CIRPIN:学习循环变不变表示法以发现假定蛋白质同类物
Aiden R Kolodziej1, S Mazdak Abulnaga2, Sergey Ovchinnikov3
1Department of Biology, Massachusetts Institute of Technology Cambridge, MA.
bioRxiv : the preprint server for biology
|December 3, 2025
概括
深度学习现在可以检测蛋白质的相似性,即使是循环排列 (CP). 一种名为CIRPIN的新方法识别了这些相关的蛋白质,揭示了成千上万的新型CP对和四种自然形式的PDZ域.
科学领域:
- 结构生物信息学 结构生物信息学
- 计算生物学是一种计算生物学.
- 蛋白质科学中的深度学习
背景情况:
- 深度学习彻底改变了基于蛋白质结构的同质检测.
- 目前的方法错过了具有拓重新排列的蛋白质,例如循环顺序 (CP).
- CP涉及相同的折叠,但不同的终端位置.
研究的目的:
- 介绍一种新的方法来检测蛋白质结构中的循环变异.
- 开发一个深度学习模型,不变于循环 permutation.
- 在大型数据库中识别新的循环 permuted 蛋白质对.
主要方法:
- 开发了CIRPIN,一个循环 permutation-invariant图形神经网络.
- 利用一种新的数据增强策略,使用合成循环排列 (synCPs).
- 使用CIRPIN和传统对齐工具搜索了SCOPe和AFDB-ClusterR数据库.
主要成果:
- CIRPIN学习了变不变的蛋白质表示.
- 在SCOPe和AFDB-ClustR.中成功识别了类似的蛋白质.
- 发现了成千上万的新型蛋白质对,它们通过循环变换相互关联.
- 发现的PDZ域自然存在于四种循环 permuted 形式.
结论:
- CIRPIN有效地解决了缺少循环 permuted 蛋白质的限制.
- 该方法使得能够大规模发现新型的循环变换.
- 揭示了PDZ域等蛋白质家族中广泛的循环变异的自然发生.
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