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

用于有效估计长时间分子热力学和动力学的潜在空间模拟器

• 批准号: 2152521
• 负责人: Andrew Ferguson
• 金额: $ 38.79万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152521&HistoricalAwards=false
• 关键词: Latent; Space; Simulators; Efficient; Estimation

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.

芝加哥大学的安德鲁·弗格森 (Andrew Ferguson) 获得了化学系化学理论、模型和计算方法项目的奖项，以建立新的理论和计算工具来模拟蛋白质和 DNA 的动力学。计算机模拟提供了一种以实验无法达到的详细程度建模和理解这些分子的结构、动力学和特性的方法。这些计算的高计算成本意味着它们的预测准确性受到限制，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，弗格森将开发由机器学习和新数学定理支持的新颖模拟方法，以比目前快数百万倍的速度模拟生物分子。该方法的关键在于开发超高效模拟器，该模拟器仅识别和模拟驱动长期分子行为的关键变量。该方法正在开发中，并在众所周知的快速折叠微型蛋白上进行测试，然后用于更好地了解与癌症有关的蛋白质的功能障碍，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脱氧核糖核酸。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，弗格森正在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，并开发分子为 NSF 支持的 nanoHUB.org 提供模拟培训材料。芝加哥大学的安德鲁·弗格森 (Andrew Ferguson) 将开发一种方法的理论和算法基础，以生成在空间中连续的生物分子的超长原子分子模拟轨迹和时间。这种方法被称为潜在空间模拟器（LSS），通过短的、不连续的、增强的采样模拟数据进行训练，然后产生遵循正确的结构、热力学和动力学统计的连续全原子模拟轨迹，其成本比传统方法低几个数量级。常规分子动力学。加速是通过在低维慢子空间内传播动力学的成本大大降低来实现的，该子空间由控制分子系统长期动态演化的集体变量所跨越。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）在这个慢子空间内传播动态，以及（iii）解码回分子空间。 Ferguson 正在开发快速折叠微型蛋白 Trp-cage 和蛋白 G 的方法，然后应用它来恢复在癌症中过度表达的 c-Src 激酶的结构转变，以设计 DNA 寡聚物的序列依赖性杂交动力学，以及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解决现有方法无法达到的时间尺度上的动力学机制，并且正在作为免费开源软件广泛提供。弗格森还将提供受指导的本科生和高中研究机会，并通过在芝加哥城市学院举办研讨会来接触社区学院的学生。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的评估进行评估，被认为值得支持。影响审查标准。

项目成果

Molecular Latent Space Simulators for Distributed and Multimolecular Trajectories

用于分布式和多分子轨迹的分子潜在空间模拟器

• DOI: 10.1021/acs.jpca.3c01362
• 发表时间: 2023-01
• 期刊: The Journal of Physical Chemistry A
• 作者: Jones, Michael S.;McDargh, Zachary A.;Wiewiora, Rafal P.;Izaguirre, Jesus A.;Xu, Huafeng;Ferguson, Andrew L.
• 通讯作者: Ferguson, Andrew L.

Molecular insight into how the position of an abasic site modifies DNA duplex stability and dynamics

从分子角度洞察无碱基位点的位置如何改变 DNA 双链体的稳定性和动力学

• DOI: 10.1016/j.bpj.2023.11.022
• 发表时间: 2023-11
• 期刊: Biophysical Journal
• 影响因子: 3.4
• 作者: Ashwood, Brennan;Jones, Michael S.;Lee, Yumin;Sachleben, Joseph R.;Ferguson, Andrew L.;Tokmakoff, Andrei
• 通讯作者: Tokmakoff, Andrei

Thermodynamics and kinetics of DNA and RNA dinucleotide hybridization to gaps and overhangs

DNA 和 RNA 二核苷酸杂交到间隙和突出端的热力学和动力学

• DOI: 10.1016/j.bpj.2023.07.009
• 发表时间: 2023-08
• 期刊: Biophysical Journal
• 影响因子: 3.4
• 作者: Ashwood, Brennan;Jones, Michael S.;Radakovic, Aleksandar;Khanna, Smayan;Lee, Yumin;Sachleben, Joseph R.;Szostak, Jack W.;Ferguson, Andrew L.;Tokmakoff, Andrei
• 通讯作者: Tokmakoff, Andrei

Girsanov Reweighting Enhanced Sampling Technique (GREST): On-the-Fly Data-Driven Discovery of and Enhanced Sampling in Slow Collective Variables

Girsanov 重新加权增强采样技术 (GREST)：慢速集体变量的动态数据驱动发现和增强采样

• DOI: 10.1021/acs.jpca.3c00505
• 发表时间: 2023-01
• 期刊: The Journal of Physical Chemistry A
• 作者: Shmilovich, Kirill;Ferguson, Andrew L.
• 通讯作者: Ferguson, Andrew L.

DiAMoNDBack: Diffusion-Denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Traces

DiAMo​​NDBack：Cα 蛋白迹线非确定性反向映射的扩散去噪自回归模型

• DOI: 10.1021/acs.jctc.3c00840
• 发表时间: 2023-10
• 期刊: Journal of Chemical Theory and Computation
• 影响因子: 5.5
• 作者: Jones, Michael S.;Shmilovich, Kirill;Ferguson, Andrew L.
• 通讯作者: Ferguson, Andrew L.