将结构同质学与深度学习相结合,实现对人类互动组的高精度蛋白质-蛋白质接口预测
bioRxiv : the preprint server for biology
|July 15, 2025
概括
PIONEER2.0准确地预测了蛋白质结合部位,在具有挑战性的情况下表现优于AlphaFold3. 该工具通过在规模上分析人类蛋白质相互作用,有助于理解疾病机制.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物学是结构生物学.
- 基因组学就是基因组学.
背景情况:
- 许多疾病突变会影响蛋白质-蛋白质接口.
- 对于多蛋白质复合体的结构数据有限.
- 准确预测这些接口对于了解疾病至关重要.
研究的目的:
- 开发一个计算管道,PIONEER2.0,用于预测蛋白质结合伙伴特定的接口残留物.
- 创建一个全面的,结构性知情的人类互动组.
- 评估预测接口在优先考虑疾病相关突变方面的有用性.
主要方法:
- 整合3D结构相似性和几何深度学习.
- 对实验观察到的人类二进制蛋白质-蛋白质相互作用的界面残留物的预测.
- 实验验证PIONEER2.0预测的实验验证,使用定位突变发生.
主要成果:
- PIONEER2.0准确地预测了接口残留物,在具有挑战性的情况下表现优于AlphaFold3.
- 实验验证证证实PIONEER2.0预测与实验确定接口可比.
- 创建了一个包括352,124个相互作用的人类互动组.
结论:
- PIONEER2.0是研究蛋白质与蛋白质相互作用和疾病病因学的宝贵工具.
- 该框架有助于优先考虑与疾病相关的突变,并了解分子机制.
- PIONEER2.0通过提供对人类互动组的结构性见解来推进个性化医疗.
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