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Daily Signal April 30, 2026 · 9 min read

Issue #98: The Influence of Ligands on AlphaFold3 Prediction of Cryptic Pockets

Protein Design Digest #98: Identification of paucinervin D as a natural sphingosine-1-phosphate rec…

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The Influence of Ligands on AlphaFold3 Prediction of Cryptic Pockets

Cryptic pockets are binding sites that are formed or exposed upon a conformational change. They represent an important class of potentially druggable binding sites. Reliably predicting cryptic pockets capable of binding ligands, however, remains a challenge. Herein we examine the use of AlphaFold 3 (AF3) for generating realistic conformational ensembles that include known cryptic pockets. We find that AF3 is generally able to reproduce the scale of conformational change required for cryptic site formation. When given a cryptic-site ligand for the protein, AF3 predominantly predicts conformations competent to bind the ligand in the cryptic site; without the ligand, conformations lacking the cryptic pocket generally dominate. While the results may reflect a bias toward memorized structural priors, the level of detrimental memorization appears to be limited. We also show that the choice of the ligand can significantly impact the predictions, and that AF3 is able to produce models with the ligand correctly positioned. Variability in ligand position, however, suggests that generating ensembles of co-folded predictions is critical to enhancing the likelihood of obtaining a correct binding mode. Overall, AF3-generated protein-ligand structural ensembles have potential utility in cryptic-site drug discovery, and they can reveal ligands likely to bind to those sites.

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Molecular docking approaches in mycetoma: Toward improved patient management.

Mycetoma is a neglected tropical disease characterised by chronic, granulomatous inflammation of the subcutaneous tissues, often leading to disfigurement, disability, and significant socioeconomic burdens. Caused by a diverse array of bacterial and fungal pathogens, eumycetoma is predominantly driven by Madurella mycetomatis, and current treatment strategies are limited and often ineffective. Conventional antifungal therapies, such as itraconazole, require prolonged administration, frequently combined with surgical interventions, yet cure rates remain suboptimal, and recurrence is common. The formidable protective grain, comprising microbial material, melanin, and host-derived substances, acts as a physical and biochemical barrier, impeding the penetration and efficacy of drugs. Additionally, issues such as toxicity, resistance, and high costs further complicate management, underscoring the urgent need for novel therapeutic strategies. Recent advancements in computational drug discovery, particularly molecular docking, offer promising avenues to accelerate the identification of effective anti-mycetoma agents. Molecular docking simulates the interaction between small molecules and target proteins, enabling rapid virtual screening of large compound libraries, including natural products, existing drugs, and synthetic molecules, against key pathogenic targets. This structure-based approach helps prioritise candidates with high binding affinity, guiding subsequent experimental validation and reducing both time and financial costs associated with traditional drug development. When integrated with artificial intelligence (AI) and machine learning (ML), these methods can enhance predictive accuracy, uncover novel bioactive scaffolds, and facilitate the repurposing of FDA-approved drugs such as montelukast and vilanterol. Key molecular targets in M. mycetomatis include enzymes and pathways critical for pathogen survival and virulence, notably cytochrome P450 (CYP51), dihydrofolate reductase (DHFR), chitin synthase, melanin biosynthesis pathways, and metal ion acquisition systems. Melanin production, via DHN-melanin, DOPA-melanin, and pyomelanin pathways, contributes to grain pigmentation and structural integrity, while metal ions such as iron and zinc are vital for enzymatic activities, grain formation, and fungal virulence. Disrupting metal ion homeostasis through targeting zincophores, siderophores, and zinc-binding proteins represents a promising therapeutic strategy to weaken grain robustness and enhance drug penetration. Despite the potential of molecular docking, limitations such as reliance on homology models, static protein structures, and the absence of cellular context necessitate complementary approaches, including molecular dynamics simulations and in vitro validation. These combined efforts can refine candidate compounds, optimise binding affinities, and predict pharmacokinetic properties. Furthermore, integrating docking results with clinical data and global collaboration platforms can accelerate the discovery of affordable, effective treatments tailored to endemic regions. In conclusion, leveraging molecular docking and computational methods to target essential M. mycetomatis pathways offers a promising frontier in mycetoma research. By identifying novel inhibitors and understanding pathogen biology at a molecular level, these approaches can inform targeted therapies, reduce treatment durations, and improve patient outcomes. Future research should focus on validating computational predictions experimentally and translating these findings into clinical practice, with an emphasis on accessible, cost-effective interventions for vulnerable populations affected by this neglected disease.

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Diethyl phthalate (DEP), a common plasticiser and endocrine disruptor, has been linked to cancer, but its role in osteosarcoma (OS) remains unclear. This study integrated network toxicology, transcriptomics, protein-protein interaction (PPI) analysis, machine learning, molecular docking, molecular dynamics (MD), single-cell RNA sequencing (scRNA-seq), and external validation to investigate DEP-related mechanisms in OS. We identified 45 DEP-responsive genes enriched in extracellular matrix-related pathways. PPI network analysis revealed 11 hub genes, of which LASSO, SVM-RFE, and Boruta algorithms consistently prioritised P4HA2, COL18A1, and COL10A1. Docking and MD simulations supported stable binding of DEP to P4HA2 and COL18A1 via hydrogen bonds and hydrophobic interactions. scRNA-seq demonstrated celltype-specific expression of these genes. Validation cohorts confirmed their upregulation in OS, with AUC values up to 0.950. These findings suggest that DEP may promote OS progression by targeting extracellular matrix remodelling, offering new diagnostic biomarkers and hypothesis-generating evidence for environmental osteocarcinogenesis.

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