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.

几种人胰高血糖素受体 (GCGR) 和胰高血糖素样肽-1 受体 (GLP-1R) 的肽双重激动剂正在开发中，用于治疗 2 型糖尿病、肥胖及其相关并发症。候选者必须对两种受体都具有高效力，但尚不清楚现有的有限实验数据是否可用于训练准确预测新肽变体对两种受体的活性的模型。在这里，我们使用标记有人类 GCGR 和 GLP-1R 体外效力的肽序列数据来训练多个模型，包括使用多重损失优化的深度多任务神经网络模型。使用模型引导的序列优化来设计三组肽变体，具有不同的预测双活性范围。我们发现模型设计的三个序列是具有优异生物活性的有效双重激动剂。通过我们的设计，与训练集中最好的双激动剂相比，我们能够同时在两个受体上实现高达七倍的效力提高。