Protein origami: New computational methods to predict protein folding

蛋白质是如何折叠成天然状态的？这是化学科学中最重要和最具挑战性的问题之一，也是理解由蛋白质错误折叠和聚集驱动的疾病（如帕金森病）的关键问题。在这个项目中，我们将开发和采用一种新的计算方案，通过映射到一个离散的基于连接性的蛋白质结构描述来访问长时间尺度的蛋白质折叠事件。这种新方法将使我们能够研究序列特异性折叠效应和翻译折叠，并为蛋白质结构预测提供一种新的方案。预测蛋白质折叠途径是具有挑战性的，因为涉及的时间尺度;折叠通常需要微秒到毫秒，这远远超出了正常分子模拟的范围。此外，蛋白质折叠强烈耦合到溶剂环境（例如水），需要新的方法，可以访问在异质溶剂/蛋白质环境中的长时间尺度模拟为了解决这个前沿挑战，我们将开发一个新的计算方案来预测蛋白质如何折叠到它们的天然状态。基于我们以前在反应发现领域的工作，蛋白质折叠途径将被编码为使用残基水平邻接矩阵定义的离散中间结构之间的一系列“跳跃”;基于在我们最近的工作中引入的矩阵空间搜索，折叠机构的集合可以容易地在该离散的折叠图片内生成，而对不同途径的热力学和动力学描述符的后分析使得能够鉴定最可能的折叠机制。除了提供用于生成折叠机制集成的新开放软件外，我们的新方法还将使我们能够独特地研究（i）序列特异性折叠效应，（ii）蛋白质折叠景观在合成过程中如何演变，以及（iii）折叠机制的知识如何为稳定的天然结构的预测提供信息。

How does a protein fold into its native state? This is one of the most important and challenging problems in the chemical sciences, and a key question in understanding diseases driven by protein misfolding and aggregation (such as Parkinson's disease). In this project, we will develop and employ a new computational scheme to access long time-scale protein folding events by mapping onto a discretized connectivity-based description of protein structure. This new approach will then enable us to investigate sequence-specific folding effects and translational folding, as well as providing a new scheme for protein-structure prediction.Predicting protein-folding pathways is challenging because of the timescales involved; folding usually takes micro- to milliseconds, a regime which is far beyond the reach of normal molecular simulations. Furthermore, protein folding is strongly coupled to the solvent environment (e.g. water), demanding new approaches which can access long timescales simulations in heterogeneous solvent/protein environmentsTo address this frontier challenge, we will develop a new computational scheme to predict how proteins fold into their native states. Building on our previous work in the field of reaction discovery, protein folding pathways will be encoded as a series of "hops" between discrete intermediate structures defined using residue-level adjacency matrices; based on a matrix-space search introduced in our recent work, ensembles of folding mechanisms can be readily generated within this discretised folding picture, while post-analysis of thermodynamic and kinetic descriptors for different pathways enables identification of the most likely folding mechanism(s). As well as delivering new open software for generating folding-mechanism ensemble, our new approach will uniquely enable us to investigate (i) sequence-specific folding effects, (ii) how the protein-folding landscape evolves during synthesis, and (iii) how knowledge of folding mechanism can inform prediction of stable native structures.