The SARS-CoV-2 pandemic has created a global race for a cure. One approach focuses on designing a novel variant of the human angiotensin-converting enzyme 2 (ACE2) that binds more tightly to the SARS-CoV-2 spike protein and diverts it from human cells. Here we formulate a novel protein design framework as a reinforcement learning problem. We generate new designs efficiently through the combination of a fast, biologically-grounded reward function and sequential action-space formulation. The use of Policy Gradients reduces the compute budget needed to reach consistent, high-quality designs by at least an order of magnitude compared to standard methods. Complexes designed by this method have been validated by molecular dynamics simulations, confirming their increased stability. This suggests that combining leading protein design methods with modern deep reinforcement learning is a viable path for discovering a Covid-19 cure and may accelerate design of peptide-based therapeutics for other diseases.
翻译:SARS-COV-2大流行创造了一种全球治愈竞赛。一种方法侧重于设计一种新型的人类血管变异酶2(ACE2)变体,这种变体更紧密地结合SARS-COV-2钉钉蛋白质,并将它从人体细胞中转移出来。在这里,我们制定了一个新的蛋白质设计框架,作为强化学习问题。我们通过将快速、生物基础的奖励功能和相继的行动空间配方相结合,产生了高效的新设计。 政策梯度的使用至少减少了与标准方法相比达到一致、高质量设计所需的计算预算。这种方法设计的复杂体已被分子动态模拟验证,证实了其稳定性的提高。这表明将领先的蛋白设计方法与现代深度强化学习相结合是发现Covid-19治愈法的可行途径,并可能加速设计其他疾病的基于peptide的治疗方法。