AIoptamer: Artificial Intelligence-Driven Aptamer Optimization Pipeline for Targeted Therapeutics in Healthcare.

IF 4.5 2区 医学 Q2 MEDICINE, RESEARCH & EXPERIMENTAL
Tushar Gupta, Priyanka Sharma, Sheeba Malik, Pradeep Pant
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引用次数: 0

Abstract

Aptamers are short, single-stranded DNA or RNA molecules known for their high specificity and affinity toward target biomolecules, making them powerful tools in drug discovery, diagnostics, and biosensing. However, conventional aptamer selection methods such as SELEX (Systematic Evolution of Ligands by EXponential Enrichment) are often labor-intensive, time-consuming, and resource-demanding. To overcome these limitations, we introduce a novel AI-driven aptamer optimization pipeline (AIoptamer: AI-driven optimization of aptamers) that integrates artificial intelligence with advanced classical computational approaches to accelerate aptamer discovery and design. The workflow begins with a known aptamer-host complex and systematically generates all possible aptamer sequence variants to target the same host. These variants are then screened using AI-based models that rank them based on sequence features and predicted binding affinity. Top candidates undergo structural modeling through CHIMERA_NA, an in-house mutagenesis tool designed to perform structural mutations in nucleic acids. The modeled structures are further evaluated using PredPRBA, a deep learning-based scoring function tailored for RNA-protein binding affinity prediction and PDA-Pred, a machine learning based model for predicting DNA-protein binding affinity. The highest-ranking aptamer-host complexes are then refined through molecular dynamics (MD) simulations to assess structural stability and interaction strength at the atomic level. Our pipeline demonstrates effectiveness across both RNA and DNA aptamer complexes, offering a generalized and robust framework for aptamer optimization. By combining AI-powered prediction with conventional computational techniques, our method advances the rational design of aptamers and significantly reduces reliance on traditional experimental trial-and-error strategies, making aptamer optimization faster, scalable and more efficient.

AIoptamer:人工智能驱动的医疗保健靶向治疗适体优化管道。
适配体是短的单链DNA或RNA分子,以其对目标生物分子的高特异性和亲和力而闻名,使其成为药物发现,诊断和生物传感的强大工具。然而,传统的适体选择方法,如SELEX(配体的系统进化指数富集)往往是劳动密集型的,耗时和资源需求。为了克服这些限制,我们引入了一种新的人工智能驱动的适配体优化管道(AIoptamer:人工智能驱动的适配体优化),它将人工智能与先进的经典计算方法相结合,以加速适配体的发现和设计。该工作流程从已知的适体-宿主复合物开始,系统地生成针对同一宿主的所有可能的适体序列变体。然后使用基于人工智能的模型对这些变体进行筛选,这些模型根据序列特征和预测的结合亲和力对它们进行排序。通过CHIMERA_NA进行结构建模,这是一种内部诱变工具,用于在核酸中进行结构突变。使用基于深度学习的评分函数PredPRBA和基于机器学习的预测dna -蛋白质结合亲和力的模型PDA-Pred对模型结构进行进一步评估。然后通过分子动力学(MD)模拟来优化最高级的适体-宿主复合物,以评估原子水平上的结构稳定性和相互作用强度。我们的产品线证明了RNA和DNA适配体复合物的有效性,为适配体优化提供了一个广泛而强大的框架。通过将人工智能预测与传统计算技术相结合,我们的方法推进了适体的合理设计,并显著减少了对传统实验试错策略的依赖,使适体优化更快、更可扩展、更高效。
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来源期刊
Molecular Pharmaceutics
Molecular Pharmaceutics 医学-药学
CiteScore
8.00
自引率
6.10%
发文量
391
审稿时长
2 months
期刊介绍: Molecular Pharmaceutics publishes the results of original research that contributes significantly to the molecular mechanistic understanding of drug delivery and drug delivery systems. The journal encourages contributions describing research at the interface of drug discovery and drug development. Scientific areas within the scope of the journal include physical and pharmaceutical chemistry, biochemistry and biophysics, molecular and cellular biology, and polymer and materials science as they relate to drug and drug delivery system efficacy. Mechanistic Drug Delivery and Drug Targeting research on modulating activity and efficacy of a drug or drug product is within the scope of Molecular Pharmaceutics. Theoretical and experimental peer-reviewed research articles, communications, reviews, and perspectives are welcomed.
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