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arXiv 2607.22143cs.LGcs.AI

TriGlue:一种用于生成分子胶水诱导三元复合物的生物启发式生成模型

TriGlue: a Biology-Inspired Generative Model for Generating Molecular Glue-Induced Ternary Complex

发表机构香港科技大学(广州) · 吉林大学 · 悉尼大学
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  • The Hong Kong University of Science and Technology (Guangzhou)(香港科技大学(广州))
  • Jilin University(吉林大学)
  • University of Sydney(悉尼大学)

机构由 AI 辅助整理,请以论文原文为准。

Yuliang Yan, Shuo Yan, Haochun Tang, Yiqin Sun, Enyan Dai

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中文总结 AI 辅助

研究针对分子胶水计算设计未充分探索的问题,提出生物启发式生成框架TriGlue,将三元复合物生成分解为界面估计和条件复合物生成两阶段,经实验验证其能生成有效分子和合理复合物,加速分子胶水发现。

中文摘要 AI 辅助

分子胶水降解剂作为一种有前景的靶向蛋白质降解策略,通过诱导E3泛素连接酶和靶蛋白之间形成三元复合物发挥作用。尽管具有治疗潜力,但分子胶水的计算设计仍未得到充分探索。与传统基于结构的药物设计不同,分子胶水设计受未知蛋白质-蛋白质界面支配,需要同时对配体生成、蛋白质-蛋白质对接和三元复合物组装进行建模。在这项工作中,我们将分子胶水设计表述为三元复合物生成问题,并提出了一个生物启发式生成框架TriGlue。受分子胶水作用机制的启发,我们将三元复合物生成分解为两个耦合阶段:界面估计和界面条件复合物生成。首先,我们开发了一个SE(3)等变界面估计模块,从未结合的单体结构预测几何约束的蛋白质-蛋白质界面。其次,我们引入了一个界面条件三元流匹配网络,联合生成分子胶水并预测组装三元复合物所需的刚体变换。广泛的实验表明TriGlue生成化学上有效的分子并产生合理的三元复合物,突出了生物启发式生成建模在加速分子胶水发现方面的潜力。我们的代码可在该https网址获取。

英文摘要

Molecular glue degraders have emerged as a promising strategy for targeted protein degradation by inducing ternary complex formation between an E3 ubiquitin ligase and a target protein. Despite their therapeutic potential, computational design of molecular glues remains largely unexplored. Unlike conventional structure-based drug design, molecular glue design is governed by the unknown protein-protein interface and requires the simultaneous modeling of ligand generation, protein-protein docking, and ternary complex assembly. In this work, we formulate molecular glue design as a ternary complex generation problem and propose a biology-inspired generative framework, TriGlue. Motivated by the mechanism of molecular glue action, we decompose ternary complex generation into two coupled stages: interface estimation and interface-conditioned complex generation. First, we develop an SE(3)-equivariant interface estimation module that predicts a geometrically constrained protein-protein interface from unbound monomer structures. Second, we introduce an interface-conditioned ternary flow matching network that jointly generates the molecular glue and predicts the rigid-body transformation required to assemble the ternary complex. Extensive experiments demonstrate that TriGlue generates chemically valid molecules and produces plausible ternary complexes, which highlight the potential of biology-inspired generative modeling for accelerating molecular glue discovery. Our code is available at https://github.com/yuliangyan0807/molecular-glue-design.

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