适体
指数富集配体系统进化
计算机科学
生成语法
SELEX适体技术
计算生物学
人工智能
机器学习
寡核苷酸
生物
核糖核酸
分子生物学
遗传学
DNA
基因
作者
Shi‐Jian Ding,Xin Yang,Chi Ho Chan,Yuan Ma,Sifan Yu,Luyao Wang,Aiping Lyu,Bao‐Ting Zhang,Liang Yu,Ge Zhang
标识
DOI:10.1101/2024.05.23.594910
摘要
Aptamers, synthetic oligonucleotide ligands, have shown significant promise for therapeutic and diagnostic applications owing to their high specificity and affinity for target molecules. However, the conventional Systematic Evolution of Ligands by Exponential Enrichment (SELEX) for aptamer selection is time-consuming and often yields limited candidates. To address these limitations, we introduce AptaGPT, a novel computational strategy that leverages a Generative Pre-trained Transformer (GPT) model to design and optimize aptamers. By training on SELEX data from early rounds, AptaGPT generated a diverse array of aptamer sequences, which were then computationally screened for binding using molecular docking. The results of this study demonstrated that AptaGPT is an effective tool for generating potential high-affinity aptamer sequences, significantly accelerating the discovery process and expanding the potential for aptamer research. This study showcases the application of generative language models in bioengineering and provides a new avenue for rapid aptamer development.
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