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拉普斯科尔 (RaptScore):一个基于大型语言模型的算法,用于多用途的aptamer评估

Akira Kimura-Yamazaki¹, Tatsuo Adachi², Shigetaka Nakamura²

¹Graduate School of Advanced Science and Engineering, Waseda University, Shinjuku-ku Okubo 3-4-1, 169-0072 Tokyo, Japan.

Nucleic acids research

|January 14, 2026

概括

研究人员开发了RaptScore,这是一种使用大型语言模型评估RNA受体结合活性的新指标. 这种工具可以识别更短,更有效的体,并提高发现效率,减少实验工作.

科学领域:

生物技术是生物技术.
计算生物学计算生物学
药物发现药物发现药物发现

背景情况:

RNA亚体是生命科学中宝贵的工具,特别是在药物发现方面.
通过指数式丰富 (SELEX) 的联体的系统进化是一种常见的aptamer生成方法,但具有局限性.
现有的aptamer评估指标仅限于SELEX中发现的序列,不能评估不同的长度.

研究的目的:

开发一种新的结合活性评估指标,用于克服 SELEX 的局限性.
为了能够评估任意序列,包括未被SELEX识别的序列,并适应序列长度的变化.
为了提高aptamer设计优化和发现效率.

主要方法:

开发RaptScore,一种利用大型语言模型进行RNA胺酶结合活性评估的新指标.
整合RaptScore与in silico成熟用于aptamer序列优化.
将RaptScore与RaptGen结合起来,这是一个基于自编码器的变异性aptamer发现工具.

主要成果:

拉普斯科尔 (RaptScore) 显示,与实际结合活动有很强的相关性.
识别具有维持或增强结合性质的较短RNA体.
通过RaptScore和in silico成熟实现了10个核酸序列的截断,同时保持了结合效率.
当RaptScore与RaptGen.Gen结合使用时,提高了阿帕特默发现效率.

结论:

拉普斯科尔 (RaptScore) 是一个强大的工具,用于评估和优化RNA胺基序列.
该指标有助于发现高活性体,包括较短的变体.
拉普斯科尔显著减少了在aptamer研究和开发中的实验力度.

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