从单分子时间序列重建蛋白质结构
Reconstruction of Protein Structures from Single-Molecule Time Series
摘要：
单分子实验技术通过记录少量的实验观测值来跟踪分子的实时动态。这些观测提供了一个粗粒度的，低维的构象动力学表示，但不提供一个原子的瞬时分子结构表示。Takens的延迟嵌入定理断言，在相当一般的条件下，这些低维时间序列可以包含足够的信息来重建系统的完整分子构型，直到一个先验未知的变换。通过将Takens定理与统计热力学，流形学习，人工神经网络和刚性图论的工具相结合，我们建立了一种方法单分子TAkens重建（STAR）来学习这种变换并从实验可测量的可观测量（如单分子Förster共振能量转移可获得的分子内距离）中的时间序列重建分子构型。我们证明了该方法的应用程序的分子动力学模拟的C24 H50聚合物链和人工微型蛋白质Chignolin。训练的模型从合成的时间序列数据中重建分子构型，从头到尾的分子距离，原子均方根偏差精度优于0.2 nm。这项工作表明，它是可以准确地重建蛋白质结构的时间序列在实验上可测量的可观的，并建立了理论和算法基础，这样做的应用程序，以真正的实验数据。
英文摘要：
Single-molecule experimental techniques track the real-time dynamics of molecules by recording a small number of experimental observables. Following these observables provides a coarse-grained, low-dimensional representation of the conformational dynamics but does not furnish an atomistic representation of the instantaneous molecular structure. Takens' Delay Embedding Theorem asserts that, under quite general conditions, these low-dimensional time series can contain sufficient information to reconstruct the full molecular configuration of the system up to an a priori unknown transformation. By combining Takens' Theorem with tools from statistical thermodynamics, manifold learning, artificial neural networks, and rigid graph theory, we establish an approach Single-molecule TAkens Reconstruction (STAR) to learn this transformation and reconstruct molecular configurations from time series in experimentally-measurable observables such as intramolecular distances accessible to single molecule Förster resonance energy transfer. We demonstrate the approach in applications to molecular dynamics simulations of a C24H50 polymer chain and the artificial mini-protein Chignolin. The trained models reconstruct molecular configurations from synthetic time series data in the head-to-tail molecular distances with atomistic root mean squared deviation accuracies better than 0.2 nm. This work demonstrates that it is possible to accurately reconstruct protein structures from time series in experimentally-measurable observables and establishes the theoretical and algorithmic foundations to do so in applications to real experimental data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
