openalexInternational Journal of Molecular Sciences2026-07-23

Mutant-Selective Binding of Phyllanthus niruri Phytochemicals to EGFR T790M: A Quantum-Classical Mechanistic Study

William D. Lituma-González, Devender Kumar, Amogh V. Arunprasad, Tanishque Verma, Shrutika Pillai, Fabián Jiménez, J. M. Sasikumar, Shanmugamurthy Lakshmanan

Epidermal growth factor receptor (EGFR) mutations drive hepatocellular carcinoma (HCC) progression, and the T790M gatekeeper substitution is the predominant mechanism of acquired resistance to EGFR-targeted therapies. Herein, we present multiscale quantum-classical in silico predictions of Phyllanthus niruri phytochemicals as non-covalent EGFR T790M binders, employing molecular docking, 100 ns molecular dynamics, MM-PBSA/MM-GBSA, per-residue decomposition, PCA/LDA, DFT at B3LYP-D3(BJ)/def2-TZVP, and comparative wild-type EGFR simulations. Five phytochemicals exhibited computationally predicted binding affinities against EGFR T790M exceeding the non-covalent binding component of osimertinib (−25.74 kcal/mol): corilagin (−53.71 ± 5.05 kcal/mol), eriodictyol-7-rhamnopyranoside (−44.35 ± 4.51 kcal/mol), isoquercetin (−44.23 ± 2.92 kcal/mol), rutin (−42.15 ± 4.50 kcal/mol), and kaempferol-4-rhamnoside (−41.68 ± 3.69 kcal/mol). Wild-type EGFR simulations (PDB 1M17) yielded a selectivity index (IS) of 2.76 for corilagin (

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Metainflammation, Mitochondrial Dysfunction, and Organokine Crosstalk: A Central Axis Linking Metabolic Syndrome to Cardiovascular Diseases

Metabolic Syndrome (MetS) is a complex and multifactorial condition characterized by insulin resistance, visceral obesity, dyslipidemia, hypertension, and chronic low-grade inflammation, all of which contribute to increased cardiovascular risk. Central to its pathophysiology is m…

Circadian Disruption Is Associated with Elevated Whole-Semen mtDNA Copy Number and Implicates CRY1 as a Candidate Regulator in Humans and Mice

Circadian disruption has been linked to impaired male fecundity, but its association with semen molecular phenotypes and circadian genes remains unclear. We analyzed 441 men from the Male Reproductive Health in Chongqing College Students cohort to assess whether social jetlag, an…

Machine Learning and Deep Learning Frameworks for Human–Virus Protein–Protein Interaction Prediction: Emerging Architectures, Methods, Benchmarks, and Challenges

The outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has emerged as one of the most significant global health crises in recent history. Coronaviruses are a diverse group of RNA viruses classified into alpha,…

Potential Molecular Associations Between Triphenyl Phosphate Exposure and Thyroid Cancer: Integration of Network Toxicology and Machine Learning for Core Target Identification with Molecular Docking

Triphenyl phosphate (TPhP) is a ubiquitous environmental contaminant and endocrine disruptor potentially associated with an increased risk of thyroid cancer (TC). However, whether TPhP directly contributes to TC remains unclear. This study integrated network toxicology and machin…

Integrative Network Toxicology, Machine Learning, Single-Cell Analysis, scTenifoldKnk-Based Virtual Knockout, and Molecular Docking Suggest a Potential Molecular Link Between Aspartame and Rheumatoid Arthritis Involving HLA-DRB1

Aspartame is a widely used artificial sweetener, but its possible relationship with rheumatoid arthritis (RA) remains insufficiently understood. This study aimed to explore, rather than prove, potential molecular links between aspartame-related targets and RA-associated gene netw…

Ensemble Machine Learning- and Deep Learning-Driven Identification and Validation of Sennidin B as a Novel Dipeptidyl Peptidase-4 Inhibitor

Dipeptidyl peptidase-4 (DPP-4) is a key therapeutic target for type 2 diabetes (T2D). Several synthetic anti-DPP-4 drugs are currently available for the treatment of T2D; however, the need for safe and effective therapies remains unmet due to the side effects associated with exis…