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An Iterative Refinement Model for Protac Induced Structure Prediction

AIhub 12:24 am on June 3, 2024

Alphafold-Multimer, PROsettaC, and PROTACability are evaluated for predicting E3-POI structure using bound (holo) or unbound (apo) protein conformations. ProFLOW outperforms others in speed and accuracy, demonstrating a statistically significant correlation between solvent-accessible area buried at the interface (dSASA) and DC50, implying larger interfaces correlate with potency degraders. This indicates PROFLOW's potential for facilitating PROTAC design understanding and rationalization.

Methodology: Alphafold-Multimer, PROsettaC, and PROTACability assessed E3-POI structure prediction.
ProFLOW Performance: Exceeds competitors in speed (44 sec), accuracy, and runs significant correlation between dSASA and DC50.
Correlation Insight: Large interfaces correlate with high potency degraders; implying importance for PROTAC design.
Impact: ProFLOW enhances understanding of PROTAC design and rationalization processes.
Acknowledgments & Support: Credit to Basis research, PhD candidates at MIT/MITDTU, Tsinghua University.
https://aihub.org/2024/05/31/an-iterative-refinement-model-for-protac-induced-structure-prediction/
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An Iterative Refinement Model for Protac Induced Structure Prediction

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