Division of Artificial Intelligence, Pusan National University, Busan, Republic of Korea.
Research & Development, NuclixBio, Seoul, Republic of Korea.
BMC Bioinformatics. 2023 Nov 27;24(1):447. doi: 10.1186/s12859-023-05577-6.
Aptamers, which are biomaterials comprised of single-stranded DNA/RNA that form tertiary structures, have significant potential as next-generation materials, particularly for drug discovery. The systematic evolution of ligands by exponential enrichment (SELEX) method is a critical in vitro technique employed to identify aptamers that bind specifically to target proteins. While advanced SELEX-based methods such as Cell- and HT-SELEX are available, they often encounter issues such as extended time consumption and suboptimal accuracy. Several In silico aptamer discovery methods have been proposed to address these challenges. These methods are specifically designed to predict aptamer-protein interaction (API) using benchmark datasets. However, these methods often fail to consider the physicochemical interactions between aptamers and proteins within tertiary structures.
In this study, we propose AptaTrans, a pipeline for predicting API using deep learning techniques. AptaTrans uses transformer-based encoders to handle aptamer and protein sequences at the monomer level. Furthermore, pretrained encoders are utilized for the structural representation. After validation with a benchmark dataset, AptaTrans has been integrated into a comprehensive toolset. This pipeline synergistically combines with Apta-MCTS, a generative algorithm for recommending aptamer candidates.
The results show that AptaTrans outperforms existing models for predicting API, and the efficacy of the AptaTrans pipeline has been confirmed through various experimental tools. We expect AptaTrans will enhance the cost-effectiveness and efficiency of SELEX in drug discovery. The source code and benchmark dataset for AptaTrans are available at https://github.com/pnumlb/AptaTrans .
适体是由形成三级结构的单链 DNA/RNA 组成的生物材料，具有作为下一代材料的巨大潜力，特别是在药物发现方面。指数富集的配体系统进化（SELEX）方法是一种关键的体外技术，用于鉴定特异性结合靶蛋白的适体。虽然有先进的基于 SELEX 的方法，如细胞 SELEX 和 HT-SELEX，但它们经常遇到耗时延长和准确性欠佳等问题。已经提出了几种计算适体发现方法来解决这些挑战。这些方法专门用于使用基准数据集预测适体-蛋白相互作用（API）。然而，这些方法往往忽略了三级结构中适体和蛋白质之间的物理化学相互作用。
在这项研究中，我们提出了 AptaTrans，这是一种使用深度学习技术预测 API 的流水线。AptaTrans 使用基于转换器的编码器在单体水平上处理适体和蛋白质序列。此外，还利用预训练的编码器进行结构表示。在使用基准数据集进行验证后，AptaTrans 已被集成到一个全面的工具集中。该流水线与 Apta-MCTS 协同工作，Apta-MCTS 是一种推荐适体候选物的生成算法。
结果表明，AptaTrans 在预测 API 方面优于现有的模型，并且通过各种实验工具验证了 AptaTrans 流水线的功效。我们期望 AptaTrans 将提高 SELEX 在药物发现中的成本效益和效率。AptaTrans 的源代码和基准数据集可在 https://github.com/pnumlb/AptaTrans 获得。
