Recep Adiyaman
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Weekly Digest: Jan 05 - Jan 09, 2026

January 09, 2026 Daily Intelligence
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šŸ§¬ Protein Design Digest

Curated protein signals by Recep Adiyaman

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šŸ§¬ Weekly Recap

Jan 05 - Jan 09, 2026

Missed a day? Here are the top research signals and tools from Monday to Friday, summarized in one place.

šŸ† Top Signals of the Week

šŸ—“ļø Friday, Jan 09

Discovery of PPARĪ³ Partial Agonists for Treatment of Type 2 Diabetes Based on an Integrated Virtual Screening Strategy that Combines Fragment Molecular Orbital Calculations, Machine Learning, Molecular Docking, Interaction Fingerprint Filtering, and Molecular Dynamics Simulations.

šŸ§¬ Abstract

Peroxisome proliferator-activated receptor Ī³ (PPARĪ³) is a key therapeutic target for type 2 diabetes and cardiovascular diseases due to its central role in regulating glucose and lipid metabolism. While full PPARĪ³ agonists exhibit efficacy, they are linked to adverse effects; in contrast, PPARĪ³ partial agonists retain metabolic regulatory functions with improved safety, representing promising candidates for type 2 diabetes treatment. However, their action mechanisms and structure-activity relationships remain unclear. Herein, we developed an integrated virtual screening strategy combining fragment molecular orbital (FMO) calculations, machine learning, molecular docking, interaction fingerprint (IFP) filtering, and molecular dynamics (MD) simulations to identify potential PPARĪ³ partial agonists and elucidate their interaction mechanisms. FMO analysis first confirmed interaction differences between PPARĪ³ agonist classes at the binding pocket, pinpointing critical residues (CYS285, ARG288, ILE341, and SER342) for partial agonist activity. Using three machine learning algorithms (random forest, extra trees, and XGBoost) with extended connectivity fingerprints (ECFP), we constructed QSAR classification models and screened 9630 compounds. SHAP analysis highlighted key fingerprint fragments (positions 45, 1034, and 1243) governing bioactivity. Molecular docking and IFP refinement yielded six high-potency candidates, whose binding stability and partial agonist properties were validated via MD simulations, MM/PBSA binding free energy calculations, hydrogen bond analysis, and FMO calculations. Notably, these candidates did not directly interact with the AF2 domain, consistent with the canonical partial agonist mode of action. This multidisciplinary approach provides a framework for rational design of novel PPARĪ³ partial agonists, and the identified molecules serve as promising leads for type 2 diabetes therapeutics.

Why it matters: Expands the searchable sequence space for novel folds and high-affinity binders.

āš” Selected Quick Reads

  • Exploring the Anti-Inflammatory Molecular Mechanism of Gentiana szechenyii Kanitz. Based on UPLC-MS/MS Combined With Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation.: This study explored the anti-inflammatory mechanisms of Gentiana szechenyii Kanitz. (GS), a Tibetan medicinal herb, by combining UPLC-MS/MS, network pharmacology, molecular docking, and molecular dynamics (MD) simulation. Using the lipopolysaccharide (LPS)-induced RAW264.7 cell inflammation model, the anti-inflammatory effect of GS was confirmed by detecting the release amount of nitric oxide (NO) and the levels of inflammatory factors tumor necrosis factor (TNF) and interleukin-6 (IL-6). UPLC-MS/MS identified 40 constituents, whereas network analysis predicted 5 core compounds (isovitexin 4’,7-diglucoside, loganin, isoorientin-2ā€³-O-glucoside, gentiopicroside, sweroside), 5 key targets (TNF, IL-6, GAPDH, epidermal growth factor receptor [EGFR], HSP90AA1), and three critical pathways (PI3K-Akt, hypoxia inducible factor-1 [HIF-1], IL-17). Molecular docking showed strong binding between core compounds and targets; the binding energies were all lower than -5 kcal mol -1 , among which isovitexin 4’,7-diglucoside had the lowest binding energy to EGFR (-9.4 kcal mol -1 ). MD simulation confirmed stable binding of TNF with the five core compounds. This study comprehensively clarifies the pharmacodynamic material basis and mechanism of action of GS in anti-inflammation, providing an experimental basis for further development and utilization. It is expected to be applied to the adjuvant treatment of inflammation-related diseases such as chronic bronchitis and pharyngitis in the future, thereby promoting the modernization of Tibetan medicine.

šŸ› ļø Tools & Datasets

  • šŸ›  Tool: Chai-1 - Multi-modal foundation model for molecular structure prediction.
  • šŸ›  Tool: Boltz-1 - Open-source biomolecular structure prediction model.
  • šŸ’¾ Dataset: SCOPe - Curated structural classification of proteins for fold analysis.
  • šŸ’¾ Dataset: Pfam - Protein families database with curated multiple sequence alignments.

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