Abstract
Characterizing conformational transitions in physical systems remains a fundamental challenge in the computational sciences. Traditional sampling methods like molecular dynamics (MD) or MCMC often struggle with the high-dimensional nature of molecular systems and the high energy barriers of transitions between stable states. While these transitions are rare events in simulation timescales, they often represent the most biologically significant processes - for example, the conformational change of an ion channel protein from its closed to open state, which controls cellular ion flow and is crucial for neural signaling. Such transitions in real systems may take milliseconds to seconds but could require months or years of continuous simulation to observe even once. We present a method that reformulates transition path generation as a continuous optimization problem solved through physics-informed neural networks (PINNs) inspired by string methods for minimum-energy path (MEP) generation. By representing transition paths as implicit neural functions and leveraging automatic differentiation with differentiable molecular dynamics force fields, our method enables the efficient discovery of physically realistic transition pathways without requiring expensive path sampling. We demonstrate our method's effectiveness on two proteins, including an explicitly hydrated bovine pancreatic trypsin inhibitor (BPTI) system with over 8,300 atoms.
Abstract (translated)
在物理系统中表征构象转换仍然是计算科学中的一个基本挑战。传统的采样方法，如分子动力学（MD）或马尔可夫链蒙特卡罗（MCMC），往往难以应对分子系统的高维特性以及稳定状态之间过渡的高能量障碍。尽管这些转变在模拟时间尺度上是罕见事件，但它们通常代表最具生物学意义的过程——例如，离子通道蛋白从闭合到开放的状态转换，这种转换控制细胞中的离子流动，并对神经信号至关重要。在这种真实系统中，这样的转换可能需要几毫秒到几秒钟的时间，在连续模拟中观察一次甚至可能需要数月或数年。 我们提出了一种方法，通过将过渡路径生成重新表述为一个可以通过物理信息神经网络（PINNs）解决的连续优化问题来克服这一挑战。该方法受到最小能量路径（MEP）生成弦法的启发。我们的方法将过渡路径表示为隐式神经函数，并利用自动微分与可微分子动力学力场，从而在无需昂贵的路径采样情况下高效发现物理现实中的转换途径。 我们在两种蛋白质系统上展示了我们方法的有效性，包括一个显式水化的牛胰蛋白酶抑制剂（BPTI）系统，该系统包含超过8300个原子。
URL
https://arxiv.org/abs/2504.16381
