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SaLT&PepPr是一个界面预测语言模型,用于设计导蛋白质降解剂

Garyk Brixi¹, Tianzheng Ye², Lauren Hong¹

¹Department of Biomedical Engineering, Duke University, Durham, NC, USA.

Communications biology

|October 24, 2023

概括

使用基于序列的AI模型预测蛋白质-蛋白质相互作用部位,可以创建定制. 这些工程可以向和降解特定的蛋白质,为癌症等疾病提供新的治疗策略.

科学领域:

生物化学生化学
计算生物学计算生物学
分子生物学分子生物学

背景情况:

蛋白与蛋白相互作用 (PPI) 是细胞功能的基础.
预测PPI部位对于理解生物机制和开发治疗方法至关重要.
当前的方法通常依赖于结构信息,限制了它们的适用性.

研究的目的:

开发一种新的计算管道,仅使用蛋白质序列来预测PPI位点.
从预测的PPI位点生成性结合基因,用于治疗应用.
为了证明基于序列的PPI预测对可编程蛋白质向的潜力.

主要方法:

微调ESM-2蛋白语言模型 (pLM) 用于PPI位点的位置预测.
使用蛋白质数据库 (PDB) 来获取训练数据.
编辑PPI数据库以获得"指南".
融合衍生酸到E3泛酸酶域以进行蛋白质降解.

主要成果:

结构不可知语言转换器和优先排序 (SaLT&PepPr) 管道准确地预测PPI站点仅从序列.
该模型显示了与基于结构同质学的方法相比具有竞争力的性能.
工程化体显示出纳米分子结合亲和力,并在癌细胞中有效降解标蛋白 (β-catenin,4E-BP2,TRIM8).
衍生的降解剂表现出较低的偏离目标倾向和功能改变能力.

结论:

使用pLMs对PPI的基于序列的预测是识别交互接口的可行方法.
这种方法促进了针对蛋白质调节的基的生成.
这项研究提出了一种通过可编程蛋白质向和降解来开发新疗法的有希望的策略.

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相关概念视频

Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...