AptaTrans:使用预训练的编码器预测aptamer-protein相互作用的深度神经网络
Incheol Shin1, Keumseok Kang1, Juseong Kim1
1Division of Artificial Intelligence, Pusan National University, Busan, Republic of Korea.
BMC bioinformatics
|November 27, 2023
概括
新的深度学习管道AptaTrans准确地预测了aptamer-protein相互作用 (API),以提高药物发现效率. 这种方法增强了通过指数式丰富 (SELEX) 过程的连接体的系统演化,使其更具成本效益.
科学领域:
- 生物分子工程是生物分子工程.
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
背景情况:
- 亚体,DNA/RNA生物材料,显示出药物发现的前景.
- 通过指数式丰富 (SELEX) 的连接体的系统演化识别了aptamers,但面临时间和精度的限制.
- 现有的in silico方法用于预测胺蛋白相互作用 (API) 往往忽略了物理化学相互作用.
研究的目的:
- 开发一种准确和高效的计算方法,用于预测胺蛋白相互作用 (API).
- 通过提高aptamer选择的效率来增强药物发现过程.
主要方法:
- 开发了一个深度学习管道,AptaTrans,用于API预测.
- AptaTrans使用基于变压器的编码器来对阿巴和蛋白质序列进行单体级分析.
- 预先训练有素的编码器被用于结构表示,管道在基准数据集上得到了验证.
主要成果:
- 与现有模型相比,AptaTrans在预测API方面表现优越.
- 该管道的有效性通过实验验证得到证实.
- AptaTrans与Apta-MCTS集成到一个工具集中,用于aptamer候选生成.
结论:
- 在药物发现中,AptaTrans显著提高了SELEX的成本效益和效率.
- 开发的管道为预测API提供了更准确的方法.
- 源代码和数据集是公开可用的AptaTrans.
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