溶液中RNase P RNA的构象空间

　　将RNase P RNA在体外在转录缓冲液（20 mM钾 - hepes pH 7.5，25 mm mgcl2，1 mm dtt）中进行3 h，使用重组的T7蛋白噬菌体RNA聚合酶和双链DNA模板由PCR放大了PCR。该质粒从细菌菌株G. stearothermophilus（GenBank访问号M19021.1）中编码全长RNase P RNA序列，并具有上游T7 RNA聚合酶启动子启动子序列，GGATCCCCAGCTCGAAATTAATACGACTCACTATATA。体外转录（IVT）后，使用高速台式离心机以13,000 rpm的自旋速率离心，以13,000 rpm的自旋速率离心10分钟，去除焦磷酸镁沉淀物。将无RNase DNase I（新英格兰Biolabs）和最终浓度5 mM CaCl2添加到IVT上清液中，并将溶液在37°C下孵育，以完全消化双链DNA模板，以增加30分钟。然后将200 mM氯化钠（NaCl）的最终浓度添加到IVT溶液中，以在4°C下过夜，然后再纯化。将重折叠的RNA进行快速蛋白质液相色谱（GE HealthcareäktaPure），并使用建议的非替代方法10使用尺寸 - 排斥色谱法（SEC）（SEC）（HILOAD 16/600 SUPERDEX 200 PG）纯化。纯化前用SEC洗脱缓冲液（25 mM Tris pH 7.5、100 mM NaCl，1 mM MGCL2）预先校准，并在单体RNase P RNA分子中以1.0 mL min-1的流速洗脱单体RNase P RNA分子，并与聚集物种分离。通过280和260 nm的吸光度检测到洗脱的RNase P RNA；根据SEC色谱图（补充图4A）收集峰值分数，并在AFM可视化之前在4°C下储存几分钟（通常少于30分钟）。从洗脱部分中取出一个小等分试样，使用8％天然页面和电喷雾电离（ESI）质谱法检查纯度和折叠（补充图4B，C）。 　　所有AFM实验均在生理相关的缓冲溶液中使用Cypher VRS AFM（庇护研究，牛津仪器）在4°C下使用振幅调节的动态A.C进行。模式，称为敲击模式。为了在AFM云母表面进行RNA粘附，用1-（3-氨基丙基）Silatrane（APS）（内部合成）新鲜处理云母载体。在使用前，将50 mm APS的储备溶液在超纯水中稀释300倍，并在新鲜裂解的白云母云母（最高级V1云母碟片，TED PELLA）上涂覆。30分钟后，将云母表面用超纯水（PICO纯水系统，亲发）冲洗，并用过滤的氮气轻轻干燥。然后，将10μl8 nm RNase P RNA在纯化的缓冲液（25 mM Tris pH 7.5，100 mm NaCl，1 mm mg2+）中沉积在APS官能化的云母表面上20分钟，并用500μLAFMAFM缓冲液（10 mm MES pH 6.8，10 mm kCl和1 mm kcl和1毫米毫米毫秒）洗涤500μLAFM缓冲液（10 mm MES）。FastScan-D-SS AFM探针（Bruker，流体的共振频率为110 kHz，弹簧常数为0.25 n m-1），尖端顶端半径为1 nm，用于高分辨率成像。配备了AFM仪器的脉冲蓝色激光器（Bluedrive）在敲击模式下用于光热激发，位于悬臂的后部，而超发光的光电二极管位于悬臂头附近，以检测悬臂偏转。收集粒子裁剪的AFM图像，扫描尺寸为500×500 nm2、1,024×1,024 Pixels2，扫描速率为1.0 Hz。将AFM尖端小心地降落在RNA分子表面上，这是由450 mV的初始设定值和500 mV的自由振幅驱动的压电扫描仪。在尖端接近表面并根据成像过程中的图像质量进行调整时，以逐步的方式（每步10 mV）降低了设定点电压。对于用于3D拓扑结构确定的图像， 使用以下步骤将原始图像用GWYDDION62处理：（1）通过将二阶多项式水平应用于无粒子区域，平面平面度校正；（2）通过正确的水平扫描进行过滤以删除字符串伪像；（3）快速傅立叶变换分析以消除傅立叶变换空间中的高频噪声。从处理的图像中裁剪了单粒子图像，并将其转换为带有X，Y，Z地形信息以进行结构概括的文本文件。 　　使用T7噬菌体RNA聚合酶在体外转录产生人类前trnagln。作为DNA模板，为了避免T7 RNA聚合酶的非特异性N+1活性，使用了在模板链的5'端上带有两次连续的2'-O-甲基修饰的PCR产物。使用8 M尿素剥落页面纯化trnagln，并在300 mm乙酸钠缓冲液中洗脱（pH 5.3）。使用逐步和温度突破的方案（90°C持续3分钟，以完全变性的Pre-tRNA，然后以最大的最大速率以5°C S-1的最大速率将洗脱的trnagln进一步以10μm的形式重折叠，以完全变性，然后在PCR机上迅速升至4°C。预先重新折叠后，在25 mm Tris-Hcl pH 7.5、100 mm NaCl存在下，将每种体外转录的预先tRNA的2μm与2μM纯化的RNase P复合物一起在37°C下孵育。通过添加变性凝胶加载缓冲液并在4°C下孵育来终止反应。在8 M尿素 - 硼酸酯 - EDTA定型的制剂上分析样品10％聚丙烯酰胺（29：1丙烯酰胺：双烯酰胺）凝胶，并用Sybr Gold染色（Thermo Fisher Scientific的Invitrogen）。trnagln通过RNase P RNA的5'加工产生76-核苷酸产物，可以通过变性页与其余的trnagln分离（扩展数据图8b，c和补充图5）。使用ImageJ软件进行了定量，并使用GraphPad Prism 10分析数据。所有实验至少在重复中进行。 　　在动态光散射（DLS）测量值之前，将RNA样品离心（5分钟，10,000 rpm，11,000g）在冷藏的Sigma台式离心机中。DLS experiments were performed at the Biophysics Resource facility, National Cancer Institute, using the DynaPro Plate Reader III Dynamic Light Scattering instrument (Wyatt Technologies) composed of a laser light source (830 nm laser diode), a plate reader cell, a detector placed at a fixed angle of 90°, a photomultiplier amplifying the signal and a correlator.将20μl的样品溶液加载到384孔微孔板的相应样品孔中。样品板将台式离心机的四个位置摇篮转子的垫板支架成对放置，在1,500克以1,500克离心5分钟，以消除样品井中的气泡。在将板放入仪器中之前，将其底部表面用一片软透镜清洁组织（Olympus光学）轻轻擦拭。等待时间1分钟后，每个样品进行至少连续的DLS测量值，以使溶液达到平衡。在固定的RNA浓度为0.20 mg ML -1的情况下，通过平均一系列自相关谱的平均计算平移扩散系数（DT），分子量（MW）和流体动力半径（RH）。 　　液相色谱 - 质谱法（LC-MS）实验在6520精确的质量Q-TOF LC/MS系统上，配备了双电喷雾源，以正离子模式运行。样品包括在无RNase双动溶液中2μMRNaseP RNA。将乙腈添加到所有样品中，最终浓度为10％。使用大众猎人工作站（V.B.06.01）进行数据采集和分析。为了进行质谱的数据分析和反卷积，使用了具有BioConfirm工作流程的群众猎人定性分析软件（V.B.07.00）。将上清液转移到聚丙烯注射小瓶中进行LC -MS分析。使用阳性ESI和Shimadzu 20AC-XR系统使用2.1×50 mm2，2.7 µm Waters Cortecs C18列的Shimadzu 20AC-XR系统，在选定的反应监测模式下以选定的反应监测模式和Shimadzu 20AC-XR系统运行的TSQ Quantiva三重四极质谱仪（Thermo Fisher Scientific）进行LC-MS。 　　在分子表面上，全长RNase P RNA的AFM图像显示出广泛的不同粒子形状。在图像处理之后，如上所述，我们选择了且均匀裁剪的孤立颗粒，即，粒子与任何邻居分子没有重叠，总共导致161个单个拓扑构象异构体。计算了161个AFM图像的单个3D结构，并使用黄蜂软件包估算结构质量和准确性：https：//github.com/pnai-csb-nci-nci-nih/hornet（参考文献44）。在合理的计算时间内，这161个颗粒中的三个没有收敛。这些粒子显然是伸长的，可以通过部分展开来解释，因此被排除在进一步分析之外。简而言之，使用晶体模型（PDB 2A64），SIMRNA63和COOT64的组合构建了天然信息和初始3D折叠。最后两个软件包分别用于建模晶体模型中未解决的缺失残基，然后进行结构改进程序。然后，将初始模型与AFM图像对齐，在该图像中，该模型的优化旋转和翻译在实验AFM图像与相应模型方向的计算图像之间给出了最大一致的分数。接下来，保存了优化的初始模型取向，并为动态拟合步骤创建了配置文件，用于应用CAFEMOL65,66，该轨迹总计为2000万帧（约为0.1μs）。值得注意的是，分别以5、9和9的权重系列缩放局部接触，堆叠和碱基配对能的天然结构信息。最后，通过大黄蜂进行了分析和评估轨迹和结构的精度，其中选择了顶部模型。对所有158个颗粒进行了相同的程序（补充图1-3）。 　　SAXS实验是使用位于国家癌症研究所的NCI SAXS核心设施（BioSaxs-2000，Rigaku）的​​内部仪器进行的。所使用的光子能为8.04 keV（λ=1.54Å）。Optisaxs视频，二维KRATKY准直的和样品对检测器距离为0.484米的组合，使我们获得了0.0051的Q范围< q < 0.6767 Å−1, where q is the magnitude of the momentum transfer, q = (4π/λ)sinθ, 2θ is the scattering angle and λ is the wavelength of the radiation. To minimize radiation damage and obtain a good signal-to-noise ratio, 8 image frames were captured for each sample at 0.55 mg ml−1 (4 μM), in the same buffer conditions used for AFM imaging at various Mg2+ concentrations, using a flow cell with an exposure time of 900 s per frame. The two-dimensional scattering patterns were collected using a Dectris PILATUS 100K detector and then converted to one-dimensional SAXS curves through radial averaging. The one-dimensional data from the 8 frames were subsequently averaged after per-curve evaluation of outliers using the software SAXSLab v.4.0.2 (Rigaku). To examine the effect of sample concentration on the scattering profiles, we repeated the SAXS experiments with samples at lower and higher concentrations: 0.38 and 0.76 mg ml−1. The similarity between the SAXS intensity profiles obtained from the two datasets was assessed using CorMap67 and the reduced χ2 values, neither of which showed any indication of concentration-dependent effects with statistical significance (Supplementary Fig. 6). 　　To orthogonally classify the conformers determined from HORNET and AFM (Fig. 2a), we applied SAXS experiments, which are non-correlated to AFM-derived results. 　　The SAXS data and standard analysis for all recorded scattering experiments at different Mg2+ concentrations are available in the SAXSBDB public repository68; the accession codes can be found in the data availability section of this article. Structural models derived from HORNET and AFM experimental data collected at 1 mM Mg2+ (Fig. 2a) were used to fit the SAXS experimental profile collected at each Mg2+ concentration. SAXS and AFM present distinct yet complementary experimental approaches for structural investigation. SAXS provides averaged information over the entire ensemble of molecules, in a concentration range from μM to mM, in which the scattering signal is collated over a timescale of minutes. AFM, on the other hand, is a direct visualization of individual particles, at low nM sample concentrations, where each particle represents a snapshot of a single conformer of the ensemble at the time of immobilization. However, the structure calculations of thousands of particles observed by AFM is impractical in terms of both labour and computational resources. A complementary method, such as SAXS, therefore, is more suitable for characterizing the solution ensemble of models derived from AFM data. 　　As the population fraction of each conformer is unknown, including interconversion of species on various timescales, we assume that the experimental SAXS profile can be described by an ensemble of models where the total SAXS intensity (ITotal) is a linear combination of n conformers. The contribution of each conformer is determined by the minimization of the discrepancy between the calculated profile and experimentally recorded data, that is: 　　where νi represents the volume fraction of particle i with a scattering intensity profile equal to Ii(q). The 158 AFM-derived conformers were used as a pool of reference structures for SAXS profile fitting, and the synthesized profile of I(q) for each particle was determined using CRYSOL69. The optimized volume fraction, ν, for each component of the ensemble is obtained by minimizing the discrepancy between the back-calculated ITotal and Iexperimental (χ2) curves using an in-house Python script that implements an iterative least-squares process70 by applying a trust region reflective algorithm71, with boundaries of 0 ≤ νi  ≥1. 　　The best fit to the experimental data (χ2 = 1.7) was obtained with an ensemble of 3 of the 158 conformers, with volume fraction percentages of 18% (S31), 76% (S69) and 6% (S53) (Fig. 2b,c). However, different combinations of different models could achieve a similar fit to the SAXS profile, with χ2 ranging from 1.8 to 15 (Fig. 2f). This finding is expected, given that the more populated conformers throughout the course of the data collection contribute more to the total scattered intensity, and our calculated particles from AFM images represent only a sampling of the billions of particles immobilized on the mica surface. 　　The three conformers that best fit the SAXS data exhibited different levels of compactness, from very compact (S31), to partially open (S69), to fully extended (S53). Based on this observation, we then classified the 155 models into 3 clusters (classes C1, C2 and C3) using the 3 representative structures as reference. For this task we partitioned the theoretical SAXS profile, respectively, for each of the 155 models using the cosine distance72 among all variations of intensity as a function of q (I(q)). A minimum threshold of 0.1 was set per similarity cluster. The largest group was C2 with 117 models (S69 as reference), which was also the largest volume fraction observed by SAXS, followed by C1 with 31 models (S31 as reference) and C3 with 7 models (S53 as reference). 　　Indeed, the classified particles based on the SAXS profile similarity to representative structures show similar topological features, as shown in Fig. 2d, overlaid with the reference structure for each class. This analysis hinges on global conformational similarity by ignoring local conformational fluctuation (Fig. 2d). The three topological classes of conformers are defined primarily by the relative orientations of the two modular domains of RNase P RNA, namely the substrate specificity (S-) and catalytic (C-) domains, which are linked by a flexible linker (Fig. 2g), giving Rg values ranging from 46 to 58 Å (Fig. 2e). 　　To further investigate the variation of χ2 during ensemble fitting, we performed an optimization of the volume fraction settings using different combinations of the 158 particles. In this procedure, we start with the fitting of each of 158 conformers, independently, mapping the χ2 values, and then correlating them with the class (C1,C2 and C3) to which that particle belongs. Then, we add in permutations of a given number (1, 2, 3, 5, 10, 20, 50) of random conformers from each class, map the χ2 for 10,000 rounds, in which (1) assumes one structure from each class, (2) assumes two structures from each class, (3) assumes three structures from each class, and so on. 　　In terms of χ2 fluctuation, we observed that C2-like particles have better agreement with the experimental SAXS profile, as those particles indeed represent an intermediate topology between closed and open conformations with respect to the S- and C-domains. The combination of C2 and C1 can reach a χ2 as small as 2.1, but the combination of C1, C2 and C3 gives the best χ2 and the lowest standard deviation (Fig. 2f). Combinations including a greater number of conformers yield a better fit in terms of the χ2 mean (µ), standard deviation (σ) and minimum value, but do not improve beyond 20 from each class (µ = 2.0, σ = 0.5). C3 conformers show the largest deviation from the experimental SAXS data (µ = 31.4), the largest χ2 range (σ = 7.7) and the largest minimum χ2 (24.2). 　　Given that every method has its limitations, we applied two additional independent methods to classify the 158 AFM-derived conformers: clustering in PC space73 and the ensemble optimization method (EOM)45,74. The analysis of clustering in PC space makes use of orthogonal eigenvectors to describe the maximal variance of the space distribution among the 158 models. We observed that seven components were sufficient to cover more than 70% of the variance. Making use of these components, we clustered the 158 models into three main clusters (Extended Data Fig. 4c). The PC analysis and clustering were performed using the Bio3d package. SAXS data were analysed using the EOM package, applying the genetic algorithm (GAJOE) module, which uses a searching process to select a subensemble of models from a pool that is sufficient to describe the SAXS data. For this analysis, we used a maximum number of conformers per ensemble of 50, a number of ensembles per generation of 50 and a minimum number of models per ensemble of 1, with no curve repetition. The EOM performed the fitting for 100 cycles of repeated searching. The best fits achieved using EOM had χ2 values of 2.3 and 1.1, respectively, for SAXS data recorded at 1 mM and 5 mM Mg2+. As the results in Extended Data Fig. 4d show, RNase P RNA presents three main distributions of Rg with high frequency, the largest of which is around 51 Å, the next largest between 47.5 and 50 Å and the smallest population showing Rg >55Å。在5 mm mg2+时，最大的人口转移到较小的RG值，RG< 47.5 Å, and no significant counts are observed for the extended conformers with Rg >52Å。 　　在ITC测量之前，将RNase P RNA（4 µM）透析在4°C下与等温滴定热量法（ITC）缓冲液（ITC）缓冲液（ITC）缓冲液（20 mM HEPES pH 7.5，100 mM NaCl，0.1 mM MGCL2）一起过夜。透析缓冲液用于溶解MGCL2六水合物（Sigma-Aldrich）终最终浓度为20 mM MGCL2，该浓度用作滴定剂。使用微局部PEAQ-ITC仪器（MALVERN）监测MG2+诱导的RNase P RNA压实的差分热。在37°C和初始延迟为180 s的预取平衡后，注入了0.4 µL滴定剂，然后进行18个串行注射（每次2.0 µL），间距为720 s。搅拌速度为750 rpm，将参考功率设置为8 µcal s -1。随着时间的推移，将热图数据记录为功率（µCAL S -1）。之后，将与每个滴定步骤相关的热量集成并绘制在MG2+和RNA的摩尔比。在ITC实验后，使用纳米球量化了校正的RNA浓度，对每个结合等温线进行校准，从而以稀释效应。 　　Beet Western Yellow病毒（BWYV）伪KNOT RNA通过IVT转录，然后使用HILOAD SUPERDEX 75 PG 16/60预涂层（Cytiva）用洗脱缓冲液（20 mm Hepes pH 7.5，100 mm Nacl）使用SEC纯化（补充图7）。通过将200 mM EDTA补充到缓冲液中并在80°C加热10分钟，将纯化的BWYV pseudoknot RNA变性。然后将2 µM变性的BWYV伪not RNA透析在4°C下透析过夜，含有20 mM HEPES pH 7.5的缓冲液。通过将透析缓冲液中的MGCL2（Sigma-Aldrich）溶解至最终浓度为60 mm，制备了滴定剂。使用Microcal Peaq-ITC（Malvern）监测MG2+诱导的BWYV PSEUDOKNOT RNA的差异热。在25°C和90 s的初始延迟之前进行预衡后，注入0.4 µL的滴定剂，然后进行22个串行注射（每次1.0 µL），间距为100 s。搅拌速度为750 rpm，将参考功率设置为4 µcal s -1。 　　为了处理ITC数据，使用了每个热补偿轮廓的原始热图（补充图8）来得出等温线（图2B）。使用PEAQ-ITC分析软件套件（MALVERN）进行数据整合和背景热量减法。 　　为了评估RNase P RNA的所有158个解决构象体之间的结构关系，我们应用了Bio3D Package46，并具有实现功能，以确定不变的核心残基和PCA方法。不变的核心分析解决了模型池的刚性和3D不变区域。在此过程中，通过所有结构坐标的交互式对齐来量化原子位移，其中每轮叠加决定了结构之间X，Y和Z坐标的方差的椭圆形体积，并且从下一个相互作用的相互作用46,75中删除了具有最大波动的残基。一系列圆形后，将其余的残基在小椭圆形体积上定义了核心区域。为了定义代表不变核心的椭圆形体积的截止，我们计算了椭圆体体积的衍生物，这是其余精制结构中存在的残基数的函数，并以150Å3的椭圆形体积达到最小山谷（扩展数据图9）。遵循定义的核心残基，我们使用PCA分发了所有解决的158个构象异构体的最重要方向运动。PCA是一种非常适合组合和传播同一分子不同构象体之间构象空间的相似性和差异的方法。简而言之，命名为主要组件（PC）的正交特征向量通过降低特征的维度来描述结构数据的最大差异，但在黄蜂中保持信息46和数学描述。使用核心作为参考进行PCA，首先应用结构模型的结构叠加，然后获得158个结构池的主要成分。使用组件图的方差和数量来确定组件的数量（扩展数据图5A）。五个和七个组件分别覆盖了总波动的70％和80％。 　　通过考虑序列同源性，二级结构保护和通过长距离级别相互作用来计算SC。从RFAM数据库61明确定义了114种细菌型B RNase P序列的多个序列比对，并使用Jalview Cross-Platform76，从RFAM数据库中明确定义了114种细菌型B RNase P序列。使用Consurf Server77在概率框架中进一步转化了从多个序列对齐的主要序列保护。在所有158个构象异构体中，在0到1之间标准化的所有158个构象异构体中，根据“ 1个减去分数R.M.S.F.”计算3D结构保护评分（图3A）。 　　有关研究设计的更多信息可在与本文有关的自然投资组合报告摘要中获得。