Predicting PROTAC-mediated ternary complexes with AlphaFold3 and Boltz-1

Abstract

Accurate prediction of protein–ligand and protein–protein interactions is essential for computational drug discovery, yet remains a significant challenge, particularly for complexes involving large, flexible ligands. In this study, we assess the capabilities of AlphaFold 3 (AF3) and Boltz-1 for modeling ligand–mediated ternary complexes, focusing on proteolysis-targeting chimeras (PROTACs). PROTACs facilitate targeted protein degradation by recruiting an E3 ubiquitin ligase to a protein of interest, offering a promising therapeutic strategy for previously undruggable intracellular targets. However, their size, flexibility, and cooperative binding requirements pose significant challenges for computational modeling. To address this, we systematically evaluated AF3 and Boltz-1 on 62 PROTAC complexes from the Protein Data Bank. Both models achieve high structural accuracy by integrating ligand input during inference, as measured by RMSD, pTM, and DockQ scores, even for post-2021 structures absent from AF3 and Boltz-1 training data. AF3 demonstrates superior ligand positioning, producing 33 ternary complexes with RMSD < 1 Å and 46 with RMSD < 4 Å, compared to Boltz-1's 25 and 40, respectively. We explore different input strategies by comparing molecular string representations and explicit ligand atom positions, finding that the latter yields more accurate ligand placement and predictions. By analyzing the relationships between ligand positioning, protein–ligand interactions, and structural accuracy metrics, we provide insights into key factors influencing AF3's and Boltz-1's performance in modeling PROTAC–mediated binary and ternary complexes. To ensure reproducibility, we publicly release our pipeline and results via a GitHub repository and website (https://protacfold.xyz), providing a framework for future PROTAC structure prediction studies.