英国剑桥大学Colwell, Lucy J.研究团队报道了人工智能设计新的具有增强生物效力的GCGR/GLP-1R双重激动剂。相关研究成果发表在2024年5月16日出版的国际知名学术期刊《自然—化学》。

人类胰高血糖素受体（GCGR）和胰高血糖素样肽-1受体（GLP-1R）的几种肽双激动剂正在开发中，用于治疗2型糖尿病、肥胖及其相关并发症。候选物必须对这两种受体都具有很高的效力，但尚不清楚有限的实验数据是否可以用于，训练准确预测新肽变体的两种受体活性的模型。

该文中，研究人员使用标记有人GCGR和GLP-1R体外效力的肽序列数据来训练几个模型，包括使用多重损失优化的深度多任务神经网络模型。模型引导的序列优化用于设计三组肽变体，具有不同的预测双重活性范围。研究发现，模型设计的序列中有三个是具有优异生物活性的强效双激动剂。与训练集中最好的双激动剂相比，通过该设计能够同时实现两种受体效力的七倍提高。

附：英文原文

Title: Machine learning designs new GCGR/GLP-1R dual agonists with enhanced biological potency

Author: Puszkarska, Anna M., Taddese, Bruck, Revell, Jefferson, Davies, Graeme, Field, Joss, Hornigold, David C., Buchanan, Andrew, Vaughan, Tristan J., Colwell, Lucy J.

Issue&Volume: 2024-05-16

Abstract: Several peptide dual agonists of the human glucagon receptor (GCGR) and the glucagon-like peptide-1 receptor (GLP-1R) are in development for the treatment of type 2 diabetes, obesity and their associated complications. Candidates must have high potency at both receptors, but it is unclear whether the limited experimental data available can be used to train models that accurately predict the activity at both receptors of new peptide variants. Here we use peptide sequence data labelled with in vitro potency at human GCGR and GLP-1R to train several models, including a deep multi-task neural-network model using multiple loss optimization. Model-guided sequence optimization was used to design three groups of peptide variants, with distinct ranges of predicted dual activity. We found that three of the model-designed sequences are potent dual agonists with superior biological activity. With our designs we were able to achieve up to sevenfold potency improvement at both receptors simultaneously compared to the best dual-agonist in the training set.

DOI: 10.1038/s41557-024-01532-x

Source: https://www.nature.com/articles/s41557-024-01532-x