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AptaDiff:基于扩散模型的aptamer的新设计和优化

Zhen Wang^1,2, Ziqi Liu^1,3, Wei Zhang¹

¹Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, 310018 Zhejiang, China.

Briefings in bioinformatics

|October 21, 2024

概括

AptaDiff是一种新的计算方法,使用扩散模型设计和优化aptamers,克服传统体外选择的局限性. 这加快了对分子标的高亲和度体的发现.

科学领域:

计算生物学计算生物学
生物技术是生物技术.
生物信息学是一种生物信息学.

背景情况:

亚胺是核酸连接体,对目标分子具有很高的亲和力和特异性.
像SELEX这样的传统的aptamer发现方法受到图书馆大小和测序能力的限制.
现有的方法只捕获理论序列空间的一小部分,限制了aptamer发现.

研究的目的:

介绍AptaDiff,这是基于扩散模型的第一个in silico aptamer设计和优化方法.
为了产生超越高通量测序数据的限制的aptamers.
通过引导生成过程来优化阿普坦酶亲和力.

主要方法:

AptaDiff使用扩散模型进行in silico aptamer设计和优化.
采用来自变化自编码器的依赖于动机的潜伏嵌入.
通过贝叶斯优化结合了亲和导向的aptamer生成.

主要成果:

在四个数据集中,AptaDiff在aptamer质量和忠实性方面表现出了对现有方法的优越性.
经验证的阿普坦体显示,结合亲和力显著增加 (87.9%和60.2%的RU提升),解离常数降低 (3.6倍和2.4倍的KD降低).
与SELEX衍生候选物相比,优化的体表现出增强的结合亲和力.

结论:

AptaDiff有效地设计和优化超越传统实验约束的aptamers.
这种方法加快了发现高亲和度体的速度.
AptaDiff代表了在计算上的aptamer发现的重大进步.

关键词:

贝叶斯优化的贝叶斯优化亚胺基的生成过程深度学习是一种深度学习.离散扩散模型的离散扩散模型.

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