Latent Space Simulators for the Efficient Estimation of Long-time Molecular Thermodynamics and Kinetics

芝加哥大学（University of Chicago）的安德鲁·弗格森（Andrew Ferguson）建立了新的理论和计算工具来模拟蛋白质和DNA的动力学，他获得了化学系化学理论、模型和计算方法项目的奖励。计算机模拟提供了一种方法来模拟和理解这些分子的结构、动力学和特性，而这些细节是实验无法达到的。这些计算的高计算成本意味着他们预测的准确性是有限的，因为即使在最强大的超级计算机上也很难模拟超过微秒的生物分子动力学。在这项工作中，Ferguson将开发新的模拟方法，通过机器学习和新的数学定理来模拟生物分子，比目前可能的速度快数百万倍。该方法的关键在于超高效模拟器的开发，该模拟器仅识别和模拟驱动长期分子行为的关键变量。目前，该方法正在开发和测试快速折叠的微型蛋白质，然后应用于更好地理解与癌症有关的蛋白质功能障碍，控制DNA双螺旋形成的动力学，并确定蛋白质如何识别和结合DNA。作为工作的一部分，新的计算工具将作为免费的开源软件提供，弗格森为本科生和高中生提供指导研究经验，担任芝加哥城市学院学生研讨会的讲师，芝加哥大学的andrew Ferguson将开发一种方法的理论和算法基础，以生成在空间和时间上连续的生物分子的超长原子分子模拟轨迹。这种方法被称为潜在空间模拟器（LSS），通过对短的、不连续的、增强的采样模拟数据进行训练，然后产生符合正确结构、热力学和动力学统计的连续全原子模拟轨迹，其成本比传统分子动力学低几个数量级。在由控制分子系统长期动态演化的集体变量所跨越的低维慢子空间中传播动力学的成本大大降低，从而实现了加速度。计算实现采用三种专门的深度学习架构(i)识别慢集体变量，（ii）在慢子空间内传播动态，以及（iii）解码回分子空间。Ferguson正在开发快速折叠迷你蛋白Trp-cage和G蛋白的方法，然后将其应用于恢复癌症中过度表达的c-Src激酶的结构转变，设计DNA寡聚物的序列依赖杂交动力学，并了解转录因子蛋白与DNA的结合。LSS生成的超长分子轨迹可以在现有方法无法达到的时间尺度上解决动力学机制，并且正在作为免费和开源软件广泛提供。弗格森还将为本科生和高中生提供指导研究的机会，并通过在芝加哥城市学院举办研讨会来接触社区大学生。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Andrew Ferguson of the University of Chicago is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to establish new theoretical and computational tools to simulate the dynamics of proteins and DNA. Computer simulations provide a means to model and understand the structure, dynamics, and properties of these molecules at a level of detail inaccessible to experiment. The high computational cost of these calculations mean that the accuracy of their predictions is limited since it is difficult to simulate the dynamics of biomolecules for longer than microseconds even on the most powerful supercomputers. In this work, Ferguson will develop novel simulation approaches enabled by machine learning and new mathematical theorems to simulate biomolecules millions of times faster than is currently possible. The crux of the approach rests on the development of ultra-efficient simulators that identify and model only the key variables driving the long-time molecular behavior. The approach is being developed and tested on well-understood fast-folding mini-proteins, and then applied to better understand dysfunction in proteins implicated in cancer, to control the kinetics of DNA double helix formation, and to determine how proteins recognize and bind to DNA. As part of the work, the new computational tools will be made available as free open-source software and Ferguson is offering mentored research experiences for undergraduate and high school students, serving as an instructor in workshops for City Colleges of Chicago students, and developing molecular simulation training materials for the NSF-supported nanoHUB.org.Andrew Ferguson of the University of Chicago will develop the theoretical and algorithmic foundations of an approach to generate ultra-long atomistic molecular simulation trajectories of biomolecules that are continuous in space and time. This approach, termed latent space simulators (LSS), is trained over short, discontinuous, enhanced sampling simulation data, and then produces continuous all-atom simulation trajectories obeying the correct structural, thermodynamic, and kinetic statistics at several orders of magnitude lower cost than conventional molecular dynamics. Accelerations are realized by the vastly lower cost of propagating the dynamics within a low-dimensional slow subspace spanned by the collective variables governing the long-time dynamical evolution of the molecular system. The computational implementation employs three specialized deep learning architectures that (i) identify the slow collective variables, (ii) propagate the dynamics within this slow subspace, and (iii) decode back to molecular space. Ferguson is developing the approach for fast-folding mini-proteins Trp-cage and protein G, and then applying it to recover structural transitions in c-Src kinase that is overexpressed in cancers, to engineer sequence-dependent hybridization kinetics of DNA oligomers, and to understand binding of transcription factor proteins to DNA. The ultra-long molecular trajectories generated by the LSS can resolve kinetic mechanisms at time scales inaccessible to existing approaches and is being made broadly available as free and open-source software. Ferguson will also offer mentored undergraduate and high school research opportunities and reach out to the community college students through hosting workshops at City Colleges of Chicago.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.