Salvato in:
| Autore principale: | |
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| Natura: | Recurso digital |
| Lingua: | inglese |
| Pubblicazione: |
Zenodo
2026
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| Soggetti: | |
| Accesso online: | https://doi.org/10.5281/zenodo.19397987 |
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Sommario:
- Geometric Deep Learning (GDL) has emerged as a revolutionary framework in computational structural biology, fundamentally changing how researchers analyze and design protein structures. Traditional deep learning struggles with non-Euclidean data, but GDL explicitly incorporates geometric priors such as symmetry, equivariance to 3D rotations and translations (E(3) and SE(3) groups), and multiscale representations, allowing it to process complex molecular graphs and 3D surfaces natively. This comprehensive guide details the core data representations used in GDL, contrasting grid-based, surface-based, and spatial graph models, and evaluates the shift from relying solely on experimental structures to incorporating highly accurate computational predictions from AlphaFold. The article delves into advanced GDL architectures, particularly Graph Neural Networks (GNNs) and equivariant diffusion models, highlighting their state-of-the-art performance across diverse applications. Key use cases include predicting protein-protein interactions (especially involving intrinsically disordered regions via models like SpatPPI), molecular docking (using tools like DiffDock and DeltaDock), and de novo protein design (exemplified by CARBonAra and MaSIF-neosurf). These tools are significantly accelerating structure-based drug discovery by enabling the creation of novel binders for complex therapeutic targets, including protein-ligand neosurfaces. Despite these remarkable successes, the field faces substantial challenges. The article critically examines issues such as data scarcity, the oversimplification of dynamic protein conformations by static models, and the black-box nature of deep learning. To overcome these hurdles, researchers are actively integrating GDL with Protein Language Models (PLMs) to leverage evolutionary data, employing molecular dynamics simulations to capture conformational flexibility, and utilizing Explainable AI (XAI) techniques to extract mechanistic biological insights. Furthermore, rigorous benchmarking frameworks like PoseBench are being adopted to ensure models generalize well to real-world, out-of-distribution scenarios. Ultimately, the convergence of GDL with high-throughput experimentation and generative AI is poised to become an indispensable engine for next-generation protein engineering and precision medicine. Source: https://www.proteineng.com/posts/geometric-deep-learning-for-protein-structures-a-new-paradigm-in-drug-discovery-and-protein-design