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Benchmarking Deep Learning for PROTAC Ternary Complex Prediction
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DOI：10.1002/prot.70117.png)
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Abstract
En 中文
Proteolysis Targeting Chimeras (PROTACs) represent a transformative approach to drug development by leveraging the intracellular ubiquitin-proteasome system (UPS) for the selective degradation of target proteins. A PROTAC molecule comprises three essential components: a ligand that binds to the E3 ubiquitin ligase, a ligand that targets the protein of interest, and a flexible linker that connects the two. This distinctive structure enables the PROTAC to simultaneously engage with both the target protein and the E3 ligase, facilitating their interaction. Such proximity initiates the ubiquitination of the target protein, marking it for recognition and subsequent degradation. In this study, we benchmark ternary complexes based on PROTACs using four recently employed predictive tools: Chai-1, AlphaFold2, AlphaFold3, and Protenix. Comparative analysis indicated that the ternary complexes predicted by the four prediction tools demonstrated satisfactory accuracy (Cα-RMSD < 10 Å). Among the evaluated tools, three—Chai-1, AlphaFold3, and Protenix—demonstrated superior performance in over half of the tests, while AlphaFold2 exhibited comparatively lower performance. However, significant challenges remained in accurately predicting the orientation of POI and the E3 ligase (Cα-RMSD < 10 Å when POI or E3 ligase were used as reference), as well as the position of the small molecule PROTAC (RMSD < 5 Å). By benchmarking these tools, we underscore recent advancements in protein structure prediction, enhance our understanding of the mechanisms underpinning PROTAC complexes, and provide a valuable reference for evaluating the binding conformations of other ternary complexes, as well as for the development of future predictive tools.
Keywords:
AlphaFold3
deep learning
PROTAC
structure prediction benchmarking
ternary complex prediction
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