Abstract
Designing protein nanomaterials of predefined shape and characteristics has the potential to dramatically impact the medical industry. Machine learning (ML) has proven successful in protein design, reducing the need for expensive wet lab experiment rounds. However, challenges persist in efficiently exploring the protein fitness landscapes to identify optimal protein designs. In response, we propose the use of AlphaZero to generate protein backbones, meeting shape and structural scoring requirements. We extend an existing Monte Carlo tree search (MCTS) framework by incorporating a novel threshold-based reward and secondary objectives to improve design precision. This innovation considerably outperforms existing approaches, leading to protein backbones that better respect structural scores. The application of AlphaZero is novel in the context of protein backbone design and demonstrates promising performance. AlphaZero consistently surpasses baseline MCTS by more than 100% in top-down protein design tasks. Additionally, our application of AlphaZero with secondary objectives uncovers further promising outcomes, indicating the potential of model-based reinforcement learning (RL) in navigating the intricate and nuanced aspects of protein design
Abstract (translated)
设计具有预定形状和特性的蛋白质纳米材料，将对医疗行业产生重大影响。机器学习（ML）在蛋白质设计方面取得了成功，减少了昂贵的湿实验室实验轮数。然而，在 efficiently 探索蛋白质适应度景观以确定最优蛋白质设计方面仍然存在挑战。因此，我们提出了使用AlphaZero生成蛋白质骨架，满足形状和结构评分要求。我们通过引入一种新颖的基于阈值的奖励和次要目标扩展了现有的蒙特卡洛树搜索（MCTS）框架，以提高设计精度。这一创新大大超过了现有方法，导致设计出的蛋白质骨架更尊重结构评分。AlphaZero在蛋白质骨架设计方面的应用是新颖的，并展示了具有潜力的应用。AlphaZero在自顶向下蛋白质设计任务中 consistently超过基线MCTS超过100%。此外，我们使用AlphaZero进行次要目标的应用揭示了进一步 promising 的结果，表明了基于模型的强化学习（RL）在导航蛋白质设计中的复杂和细微方面具有潜力。
URL
https://arxiv.org/abs/2405.01983
