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Daily Signal March 23, 2026 · 9 min read

Issue #73: AlphaFold3: A Transformer in Life Sciences.

Protein Design Digest - 2026-03-23 - Enhancing CYP450-Ligand Binding Predictions: A Comparative Analysis of Ligand-Based and Hybrid Machine Learning Models.

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Signal of the Day

AlphaFold3: A Transformer in Life Sciences.

The development of AlphaFold2 (AF2) marked a revolutionary milestone in the field of life sciences, such as structural and computational biology, offering highly accurate atomic-level predictions of individual protein structures using deep learning techniques. Its unprecedented performance has transformed structural biology by providing insights that were previously dependent on time-consuming experimental methods. However, despite its success, AF2 has notable limitations. It struggles with accurately modeling protein-protein interactions and fails to reliably predict the presence and positioning of non-protein components, such as nucleic acids, metal ions, ligands, and posttranslational modifications, which are critical for understanding full biological functionality. In response to these shortcomings, AlphaFold3 (AF3) has emerged as a more comprehensive solution by integrating sequence, structural, and chemical context to predict a broader range of biomolecular structures and their interactions. However, AF3 is not without limitations. It still struggles with intrinsically disordered regions, low-homology sequences, and RNA structures, particularly long or unvalidated ones. Moreover, antibody- antigen docking and flexible binding site modeling remain challenging. Addressing these gaps may require hybrid approaches that combine AF3 with experimental data, molecular dynamics simulations, or network-based models. This review explores the technical innovations underlying AF3, evaluates its current performance across different biological contexts, and presents its transformative potential in fields, such as antibodies and vaccine development for infectious diseases, cancer, and other diseases, as well as basic biological research. Finally, we highlight the remaining challenges and propose future research directions to further improve the prediction of protein complexes and other biomolecular structures.

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Also Worth Reading

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.

Mechanisms of Okanin against wound healing based on network pharmacology, molecular docking and molecular dynamics simulation.

Wound healing is a critical aspect of modern medicine, impacting patient health, quality of life, and healthcare resource allocation. Okanin, a flavonoid from the Asteraceae family, has shown potential in promoting wound healing. This study investigates okanin’s key molecular targets, binding affinity, and mechanisms of action using network pharmacology, molecular docking, molecular dynamics simulations, and in vivo experimental validation. Okanin’s potential targets were identified using the Comparative Toxicogenomics Database (CTD) and SwissTargetPrediction, while wound healing-related targets were sourced from GeneCards and DrugBank. Overlap analysis of these datasets revealed common targets. Key target proteins were filtered through protein-protein interaction (PPI) analysis using the STRING database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using Metascape to build a drug-target-pathway-disease network. Molecular docking was performed with AutoDockTools, and binding affinity was evaluated through energy scores, particularly with AURKA and HDAC1. Molecular dynamics simulations with GROMACS confirmed the stability of okanin-target complexes. ADME/T properties were assessed using SwissADME and ProTox-3.0 to evaluate pharmacokinetics and toxicity. In vivo quantitative real-time PCR (qRT-PCR) was performed to assess the expression of selected target genes in a mouse wound model following topical okanin treatment. A total of 72 common targets were identified between okanin and wound healing. PPI network analysis highlighted 17 key targets, with molecular docking revealing the highest binding affinity for AURKA and HDAC1 (ΔG = - 8.8 kcal/mol for both). GROMACS were then run on the top complexes. Target-ligand stability was quantified by convergence of RMSD/Rg, sustained hydrogen-bond counts, and MM/GBSA binding free energies (AURKA, - 24.27 ± 3.65 kcal/mol; HDAC1, - 47.7 ± 1.60 kcal/mol), confirming robust interactions. SwissADME predicted good drug-likeness (MW = 288.25 g/mol; logP = 1.69; high GI and moderate skin permeability) and no P-gp liability, while ProTox-3.0 indicated low systemic toxicity (LD₅₀ = 2500 mg/kg). qRT-PCR results demonstrated that okanin treatment significantly downregulated AURKA and PIK3R1, while upregulating HDAC1, in wounded skin, supporting the predicted molecular interactions and regulatory functions. Okanin promotes wound healing through multiple molecular targets and pathways, including antioxidant, anti-inflammatory, and cell proliferation mechanisms. Its high binding affinity for AURKA and HDAC1, along with modulation of the IL-17 and AMPK signaling pathways, underscores its therapeutic potential. This study provides a comprehensive theoretical and experimental framework for the development of okanin as a topical agent for wound healing, with future research focusing on formulation development and translational applications.

In silico prediction, molecular docking and simulation of natural flavonoid apigenin and xanthoangelol E against human metapneumovirus.

Human metapneumovirus (hMPV) is one of the potential pandemic pathogens, and it is a concern for elderly subjects and immunocompromised patients. There is no vaccine or specific antiviral available for hMPV. We conducted an in-silico study to predict initial antiviral candidates against human metapneumovirus. Our methodology included protein modeling, stability assessment, molecular docking, molecular simulation, analysis of non-covalent interactions, bioavailability, carcinogenicity, and pharmacokinetic profiling. We pinpointed four plant-derived bio-compounds as antiviral candidates. Among the compounds, apigenin showed the highest binding affinity, with values of - 8.0 kcal/mol for the hMPV-F protein and - 7.6 kcal/mol for the hMPV-N protein. Molecular dynamic simulations and further analyses confirmed that the protein-ligand docked complexes exhibited acceptable stability compared to two standard antiviral drugs. Additionally, these four compounds yielded satisfactory outcomes in bioavailability, drug-likeness, and ADME-Tox (absorption, distribution, metabolism, excretion, and toxicity) and STopTox analyses. This study highlights the potential of apigenin and xanthoangelol E as an initial antiviral candidate, underscoring the necessity for wet-lab evaluation, preclinical and clinical trials against human metapneumovirus infection. Supplementary information The online version contains supplementary material available at 10.1007/s40203-025-00539-7.

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AlphaFold3: A Transformer in Life Sciences.

The development of AlphaFold2 (AF2) marked a revolutionary milestone in the field of life sciences, such as structural and computational biology, offering highly accurate atomic-level predictions of individual protein structures using deep learning techniques. Read more →

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Pipeline Tip

Check for missing residues in PDB files using PDB-Fixer before simulation.

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

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