发表机构
University of British Columbia; University of Cambridge（英属哥伦比亚大学; 剑桥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
METALICA通过副本交换在扩散模型上实现元动力学，沿集体变量累积偏置以高效采样稀有构象，并在蛋白质去折叠中成功发现第二个自由能最小值。
AI 中文摘要
许多蛋白质的功能依赖于构象状态之间的转变，然而，基于平衡系综训练的扩散模型很少能采样到稀有状态，因此需要更好的采样方法。我们提出了METALICA，该方法通过副本交换在预训练的扩散模型上实现元动力学。它沿集体变量累积偏置势，通过偏置采样使新样本远离先前样本，并将样本重新加权到无偏分布。METALICA在每一个扩散层级持有一个副本，形成一个通过副本间通信演化的马尔可夫链，并随着偏置的增长在原位进行细化。METALICA是序列控制的对偶方法，后者通过序贯蒙特卡洛在一批粒子上并行化采样器。相反，METALICA利用扩散时间调度层级上的并行性，能够从长链中生成样本，这对于发现稀有事件至关重要，其精度由运行长度而非可用内存决定。我们在一个具有已知自由能的双峰目标上进行了验证，然后将METALICA应用于蛋白质的去折叠。在序列控制在预算内无法产生任何去折叠结构的条件下，METALICA成功填充了该盆地并解析出第二个自由能最小值。
英文摘要
Many proteins function through transitions between conformational states, yet rare states are rarely sampled by diffusion models trained on an equilibrium ensemble, demanding better sampling methods. We introduce METALICA, which implements Metadynamics on a pretrained diffusion model via Replica Exchange. It accumulates a bias potential along a Collective Variable, repels new samples from previous ones through biased sampling, and reweights samples onto the unbiased distribution. METALICA holds one replica per diffusion level, forming a Markov Chain that evolves through inter-replica communication and is refined in place as the bias grows. METALICA is the dual of sequential control, in which Sequential Monte Carlo parallelizes the sampler over a batch of particles. Parallelism over the levels of the diffusion-time schedule instead allows METALICA to generate samples from long chains, essential for the discovery of rare events, with accuracy set by run length rather than by the memory available. We validate on a bimodal target with known free energies, then apply METALICA to the unfolding of a protein. At a budget for which sequential control yields no unfolded structure, METALICA populates the basin and resolves a second free energy minimum.
Comments14 pages, 6 figures
