AptaDiff:基于扩散模型的全新设计和优化适配体。

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Briefings in bioinformatics Pub Date : 2024-09-23 DOI:10.1093/bib/bbae517
Zhen Wang, Ziqi Liu, Wei Zhang, Yanjun Li, Yizhen Feng, Shaokang Lv, Han Diao, Zhaofeng Luo, Pengju Yan, Min He, Xiaolin Li
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引用次数: 0

摘要

Aptamers 是单链核酸配体,对目标分子具有高亲和力和特异性。传统上,它们是通过体外方法(如通过指数富集配体的系统进化(SELEX))从大型 DNA/RNA 文库中鉴定出来的。然而,这些文库只能捕获理论序列空间的一小部分,而且各种适配体候选物受到实验实际测序能力的限制。针对这一问题,我们提出了 AptaDiff,这是第一种基于扩散模型的硅学适配体设计和优化方法。我们的 Aptadiff 可以超越高通量测序数据的限制,利用变异自动编码器中依赖于主题的潜在嵌入来生成适配体,并可以根据贝叶斯优化法通过亲和力引导生成适配体来优化适配体。对比评估显示,在针对不同蛋白质的四种高通量筛选数据中,AptaDiff 在质量和保真度方面优于现有的适配体生成方法。此外,还进行了表面等离子体共振实验,以验证通过贝叶斯优化生成的适配体对两种目标蛋白质的结合亲和力。结果表明,RU值分别显著提高了87.9%$和60.2%$,KD值分别降低了3.6倍和2.4倍。值得注意的是,与通过 SELEX 筛选出的顶级实验候选物相比,优化后的适配体表现出了更高的结合亲和力,这突显了我们的 AptaDiff 在加速发现优质适配体方面取得的可喜成果。
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AptaDiff: de novo design and optimization of aptamers based on diffusion models.

Aptamers are single-stranded nucleic acid ligands, featuring high affinity and specificity to target molecules. Traditionally they are identified from large DNA/RNA libraries using $in vitro$ methods, like Systematic Evolution of Ligands by Exponential Enrichment (SELEX). However, these libraries capture only a small fraction of theoretical sequence space, and various aptamer candidates are constrained by actual sequencing capabilities from the experiment. Addressing this, we proposed AptaDiff, the first in silico aptamer design and optimization method based on the diffusion model. Our Aptadiff can generate aptamers beyond the constraints of high-throughput sequencing data, leveraging motif-dependent latent embeddings from variational autoencoder, and can optimize aptamers by affinity-guided aptamer generation according to Bayesian optimization. Comparative evaluations revealed AptaDiff's superiority over existing aptamer generation methods in terms of quality and fidelity across four high-throughput screening data targeting distinct proteins. Moreover, surface plasmon resonance experiments were conducted to validate the binding affinity of aptamers generated through Bayesian optimization for two target proteins. The results unveiled a significant boost of $87.9\%$ and $60.2\%$ in RU values, along with a 3.6-fold and 2.4-fold decrease in KD values for the respective target proteins. Notably, the optimized aptamers demonstrated superior binding affinity compared to top experimental candidates selected through SELEX, underscoring the promising outcomes of our AptaDiff in accelerating the discovery of superior aptamers.

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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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