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An Integrative Computational Biophysical Workflow for Molecular Glue Discovery
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AI
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DOI：10.1142/S2737416526500547.png)
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Abstract
En 中文
Targeted protein degradation (TPD), including PROTACs and molecular glues (MG), offers a path to modulate previously undruggable targets, but the multicomponent nature of MG systems complicates binding affinity evaluation, especially without crystal structures. We present a workflow integrating AI-assisted prediction with physics-driven free energy calculations. Across 28 crystal structures, AI-assisted structure prediction models perform well in reconstructing quaternary complexes, despite the fact that all tested entries are deposited after the proposal of the AI tool (i.e., not in the training set). For free energy estimation, instead of using individual scoring regimes, we propose a combined ASGBIE + Vinardo strategy that significantly outperforms either method alone. The proposed approach enables the estimation of the bioactivity of MG from only sequence and ligand input, enabling a practical virtual screening. Further, energy decomposition enables the elucidation of protein-MG interaction mechanisms and highlights pi-cation and pi-methyl interactions as key contributors to MG-induced cooperativity. This integrated workflow provides mechanistic insight and practical guidance for MG design, supporting in silico discovery when experimental structures are unavailable.
Keywords:
Targeted protein degradation
molecular glue
DDB1-CDK12-Cyclin K
AI-assisted modeling
free energy calculation
Journal
J
IF:
2.3
Papers:
98
Citations:
0
Organization
Cited Papers