α7烟碱型乙酰胆碱受体闭合、打开和脱敏的对称适应马尔可夫状态模型-动脉网

Symmetry-adapted Markov state models of closing, opening, and desensitizing in α 7 nicotinic acetylcholine receptors

Nature 等信源发布 2024-10-18 02:14

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Abstractα7 nicotinic acetylcholine receptors (nAChRs) are homopentameric ligand-gated ion channels with critical roles in the nervous system. Recent studies have resolved and functionally annotated closed, open, and desensitized states of these receptors, providing insight into ion permeation and lipid binding.

摘要α7烟碱型乙酰胆碱受体（nAChRs）是同五聚体配体门控离子通道，在神经系统中起关键作用。。

However, the process by which α7 nAChRs transition between states remains unclear. To understand gating and lipid modulation, we generated two ensembles of molecular dynamics simulations of apo α7 nAChRs, with or without cholesterol. Using symmetry-adapted Markov state modeling, we developed a five-state gating model.

然而，α7 nAChRs在状态之间转换的过程仍不清楚。为了了解门控和脂质调节，我们生成了两组载脂蛋白α7 nAChRs的分子动力学模拟，有或没有胆固醇。使用对称自适应马尔可夫状态建模，我们开发了一个五状态门控模型。

Free energies recapitulated functional behavior, with the closed state dominating in absence of agonist. Open-to-nonconducting transition rates corresponded to experimental open durations. Cholesterol relatively stabilized the desensitized state, and reduced open-desensitized barriers. These results establish plausible asymmetric transition pathways between states, define lipid modulation effects on the α7 nAChR conformational cycle, and provide an ensemble of structural models applicable to rational design of lipidic pharmaceuticals..

自由能概括了功能行为，在没有激动剂的情况下，闭合状态占主导地位。开放到非导电的过渡速率对应于实验开放持续时间。胆固醇相对稳定了脱敏状态，并减少了开放的脱敏屏障。这些结果建立了状态之间合理的不对称过渡途径，定义了脂质对α7 nAChR构象周期的调节作用，并提供了一套适用于脂质药物合理设计的结构模型。。

IntroductionPentameric ligand-gated ion channels (pLGICs) play a crucial role in the transmission of signals within the nervous system1,2,3. Among pLGICs, nicotinic acetylcholine receptors (nAChRs) constitute a family that can be activated by the neurotransmitter acetylcholine (ACh). These receptors are widely distributed across different species and can be found at both neuromuscular junctions and synaptic clefts4.

。在PLGIC中，烟碱乙酰胆碱受体（nAChRs）构成了一个可以被神经递质乙酰胆碱（ACh）激活的家族。这些受体广泛分布于不同物种，可以在神经肌肉接头和突触间隙中发现4。

The nAChRs exhibit heterogeneity through the assembly of different combinations of subtypes, among which 16 have been identified within the human genome. These receptors demonstrate diverse physiological and pharmacological properties. Notably, the α 7 subtype has the ability to form homomeric receptors and is abundant in the central nervous system, predominantly located pre- or peri-synaptically.

。这些受体表现出多种生理和药理学特性。值得注意的是，α7亚型具有形成同源受体的能力，并且在中枢神经系统中含量丰富，主要位于突触前或突触周围。

Dysfunction of the α 7 subtype can contribute to various neurological and inflammatory disorders5,6,7,8,9. Consequently, this subtype has emerged as a prominent target for drug development. However, the lack of a comprehensive understanding of its gating mechanism has hindered the development of effective therapeutic interventions in clinical settings10,11.The gating process of nAChRs primarily encompasses three functional states: closed, open, and desensitized12.

α7亚型的功能障碍可导致各种神经和炎症性疾病5,6,7,8,9。因此，这种亚型已成为药物开发的突出目标。然而，对其门控机制缺乏全面了解阻碍了临床环境中有效治疗干预的发展10,11。nAChRs的门控过程主要包括三种功能状态：闭合，开放和脱敏12。

The resting, or closed, state predominates when no agonists are bound to the receptors. Upon binding of agonists in the extracellular domain (ECD), the receptors undergo a subsequent conformational change, leading to the opening of the hydrophobic pore in the transmembrane domain (TMD) and allowing cations to pass through.

当没有激动剂与受体结合时，静止或闭合状态占主导地位。激动剂在细胞外结构域（ECD）中结合后，受体会发生随后的构象变化，导致跨膜结构域（TMD）中疏水孔的开放并允许阳离子通过。

Following prolonged exposure to agonists, the pore closes again, entering a distinct non-conductive desensitized state. The α 7 subtype also displays a distinct kinetic profile compared.

长时间暴露于激动剂后，孔再次闭合，进入明显的非导电脱敏状态。与之相比，α7亚型也显示出明显的动力学特征。

(1)

The simulation sampling, thus, can be augmented by applying permutations to the original feature array from simulations when they are grouped based on the system’s symmetry, which effectively improves the simulation sampling fivefold. Note that features in this system are organized into five subsystem blocks, rather than subunit blocks.

因此，当根据系统的对称性对模拟进行分组时，可以通过对来自模拟的原始特征阵列应用置换来增强模拟采样，这有效地将模拟采样提高了五倍。请注意，此系统中的功能被组织为五个子系统块，而不是子单元块。

The first block encompasses contacts within subunit 1, as well as contacts between subunit 1 and other subunits (primarily subunit 2) taking care to avoid double counting of the contacts.Symmetry-adapted time-lagged independent component analysis (SymTICA)Time-lagged independent component analysis (TICA) is a dimensionality reduction technique utilized to extract the slowest dynamics present in a given feature space37.

第一块包含子单元1内的接触，以及子单元1和其他子单元（主要是子单元2）之间的接触，注意避免重复计数接触。对称自适应时滞独立分量分析（SymTICA）时滞独立分量分析（TICA）是一种降维技术，用于提取给定特征空间中存在的最慢动力学37。

It operates on a sequence of time series data denoted as ${{{{\bf{X}}}}}_{{{{\bf{t}}}}}={\{{x}_{1},{x}_{2},...,{x}_{N}\}}_{t}$ and computes the mean-free covariance matrix C0 and the time-lagged covariance matrix Cτ given a specific lag time τ. Under the assumption of reversible dynamics in TICA, the symmetry of Cτ is enforced numerically during the estimation process.

它对时间序列数据序列进行操作，表示为\（{{{{\bf{X}}}}}{{{\bf{t}}}}={\{{x}_{1} ，{x}_{2} ，。。。，{x}_{N} \}}{t}\），并在给定特定滞后时间τ的情况下计算无均值协方差矩阵C0和时滞协方差矩阵Cτ。在TICA中可逆动力学的假设下，在估计过程中在数值上强制Cτ的对称性。

The symmetric Koopman matrix, which encodes the kinetic information of the system, is then obtained. This matrix is further decomposed into a spectrum of eigenvalues λ and corresponding eigenvectors ν. These eigenvalues and eigenvectors provide insights into the dominant slow modes or collective motions of the system, allowing for a reduced-dimensional representation of the dynamics.In the presence of symmetry within the dataset, the covariance matrix of the Koopman matrix exhibits a distinct block form.

然后获得编码系统动力学信息的对称Koopman矩阵。该矩阵进一步分解为特征值λ和相应特征向量ν的谱。这些特征值和特征向量提供了对系统主要慢模式或集体运动的见解，从而可以减少动力学的维数表示。在数据集中存在对称性的情况下，Koopman矩阵的协方差矩阵表现出独特的块形式。

For instance, in the case of 5-fold symmetry, the covariance matrix (N × N) can be represented as:$$\left[\begin{array}{ccccc}{{{{\bf{C}}}.

例如，在5倍对称的情况下，协方差矩阵（N × N）可以表示为：$$\ left[\ begin{array}{ccccc}{{{{\ bf{C}}。

(2)

Here, Cd (N/5 × N/5) represents the diagonal block of the covariance matrix; Co1 (N/5 × N/5) represents the off-diagonal block of the covariance matrix between features in subsystem i and i+1; Co2 (N/5 × N/5) represents the off-diagonal block of the covariance matrix between features in subsystem i and i + 2.Similarly, the Koopman matrix (N × N) follows the same block structure, and it can be expressed as:$$\left[\begin{array}{ccccc}{{{{\bf{K}}}}}_{d}&{{{{\bf{K}}}}}_{o1}&{{{{\bf{K}}}}}_{o2}&{{{{\bf{K}}}}}_{o2}^{\top }&{{{{\bf{K}}}}}_{o1}^{\top }\\ {{{{\bf{K}}}}}_{o1}^{\top }&{{{{\bf{K}}}}}_{d}&{{{{\bf{K}}}}}_{o1}&{{{{\bf{K}}}}}_{o2}&{{{{\bf{K}}}}}_{o2}^{\top }\\ {{{{\bf{K}}}}}_{o2}^{\top }&{{{{\bf{K}}}}}_{o1}^{\top }&{{{{\bf{K}}}}}_{d}&{{{{\bf{K}}}}}_{o1}&{{{{\bf{K}}}}}_{o2}\\ {{{{\bf{K}}}}}_{o2}&{{{{\bf{K}}}}}_{o2}^{\top }&{{{{\bf{K}}}}}_{o1}^{\top }&{{{{\bf{K}}}}}_{d}&{{{{\bf{K}}}}}_{o1}\\ {{{{\bf{K}}}}}_{o1}&{{{{\bf{K}}}}}_{o2}&{{{{\bf{K}}}}}_{o2}^{\top }&{{{{\bf{K}}}}}_{o1}^{\top }&{{{{\bf{K}}}}}_{d}\end{array}\right]$$.

这里，Cd（N/5×N/5）表示协方差矩阵的对角块；Co1（N/5×N/5）表示子系统i和i+1中特征之间协方差矩阵的非对角块；Co2（N/5××N/5）表示子系统i和i++2中特征之间协方差矩阵的非对角块。类似地，Koopman矩阵（N××N）遵循相同的块结构，它可以表示为：$$\左[\开始{数组}{ccccc}{{{\ bf{K}}}}{{{\ bf{K}}}}}&{{{{{\ bf K}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}{\ top}&{{{\ bf{K}}}}}}}}{{{\ bf{K}}}}}}}}}{{{\ bf{K}}}}}}}}}}{{\ bf{K}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}{{{\bf{K}}}}}}}}}}{{{{\bf{K}}}}}}}}}}}}}}}}}}}}}}}{{{\bf{K}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}{{o1}\{{{{bf{K}}}}}}}}}{{{{bf{K}}}}}}}}}}{{{{bf{K}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}{\bf{K}}}}\ud}\end{array}\right]$$。

(3)

As a result, when solving the eigenvalue problem of the Koopman matrix Kν = λν, the N-dimensional eigenvector ν can be decomposed as a concatenation of νA, νB, νC, νD, νE (N/5-dimensional). The first component of the equation becomes ${{{{\bf{K}}}}}_{d}\cdot {{{\nu }}}_{{{{\bf{A}}}}}+{{{{\bf{K}}}}}_{o1}\cdot {{{\nu }}}_{{{{\bf{B}}}}}+{{{{\bf{K}}}}}_{o2}\cdot {{{\nu }}}_{{{{\bf{C}}}}}+{{{{\bf{K}}}}}_{o2}^{\top }\cdot {{{\nu }}}_{{{{\bf{D}}}}}+{{{{\bf{K}}}}}_{o1}^{\top }\cdot {{{\nu }}}_{{{{\bf{E}}}}}=\lambda {{{\nu }}}_{{{{\bf{A}}}}}$.To enhance data efficiency and reduce estimation error in a large kinetic model (see the toy model system in Supplementary Information), we aim to understand the system dynamics under degeneracy.

因此，当解决Koopman矩阵Kν= λν的特征值问题时，N维特征向量ν可以分解为νa，νB，νC，νD，νE（N/5维）的级联。方程的第一个分量是\（{{{{{\bf{K}}}}}}}}{{\d}\cdot{{{{\nu}}}}}}}{{{{\bf{A}}}}+{{{{\bf{K}}}}}}}}u1}\cdot{{\nu}}}}}}u{{{{\ bf{B}}}}}}+{{{{\bf{K}}}}}}}}}{{o2}\cdot{{{\nu}}}}}}}{{{{\bf{C}}}}}}+{{{{{\bf{K}}}}}}}}}{o2}^{\top}\cdot{{{\nu}}}}}}}}{{{{{\bf d}}}}}}+{{{{}}}{{\bf{K}}}}}}}}{o1}}{\top}\cdot{{{\nu}}}}}}{{{\bf{E}}}}=\lambda{{\nu}}}}{{{\bf{A}}}}}。为了提高数据效率并减少大型动力学模型中的估计误差（请参阅补充信息中的玩具模型系统），我们旨在了解简并下的系统动力学。

This involves seeking a symmetry-adapted mapping that remains invariant under the symmetry group. In this case, we have νA = νB = νC = νD = νE, and the eigenvalue problem can be simplified to KsumνA = λνA, where ${{{{\bf{K}}}}}_{{{{\bf{sum}}}}}={{{{\bf{K}}}}}_{d}+{{{{\bf{K}}}}}_{o1}+{{{{\bf{K}}}}}_{o2}+{{{{\bf{K}}}}}_{o2}^{\top }+{{{{\bf{K}}}}}_{o1}^{\top }$.The features xi can be projected onto the dominant eigenspace (independent components, ICs) by summing over all five subsystems (subICs):$${{{{\bf{x}}}}}_{{{{\bf{i}}}}}=\left[\begin{array}{c}{{{{\bf{x}}}}}_{{{{\bf{iA}}}}}\\ {{{{\bf{x}}}}}_{{{{\bf{iB}}}}}\\ {{{{\bf{x}}}}}_{{{{\bf{iC}}}}}\\ {{{{\bf{x}}}}}_{{{{\bf{iD}}}}}\\ {{{{\bf{x}}}}}_{{{{\bf{iE}}}}}\end{array}\right],\quad {{{\bf{subICs}}}}=\left[\begin{array}{c}{{{{{\bf{v}}}}}_{{{{\bf{A\cdot x}}}}}}_{{{{\bf{iA}}}}}\\ {{{{{\bf{v}}}}}_{{{{\bf{A\cdot x}}}}}}_{{{{\bf{iB}}}}}\\ {{{{{\bf{v}}}}}_{{{{\bf{A\cdot x}}}}}}_{{{{\bf{iC}}}}}\\ {{{{{\bf{v}}}}}_{{{{\bf{A\cdot x}}}}}}_{{{{\bf{iD}}}}}\\ {{{{{\bf{v}}}}}_{{{{\bf{A\cdot x}}}}}.

这涉及寻求在对称群下保持不变的对称自适应映射。在这种情况下，我们有νA = νB = νC = νD = νE，特征值问题可以简化为KsumνA = λνA，其中\（{{{{{bf{K}}}}}}}u{{{{{{{bf sum}}}}}={{{{{bf K}}}}}}}}}}{{{{{\bf{K}}}}}}}}{{{{\bf{K}}}}}}}}}}{{{\bf{K}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}{{\bf K}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}\）。通过对所有五个子系统（子系统）求和，可以将特征xi投影到主要特征空间（独立分量，iC）上：$$${{{{{bf{x}}}}}}}{{{{bf{i}}}=\左[\ begin{array}{c}{{{{bf{x}}}}}}}}U{{{{{{{bf iA}}}}}}}{{{{{\bf x}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}{{{{{{}}}}}}}}}}}}}}}}即}}}}}\结束{数组}\右]，\四元组{{{\ bf{子类}}}=\左[\开始{数组}{c}{{{{\ bf{v}}}}}U{{{\ bf{A \ cdot x}}}}}}U{{{{\ bf{iA}}}}}\{{{\ bf{v}}}}}}}}{{{{\bf{A\cdot x}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}{A\cdot x}}}}}}}}}}}}{{{{\bf{iD}}}}}}}}}{{{\bf{v}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}的时间。

(4)

$${{{\bf{ICs}}}}={{{\bf{v}}}_{{{\bf{A }}}} \cdot x}_{{{{\bf{iA}}}}}+{{{\bf{v}}}_{{{\bf{A }}}} \cdot x}_{{{{\bf{iB}}}}}+{{{\bf{v}}}_{{{\bf{A }}}} \cdot x}_{{{{\bf{iC}}}}}+{{{\bf{v}}}_{{{\bf{A }}}} \cdot x}_{{{{\bf{iD}}}}}+{{{\bf{v}}}_{{{\bf{A }}}} \cdot x}_{{{{\bf{iE}}}}}$$

$${{{\bf{ICs}}}}={{{\bf{v}}}}{{{\bf{A}}}\cdot x}}{{{{\bf{iA}}}}+{{\bf{v}}}}{{\bf{A}}}}\cdot x}}{{{{{{{{}}}\bf{iB}}}}+{{{\bf{v}}}}}U{{\ bf{A}}}\cdot x}}U{{{{\ bf{iC}}}+{{\bf{v}}}U{{\ bf{A}}}\cdot x}U{{{{{{\ bf{iD}}}}}+{{{\bf{v}}}}}{{\bf{A}}}}\cdot x}}}}{{{\bf{iE}}}}$$

(5)

Meanwhile, symmetry can be evaluated by analyzing the differences between each subICs, such as the standard deviation. Alternatively, symmetry-aware MDS70 can be employed. Here, the distance matrix between samples in each macrostate was calculated from the minimum Euclidean distances of picked subIC(s) across all five possible permutations.

同时，可以通过分析每个子词之间的差异来评估对称性，例如标准差。或者，可以使用对称感知MDS70。在这里，每个宏状态中样本之间的距离矩阵是根据所有五种可能排列中拾取的subIC的最小欧几里得距离计算出来的。

It ensures that similar asymmetric conformations are embedded similarly, regardless of which subsystem differs. The distance matrix is then decomposed into a low-dimensional representation using classical MDS. The symmetry-aware MDS was performed using the scikit-learn package71 and is available in the Zenodo repository72..

它确保类似的不对称构象被类似地嵌入，而不管哪个子系统不同。然后使用经典MDS将距离矩阵分解为低维表示。对称感知MDS是使用scikit学习软件包71执行的，可在Zenodo repository72中找到。。

Algorithm 1

算法1

Get distance between two samples for five-fold symmetric systems

求五重对称系统的两个样本之间的距离

Si ← (SiA, SiB, SiC, SiD, SiE)

Si←↔（SiA、[UNK]SiB、[UNK]SiC、[UNK-]SiD、[UNK]SiE）

Sj ← (SjA, SjB, SjC, SjD, SjE)

Sj←（SjA、SjB、SjC、SjD、SjE）

D ← inf

D←INF

for i ← 1 to 5 do

Sj ← roll(Sj, 1)

Sj←辊（Sj，1）

$D\leftarrow \min (D,\,{\mbox{Euclidean}}\,({S}_{i},{S}_{j}))$

\（D \ leftarrow \ min（D，\，{\ mbox{欧几里得}}\({S}_{i} ，{S}_{j} ））\）

end for

结束于

return D

返回D

Markov state modelThe SymTICA analysis was conducted using a lag time of 50 ns, and the first two independent components (ICs) that separate the three starting states were selected. Other faster dimensions with a single Gaussian shape distribution in their sampling were excluded from further analysis73.After cross-validation based on the VAMP-2 score74, 1000 microstates were clustered using the k-means algorithm75 with k-means++76 initialization, implemented in the Deeptime package77.

马尔可夫状态模型使用50 ns的滞后时间进行符号分析，并选择分离三个起始状态的前两个独立组件（IC）。其他在采样中具有单一高斯形状分布的更快维度被排除在进一步分析之外73。在基于VAMP-2得分74的交叉验证之后，使用k-means算法75和k-means++76初始化对1000个微状态进行聚类，在Deeptime软件包77中实现。

Bayesian MSMs78 were estimated from the discrete microstate trajectories with a lag time of 100 ns after the Markovian property was validated using the corresponding implied timescales38 ranging from 50 ns to 500 ns.Subsequently, a five-state coarse-grained MSM was constructed using the PCCA+ algorithm79,80, which partitions the microstates into kinetically distinct sets.

在使用相应的隐含时间尺度38（范围从50 ns到500 ns）验证马尔可夫特性后，贝叶斯MSMs78是从离散的微状态轨迹估计的，滞后时间为100 ns。随后，使用PCCA+算法79,80构建了一个五态粗粒度MSM，该算法将微观状态划分为动力学上不同的集合。

The MSM was validated using the Chapman-Kolmogorov test38.All MSM analyses were performed using either the Deeptime77 or PyEMMA81 packages, and the results were visualized using seaborn82, Matplotlib83, or prettypyplot84.Relevant feature analysisThe contact features, as well as other geometric features, were computed using custom scripts available at the GitHub repository: https://github.com/yuxuanzhuang/msm_a7_nachrs; the scripts utilized MDAnalysis v.

MSM使用Chapman-Kolmogorov测试38进行了验证。所有MSM分析均使用Deeptime77或PyEMMA81软件包进行，结果使用seaborn82，Matplotlib83或prettypyplot84进行可视化。相关功能分析使用GitHub存储库中可用的自定义脚本计算接触特征以及其他几何特征：https://github.com/yuxuanzhuang/msm_a7_nachrs；这些脚本使用了MDAnalysis v。

2.8.085 and ENPMDA72.To calculate membrane thickness, the lipophilic package86 was employed. The thickness was determined by selecting phosphorus (P) atoms from POPC molecules and measuring the distance between the upper and lower leaflets of the membrane.Pore hydration around residue ${9}^{{\prime} }$ or ${2}^{{\prime} }$ was defined as the number of water molecules within a cylindrical region centered at the specific residue and spanned ± 2 Å along the pore axis.

2.8.085和ENPMDA72。为了计算膜厚度，使用亲脂性包装86。通过从POPC分子中选择磷（P）原子并测量膜的上下小叶之间的距离来确定厚度。残留物周围的孔隙水合作用被定义为以特定残留物为中心的圆柱形区域内的水分子数量，并沿着孔轴跨越2Å。

The.

的。

Data availability

数据可用性

Representative snapshots from each microstate and final MSM models have been deposited on Zenodo at https://zenodo.org/records/11117001. The source data underlying Figs. 1D–E, 2B, D, E, 3A, B, 4B–G, 5A–C, 6B, D, and Supplementary Figures are provided as a Source Data file. Structures with accession codes 7KOX, 7KOO, and 7KOQ were used as starting models. Source data are provided with this paper..

每个微州和最终MSM模型的代表性快照已保存在Zenodo上https://zenodo.org/records/11117001.图1D–E，2B，D，E，3A，B，4B–G，5A–C，6B，D和补充图的基础源数据作为源数据文件提供。登录号为7KOX，7KOO和7KOQ的结构被用作起始模型。本文提供了源数据。。

Code availability

代码可用性

The scripts to generate figures can be found on GitHub at https://github.com/yuxuanzhuang/msm_a7_nachrs. The implementation of SymTICA can be found at https://github.com/yuxuanzhuang/sym_msm. Code and scripts that reproduce the results are also available on Zenodo at https://zenodo.org/records/1111700172..

生成数字的脚本可以在GitHub上找到https://github.com/yuxuanzhuang/msm_a7_nachrs.SymTICA的实现可以在https://github.com/yuxuanzhuang/sym_msm.复制结果的代码和脚本也可以在Zenodo上获得https://zenodo.org/records/1111700172..

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Download referencesAcknowledgementsThis work was supported by grants from the Knut and Alice Wallenberg Foundation, the Swedish Research Council (2019-02433, 2021-05806), the Swedish e-Science Research Centre, and the BioExcel Center of Excellence (101093290). Computational resources were provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS/SNIC 2022/3-40, 2022/21-16), the European High-Performance Computing Joint Undertaking (EuroHPC JU), and the Swiss National Supercomputing Centre (CSCS) through the Partnership for Advanced Computing in Europe (PRACE).

下载参考文献致谢这项工作得到了Knut和Alice Wallenberg基金会，瑞典研究委员会（2019-024332021-05806），瑞典电子科学研究中心和BioExcel卓越中心（101093290）的资助。计算资源由瑞典国家超级计算学术基础设施（NAISS/SNIC 2022/3-402022/21-16），欧洲高性能计算联合企业（EuroHPC JU）和瑞士国家超级计算中心（CSCS）通过欧洲高级计算伙伴关系（PRACE）提供。

The authors are grateful to Prof. Ryan E. Hibbs and Prof. Lucie Delemotte for helpful feedback and discussions.FundingOpen access funding provided by Stockholm University.Author informationAuthors and AffiliationsDepartment of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Stockholm, SwedenYuxuan Zhuang, Rebecca J.

作者感谢Ryan E.Hibbs教授和Lucie Delemotte教授的有益反馈和讨论。资金斯德哥尔摩大学提供的开放获取资金。作者信息作者和附属机构斯德哥尔摩大学生命科学实验室生物化学与生物物理学系，索尔纳，斯德哥尔摩，瑞典宇轩庄，丽贝卡J。

Howard & Erik LindahlDepartment of Applied Physics, Swedish e-Science Research Center, KTH Royal Institute of Technology, Solna, Stockholm, SwedenErik LindahlAuthorsYuxuan ZhuangView author publicationsYou can also search for this author in.

Howard＆Erik LindahlDepartment of Applied Physics，Swedish e-Science Research Center，KTH Royal Institute of Technology，Solna，Stockholm，SwedenErik LindahlAuthorsYuxuan Zhuagview author Publications您也可以在中搜索这位作者。

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PubMed Google ScholarContributionsY.Z., R.J.H., and E.L. designed research; Y.Z. performed research and analyzed data; Y.Z., R.J.H., and E.L. wrote the paper; Y.Z., R.J.H., and E.L. revised the paper.Corresponding authorCorrespondence to

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转载和许可本文引用本文Zhuang，Y.，Howard，R.J。＆Lindahl，E。对称适应的马尔可夫状态模型，用于α7烟碱型乙酰胆碱受体的关闭，打开和脱敏。

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