Pereira Gilberto P, Gouzien Corentin, Souza Paulo C T, Martin Juliette
Laboratoire de Biologie et Modelisation de la Cellule, Ecole Normale Superieure de Lyon, CNRS, UMR 5239, Universite Claude Bernard Lyon 1, Inserm, U1293, Lyon F-69364, France.
Centre Blaise Pascal de Simulation et de Modelisation Numerique, Ecole Normale Superieure de Lyon, Lyon 69364, France.
Bioinform Adv. 2025 Mar 14;5(1):vbaf056. doi: 10.1093/bioadv/vbaf056. eCollection 2025.
Proteolysis Targeting Chimeras (PROTACs) are heterobifunctional molecules composed by ligands binding to a target protein and a E3-ligase complex, connected by a linker, that induce proximity-based target protein degradation. PROTACs are promising alternatives to conventional drugs against cancer. Predicting PROTAC-mediated complexes is often the first step for PROTAC design pipelines. We previously noted that AlphaFold2 (AF2) fails to predict PROTAC-mediated complexes.
Here, we investigate the potential causes of this limitation. We consider a set of 326 protein heterodimers orthogonal to the AF2 training set, and evaluate AF2 models focusing on the interface size and presence of interface ligand. Our results show that AF2-multimer predictions are sensitive to the size of the interface to predict even in the absence of ligands, with the majority of models being incorrect for the smallest interfaces. We also benchmark both AF2 and AF3 on a set of 28 PROTAC-mediated dimers and show that AF3 does not significantly improve upon the accuracy of AF2. The low accuracy of AF2 on complexes with small interfaces has strong implications for computational pipelines for PROTAC design, as these stabilize typically small interfaces, and more generally on any prediction task that involves small interfaces.
All the models analyzed in this article are available in the Zenodo archive https://zenodo.org/records/14810843.
蛋白酶靶向嵌合体(PROTACs)是一种异双功能分子,由与靶蛋白结合的配体和E3连接酶复合物组成,通过连接子相连,可诱导基于邻近性的靶蛋白降解。PROTACs是对抗癌症的传统药物的有前景的替代物。预测PROTAC介导的复合物通常是PROTAC设计流程的第一步。我们之前注意到AlphaFold2(AF2)无法预测PROTAC介导的复合物。
在此,我们研究了这一局限性的潜在原因。我们考虑了一组与AF2训练集正交的326种蛋白质异二聚体,并评估了关注界面大小和界面配体存在情况的AF2模型。我们的结果表明,即使在没有配体的情况下,AF2多聚体预测对要预测的界面大小也很敏感,大多数模型对于最小的界面预测错误。我们还在一组28种PROTAC介导的二聚体上对AF2和AF3进行了基准测试,结果表明AF3在预测准确性上并没有比AF2有显著提高。AF2在具有小界面的复合物上的低准确性对PROTAC设计的计算流程有很大影响,因为这些流程通常会稳定小界面,更普遍地说,对任何涉及小界面的预测任务都有影响。
本文分析的所有模型都可在Zenodo存档库https://zenodo.org/records/14810843中获取。