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Daily Signal January 04, 2026 · 8 min read

Issue #13: AlphaFold for Docking Screens.

Protein Design Digest - 2026-01-04 - AlphaFold for Docking Screens.

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Signal of the Day

AlphaFold for Docking Screens.

AlphaFold is an AI system developed by Google DeepMind to generate three-dimensional structures of proteins without experimental data. The models created with AlphaFold are available on the AlphaFold Protein Structure Database (AlphaFoldDB) ( https://alphafold.ebi.ac.uk/ ). The AlphaFold database is searchable by sequence and protein identification. This chapter focuses on an AlphaFold model and its use for docking screens using Molegro Virtual Docker. We rely on Jupyter Notebooks to integrate docking simulations and build regression models based on the atomic coordinates of protein-pose complexes. Our study focuses on constructing a neural network regression model to predict the inhibition of cyclin-dependent kinase 19 (CDK19). This enzyme is a target for anticancer drugs and does not have experimental data for its atomic coordinates. We utilize the Molegro Data Modeller to construct a regression model based on docking results of inhibitors for which binding affinity data is available. All CDK19 datasets and Jupyter Notebooks discussed in this work are available at GitHub: https://github.com/azevedolab/docking#readme .

Why this matters: Provides actionable mutations to enhance catalytic efficiency or thermostability.

Also Worth Reading

Geometric deep learning assists protein engineering. Opportunities and Challenges.

Protein engineering is experiencing a paradigmatic transformation through the integration of geometric deep learning (GDL) into computational design workflows. While traditional approaches such as rational design and directed evolution have achieved significant progress, they remain constrained by the vastness of sequence space and the cost of experimental validation. GDL overcomes these limitations by operating on non-Euclidean domains and by capturing the spatial, topological, and physicochemical features that govern protein function. This perspective provides a comprehensive and critical overview of GDL applications in stability prediction, functional annotation, molecular interaction modeling, and de novo protein design. It consolidates methodological principles, architectural diversity, and performance trends across representative studies, emphasizing how GDL enhances interpretability and generalization in protein science. Aimed at both computational method developers and experimental protein engineers, the review bridges algorithmic concepts with practical design considerations, offering guidance on data representation, model selection, and evaluation strategies. By integrating explainable artificial intelligence and structure-based validation within a unified conceptual framework, this work highlights how GDL can serve as a foundation for transparent, interpretable, and autonomous protein design. As GDL converges with generative modeling, molecular simulation, and high-throughput experimentation, it is poised to become a cornerstone technology for next-generation protein engineering and synthetic biology.

Modeling Protein-Protein Complexes by Combining pyDock and AlphaFold.

The lack of experimental structures for the majority of protein-protein complexes has motivated the development of a variety of strategies for the structural modeling of protein complexes, such as computational docking, in active development for the last decades, and the more recent artificial intelligence (AI)-based ground-breaking methodologies. Among the existing computational docking methods, Python docking (pyDock) has shown competitive predictive rates and high robustness over the years. However, the field has dramatically changed with the appearance of artificial intelligence (AI)-based methods, like AlphaFold. While structure prediction of individual proteins is virtually solved by this program, the focus is now on how to improve the prediction of challenging cases like antibody-antigen complexes, multiprotein complexes, weak interactions, or highly flexible interacting proteins. Successful strategies are based on the generation of more diverse sets of models and the integration with other “classical” approaches that facilitate the identification of the correct models. Here, we will show in practical terms how to combine the structural modeling capabilities of AlphaFold with the energy-based scoring function in pyDock to improve structural predictions in challenging protein-protein complexes.

Combining network pharmacology, machine learning, molecular docking, molecular simulation dynamics and experimental validation to explore the mechanism of Zhenwu decoction in treating major depression through TNF-α pathways.

Background Major depressive disorder (MDD) is a severe psychophysiological condition characterized by cognitive decline, low energy, weight loss, insomnia, and increased suicide risk, posing a significant burden on global health. Zhenwu decoction (ZWD), a traditional Chinese medicine, has shown therapeutic potential in alleviating MDD symptoms. However, its complex composition has limited the understanding of its underlying pharmacological mechanisms. This study aimed to explore the antidepressant mechanisms of ZWD in the treatment of MDD. Methods Active compounds and potential targets of ZWD were identified through database screening and network pharmacology analysis. These targets were intersected with MDD-related genes to construct a protein-protein interaction network. Core targets were further refined using random forest algorithms. Molecular docking and molecular dynamics simulations were employed to evaluate the binding affinity and stability between key compounds and core targets. Experimental validation was conducted in a lipopolysaccharide (LPS)-induced mice model of depression using behavioral testing, measurement of inflammatory cytokines, and gene expression analysis. Results Network pharmacology and machine learning identified TNF-α signaling as key pathways in the antidepressant effects of ZWD. Enrichment analysis highlighted the involvement of Lipid and atherosclerosis, the IL-17 signaling pathway. Core targets, including PPARG, F10, AR, TNF, PIK3CG, ADH1C, and GABRA6, were predicted to mediate its effects. Molecular docking and dynamics simulations confirmed strong binding of ZWD components, especially kaempferol, to TNF-α, inhibiting its expression. In vivo, ZWD improved anxiety/depressive-like behaviors in LPS-treated mice, evidenced by better performance in the behavioral tests. ZWD also reduced neuroinflammation, with decreased Tnf-α levels, and reduced IBA-1 and GFAP staining, indicating reduced microglial and astrocyte activation. These results suggest that ZWD alleviates depression through modulation of TNF-α-mediated inflammation. Conclusions These findings suggest that ZWD exerts antidepressant effects primarily by modulating TNF-α-mediated inflammatory pathways, providing a comprehensive molecular and experimental framework supporting its clinical potential in MDD treatment.

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Pipeline Tip

Pin reference genomes by checksum to avoid version drift.

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

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