Mahrosh, Saad Serfraz and Amer Jamil of the University of Agriculture, Faisalabad, set out to close this gap by screening plant-derived molecules against two of the virus’s most strategically important proteins. The team assembled a library of 1,000 phytochemicals with documented antiviral, antioxidant or anticancer activity, drawn from the IMPPAT database, Dr. Duke’s Phytochemical and Ethnobotanical Database and published literature, and docked them against glycoprotein 42, the envelope protein that mediates viral entry into B cells, and Epstein–Barr nuclear antigen 1, the latency protein that maintains and partitions the viral episome during cell division.

The choice of targets reflects a deliberate dual strategy. Glycoprotein 42 is a surface-exposed glycoprotein that binds HLA class II molecules and works alongside the gH and gL glycoproteins to drive membrane fusion, making it an accessible point of attack during the earliest stages of infection. Peptide inhibitors directed at this protein have already shown nanomolar antiviral activity in prior work, validating its therapeutic potential. EBNA1, by contrast, is a 641-amino-acid nuclear protein expressed in every EBV-linked cancer and is essential for viral genome replication and persistence; no FDA-approved inhibitor of it exists. By targeting both an entry factor and a latency factor simultaneously, the researchers aimed to identify compounds with combined antiviral and antineoplastic potential, an approach that distinguishes their workflow from earlier studies focused on single targets or synthetic libraries.

The docking screen, performed with the AutoDock Vina engine inside PyRx, produced clear front-runners. Against glycoprotein 42, the steroidal saponin hecogenin achieved the strongest binding energy of −8.9 kcal/mol, forming a hydrogen bond with glutamine 92 and hydrophobic contacts with tyrosine 167, phenylalanine 129 and histidine 191. Other strong performers included baicalin, islandicin, swertisin, hortensin, pedalitin, epigallocatechin, pinoresinol, hesperetin and verbenalin. Against EBNA1, the plant sterol cyclobranol led with a score of −8.2 kcal/mol, anchoring itself in a hydrophobic pocket through contacts with phenylalanine 508, tyrosine 510 and leucine 539. Notably, acyclovir, used as a comparator control, scored only −4.7 kcal/mol against glycoprotein 42 and −4.9 kcal/mol against EBNA1, placing it last among the screened ligands and suggesting that the top phytochemicals bind both viral proteins more favorably than the standard clinical antiviral.

The authors took care to validate their docking protocol. The native co-crystallized ligand of EBNA1 was extracted and redocked under identical parameters, reproducing the experimental binding geometry with a heavy-atom root-mean-square deviation of 0.958 angstroms, well within the accepted threshold of 2 angstroms. Redocking validation was not possible for glycoprotein 42, which was solved in its unbound form without a co-crystallized ligand, a structural limitation the team acknowledged openly. Docking scores, they stressed, are estimates of binding compatibility rather than predictions of biological potency, and experimental confirmation remains essential.

From the top-ranked compounds, the researchers applied Lipinski’s rule of five as an initial filter for oral drug-likeness. Hesperetin, hecogenin, islandicin, hortensin, pedalitin, pinoresinol and verbenalin passed with no violations, while brassicasterol, swertisin, caftaric acid and epigallocatechin each violated a single criterion. Compounds with more than two violations, including cyclobranol, stigmasterol, baicalin and taraxerol, were excluded. The authors noted that natural products often carry greater structural complexity than synthetic drugs, so broader physicochemical descriptors were reserved for the later pharmacokinetic assessment rather than used as absolute exclusion criteria at this early stage.

The study then moved into network pharmacology, linking the surviving compounds to their predicted human gene targets through SwissTargetPrediction and the STITCH database, and cross-referencing these against EBV- and EBV-cancer-associated genes retrieved from GeneCards and DisGeNET. The intersection yielded 132 shared targets, which were mapped into a protein–protein interaction network using STRING and visualized in Cytoscape. The network contained 132 nodes and 827 edges, and degree-based analysis with the CytoHubba plugin identified ten hub genes: TP53, HSP90AA1, AKT1, SRC, BCL2, EGFR, TNF, ESR1, ERBB2 and PIK3CA. Gene Ontology and KEGG enrichment showed these genes clustered in apoptosis regulation, kinase signaling, the PI3K-Akt pathway and viral oncogenesis pathways, including EBV infection itself and Kaposi’s sarcoma-associated herpesvirus infection, consistent with the virus’s known strategy of suppressing host cell death and hijacking kinase cascades to maintain latency and drive proliferation.

When the eight drug-like phytochemicals were docked against the EBV-associated cancer proteins, hecogenin again dominated, achieving the best score against nine of the ten targets. Its strongest interaction was with the TP53 DNA-binding domain at −10.9 kcal/mol, stabilized by hydrogen bonds with glycine 112, phenylalanine 113 and serine 116, followed by −10.6 kcal/mol against PIK3CA and −10.4 kcal/mol against SRC. Pinoresinol, swertisin and hesperetin also performed well. To test whether the hecogenin–TP53 interaction could survive realistic physical conditions, the team ran a 200-nanosecond molecular dynamics simulation in GROMACS using the CHARMM36 force field on an NVIDIA H100 GPU. The protein backbone remained stable throughout, with RMSD values of 2.0 to 2.8 angstroms and a nearly constant radius of gyration, while the ligand’s internal geometry barely changed, with a ligand RMSF of only about 0.35 angstroms. The ligand did, however, wander considerably relative to its docked pose, and hydrogen bond analysis revealed mostly transient polar contacts, indicating that the complex is held together primarily by hydrophobic and van der Waals forces in a dynamic rather than rigid binding mode.

Predicted pharmacokinetics added further support. ADMET profiling with SwissADME and pkCSM indicated no AMES toxicity, hepatotoxicity or skin sensitization for the lead compounds, and both hecogenin and pinoresinol were predicted to cross the blood–brain barrier and show high gastrointestinal absorption, placing them in the favorable yolk region of the Boiled Egg plot. As P-glycoprotein substrates, both would be actively pumped out of the central nervous system, limiting CNS exposure but potentially improving peripheral safety. The authors were candid about the caveats: hecogenin’s broad affinity across structurally unrelated proteins raises the possibility of promiscuous, hydrophobicity-driven binding rather than genuine multitarget specificity, and although the compound triggered no PAINS alerts, orthogonal assays such as surface plasmon resonance and thermal shift experiments will be needed to separate true polypharmacology from assay interference.

Hecogenin itself is a steroidal saponin from Agave sisalana with a prior record of anti-inflammatory, antimicrobial and antitumor research, while pinoresinol is a lignan from sesame seeds and olive oil, and hesperetin and swertisin are flavonoids with documented anticancer and immunomodulatory properties. The study’s real contribution may lie as much in its framework as in its lead compound: an integrated pipeline that couples large-scale docking, network pharmacology, enrichment analysis, ADMET prediction and molecular dynamics into a reproducible workflow for discovering mechanism-based antiviral agents from natural sources. The researchers emphasize that all findings are computational and hypothesis-generating, and that in vitro and in vivo validation is required before hecogenin or its counterparts can be considered genuine therapeutic candidates against EBV infection and the cancers it leaves in its wake.

Subject of Research: Computational screening of phytochemicals as multitarget inhibitors of Epstein–Barr virus infection and EBV-associated malignancies

Article Title: Network pharmacology and molecular docking-based screening of phytochemicals as multitarget inhibitors of Epstein–Barr virus infection and associated malignancies

Article References: Javaid, Z., Mahrosh, H. S., Serfraz, S., & Jamil, A. (2026). Network pharmacology and molecular docking-based screening of phytochemicals as multitarget inhibitors of Epstein–Barr virus infection and associated malignancies. Discover Chemistry, 3(1), Article 577. https://doi.org/10.1007/s44371-026-01029-1

Image Credits: AI Generated

DOI: 10.1007/s44371-026-01029-1

Keywords: Epstein–Barr virus, network pharmacology, molecular docking, hecogenin, phytochemicals, EBNA1, glycoprotein 42, molecular dynamics simulation, ADMET, oncogenic viruses, drug discovery, TP53