In the post-Moore era, the need for efficient solutions to non-deterministic polynomial-time (NP) problems is becoming more pressing. In this context, the Ising model implemented by the probabilistic computing systems with probabilistic bits (p-bits) has attracted attention due to the widespread availability of p-bits and support for large-scale simulations. This study marks the first work to apply probabilistic computing to tackle protein folding, a significant NP-complete problem challenge in biology. We represent proteins as sequences of hydrophobic (H) and polar (P) beads within a three-dimensional (3-D) grid and introduce a novel many-body interaction-based encoding method to map the problem onto an Ising model. Our simulations show that this approach significantly simplifies the energy landscape for short peptide sequences of six amino acids, halving the number of energy levels. Furthermore, the proposed mapping method achieves approximately 100 times acceleration for sequences consisting of ten amino acids in identifying the correct folding configuration. We predicted the optimal folding configuration for a peptide sequence of 36 amino acids by identifying the ground state. These findings highlight the unique potential of the proposed encoding method for solving protein folding and, importantly, provide new tools for solving similar NP-complete problems in biology by probabilistic computing approach.

翻译：在后摩尔时代，对非确定性多项式时间（NP）问题高效解决方案的需求日益迫切。在此背景下，由概率比特（p-bit）概率计算系统实现的伊辛模型，由于p-bit的广泛可用性及对大规模仿真的支持而备受关注。本研究首次将概率计算应用于解决蛋白质折叠这一生物学中重要的NP完全问题挑战。我们将蛋白质表示为三维网格中的疏水性（H）与极性（P）珠链序列，并引入一种新颖的基于多体相互作用的编码方法，将问题映射到伊辛模型。仿真结果表明，该方法显著简化了六氨基酸短肽序列的能量格局，使能级数量减半。此外，所提出的映射方法在识别十氨基酸序列的正确折叠构型时实现了约100倍的加速。我们通过基态识别预测了36氨基酸肽链的最优折叠构型。这些发现凸显了所提编码方法在解决蛋白质折叠问题上的独特潜力，更重要的是为通过概率计算方法解决生物学中类似NP完全问题提供了新工具。