Supplementary data for: Tackling hysteresis in conformational sampling --- how to be forgetful with MEMENTO

This is the supplementary data for our publication entitled: "Tackling hysteresis in conformational sampling --- how to be forgetful with MEMENTO". Abstract: <<< The structure of proteins has long been recognised to hold the key to understanding and engineering their function, and rapid advances in structural biology (and protein structure prediction) are now supplying researchers with an ever-increasing wealth of structural information. Most of the time, however, structures can only be determined in free energy minima, one at a time. While conformational flexibility may thus be inferred from static end-state structures, their interconversion mechanisms — a central ambition of structural biology — are often beyond the scope of direct experimentation. Given the dynamical nature of the processes in question, many studies have attempted to explore conformational transitions using molecular dynamics (MD). However, ensuring proper convergence and reversibility in the predicted transitions is extremely challenging. In particular, a commonly used technique to map out a path from a starting to a target conformation called targeted MD (tMD) can suffer from starting-state dependence (hysteresis) when combined with techniques such as umbrella sampling (US) to compute the free energy profile of a transition. Here, we study this problem in detail on conformational changes of increasing complexity. We also present a new, history-independent approach that we term “MEMENTO” (Morphing End states by Modelling Ensembles with iNdependent TOpologies) to generate paths that alleviate hysteresis in the construction of conformational free energy profiles. MEMENTO utilises template-based structure modelling to restore physically reasonable protein conformations based on coordinate interpolation (morphing) as an ensemble of plausible intermediates, from which a smooth path is picked. We compare tMD and MEMENTO on well-characterized test cases (the toy peptide deca-alanine and the enzyme adenylate kinase) before discussing its use in more complicated systems (the kinase P38α and the bacterial leucine transporter LeuT). Our work shows that for all but the simplest systems tMD paths should not in general be used to seed umbrella sampling or related techniques, unless the paths are validated by consistent results from biased runs in opposite directions. MEMENTO, on the other hand performs well as a flexible tool to generate intermediate structures for umbrella sampling. We also demonstrate that extended end-state sampling combined with MEMENTO can aid the discovery of collective variables on a case-by-case basis. >>> There are 4 folders in this dataset for the 4 simulation systems studied in the paper. Deca-alanine, ADK, P38a and LEUT. Each folder contains key coordinate files used to initialise MEMENTO and tMD runs, and data produced in the course of simulations (as labelled in the folders). In the simulation folders, 'raw_data' corresponds to the outputs produced by PLUMED during REUS or tMD, 'convergence' contains WHAM outputs taken at a certain percentage of the data included, and 'pointsfile' are the metadata files required by the Grossfield WHAM implementation.

本数据集为本研究发表论文的补充数据，论文标题为：《解决构象采样中的滞后效应——如何用MEMENTO实现无记忆性》。

摘要：
长期以来，人们已认识到蛋白质结构是理解并改造其功能的关键，而结构生物学（以及蛋白质结构预测）的快速发展正为研究者提供日益丰富的结构信息。然而在大多数情况下，人们仅能逐个测定处于自由能极小值处的蛋白质结构。尽管可从静态终态结构推知构象灵活性，但这些结构的相互转化机制——结构生物学的核心研究目标之一——往往超出直接实验的范畴。
鉴于所研究过程的动态本质，诸多研究尝试借助分子动力学（Molecular Dynamics, MD）探索构象转变过程。然而，确保预测的转变过程具备恰当的收敛性与可逆性极具挑战。具体而言，一类常用于绘制从初始构象到目标构象路径的技术——目标分子动力学（targeted MD, tMD），在与伞形采样（umbrella sampling, US）等用于计算转变自由能剖面的技术结合使用时，可能会出现初始态依赖性（即滞后效应）。
本研究针对不同复杂程度的构象变化问题，对该问题展开了详细探讨。同时，我们提出了一种全新的无记忆性方法，将其命名为MEMENTO（基于独立拓扑建模集合的构象终态形变方法，Morphing End states by Modelling Ensembles with iNdependent TOpologies），该方法可生成能够缓解构象自由能剖面构建过程中滞后效应的路径。MEMENTO利用模板驱动的结构建模技术，基于坐标插值（形变）将合理的蛋白质构象恢复为可信中间构象集合，并从中选取一条平滑路径。
我们在特征明确的测试体系（十丙氨酸肽与腺苷酸激酶（adenylate kinase））上对tMD与MEMENTO进行了对比，随后讨论了该方法在更复杂体系（激酶P38α与细菌亮氨酸转运蛋白LeuT（bacterial leucine transporter LeuT））中的应用。研究表明，除最简单的体系外，tMD路径通常不宜用于为伞形采样或相关技术提供初始输入，除非通过反向偏置运行的一致结果对路径进行了验证。相较而言，MEMENTO作为一种灵活的工具，能够为伞形采样生成合格的中间结构，表现优异。此外，我们还证明，将扩展终态采样与MEMENTO相结合，可在特定案例中助力集体变量的发现。

本数据集包含4个文件夹，分别对应论文中研究的4类模拟体系：十丙氨酸、腺苷酸激酶（ADK）、P38α激酶与LeuT细菌亮氨酸转运蛋白（LEUT）。每个文件夹内包含用于初始化MEMENTO与tMD运行的关键坐标文件，以及模拟过程中产生的对应数据（文件夹内已标注）。在模拟文件夹中，`raw_data`对应PLUMED在REUS或tMD运行中生成的输出结果；`convergence`包含基于指定比例的模拟数据所得到的加权直方图分析（WHAM）输出结果；`pointsfile`为格罗斯菲尔德开发的WHAM工具实现所需的元数据文件。