Google Research, Mountain View, CA, USA.
Aptitude Medical Systems Inc., Santa Barbara, CA, USA.
Nat Commun. 2021 Apr 22;12(1):2366. doi: 10.1038/s41467-021-22555-9.
Aptamers are single-stranded nucleic acid ligands that bind to target molecules with high affinity and specificity. They are typically discovered by searching large libraries for sequences with desirable binding properties. These libraries, however, are practically constrained to a fraction of the theoretical sequence space. Machine learning provides an opportunity to intelligently navigate this space to identify high-performing aptamers. Here, we propose an approach that employs particle display (PD) to partition a library of aptamers by affinity, and uses such data to train machine learning models to predict affinity in silico. Our model predicted high-affinity DNA aptamers from experimental candidates at a rate 11-fold higher than random perturbation and generated novel, high-affinity aptamers at a greater rate than observed by PD alone. Our approach also facilitated the design of truncated aptamers 70% shorter and with higher binding affinity (1.5 nM) than the best experimental candidate. This work demonstrates how combining machine learning and physical approaches can be used to expedite the discovery of better diagnostic and therapeutic agents.
适配体是与靶分子具有高亲和力和特异性结合的单链核酸配体。它们通常通过搜索具有理想结合特性的文库来发现。然而，这些文库实际上只局限于理论序列空间的一小部分。机器学习为智能地探索这个空间以识别高性能适配体提供了机会。在这里，我们提出了一种方法，该方法使用粒子显示 (PD) 通过亲和力对适配体文库进行分区，并使用这些数据来训练机器学习模型以在计算机上预测亲和力。我们的模型以比随机扰动高 11 倍的速率从实验候选物中预测高亲和力 DNA 适配体，并以比 PD 单独观察到的更高的速率生成新的高亲和力适配体。我们的方法还促进了截短适配体的设计，这些适配体比最佳实验候选物短 70%，结合亲和力更高（1.5 nM）。这项工作表明，如何将机器学习和物理方法结合起来可以加快更好的诊断和治疗剂的发现。
