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Daily Signal January 20, 2026 · 9 min read

Issue #26: MetalloDock: Decoding Metalloprotein-Ligand Interactions via Physics-Aware Deep Learning for Metalloprotein Drug Discovery.

Protein Design Digest - 2026-01-20 - MetalloDock: Decoding Metalloprotein-Ligand Interactions via Physics-Aware Deep Learning for Metalloprotein Drug Discovery.

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Signal of the Day

MetalloDock: Decoding Metalloprotein-Ligand Interactions via Physics-Aware Deep Learning for Metalloprotein Drug Discovery.

Accurate prediction of metalloprotein-ligand interactions is critical for metalloprotein-targeted drug discovery. Conventional docking tools and existing deep learning (DL) models fail to reliably capture metal-ligand interactions, hampering the discovery of potent metalloprotein inhibitors. Here, we propose MetalloDock, the first DL-based docking framework specially designed for metalloprotein targets. By innovatively integrating an autoregressive spatial decoding engine with a physics-constrained geometric generation paradigm, MetalloDock can precisely reconstruct metal coordination geometries and accurately capture metal-ligand interactions, which enhance both the accuracy of metalloprotein-ligand docking and binding affinity prediction. Extensive evaluations on our custom-built benchmark data set demonstrate that MetalloDock outperforms existing methods, including AlphaFold3, in docking success rate and virtual screening performance for metalloprotein targets. In real-world applications, MetalloDock successfully identified multiple novel hit compounds in a virtual screening campaign targeting the prostate-specific membrane antigen. Additionally, it enabled rational drug design for acidic polymerase endonuclease, leading to the discovery of potent inhibitors. These results highlight the broad applicability of MetalloDock in accelerating metalloprotein-targeted drug discovery and provide a standardized framework for future evaluation of metalloprotein-specific docking algorithms.

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

Also Worth Reading

In silico screening of IMPPAT-derived phytochemicals targeting ERG6 and drug resistance-associated proteins in drug-resistant Candida albicans: virtual screening and molecular dynamics using alphafold models.

Pathogenic fungi, particularly Candida albicans, have been escalating clinical problems, notably because of antifungal resistance and symptomatological comorbidity with COVID-19. This research aimed to find phytochemical inhibitors of ergosterol production, specifically targeting ERG6 (C-24 sterol methyltransferase), utilizing chemicals from the IMPPAT database. A total of 14,965 phytochemicals were computationally evaluated against AlphaFold-predicted ERG6 utilizing AutoDock Vina. Fifteen compounds exhibiting robust binding affinities (- 8.2 to - 9.2 kcal/mol) were found, from which four candidates were chosen based on advantageous ADMET profiles. The docking scores for the top four compounds targeting ERG6-Daturataturin A (- 8.8 kcal/mol), Disogluside (- 8.6), Tataramide B (- 8.4), and Floribundasaponin A (- 8.4)-exceeded those of previously identified ERG6 inhibitors D28 (- 8.0), Tomatidine (- 7.9), and H55 (- 6.4). The selected leads were further docked against other proteins associated with drug resistance and cell proliferation, specifically ERG1, ERG11, CLB2, CDR1, and CDR2. Among these, only ERG1 exhibited significant interactions, with Disogluside (- 9.3 kcal/mol), Tataramide B (- 9.9), and Floribundasaponin A (- 9.3) surpassing the reference inhibitor terbinafine (- 8.7 kcal/mol), except for Daturataturin A, which showed a comparable score of - 8.6 kcal/mol. Nevertheless, owing to steric conflicts inside the ERG1 binding sites, molecular dynamics (MD) simulations were conducted exclusively for ERG6-ligand complexes over duration of 100 ns. The RMSD values demonstrated commendable structural stability: Daturataturin A (~ 0.39 nm), Disogluside (~ 0.38 nm), Tataramide B (~ 0.27 nm), and Floribundasaponin A (~ 0.40 nm). Principal Component Analysis (PCA) validated consistent and significant movements for Daturataturin A and Floribundasaponin A, whereas Disogluside and Tataramide B exhibited increased flexibility. MM/PBSA analysis indicated robust binding free energies for Daturataturin A (- 42.26 kcal/mol), Floribundasaponin A (- 37.48 kcal/mol), and Disogluside (- 29.58 kcal/mol), however Tataramide B exhibited a detrimental + 9.81 kcal/mol. These results endorse the promise of phytochemical-derived antifungals and necessitate more experimental verification. The online version contains supplementary material available at 10.1007/s40203-025-00480-9.

Mechanistic Investigation of Astragalus Root in the Management of T2DM-NAFLD Comorbidity: An Integrated Network Pharmacology, Molecular Docking, Molecular Dynamics Simulation, and In Vitro Study

Background: /Objectives: Astragalus root is a classical qi-tonifying traditional Chinese medicine that has demonstrated potential therapeutic efficacy in T2DM and NAFLD. However, the precise mechanisms underlying its effects on the comorbidity of these two disorders remain unclear. This study investigated the molecular mechanisms by which astragalus root ameliorated T2DM-NAFLD comorbidity. Methods: Network pharmacology, molecular docking, molecular dynamics simulation, and in vitro experiments were employed to elucidate the potential roles and mechanisms of astragalus root in the management of T2DM-NAFLD comorbidity. Results: A total of 25 bioactive constituents and 152 corresponding targets associated with astragalus root were identified. PPI network analysis revealed the top ten core candidate targets, among which six possessed suitable crystal structures for molecular docking, including IL-6, AKT1, JUN, TNF, CASP3, and ESR1. KEGG analysis further identified the PI3K-AKT as the most significantly en-riched pathway. Molecular docking of the principal bioactive constituent formononetin from astragalus root with the six core targets was conducted using AutoDock4 software. Molecular dynamics simulations verified the stability of the interactions between for-mononetin and each of the six core target proteins. In vitro experiments demonstrated that formononetin obviously decreased lipid droplet accumulation, downregulated TC and TG levels, suppressed the expression of TNF-α, IL-6, and IL-1β, decreased ROS and MDA levels, and enhanced GSH content and SOD activity. These therapeutical effects were achieved through inhibition of protein expression within the PI3K/AKT/mTOR signaling pathway. Conclusions: This study determined the potential therapeutic targets and underlying mechanisms of formononetin derived from astragalus root in the T2DM-NAFLD management, thereby providing a scientific basis for its clinical application.

Exploring the mechanism of Kemofang in treating idiopathic membranous nephropathy based on LC-MS/MS combined with network pharmacology, molecular docking, and molecular dynamics simulation.

Idiopathic membranous nephropathy (IMN), an autoimmune glomerular disease, arises from in situ immune complex deposition in the glomerular subepithelial spaces, triggering complement activation and podocyte injury. Although the Kemo Formula shows therapeutic potential for IMN, its mechanisms remain unclear. This study employed LC-MS/MS, network pharmacology, molecular docking, and dynamic simulations to elucidate the mechanism of action. LC-MS/MS and the TCMSP database identified 83 bioactive components from 267 chemicals detected in the Kemo Formula. Using PubChem, Swiss Target Prediction, and GeneCards, 827 drug targets and 2581 IMN-related targets were screened, yielding 336 overlapping targets linked to 81 components. Network analysis prioritized 15 key components ( baicalein and quercetin) and 36 core targets (TP53, IL6, and AKT1). Functional enrichment revealed involvement in hormone response, MAPK cascade regulation, and kinase binding with pathways including lipid metabolism, PI3K/Akt, and MAPK signaling. Molecular docking indicated strong binding affinities between the active components and targets, while dynamic simulations predicted the stability of the galangin-AKT1 complex. The Kemo Formula likely mitigates IMN by multi-target modulation, ameliorating lipid dysregulation, suppressing podocyte apoptosis, and attenuating immune-inflammatory and oxidative stress via PI3K/Akt and MAPK pathways. This integrative approach highlights its multicomponent, multitarget therapeutic strategy against IMN, providing a foundation for further mechanistic and clinical exploration.

Research & AI Updates

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Arugula-derived isothiocyanates as novel agents for a potential regulator of AKR1B10 protein in breast cancer: an integrated transcriptomic and molecular docking approach.

Breast cancer (BC) is a leading cause of cancer-related mortality, with estrogen receptor (ER)-negative subtypes, especially triple-negative BC, comprising one-fifth of global cases. Read more →

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