感谢Guido Scarabelli和Hongyang Li博士在开发过程中进行广泛的测试，以及Bio3D用户社区和卑尔根大学结构生物信息学研讨会参与者的反馈和意见，从而改进了这一应用。

注意:典型的Bio3D-web会话通过五个连续和相关的步骤进行（参见图1的示意图）。每个步骤被​​实现为Web应用程序的连续导航选项卡，即SEARCH，ALIGN，FIT，PCA和eNMA。 结构搜索与选择（SEARCH） <ol><li> 输入结构 <ol><li>获得腺苷酸激酶（Adk）的PDB ID， 例如通过搜索PDB [http://www.rcsb.org/pdb]。或者，获得感兴趣的蛋白质氨基酸序列， 例如 UniProt [http://uniprot.org]。 </li><li>输入Adk的四个字符长的PDB ID（ 例如 1AKE）或将蛋白质序列粘贴到"输入结构或序列"面板的文本框中。 </li></ol></li><li> 点击选择 <ol><li>点击第一个面板中的蓝色"下一步"（击中选择）按钮，或者直接向下滚动到面板B）"点击选择"进一步分析。 </li><li>确保将"包含的结构的总数"滑块设置为其最大值，以将所有结构包括在截止值之上。 </li><li>降低"调整包含BitScore截止值"以包含更远的相关命中，或将其增加排除。 </li></ol></li><li> 可选点击过滤 <ol><li>单击第一个面板中的蓝色"下一步"（命中选择）按钮，或者简单地向下滚动到面板C）"可选过滤相关结构进行进一步分析"。 </li><li>通过检查表的细节， 例如 PDB名称，物种和结合的配体，确保所选择的命中代表相关结构。 </li><li>如果需要，通过单击表的行手动细化所选的结构子集。 注意:以蓝色突出显示的行描述了选择用于后续选项卡进一步分析的PDB ID。 </li></ol></li></ol> 2。多序列比对分析（ALIGN） <ol><li>单击ALIGN选项卡以从SEARCH选项卡执行所选结构的序列对齐。 </li><li> 对齐摘要 <ol><li>在面板A中查看对齐摘要）"对齐摘要"。确保感兴趣的区域是对齐的，并且不被一个或多个结构中的间隙掩蔽。 </li><li>如果需要，请切换"显示对齐编辑选项"并删除不需要的PDB ID， 例如缺少残留的PDB。 </li></ol></li><li> 序列比对分析 <ol><li>点击蓝色的"下一步"（分析）按钮，对收集的结构进行基于序列的聚类分析。 </li><li>选择绘图选项树状图。将集群调整为K组滑块以将结构分成k组。 </li><li>如果需要，可以通过切换"更多聚类和输出选项"复选框来更改聚类方法。 </li></oL&gt; </li><li> 残留保护分析 <ol><li>点击蓝色的"下一步"（保存）按钮计算列列残基守恒。 </li><li>选择对齐结构集合，以生成每个对齐位置处的残留守恒图。 </li><li>选择与PFAM种子对齐一致的结构，以显示关于包含家族代表成员的相关PFAM种子对齐计算的保守性。 </li></ol></li><li> 序列对齐显示 <ol><li>单击蓝色的"下一步"（对齐）按钮显示与浏览器中对齐可视化工具的完整序列对齐。 </li></ol></li></ol> 3.结构配套与分析（FIT） <ol><li>通过输入FIT选项卡执行结构叠加。 </li><li> 结构叠加 <ol><li>切换"显示PDB"复选框可视化对齐的蛋白质n结构浏览器。 </li><li>通过视觉检查确保蛋白质结构与相应的和相关的区域相叠加。单击并将鼠标拖动到要旋转的结构上，然后滚动以进行缩放。 </li><li>通过点击"颜色选项"来调整结构的着色。着色选项包括对齐位置，每位置的结构变异性，RMSD簇组，序列簇组，对准区域和二级结构。 </li><li>将叠加结构作为常规PDB文件或作为单个PyMOL会话文件下载，以便在专门的分子观察程序中进行可视化。 </li></ol></li><li> 结构分析 <ol><li>点击蓝色的"下一步"（分析）按钮，对采集的PDB结构进行基于结构的聚类。 </li><li>在"绘图选项"下拉菜单中切换RMSD热图。 </li><li>调整聚类选项，包括聚类方法本身，通过切换"更多聚类和输出选项"复选框。 注意:成对RMSD数据也可以被视为树形图，直方图或热图。 </li></ol></li><li> 残留波动 <ol><li>点击蓝色的"下一步"（RMSF）按钮查看每个残差的结构变异性（显示为RMSF图），其主要二级结构元素显示在x轴的边缘区域。 </li><li>切换显示B因子复选框以将参考结构的晶体B因子覆盖到RMSF图上。 </li></ol></li></ol>主成分分析（PCA） <ol><li>通过输入"PCA"选项卡执行主成分分析。 </li><li> 可视化主要组件 <ol><li>切换"显示PC轨迹"复选框，以使用浏览器可视化工具可视化PC所描述的动作。 </li><li>确保"打印机cipal组件1"从第一个下拉菜单中选择。 </li><li>为了可视化其他PC所描述的动作，请从"选择主要组件"下拉菜单中选择所需的PC。 </li><li>从"颜色选项"下拉菜单中更改轨迹的着色。 </li><li>从"颜色选项"中选择"每个位置的变量"，通过位移大小来选择颜色。 </li><li>单击"主组件可视化"面板中的"下载PDB轨迹"按钮，以获得由PC描述的运动的轨迹。 </li><li>点击"下载PyMOL"会话文件按钮，生成一个PyMOL会话文件，给出动作为一个向量域。 </li></ol></li><li> 一致性分析 <ol><li>通过点击蓝色的"下一步"（Plot）按钮将各个结构投影到两个选定的PC上。 </li><li>确保"X轴上的PC"设置为1，"PC on Y轴"到2.将结构投影到其他PC上，相应调整PC编号。 </li><li>选择"通过PC子空间集群"，通过基于PC的聚类对图中的结构进行着色; "RMSD"通过"基于RMSD"进行聚类;和"序列"按照基于序列的聚类进行着色。 </li><li>点击绘图中的任何一个点来标记结构。或者，突出显示图表下方的"PCA构图图"注释中的一个或多个结构。 </li><li>将子空间滑块中的PC滑动到包含更多/更少的PC用于聚类算法。 </li></ol></li><li> 剩余贡献 <ol><li>通过点击蓝色的"下一步"（残留贡献）按钮计算残留物对各个PC的贡献。 </li><li>通过在"选择主体组件"文本框中添加PC编号，为其他PC添加贡献。 </li><li>切换"传播李nes"复选框可以避免将残差贡献绘制在彼此之间。 </li><li>关闭"多谱图"复选框以在不同的图中绘制残差贡献。 </li><li>切换"显示RMSF"以包括RMSF值（来自FIT选项卡）。 </li></ol></li></ol>正常模式分析（eNMA） <ol><li>单击eNMA选项卡以启动正常模式（NM）计算。 </li><li> 过滤器结构 <ol><li>通过降低或增加结构包含/排除的"截止"来调整结构数量。 </li><li>点击绿色的"Run Ensemble NMA"开始NMA计算。 </li></ol></li><li> 正常模式可视化 <ol><li>向下滚动到eNMA选项卡（正常模式可视化）的第二个面板，用于NM的可视化。 注意:默认情况下，视觉上显示与PC-1重叠（相似度）最高的NM化窗口</li><li>为了可视化其他NM或其他PDB结构描述的动作，请分别从"选择模式"和"显示结构的 NM "下拉菜单中选择所需的NM和结构。 </li></ol></li><li> 残留波动 <ol><li>点击蓝色的"下一步"（波动）按钮来计算eNMA选择的结构的残留波动。 </li><li>切换"通过RMSD集群"，以通过基于RMSD的聚类对波动分布进行着色。 </li><li>通过RMSIP切换"Cluster"，通过基于RMSIP的聚类来对波动特征进行着色。 </li><li>切换"扩展行"复选框，以便将分组的波动曲线彼此分开。 </li></ol></li><li> 比较NMA和PCA <ol><li>点击蓝色的"下一步"（PCA-vs-NMA）按钮来计算各个NM和PC之间的相似度。 </li><li>选择PDB ID从"比较结构的NM"下拉菜单中计算出这个结构的NM与PCA选项卡中计算的PC之间的相似度。 </li></ol></li><li> 重叠分析 <ol><li>点击蓝色的"下一步"（重叠分析）按钮，计算计算的NM与两个选定结构之间的结构差向量之间的重叠。 </li><li>从"结构的比较NM"下拉菜单中选择一个"引用"PDB ID，或者在结构表中选择与参考PDB成对比较的一个或多个PDB ID。 </li></ol></li><li> 聚类分析 <ol><li>单击蓝色的"下一步"（聚类）按钮，根据成对的NM相似度（RMSIP）进行结构聚类。 </li></ol></li></ol>

<ol>
	<li>Kornev, A. P., Taylor, S. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamics-Driven+Allostery+in+Protein+Kinases.">Dynamics-Driven Allostery in Protein Kinases.</a> Trends Biochem. Sci. 40 (11), 628-647 (2015).</li><li>Yao, X. -. Q., Grant, B. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Domain-opening+and+dynamic+coupling+in+the+&#945;-subunit+of+heterotrimeric+G+proteins.">Domain-opening and dynamic coupling in the &#945;-subunit of heterotrimeric G proteins.</a> Biophys. J. 105 (2), L08-L10 (2013).</li><li>Henzler-Wildman, K. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intrinsic+motions+along+an+enzymatic+reaction+trajectory.">Intrinsic motions along an enzymatic reaction trajectory.</a> Nature. 450 (7171), 838-844 (2007).</li><li>Boehr, D., Nussinov, R., Wright, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+role+of+dynamic+conformational+ensembles+in+biomolecular+recognition.">The role of dynamic conformational ensembles in biomolecular recognition.</a> Nat. Chem. Biol. 5 (11), 789-796 (2009).</li><li>Teilum, K., Olsen, J. G., Kragelund, B. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+aspects+of+protein+flexibility.">Functional aspects of protein flexibility.</a> Cell Mol Life Sci. 66 (14), 2231-2247 (2009).</li><li>Henzler-Wildman, K., Kern, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+personalities+of+proteins.">Dynamic personalities of proteins.</a> Nature. 450 (7172), 964-972 (2007).</li><li>Grant, B. J., Gorfe, A. A., McCammon, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Large+conformational+changes+in+proteins:+signaling+and+other+functions.">Large conformational changes in proteins: signaling and other functions.</a> Curr. Opin. Struct. Biol. 20 (2), 142-147 (2010).</li><li>Grant, B. J., Rodrigues, A. P. C., ElSawy, K. M., McCammon, J. A., Caves, L. S. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bio3d:+an+R+package+for+the+comparative+analysis+of+protein+structures.">Bio3d: an R package for the comparative analysis of protein structures.</a> Bioinformatics. 22 (21), 2695-2696 (2006).</li><li>Bakan, A., Meireles, L. M., Bahar, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ProDy:+protein+dynamics+inferred+from+theory+and+experiments.">ProDy: protein dynamics inferred from theory and experiments.</a> Bioinformatics. 27 (11), 1575-1577 (2011).</li><li>Zimmermann, M. T., Kloczkowski, A., Jernigan, R. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MAVENs:+motion+analysis+and+visualization+of+elastic+networks+and+structural+ensembles.">MAVENs: motion analysis and visualization of elastic networks and structural ensembles.</a> BMC Bioinformatics. 12 (1), 264 (2011).</li><li>Yang, L. -. W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=oGNM:+online+computation+of+structural+dynamics+using+the+Gaussian+Network+Model.">oGNM: online computation of structural dynamics using the Gaussian Network Model.</a> Nucleic Acids Res. 34, 24-31 (2006).</li><li>Suhre, K., Sanejouand, Y. -. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ElNemo:+a+normal+mode+web+server+for+protein+movement+analysis+and+the+generation+of+templates+for+molecular+replacement.">ElNemo: a normal mode web server for protein movement analysis and the generation of templates for molecular replacement.</a> Nucleic Acids Res. 32, W610-W614 (2004).</li><li>Tiwari, S. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=WEBnm@+v2.0:+Web+server+and+services+for+comparing+protein+flexibility.">WEBnm@ v2.0: Web server and services for comparing protein flexibility.</a> BMC Bioinformatics. 15 (1), 427 (2014).</li><li>Hrabe, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PDBFlex:+exploring+flexibility+in+protein+structures.">PDBFlex: exploring flexibility in protein structures.</a> Nucleic Acids Res. 44, D423-D428 (2016).</li><li>Skj&#230;rven, L., Jariwala, S., Yao, X. -. Q., Grant, B. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Online+interactive+analysis+of+protein+structure+ensembles+with+Bio3D-web.">Online interactive analysis of protein structure ensembles with Bio3D-web.</a> Bioinformatics. , (2016).</li><li>Skj&#230;rven, L., Yao, X., Scarabelli, G., Grant, B. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrating+protein+structural+dynamics+and+evolutionary+analysis+with+Bio3D.">Integrating protein structural dynamics and evolutionary analysis with Bio3D.</a> BMC Bioinformatics. 15 (399), 1-11 (2014).</li><li>Eddy, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accelerated+Profile+HMM+Searches.">Accelerated Profile HMM Searches.</a> PLoS Comput. Biol. 7 (10), (2011).</li><li>Edgar, R. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MUSCLE:+multiple+sequence+alignment+with+high+accuracy+and+high+throughput.">MUSCLE: multiple sequence alignment with high accuracy and high throughput.</a> Nucleic Acids Res. 32 (5), 1792-1797 (2004).</li><li>Berman, H. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Protein+Data+Bank.">The Protein Data Bank.</a> Nucleic Acids Res. 28 (1), 235-242 (2000).</li><li>Finn, R. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pfam:+the+protein+families+database.">Pfam: the protein families database.</a> Nucleic Acids Res. 42, D222-D230 (2014).</li><li>Kerns, S. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+energy+landscape+of+adenylate+kinase+during+catalysis.">The energy landscape of adenylate kinase during catalysis.</a> Nat. Struct. Mol. Biol. 22 (2), 124-131 (2015).</li></ol>

作者宣称他们没有竞争的经济利益。

Bio3D-web可用于交互式探索并绘制来自可用晶体结构的蛋白质的结构，动态和功能状态。此外，基于NMA和PCA的聚类结果以及注释和基于序列的分析可以特别适用于选择代表性结构以进行更耗时的分析，例如整体小分子对接或分子动力学模拟。因此，Bio3D-web通过减少所需的技术专长水平，为更广泛的研究人员提供先进的结构生物信息学分析。 Bio3D-web的当前设计强调简单易懂，包含完整的独立Bio3D包中可用的许多分析方法。在许多情况下，设想研究人员将使用Bio3D-web来了解其蛋白质家族或感兴趣的超家族的总体趋势，然后可以提供更专业的分析。 Bio3D-web是重新设计用于快速探索生物分子结构数据集并充当假设生成工具。我们鼓励用户通过在可重复报告中提供示例Bio3D代码来进一步探索其数据，该代码也存储所有查询详细信息和分析结果。 在上述代表性的示例协议中，我们展示了Bio3D-web揭示Adk功能构象转换的结构特征的能力。 Bio3D-web的其他应用包括用户上传的PDB结构的结构和动力学分析。例如，用户可以上传新的结构或确实的蛋白质序列进行分析。前面提到的分析步骤，特别是eNMA步骤，可以揭示蛋白质运动的局部和全球趋势，集体运动具有功能意义。与apo结构的比较也可以揭示未绑定到构象转换的特征。应用的其他示例在线提供了一系列不同的蛋白质家族。 尽管所有蛋白质都是灵活和动态的实体，但并非所有蛋白质都具有在不同状态（ 例如活性和非活性状态）范围内可用的原子分辨率结构。因此，我们对蛋白质结构空间的观点是有限的，因此从诸如Bio3D-web的工具获得的洞察力对于某些蛋白质也是有限的。然而，随着目前的技术进步和结构基因组学的新举措，这里提出的协议将越来越成为了解重要的结构 - 功能关系的重要途径。在分析更远的相关蛋白质时特别重要的关键步骤是ALIGN标签中对齐错误的潜在出现。当序列相似性下降到30％以下时，不可避免地发生对准误差，并且在这种情况下用户必须双重检查并校正序列比对在ALIGN标签中。对齐错误可能导致FIT选项卡中不正确的叠加结构，并掩盖后续PCA的最相关的构象变体。此外，用户应该知道所选PDB结构中缺少的残留物，如在当前实施方案中，PCA只能对其中所有结构具有相应碳原子被解离的蛋白质残基进行。因此，如果选择的PDB对于蛋白质的特定区域具有未解决的残基，则该区域将从PCA中省略。 Bio3D-web目前仅限于单链PDB结构的分析。因此，使用当前的方案不能探索在四级水平发生的功能运动。尽管我们正在开发新的算法来将这种分析纳入Bio3D-web，但目前唯一的选择是通过传统的Bio3D使用。 Bio3D-web是唯一的在线应用程序离子，可以查询和识别结构集，解释其序列模式和结构变异性，并从其结构可塑性的分析和预测中提取机械信息。各种分子可视化工具和在线服务器使研究人员能够探索和分析各种生物分子结构。然而，用于分析大型异质蛋白家族的序列，结构和动力学的现有工具通常需要大量的计算专业知识，并且通常只有具有相关编程技能的用户才能访问。例如，Bio3D包需要R 8，PRODY需要Python和Maven需要Matlab的知识9,10。相反，Bio3D-web不需要编程知识，从而增加了可访问性，并减少了执行高级比较序列，结构和染色的进入障碍动态分析。此外，Bio3D-Web服务还包含了有效分析所需的分子结构的准备，策划，注释和清理。另外，通过我们的服务器实例，可以减轻对能力计算资源进行分析的限制，可以对任何现代Web浏览器启动和控制的许多结构进行大规模分析。 Bio3D-Web的开放性开发正在进行中（请参阅https://bitbucket.org/Grantlab/bio3d）。我们继续添加新的分析功能并改进现有的方法。未来的发展将侧重于增加基于距离矩阵的PCA和扭转PCA，更广泛的序列保守方法，包括系统发育成分，整体结合位点识别以及蛋白质家族动态网络分析的新方法。在这方面，当前的Web应用程序代表了起始点通过在用户定义的实验结构集上实现可重复和可共享的步骤，可以为许多其他协作结构生物信息学分析工作流程。我们还计划从PDB结构的不对称单位除个体和多个链之外重建生物单位坐标集的未来支持。其他功能将包括增强协同工作空间的保存和加载以及撤消可能性。 Bio3D-Web是生物分子结构数据交互式分析的在线应用。 Bio3D-web可在任何现代Web浏览器上运行，并提供以下功能:（1）将相关蛋白质结构集合识别为用户指定的相似阈值; （2）它们的多重对齐和结构叠加; （3）序列和结构守恒分析; （4）构象间关系映射与主成分分析，（5）通过集合比较预测的内部动力学mal模式分析。该综合功能为蛋白质家族和超家族中序列结构 - 动态关系的调查提供了完整的工作流程。除了方便易用的动态界面，用于探索参数和方法选择的影响，Bio3D-web还记录了用户会话的完整用户输入和后续图形结果。这允许用户轻松共享和重现创建其结果的分析步骤序列.Bio3D-Web完全以R语言实现，并基于Bio3D和Shiny R软件包。它可以从我们的在线服务器运行，也可以在任何运行R的计算机上本地运行。这包括本地服务器安装，以提供定制的多用户实例，可访问优先结构数据集，如制药行业常见的数据集。完整的源代码和广泛的文档是根据GPL-3开源许可证提供的:http://thegrantlab.org/ bio3d / webapps中

蛋白质数据库（PDB）现在包含超过120,000个蛋白质结构 - 其中许多蛋白质家族具有相同的蛋白质家族，但在不同的实验条件下被解析。这些多重结构代表了理解蛋白质形式和功能复杂性的宝贵资源。例如，这些结构合奏的严格的比较可以揭示重要的分子机制1，2，3，并通知上涉及过程，包括配体结合，酶催化和双分子识别4，5，6，7构象动力学。通常可以从蛋白质家族的序列，结构和动力学的详细的大规模分析中获得新的见解。然而，这通常需要相当大的生物ormatics和计算机程序设计专业知识，以及对正在研究的蛋白质系统的熟悉程度。例如，软件包如Bio3D，PRODY和Maven需要在R，蟒和Matlab，分别为8，9，10编程。相反，对于结构灵活性分析的在线工具一般仅限于个别结构11的调查，12。这方面的一个例外是最近开发的WebNM @服务器，其允许比较从几个预对齐用户指定结构13的正常模式分析（NMA）获得的灵活性模式。然而，该服务器缺少自动化程序来识别用于比较的结构，它们的对准或超出NMA的进一步分析。另一个最近的贡献是在线PDBFlex数据库，其中提供了c共享95％或更高序列同一性的PDB结构的有限分析14 。然而，目前尚不能分析更多样化的结构集。 我们以前介绍过Bio3D-web - 一个易于使用的Web应用程序，用于分析蛋白质序列结构 - 动态关系15 。 Bio3D-web是提供易于使用的集成功能，用于在线大型同源结构集的识别，比较和详细分析。在这里，我们提出了使用Bio3D-web在线调查蛋白质序列结构 - 动力学关系的详细协议。 Bio3D-web提供了多种功能，支持图1所示的五个主要步骤的数据分析，并在下面详细讨论。这些步骤构成了从查询序列或结构输入，跨序列结构动态分析的多个级别到总结的工作流程y报告生成。通过广泛的浏览器可视化和绘图设备，以及通过以常用格式下载结果文件，可以立即获得结果。除了方便易用的动态界面，用于探索参数和方法选择的影响，Bio3D-web还将用户会话的完整用户输入和后续图形结果记录为PDF，DOC和HTML格式的可共享的可重复报告。用户会话可以在以后的时间保存和重新加载，并在用户的本地计算机上通过Bio3D R软件包下载并进一步解释完整的结果。 Bio3D幅材由Bio3Dř包生物分子的结构，序列和分子模拟数据8，16的分析供电。特别是用于刚性核识别的Bio3D算法 8 ，叠加，主成分分析（PCA） 8 ，集合正常模式分析（eNMA） 16构成了应用的基础。我们还使用依赖于pHMMER 17的 Bio3D方案来鉴定相关的蛋白质结构，并且使用MUSCLE 18进行多重序列比对。结构和序列注释通过来自RCSB PDB 19和PFAM数据库20的 Bio3D实用程序得到。 Bio3D-web可以从我们的在线服务器运行，也可以在任何运行R的计算机上安装。Bio3D-web对所有用户都是开放的，根据GPL-3开源许可证免费提供:http:// thegrantlab。组织/ bio3d / webapps中

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Bio3D-web</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Web-site</td>
			<td>http://thegrantlab.org/bio3d-web/</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Requirements</td>
			<td>Web browser</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

investigating protein sequence-structure-dynamics relationships with bio3d-web

我们展示了Bio3D-Web在生物分子结构数据交互式分析中的应用。 Bio3D-Web应用程序提供在线功能:（1）将相关蛋白质结构集合识别为用户指定的相似阈值; （2）它们的多重对齐和结构叠加; （3）序列和结构守恒分析; （4）主成分分析的构象间关系映射，（5）通过整体正态分析比较预测的内部动力学。该集成功能提供了一个完整的在线工作流程，用于调查蛋白质家族和超家族中的序列结构 - 动态关系。

腺苷酸激酶（Adk）是普遍存在的酶，其功能是维持许多细胞过程所必需的细胞质核苷酸之间的平衡。 Adk通过催化磷酰基从ATP向AMP的可逆转移而起作用。该反应是伴随着充分研究的速率限制的构象转变3,21。在这里，我们使用Bio3D-web分析目前可用的所有Adk结构，以揭示这些基本过渡的详细功能和机制原理。 我们可以通过输入任何已知的Adk结构的RCSB PDB代码开始对Adk的Bio3D-web分析。例如，在"搜索"选项卡的面板A中输入PDB ID 1AKE会返回167个序列相似的结构，从中自动选择顶部26进行进一步分析（见图B）。注释存在在图C中表示这些选定的结构全部来自大肠杆菌，通过在一定范围的空间组中的x射线衍射来求解;分辨率范围为1.63至2.8Å，并与一系列不同的配体（不含配体，AMP，ADP，MG和抑制剂AP5）共结晶。请注意，通过单击面板C中的"显示/隐藏列"选项可以显示附加注释详细信息。 进入ALIGN选项卡时执行多序列对齐。 ALIGN选项卡的第一个面板显示对齐的摘要，提供有关序列行数（相当于PDB结构数）的详细信息以及位置数（ 即对齐列）。这包括间隙数和非间隙数列的规格。第一行右侧的图提供了序列比对的示意图。这里e灰色区域表示非间隙位置，而对齐的白色区域对应于间隙。序列保守性的表示在与指示保守位置的红色区域的对准之上显示，并且白色表示较不守恒。注意，该图中的序列是基于它们在左侧的聚类树形图提供的相似性来排序的。该选项卡的第二个面板进一步促进了所选PDB的聚类，这些PDB基于它们的成对序列相似性，可以将其视为树形图或热图。默认情况下，显示了表示簇的布置的树形图（或树形图）。树形图的y轴表示簇之间的距离（以序列同一性计）。 进入FIT标签后，会自动执行结构叠加。叠加结构，在面板A中交互显示，indic考虑到相对刚性的核心区域的存在（包括残留1-29,68-117和161-214;有关详细信息，请参阅FIT选项卡底部的"可选核心和RMSD详细信息"面板）。另外两个可变的核苷酸结合区（残基30-67和118-167）也清晰可见（ 图2 ）。基于RMSD的聚类将这些结构分成两个不同的构象。 单击PCA选项卡更清楚地显示了这些结构之间的关系，这些位置在有效结合相关结构中的结合核苷酸物种（ 图2B和2C ）的位置。大多数结构是"封闭"形式（ 图2C中的蓝色）并且与结合的配体或抑制剂相关联。相比之下，更多的"开放"构型是核苷酸和不含抑制剂的。这是一致的对Adk结构和动力学的广泛研究表明，这些区域的开放构型对于核苷酸结合是需要的，并且是有效的磷酰基转移和抑制有害水解事件的封闭构象。值得注意的是，单个PC捕获该Adk结构集合中总平均位移的97％，并且提供了开放到闭合过渡的明确和引人注目的描述以及对该功能位移的各个残差贡献（应用的面板C）和图2 ）。 访问NMA选项卡并增加考虑计算的结构数量（通过减少过滤类似结构的截止值）表明，与封闭形式结构相比，开放状态结构显示增强的局部和全局动力学（ 图2D和应用程序面板C） 。比较PCA和NMA的结果单个结构（图D）表示所有开放式结构的第一种模式与PC1显示相对较高的重叠（平均值为0.37±0.04）。相比之下，封闭形式的结构显示较低的值（平均值为0.30±0.01）。开放式结构的RMSIP值（0.62±0.003）也高于封闭结构（0.56±0.008）。此外，重叠分析表明，开放状态的第一种模式与描述开放和关闭状态差异的构象变化（图E）非常一致。基于RMSIP值的聚类再次显示了打开和关闭状态结构的一致分区（图F）。 这些结果总体上表明Adk存在两个主要的不同构象状态。这两个不同之处在于显示出不同柔性的两个核苷酸结合位点区域的低频位移核苷酸结合后的遗传。 <img alt="图1" src="/files/ftp_upload/55640/55640fig1.jpg" /> 图1: 具有PCA和NMA选项卡屏幕截图的Bio3D- Web概述。 Bio3D-web将用户提供的蛋白质结构或序列作为SEARCH选项卡（ 1 ）中的输入。服务器提供相关结构的列表，可以选择进行进一步分析。 （ 2 ）ALIGN标签提供了在SEARCH选项卡中选择的结构的序列比对和分析。 （ 3 ）在FIT标签中，所有的结构与传统的成对结构分析的结果一起叠加和可视化。 （ 4 ）结构集合的主成分分析在PCA选项卡中进行，以表征构象间关系。 （ 5 ）每个结构的正常模式分析可以在eNMA标签中进行探索可用结构状态的动态趋势。 <a href="http://ecsource.jove.com/files/ftp_upload/55640/55640fig1large.jpg" target="_blank">请点击此处查看此图的较大版本。</a> <img alt="图2" src="/files/ftp_upload/55640/55640fig2.jpg" /> 图2: 腺苷酸激酶的Bio3D-web分析结果。 （ A ）叠加在确定的不变核上的腺苷酸激酶的可用PDB结构。结构根据FIT选项卡中提供的基于RMSD的聚类进行着色。 （ B ）主成分的可视化可从PCA选项卡中获取，以表征数据集中的主要构象变化。这里，对应于第一主成分的轨迹以管表示的方式示出，表示蛋白质的大规模闭合运动。 （ C ）结构在形成图中的两个第一主要成分上，显示构象变异性的低维表示。每个点（或结构）根据用户指定的标准进行着色，在这种情况下是基于PCA的聚类结果。 （ D ）eNMA标签中的正常模式分析表明，与封闭形式（蓝色）结构相比，打开状态（红色）中结构的局部和全局动力学增强。 <a href="http://ecsource.jove.com/files/ftp_upload/55640/55640fig2large.jpg" target="_blank">请点击此处查看此图的较大版本。</a>

Watch this Scientific Journal Video about Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web at JoVE.com

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

介绍了使用Bio3D-web在线调查蛋白质序列结构 - 动力学关系的协议。

研究与Bio3D-web的蛋白质序列 - 结构 - 动力学关系

We demonstrate the usage of Bio3D-web for the interactive analysis of biomolecular structure data. The Bio3D-web application provides online functionality ...

investigating-protein-sequence-structure-dynamics-relationships-with

Research

JoVE Journal

Biochemistry

15.3K 次观看.  University of Michigan Medical School. 介绍了使用Bio3D-web在线调查蛋白质序列结构 - 动力学关系的协议。 

视频: Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Synthesis of 1,2-Azaborines and the Preparation of Their Protein Complexes with T4 Lysozyme Mutants

We describe a general synthesis of 1,2-azaborines using standard air-free techniques and protein complex preparation with T4 lysozyme mutants by vapor diffusion. Oxygen- and moisture-sensitive compounds are prepared and isolated under an inert atmosphere (N2) using either a vacuum gas manifold or a glove box. As an example of azaborine synthesis, we demonstrate the synthesis and purification of the volatile N-H-B-ethyl-1,2-azaborine by a five-step sequence involving distillation and column chromatography for the isolation of products. T4 lysozyme mutants L99A and L99A/M102Q are expressed with Escherichia coli RR1 strain. Standard protocols for chemical cell lysis followed by purification using carboxymethyl ion exchange column affords protein of sufficiently high purity for crystallization. Protein crystallization is performed in various concentrations of precipitant at different pH ranges using the hanging drop vapor diffusion method. Complex preparation with the small molecules is carried out by vapor diffusion method under an inert atmosphere. X-ray diffraction analysis of the crystal complex provides unambiguous structural evidence of binding interactions between the protein binding site and 1,2-azaborines.

A protocol for the synthesis of 1,2-azaborines and the preparation of their protein complexes with T4 lysozyme mutants is presented.

1,2- Azaborines的合成及其蛋白复合物的用T4溶菌酶突变体的制备

We describe a general synthesis of 1,2-azaborines using standard air-free techniques and protein complex preparation with T4 lysozyme mutants by vapor ...

科研

生物化学

Assaying Protein Kinase Activity with Radiolabeled ATP

Protein kinases are able to govern large-scale cellular changes in response to complex arrays of stimuli, and much effort has been directed at uncovering allosteric details of their regulation. Kinases comprise signaling networks whose defects are often hallmarks of multiple forms of cancer and related diseases, making an assay platform amenable to manipulation of upstream regulatory factors and validation of reaction requirements critical in the search for improved therapeutics. Here, we describe a basic kinase assay that can be easily adapted to suit specific experimental questions including but not limited to testing the effects of biochemical and pharmacological agents, genetic manipulations such as mutation and deletion, as well as cell culture conditions and treatments to probe cell signaling mechanisms. This assay utilizes radiolabeled [&#947;-32P] ATP, which allows for quantitative comparisons and clear visualization of results, and can be modified for use with immunoprecipitated or recombinant kinase, specific or typified substrates, all over a wide range of reaction conditions.

Protein kinases are highly evolved signaling enzymes and scaffolds that are critical for inter- and intracellular signal transduction. We present a protocol for measuring kinase activity through the use of radiolabeled adenosine triphosphate ([&#947;-32P] ATP), a reliable method to aid in elucidation of cellular signaling regulation.

用放射性标记的ATP测定蛋白激酶活性

Protein kinases are able to govern large-scale cellular changes in response to complex arrays of stimuli, and much effort has been directed at uncovering ...

Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering

The automated crystallization device is a patented technique1 especially developed for monitoring protein crystallization experiments with the aim to precisely maneuver the nucleation and crystal growth towards desired sizes of protein crystals. The controlled crystallization is based on sample investigation with in situ Dynamic Light Scattering (DLS) while all visual changes in the droplet are monitored online with the help of a microscope coupled to a CCD camera, thus enabling a full investigation of the protein droplet during all stages of crystallization. The use of in situ DLS measurements throughout the entire experiment allows a precise identification of the highly supersaturated protein solution transitioning to a new phase &#8211; the formation of crystal nuclei. By identifying the protein nucleation stage, the crystallization can be optimized from large protein crystals to the production of protein microcrystals. The experimental protocol shows an interactive crystallization approach based on precise automated steps such as precipitant addition, water evaporation for inducing high supersaturation, and sample dilution for slowing induced homogeneous nucleation or reversing phase transitions.

Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering

Here we present a protocol for controlled production of protein microcrystals. The process uses an automated device allowing controlled manipulation of several crystallization parameters. The protein crystallization is carried-out by controlled and automated addition of crystallization solutions while monitoring and investigating the radius distribution of particles in the crystallization droplet.

采用自动结晶耦合原位动态光散射的不同尺寸生长蛋白晶体

The automated crystallization device is a patented technique1 especially developed for monitoring protein crystallization experiments with the aim to ...

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy

Accurate quantification of transcription factor (TF)-DNA interactions is essential for understanding the regulation of gene expression. Since existing approaches suffer from significant limitations, we have developed a new method for determining TF-DNA binding affinities with high sensitivity on a large scale. The assay relies on the established fluorescence anisotropy (FA) principle but introduces important technical improvements. First, we measure a full FA competitive titration curve in a single well by incorporating TF and a fluorescently labeled reference DNA in a porous agarose gel matrix. Unlabeled DNA oligomer is loaded on the top as a competitor and, through diffusion, forms a spatio-temporal gradient. The resulting FA gradient is then read out using a customized epifluorescence microscope setup. This improved setup greatly increases the sensitivity of FA signal detection, allowing both weak and strong binding to be reliably quantified, even for molecules of similar molecular weights. In this fashion, we can measure one titration curve per well of a multi-well plate, and through a fitting procedure, we can extract both the absolute dissociation constant (KD) and active protein concentration. By testing all single-point mutation variants of a given consensus binding sequence, we can survey the entire binding specificity landscape of a TF, typically on a single plate. The resulting position weight matrices (PWMs) outperform those derived from other methods in predicting in vivo TF occupancy. Here, we present a detailed guide for implementing HiP-FA on a conventional automated fluorescent microscope and the data analysis pipeline.

Here we present a novel method for determining binding affinities at equilibrium and in solution with high sensitivity on a large scale. This improves the quantitative analysis of transcription factor-DNA binding. The method is based on automated fluorescence anisotropy measurements in a controlled delivery system.

荧光显微镜竞争滴定法测定转录机-dna 结合感的高灵敏度

Accurate quantification of transcription factor (TF)-DNA interactions is essential for understanding the regulation of gene expression. Since existing ...

Analysis of Protein Folding, Transport, and Degradation in Living Cells by Radioactive Pulse Chase

Radioactive pulse-chase labeling is a powerful tool for studying the conformational maturation, the transport to their functional cellular location, and the degradation of target proteins in live cells. By using short (pulse) radiolabeling times (&lt;30 min) and tightly controlled chase times, it is possible to label only a small fraction of the total protein pool and follow its folding. When combined with nonreducing/reducing SDS-polyacrylamide gel electrophoresis (SDS-PAGE) and immunoprecipitation with (conformation-specific) antibodies, folding processes can be examined in great detail. This system has been used to analyze the folding of proteins with a huge variation in properties such as soluble proteins, single and multi-pass transmembrane proteins, heavily N- and O-glycosylated proteins, and proteins with and without extensive disulfide bonding. Pulse-chase methods are the basis of kinetic studies into a range of additional features, including co- and posttranslational modifications, oligomerization, and polymerization, essentially allowing the analysis of a protein from birth to death. Pulse-chase studies on protein folding are complementary with other biochemical and biophysical methods for studying proteins in vitro by providing increased temporal resolution and physiological information. The methods as described within this paper are adapted easily to study the folding of almost any protein that can be expressed in mammalian or insect-cell systems.

Here we describe a protocol for a general pulse-chase method that allows the kinetic analysis of folding, transport, and degradation of proteins to be followed in live cells.

放射性脉冲追逐对活细胞蛋白质折叠、迁移和降解的分析

Radioactive pulse-chase labeling is a powerful tool for studying the conformational maturation, the transport to their functional cellular location, and ...

Creating Highly Specific Chemically Induced Protein Dimerization Systems by Stepwise Phage Selection of a Combinatorial Single-Domain Antibody Library

Protein dimerization events that occur only in the presence of a small-molecule ligand enable the development of small-molecule biosensors for the dissection and manipulation of biological pathways. Currently, only a limited number of chemically induced dimerization (CID) systems exist and engineering new ones with desired sensitivity and selectivity for specific small-molecule ligands remains a challenge in the field of protein engineering. We here describe a high throughput screening method, combinatorial binders-enabled selection of CID (COMBINES-CID), for the de novo engineering of CID systems applicable to a large variety of ligands. This method uses the two-step selection of a phage-displayed combinatorial nanobody library to obtain 1) &#34;anchor binders&#34; that first bind to a ligand of interest and then 2) &#34;dimerization binders&#34; that only bind to anchor binder-ligand complexes. To select anchor binders, a combinatorial library of over 109 complementarity-determining region (CDR)-randomized nanobodies is screened with a biotinylated ligand and hits are validated with the unlabeled ligand by bio-layer interferometry (BLI). To obtain dimerization binders, the nanobody library is screened with anchor binder-ligand complexes as targets for positive screening and the unbound anchor binders for negative screening. COMBINES-CID is broadly applicable to select CID binders with other immunoglobulin, non-immunoglobulin, or computationally designed scaffolds to create biosensors for in vitro and in vivo detection of drugs, metabolites, signaling molecules, etc.

Creating chemically induced protein dimerization systems with desired affinity and specificity for any given small molecule ligand would have many biological sensing and actuation applications. Here, we describe an efficient, generalizable method for de novo engineering of chemically induced dimerization systems via the stepwise selection of a phage-displayed combinatorial single-domain antibody library.

通过组合单域抗体库的分步噬菌体选择创建高度特异性化学诱导蛋白分解系统

Protein dimerization events that occur only in the presence of a small-molecule ligand enable the development of small-molecule biosensors for the ...

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening

Protein structure elucidation using X-ray crystallography requires both high quality diffracting crystals and computational solution of the diffraction phase problem. Novel structures that lack a suitable homology model are often derivatized with heavy atoms to provide experimental phase information. The presented protocol efficiently generates derivatized protein crystals by combining random microseeding matrix screening with derivatization with a heavy atom molecule I3C (5-amino-2,4,6-triiodoisophthalic acid). By incorporating I3C into the crystal lattice, the diffraction phase problem can be efficiently solved using single wavelength anomalous dispersion (SAD) phasing. The equilateral triangle arrangement of iodine atoms in I3C allows for rapid validation of a correct anomalous substructure. This protocol will be useful to structural biologists who solve macromolecular structures using crystallography-based techniques with interest in experimental phasing.

This article presents a method to generate protein crystals derivatized with I3C (5-amino-2,4,6-triiodoisophthalic acid) using microseeding to generate new crystallization conditions in sparse matrix screens. The trays can be set up using liquid dispensing robots or by hand.

使用随机微种子基质筛选与 I3C 的蛋白质晶体的衍生

Protein structure elucidation using X-ray crystallography requires both high quality diffracting crystals and computational solution of the diffraction ...

Density Gradient Ultracentrifugation for Investigating Endocytic Recycling in Mammalian Cells

Endosomal trafficking is an essential cellular process that regulates a broad range of biological events. Proteins are internalized from the plasma membrane and then transported to the early endosomes. The internalized proteins could be transited to the lysosome for degradation or recycled back to the plasma membrane. A robust endocytic recycling pathway is required to balance the removal of membrane materials from endocytosis. Various proteins are reported to regulate the pathway, including ADP-ribosylation factor 6 (ARF6). Density gradient ultracentrifugation is a classical method for cell fractionation. After the centrifugation, organelles are sedimented at their isopycnic surface. The fractions are collected and used for other downstream applications. Described here is a protocol to obtain a recycling endosome-containing fraction from transfected mammalian cells using density gradient ultracentrifugation. The isolated fractions were subjected to standard Western blotting for analyzing their protein contents. By employing this method, we identified that the plasma membrane targeting of engulfment and cell motility 1 (ELMO1), a Ras-related C3 botulinum toxin substrate 1 (Rac1) guanine nucleotide exchange factor, is through ARF6-mediated endocytic recycling.

This paper aims to present a protocol for preparing recycling endosomes from mammalian cells using sucrose density gradient ultracentrifugation.

密度梯度超速离心研究哺乳动物细胞内吞循环

Endosomal trafficking is an essential cellular process that regulates a broad range of biological events. Proteins are internalized from the plasma ...

Extraction and Visualization of Protein Aggregates after Treatment of Escherichia coli with a Proteotoxic Stressor

The exposure of living organisms to environmental and cellular stresses often causes disruptions in protein homeostasis and can result in protein aggregation. The accumulation of protein aggregates in bacterial cells can lead to significant alterations in the cellular phenotypic behavior, including a reduction in growth rates, stress resistance, and virulence. Several experimental procedures exist for the examination of these stressor-mediated phenotypes. This paper describes an optimized assay for the extraction and visualization of aggregated and soluble proteins from different Escherichia coli strains after treatment with a silver-ruthenium-containing antimicrobial. This compound is known to generate reactive oxygen species and causes widespread protein aggregation. 
The method combines a centrifugation-based separation of protein aggregates and soluble proteins from treated and untreated cells with subsequent separation and visualization by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and Coomassie staining. This approach is simple, fast, and allows a qualitative comparison of protein aggregate formation in different E. coli strains. The methodology has a wide range of applications, including the possibility to investigate the impact of other proteotoxic antimicrobials on in vivo protein aggregation in a wide range of bacteria. Moreover, the protocol can be used to identify genes that contribute to increased resistance to proteotoxic substances. Gel bands can be used for the subsequent identification of proteins that are particularly prone to aggregation.

Extraction and Visualization of Protein Aggregates after Treatment of Escherichia coli with a Proteotoxic Stressor

This protocol describes the extraction and visualization of aggregated and soluble proteins from Escherichia coli after treatment with a proteotoxic antimicrobial. Following this procedure allows a qualitative comparison of protein aggregate formation in vivo in different bacterial strains and/or between treatments.

用蛋白毒性应激器治疗大 肠杆菌 后蛋白质聚合物的提取和可视化

The exposure of living organisms to environmental and cellular stresses often causes disruptions in protein homeostasis and can result in protein ...

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Untargeted metabolomics techniques are being widely used in recent years. However, the rapidly increasing throughput and number of samples create an enormous amount of spectra, setting challenges for quality control of the mass spectrometry spectra. To reduce the false positives, false discovery rate (FDR) quality control is necessary. Recently, we developed a software for FDR control of untargeted metabolome identification that is based on a Target-Decoy strategy named XY-Meta. Here, we demonstrated a complete analysis pipeline that integrates XY-Meta and metaX together. This protocol shows how to use XY-meta to generate a decoy database from an existing reference database and perform FDR control using the Target-Decoy strategy for large-scale metabolome identification on an open-access dataset. The differential analysis and metabolites annotation were performed after running metaX for metabolites peaks detection and quantitation. In order to help more researchers, we also developed a user-friendly cloud-based analysis platform for these analyses, without the need for bioinformatics skills or any computer languages.

We constructed an untargeted metabolomic workflow that integrated XY-Meta and metaX together. In this protocol, we displayed how to use XY-Meta to generate a decoy spectral library from open access spectra reference, and then performed FDR control and used the metaX to quantitate the metabolites after identifying the metabolomics spectra.

基于FDR对照的非靶向代谢组的鉴定和定量集成工作流程

Untargeted metabolomics techniques are being widely used in recent years. However, the rapidly increasing throughput and number of samples create an ...