Name: creating and applying a reference to facilitate the discussion and classification of proteins in a diverse group
Uploaded: 2017-08-16T14:00:00.000+00:00
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Description: Watch this Scientific Journal Video about Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group at JoVE.com

正在缓步动物抗菌肽的研究得到了校内资金来自美国中西部大学研究办公室和赞助程序 (ORSP)。ORSP 没有任何作用，研究设计、 数据收集、 分析、 解释或手稿的准备。

1.确定定义功能的蛋白质组的 <ol><li>咨询以前的出版物，以确定是否有必要考虑组的一部分的功能方面达成共识。注意到的任何不一致之处或意见研究团体之间的分歧，包括可能有助于区分另一个亚组的特点。</li>
	<li>如果以往文献不能解决最大特色，使用序列作为起点来识别保守的特点被认为集团代表。</li>
</ol> 2。收集有关序列 <ol><li>如果已编写审核，包括分析序列，所代表的集团，包括这些序列中的原始数据集。检索序列使用加入数字文献中引用和保存标准序列编辑程序中 （例如， EditSeq Lasergene 套房或的众多可供一个免费在线）。</li>
	<li>如果问题组已定义在一个结构的数据库，包括数据库列出作为组的一部分使用加入数字提供了在数据库中检索序列的序列并保存在标准序列编辑程序，如上。 
	注: 例如，分类在 CS-α β （蝎子毒素样） 超家族 SCOP 数据库中的序列可以在这里找到: http://scop.mrc-lmb.cam.ac.uk/scop/data/scop.b.h.c.h.html.</li> <li>执行基本本地对齐方式搜索工具 (BLAST) 14 检索公共、 在线数据库可用通过国家中心为生物技术信息中心 (NCBI) 找到可能不在文献中包含的序列或结构数据库。因为大部分完成结果、 使用这两种蛋白质爆炸 (blastp) 和翻译爆炸与蛋白 (tblastn) 查询程序;这些都可在: https://blast.ncbi.nlm.nih.gov/Blast.cgi.
	<ol><li>使用序列已知作为查询序列是感兴趣的组的一部分。复制和粘贴在顶部，搜索框中的序列或提供基因加入号码或 gi 标识符，如果可用。</li>
		<li>从下拉菜单中选择的数据库。选择非冗余蛋白质序列 (nr) 为 blastp 和表达序列标签为 tblastn.</li>
		<li>搜索结果体内设置键入有机体或分类单元的名称，从打字时出现的列表中选择的特定类群。若要添加其他有机体或类群中排除，请单击 &#34; + &#34; 按钮和另一个字段将出现。排除任何不需要的罗汗松在有机体框中键入的有机体或分类单元的名称，从同时键入，并检查显示的列表中选择 &#34; 排除 &#34; 右侧框。</li>
		<li>通过单击访问附加参数 &#34; 算法参数 &#34; 附近的页的底部。保留默认值除非有理由要改变一个参数。</li>
		单击 <li>&#34; 高炉 &#34; 按钮以运行分析; 它可能需要一些时间来显示结果。一般情况下，检索命中与预期值 （或 e-值） 的 &#34;-05 &#34; 或更好的与标准序列编辑程序中保存。
		<ol><li>如果所有点击数高于此阈值，重新运行搜索与日益增多的靶序列 （在算法参数部分） 以获得所有相关序列。</li>
		</ol></li></ol></li> <li>如有必要，修剪序列以排除不相关的信息 （例如， CS-α β 折叠仅适用于成熟肽）。识别信号肽和 pro 肽去除使用道具 15 (可用在线)，或为更复杂的信号肽预测 16 SignalP (可在线)。</li>
</ol> 3。在电子表格中基于重要特征，确定了生成引用 <ol><li>标识的组的最大特色。例如，使用 CS-α β 折叠明确建立由昆虫防御素 Phormia terraenovae ( 图 1) 8 A 的解决方案结构。
	<ol><li>这折叠包括一个小的主题，称为半胱氨酸稳定螺旋 (CSH) 17; 确定这一主题的 CXXXC （其中 X 是任何氨基酸） 上游形成两个二硫键 CXC ( 图 1、 固体粉红色线条)。 
		注: 要完成 CS-α β 母题，第三个二硫键形成从额外半胱氨酸，摆在每个半场 CSH 图案 ( 图 1，点缀粉色线) 面前。</li>
	</ol></li> <li>输入这些到电子表格中定义的功能。请参见 图 2。
	<ol><li>使用列的保守的功能，并表示这些功能之间的空格。保持足够宽，以便容纳数字并确保他们有一个一致的宽度的列。设置宽度使用 &#34; 格式 |列宽度 &#34; 功能 ( 图 2，粉红色的箭头)。</li>
		<li>序列名称中使用行。</li>
		<li>当序列具有的特点，填写框中使用的填充函数 ( 图 2，粉红色广场)。功能部件之间的间距，之间的框中输入氨基酸数目并保持空缺。例如，利用昆虫防御素序列给出了包括六个半胱氨酸、 定义间距以及 C5 和 C6 之间 C2 和 C3 的引用。</li>
	</ol></li> <li>添加代表此前已建立的序列作为基于结构数据库和文学组的成员。 
	注: 例如，前面的文献和 SCOP 数据库确定列入几个群体: 昆虫防御素、 短链蝎毒素、 长链蝎毒素 MGD 1、 植物防御素、 ABFs 线虫、 果蝇， 从 drosomycins 和macins。文学也标识可能代表 18 本家族的祖先的只有四个半胱氨酸与细菌的序列。添加这些序列增加半胱氨酸在六至十参考但保持重要的结构特征 ( 图 3) 的对齐方式。
	<ol><li>若要添加一项功能，很容易定义序列 （例如，额外的半胱氨酸） 的一个子群，使用 &#34; 插入 &#34; 功能 ( 图 3，粉红色的箭头)。</li>
		<li>如果有的功能缺失从一个给定的序列，将填充框和结合代表介入氨基酸盒子。如有必要，合并单元格使用合并及居中功能 ( 图 3，粉红色的盒子)。</li>
	</ol></li> <li>继续将序列添加到的组，以更好地了解每个组的大家族中的变化。总结的群体特征，以便利比较 ( 图 4)。
	<ol><li>氨基酸之间主要特征数目变化时，使用连字符来指示一个范围，如 6-12 （6 至 12 氨基酸） 和一个斜杠，表明不是 / 或者，如 7/10 （7 或 10 氨基酸）。</li>
		<li>选择注释的序列可能有关，但不会经常发生，在引用中包括的功能的方式。例如，由于半胱氨酸是重要在这个家族中，标签附加半胱氨酸 ( 图 4，粉红色框)。</li>
	</ol></li> <li>添加 newly 确定序列到电子表格中使用既定的序列作为指南。例如，从缓步动物 （黄色） 添加序列显示缓步序列分为几个不同的家族 （ 图 5 显示摘要而不是每个层序空间用于行）。</li>
	<li>通过重新排列行 ( 图 6) 显示分类组内的变异。</li>
</ol> 4。使用改进的氨基酸排列的引用 注: 有很多的程序，可以用于多序列比对，但这个示范将使用分子进化遗传分析 (MEGA6) 19 因为它是可供免费下载。
<ol><li>下载并安装软件。</li>
	<li>通过选择开始新的对准线在巨型 &#34; 编辑/生成对齐 &#34; 下对齐选项卡选择 &#34; 创建一个新的路线 &#34; 在框中出现，请单击 &#34; OK。 &#34; 然后选择 &#34; 蛋白质。 &#34;</li>
	<li>选择 &#34; 从文件插入序列 &#34; 在 &#34; 编辑 &#34; 菜单导入序列。 
	注意: 序列将需要在 FASTA 格式导入到大型。默认情况下，使用反映不同氨基酸类型的背景颜色，但此选项可以关闭下 &#34; 显示 &#34; 菜单。</li>
	<li>一旦输入所有序列，请单击伸缩臂图标，然后 &#34; 对齐蛋白 &#34; 来对齐序列使用肌肉算法 20。 
	注: ClustalW 也是可用的。
	<ol><li>如果一个消息，说什么已经选定的持久性有机污染物和要求来选择所有，单击 &#34; OK。 &#34;</li> <li>注: 这将打开一个窗口，允许一个人能改变一些参数，但他们，才应该更改理由这样做。这种分析使用子集序列分析在先前的纸 12.</li>
	</ol></li> <li>检查对齐方式的基础的重要特征; 请注意，上面的序列的顶部栏将显示的任何列氨基酸在哪里完全保守 （*）。请参阅 图 7。请参阅初始对准显示只有三个四个保守半胱氨酸 ( 图 7，粉红色框);俯视列中，AlCRP 序列是明确未对齐 ( 图 7，粉红色的箭头)。</li>
	<li>要摆脱我的大差距和保守的 C，突出显示的短划线和新闻 &#34; 删除 &#34; 的关键。不突出任何氨基酸，或他们将同时删除。</li>
	<li>移动到右边的氨基酸，突出显示，按空间栏
	<ol><li>注 AlCRP 现在已经对齐结构半胱氨酸和 C 最后的 CXXXC 主题守恒整个对齐 ( 图 8)。调整对齐方式，有必要优先序列的最重要特点。</li>
	</ol></li></ol> 5。比较组确定使用从系统发育分析结果与参考 <ol><li>从初步的路线，确定哪些序列应包含在系统发育分析; 对于少量的序列，这一步可能是不必要的。
	<ol><li>保留对齐文件包含所有的序列，但系统发育分析，将删除冗余序列 （ 图 9，粉红色框显示对的冗余序列）。</li>
		<li>如果数据集包含大量的序列，运行初步分析和选择代表从群体总是形成分支。</li>
	</ol></li> <li>确定最佳的氨基酸替代模式。
	<ol><li>出口巨型格式 （在数据选项卡） 中的对齐方式。</li>
		<li>转到模型菜单并选择 &#34; 找到最佳 DNA/蛋白质模型。 &#34; 选择刚刚保存的文件并打开它; 这将打开一个窗口，可以更改某些参数。</li>
		<li>使用默认参数，除非有理由去改变它们。单击 &#34; 计算 &#34; 开始分析。</li>
	</ol></li> <li>运行的最大似然 (ML) 分析在巨型。
	<ol><li>选择 &#34; 构建/测试最大似然树 &#34; 从发展史上菜单。</li>
		<li>选择确定的模型是最适合的数据从步 5.2 (输出就能替代模型，最好 &#34; 率网站 &#34; 参数)。</li>
		<li>选择 1,000 引导复制以获得对树的支持措施。</li>
		单击 <li>&#34; 计算 &#34; 运行分析;巨型有 &#34; 树资源管理器 &#34; 可视化树。</li>
	</ol></li> <li>MrBayes 开放源码软件 21 运行贝叶斯分析。 
	注: MrBayes 手册是也可从本网站。这只提供基本步骤并不是进行贝叶斯系统发育分析的综合指南。
	<ol><li>将巨型对齐在波普 (Nexus) 格式导出到 MrBayes 程序相同的文件夹中。</li>
		<li>开放 MrBayes 和类型 &#34; exe 文件名 &#34; (例如， &#34; exe Alignment.nex &#34;)。</li>
		<li>指定的模型和分析参数。选择在步骤 5.2 中指定的任一模型或选择 &#34; 混合 &#34; 设置，会尝试各种型号和报告在树上与最好的后验概率模型的频率 (prset aamodelpr = 混合)。类型 &#34; showmodel &#34; 报告当前的模型设置和 &#34; 帮助 mcmc &#34; 显示当前参数设置，与每个的简要说明。</li>
		<li>设置的数代使用 &#34; mcmcp ngen = &#34; 命令 （100 万是典型）。</li>
		<li>型 &#34; mcmc &#34; 开始分析。</li>
		<li>代数目已完成时，程序会要求要添加更多的后代。如果分裂频率平均标准偏差均小于 0.1，键入 no。如果它是高于 0.1，分析应该再继续下去，或一些参数应改变 （见手册 》）。</li>
		<li>使用 &#34; sumt &#34; 命令以生成树文件。</li>
		<li>完成分析并生成共识树之后，可以在忐忑不安中查看树 (可在线)。</li>
	</ol></li> <li>比较，树木可以看到是否方法生成一致的结果。 
	注意: 一些序列并不提供大量的信息: 树可能不是很好的解决和分公司，可能有极少的支持 ( 图 10)。</li>
	<li>把确定使用引用来看看是否系统发育分析支持这些群体的群体树木比作。</li>
</ol>

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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>Ronquist, F., Huelsenbeck, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MrBayes+3:+Bayesian+phylogenetic+inference+under+mixed+models.">MrBayes 3: Bayesian phylogenetic inference under mixed models.</a> Bioinformatics. 19 (12), 1572-1574 (2003).</li><li>Altschul, S. F., 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=Gapped+BLAST+and+PSI-BLAST:+a+new+generation+of+protein+database+search+programs.">Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.</a> Nucleic Acids Res. 25 (17), 3389-3402 (1997).</li><li>Zhang, Z., 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=Protein+sequence+similarity+searches+using+patterns+as+seeds.">Protein sequence similarity searches using patterns as seeds.</a> Nucleic Acids Res. 26 (17), 3986-3990 (1998).</li></ol>

作者没有透露。

命名组内的蛋白质标准应该是明确的但事实并非总是如此。在许多实验室使用各种各样的生物，导致在不同系统中的术语，以及不同程度的表征，研究了有 CS-α β 折叠的序列。企图强加一个完全新的命名法是不合理的会导致大量的混乱时以前文献。参考编号系统可以蛋白质名称用于澄清其与家族的特征。
组蛋白的明确标准的命名和分类将不可能受益于生成参考在电子表格中?...

蛋白质的名称应该反映是特征及与其他蛋白质的关系。不幸的是，名称通常会发现当时被分配，并且，随着研究的继续，较大范围的理解可能更改。如果蛋白质被独立地识别由多个实验室，变化在命名或被认为是明确分配名称时的特点和充足地不再区分蛋白质的名称，这会导致多个名称从别人。
无脊椎动物防御提供很好的例子，变性的命名和分类。第一次的无脊椎动物防御宗从昆虫，和名称"昆虫防御素"，提出了基于感知的同源性为哺乳动物防御素1，2。长期防御素仍然使用，即使它是现在很清楚那无脊椎动物及哺乳动物防御素不共享一个共同的祖先3，4。根据物种，"防御"无脊椎动物可能有六个或八个半胱氨酸 （即形成三个或四个二硫键） 和各种抗菌活性。防止局势进一步复杂化，具有相同特征的蛋白质称为防御素是不总是"防御，"如最近发现的 cremycins 从秀丽 remanei5。此外，无脊椎动物大防御素是更有可能进化有关脊椎动物 β-防御素比到其他无脊椎动物防御6。尽管这样，研究人员有时依赖名称"防御"确定哪些序列应列入分析时。
结构的研究揭示昆虫防御素与蝎子毒素7，之间的相似性和 CS-α β 折叠随后成立作为昆虫防御8的定义结构特征。这折叠结构蛋白质分类 (SCOP) 数据库中9，目前包括五个家庭定义蝎子毒素样 （CS-α β） 超家族: 昆虫防御素、 短链蝎毒素、 长链蝎毒素，MGD 1 （从软体动物） 和植物防御素。这个家族是最近描述的独联体防御4和蛋白酶基因 3D 数据库10，11超 3.30.30.10 家族的代名词。从繁多的无脊椎动物类群、 植物和真菌显示的名称包含这折叠的蛋白质显然无关的半胱氨酸数量的抗菌活性或键合模式、 进化历史12的研究。
缺乏一致性和明确的标准，使它具有挑战性进行命名和分类在这个家族中的新发现序列。比较此超家族蛋白的主要障碍是半胱氨酸的编号对每个单独序列 （每个序列中的第一个半胱氨酸是 C1），没有办法解释的结构的作用。这意味着只有序列与相同数量的半胱氨酸可以进行比较。还有小序列保守性非半胱氨酸形成 CS-α β 折叠，使线路及系统发育分析困难。通过开发划分结构功能优先级编号系统，可以更轻松地比较和对齐家族序列。保守的特点，以及那些定义分组，可以快速，可视化和新序列可以更轻松地放入适当的上下文。
本文使用一个电子表格软件 （如Excel） 来生成参考编号系统的 CS-α β 超家族。它显示如何这澄清序列之间的比较，并将其应用于新的 CS-α β 序列确定从缓步动物。使用 CS-α β 超家族作为一个例子，是写议定书 》 提供指导，当使用序列的兴趣;然而，它不是要具体到这个家族或富含半胱氨酸序列。此方法可能会最有用的蛋白质，有独立研究在不同类群和 （或） 有小的整体序列同源性，与离散性的特征，不可能很容易识别的分子分析软件组。此方法要求一些先验的决定，有关重要的功能，所以它将有限的公共事业，如果没有重要的特点，确定了。主要的目标是展示如何可以实现一个简单的可视化的序列关系。这然后可以用于通知序列比对和分析，但如果对齐方式和分析的主要目标，条形码方法会有更多的容量，为自动化13个合适的选择。当前方法的线性形式，显示每个肽的特点，所以它不会有助于直接可视化的三维结构。

<table><tbody><tr><td>BLAST webpage</td><td></td><td></td><td>https://blast.ncbi.nlm.nih.gov/Blast.cgi</td></tr><tr><td>EditSeq (Lasergene suite)</td><td>DNASTAR</td><td></td><td>https://www.dnastar.com/t-allproducts.aspx</td></tr><tr><td>Excel 2013</td><td>Microsoft</td><td></td><td></td></tr><tr><td>FigTree</td><td></td><td></td><td>&amp;nbsp;http://tree.bio.ed.ac.uk/software/figtree/</td></tr><tr><td>MEGA</td><td></td><td></td><td>www.megasoftware.net</td></tr><tr><td>MrBayes</td><td></td><td></td><td>http://mrbayes.sourceforge.net/</td></tr><tr><td>SCOP database</td><td></td><td></td><td>http://scop.mrc-lmb.cam.ac.uk/scop/</td></tr></tbody></table>

creating and applying a reference to facilitate the discussion and classification of proteins in a diverse group

研究了在不同的实验室使用不同生物体的相关的蛋白可能缺乏统一的制度的命名和分类，制作困难，讨论组作为一个整体并放置到适当的上下文的新序列。发展优先重要序列特征的引用有关的结构和 （或) 活动可用于除规定名称向不同的蛋白质组中添加一些相干。本文使用的半胱氨酸稳定 α 螺旋 （CS-α β） 超家族作为一个例子显示如何在电子表格软件中生成的引用可以澄清现有蛋白超家族成员之间的关系，以及促进新的加法序列。它还演示如何引用可以帮助完善中常用的软件，系统发育分析的有效性的影响生成的序列比对。引用的使用可能会对于蛋白质组包含高度不同的序列，从广泛的类群，具有各种功能，不充分的分子分析捕捉到最有帮助。

序列在文献报道的 CS-α β 超家族中的组如图 4所示。半胱氨酸配对的基础，每个序列的编号表明五个基本组 (表 1，中间列)。第一小组有六个半胱氨酸，从三个二硫键债券，包括昆虫、 蛛形纲动物、 软体动物、 线虫、 真菌从序列。组 2、 3 和 4 已形成四个二硫键的 8 半胱氨酸。第二组包括昆虫、 蜘蛛纲动物和植物序列;第 3 组包括蛛形?...

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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

创建和应用的参考，以便不同的组中的蛋白质分类与探讨

本议定书的目标是制定一个基准不同蛋白质缺乏连贯一致的命名和分类标准的组中。此引用有助于分析和小组讨论作为一个整体，可采用除规定名称。

Related proteins that have been studied in different labs using varying organisms may lack a uniform system of nomenclature and classification, making it ...

creating-applying-reference-to-facilitate-discussion-classification

Research

JoVE Journal

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7.0K 次观看.  Midwestern University. 本议定书的目标是制定一个基准不同蛋白质缺乏连贯一致的命名和分类标准的组中。此引用有助于分析和小组讨论作为一个整体，可采用除规定名称。

视频: Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

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

We demonstrate the usage of Bio3D-web for the interactive analysis of biomolecular structure data. The Bio3D-web application provides online functionality for: (1) The identification of related protein structure sets to user specified thresholds of similarity; (2) Their multiple alignment and structure superposition; (3) Sequence and structure conservation analysis; (4) Inter-conformer relationship mapping with principal component analysis, and (5) comparison of predicted internal dynamics via ensemble normal mode analysis. This integrated functionality provides a complete online workflow for investigating sequence-structure-dynamic relationships within protein families and superfamilies.

A protocol for the online investigation of protein sequence-structure-dynamics relationships using Bio3D-web is presented.

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 ...

科研

生物化学

mRNA Interactome Capture from Plant Protoplasts

RNA-binding proteins (RBPs) determine the fates of RNAs. They participate in all RNA biogenesis pathways and especially contribute to post-transcriptional gene regulation (PTGR) of messenger RNAs (mRNAs). In the past few years, a number of mRNA-bound proteomes from yeast and mammalian cell lines have been successfully isolated through the use of a novel method called &#34;mRNA interactome capture,&#34; which allows for the identification of mRNA-binding proteins (mRBPs) directly from a physiological environment. The method is composed of in vivo ultraviolet (UV) crosslinking, pull-down and purification of messenger ribonucleoprotein complexes (mRNPs) by oligo(dT) beads, and the subsequent identification of the crosslinked proteins by mass spectrometry (MS). Very recently, by applying the same method, several plant mRNA-bound proteomes have been reported simultaneously from different Arabidopsis tissue sources: etiolated seedlings, leaf tissue, leaf mesophyll protoplasts, and cultured root cells. Here, we present the optimized mRNA interactome capture method for Arabidopsis thaliana leaf mesophyll protoplasts, a cell type that serves as a versatile tool for experiments that include various cellular assays. The conditions for optimal protein yield include the amount of starting tissue and the duration of UV irradiation. In the mRNA-bound proteome obtained from a medium-scale experiment (107 cells), RBPs noted to have RNA-binding capacity were found to be overrepresented, and many novel RBPs were identified. The experiment can be scaled up (109 cells), and the optimized method can be applied to other plant cell types and species to broadly isolate, catalog, and compare mRNA-bound proteomes in plants.

Here, we present an interactome capture protocol applied to Arabidopsis thaliana leaf mesophyll protoplasts. This method critically relies on in vivo UV crosslinking and allows for the isolation and identification of plant mRNA-binding proteins from a physiological environment.

mRNA从植物原生质体捕获

RNA-binding proteins (RBPs) determine the fates of RNAs. They participate in all RNA biogenesis pathways and especially contribute to post-transcriptional ...

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Genome annotation is central to today's proteomic research as it draws the outlines of the proteomic landscape. Traditional models of open reading frame (ORF) annotation impose two arbitrary criteria: a minimum length of 100 codons and a single ORF per transcript. However, a growing number of studies report expression of proteins from allegedly non-coding regions, challenging the accuracy of current genome annotations. These novel proteins were found encoded either within non-coding RNAs, 5' or 3' untranslated regions (UTRs) of mRNAs, or overlapping a known coding sequence (CDS) in an alternative ORF. OpenProt is the first database that enforces a polycistronic model for eukaryotic genomes, allowing annotation of multiple ORFs per transcript. OpenProt is freely accessible and offers custom downloads of protein sequences across 10 species. Using OpenProt database for proteomic experiments enables novel proteins discovery and highlights the polycistronic nature of eukaryotic genes. The size of OpenProt database (all predicted proteins) is substantial and need be taken in account for the analysis. However, with appropriate false discovery rate (FDR) settings or the use of a restricted OpenProt database, users will gain a more realistic view of the proteomic landscape. Overall, OpenProt is a freely available tool that will foster proteomic discoveries.

OpenProt is a freely accessible database that enforces a polycistronic model of eukaryotic genomes. Here, we present a protocol for the use of OpenProt databases when interrogating mass spectrometry datasets. Using OpenProt database for analysis of proteomic experiments allows for discovery of novel and previously undetectable proteins.

基于质谱的蛋白质组学分析, 利用 OpenProt 数据库揭开从非规范开放阅读框架翻译的新蛋白质

Genome annotation is central to today's proteomic research as it draws the outlines of the proteomic landscape. Traditional models of open reading frame ...

遗传学

Characterization of Proteins by Size-Exclusion Chromatography Coupled to Multi-Angle Light Scattering (SEC-MALS)

Analytical size-exclusion chromatography (SEC), commonly used for the determination of the molecular weight of proteins and protein-protein complexes in solution, is a relative technique that relies on the elution volume of the analyte to estimate molecular weight. When the protein is not globular or undergoes non-ideal column interactions, the calibration curve based on protein standards is invalid, and the molecular weight determined from elution volume is incorrect. Multi-angle light scattering (MALS) is an absolute technique that determines the molecular weight of an analyte in solution from basic physical equations. The combination of SEC for separation with MALS for analysis constitutes a versatile, reliable means for characterizing solutions of one or more protein species including monomers, native oligomers or aggregates, and heterocomplexes. Since the measurement is performed at each elution volume, SEC-MALS can determine if an eluting peak is homogeneous or heterogeneous and distinguish between a fixed molecular weight distribution versus dynamic equilibrium. Analysis of modified proteins such as glycoproteins or lipoproteins, or conjugates such as detergent-solubilized membrane proteins, is also possible. Hence, SEC-MALS is a critical tool for the protein chemist who must confirm the biophysical properties and solution behavior of molecules produced for biological or biotechnological research. This protocol for SEC-MALS analyzes the molecular weight and size of pure protein monomers and aggregates. The data acquired serve as a foundation for further SEC-MALS analyses including those of complexes, glycoproteins and surfactant-bound membrane proteins.

Characterization of Proteins by Size-Exclusion Chromatography Coupled to Multi-Angle Light Scattering SEC-MALS

This protocol describes the combination of size exclusion chromatography with multi-angle light scattering (SEC-MALS) for absolute characterization of proteins and complexes in solution. SEC-MALS determines the molecular weight and size of pure proteins, native oligomers, heterocomplexes and modified proteins such as glycoproteins.

蛋白质的表征(按尺寸-排除色谱与多角度光散射相结合(SEC-MALS)

Analytical size-exclusion chromatography (SEC), commonly used for the determination of the molecular weight of proteins and protein-protein complexes in ...

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

Proteomic technologies are powerful methodologies that can aid our understanding of mechanisms of action in biological systems by providing a global view of the impact of a disease, treatment, or other condition on the proteome as a whole. This report provides a detailed protocol for the extraction, quantification, precipitation, digestion, labeling, and subsequent data analysis of protein samples. Our optimized TMT labeling protocol requires a lower tag-label concentration and achieves consistently reliable data. We have used this protocol to evaluate protein expression profiles in a variety of mouse tissues (i.e., heart, skeletal muscle, and brain) as well as cells cultured in vitro. In addition, we demonstrate how to evaluate thousands of proteins from the resulting dataset.

We present an optimized tandem mass tag (TMT) labeling protocol that includes detailed information for each of the following steps: protein extraction, quantification, precipitation, digestion, labeling, submission to a proteomics facility, and data analyses.

蛋白质组学设施提交和后续数据分析的TMT样品制备

Proteomic technologies are powerful methodologies that can aid our understanding of mechanisms of action in biological systems by providing a global view ...

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. However, deriving biological insights from such valuable datasets is challenging. Here we introduce a systems biology-based software JUMPn, and its associated protocol to organize the proteome into protein co-expression clusters across samples and protein-protein interaction (PPI) networks connected by modules (e.g., protein complexes). Using the R/Shiny platform, the JUMPn software streamlines the analysis of co-expression clustering, pathway enrichment, and PPI module detection, with integrated data visualization and a user-friendly interface. The main steps of the protocol include installation of the JUMPn software, the definition of differentially expressed proteins or the (dys)regulated proteome, determination of meaningful co-expression clusters and PPI modules, and result visualization. While the protocol is demonstrated using an isobaric labeling-based proteome profile, JUMPn is generally applicable to a wide range of quantitative datasets (e.g., label-free proteomics). The JUMPn software and protocol thus provide a powerful tool to facilitate biological interpretation in quantitative proteomics.

We present a systems biology tool JUMPn to perform and visualize network analysis for quantitative proteomics data, with a detailed protocol including data pre-processing, co-expression clustering, pathway enrichment, and protein-protein interaction network analysis.

JUMPn:蛋白质共表达聚类和网络分析在蛋白质组学中的简化应用

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. ...

An Optimized Quantitative Pull-Down Analysis of RNA-Binding Proteins Using Short Biotinylated RNA

Protein-RNA interactions regulate gene expression and cellular functions at transcriptional and post-transcriptional levels. For this reason, identifying the binding partners of an RNA of interest remains of high importance to unveil the mechanisms behind many cellular processes. However, RNA molecules might interact transiently and dynamically with some RNA-binding proteins (RBPs), especially with non-canonical ones. Hence, improved methods to isolate and identify such RBPs are greatly needed.
To identify the protein partners of a known RNA sequence efficiently and quantitatively, we developed a method based on the pull-down and characterization of all interacting proteins, starting from cellular total protein extract. We optimized the protein pull-down using biotinylated RNA pre-loaded on streptavidin-coated beads. As a proof of concept, we employed a short RNA sequence known to bind the neurodegeneration-associated protein TDP-43 and a negative control of a different nucleotide composition but the same length. After blocking the beads with yeast tRNA, we loaded the biotinylated RNA sequences on the streptavidin beads and incubated them with the total protein extract from HEK 293T cells. After incubation and several washing steps to remove nonspecific binders, we eluted the interacting proteins with a high-salt solution, compatible with the most commonly used protein quantification reagents and with sample preparation for mass spectrometry. We quantified the enrichment of TDP-43 in the pull-down performed with the known RNA binder compared to the negative control by mass spectrometry. We used the same technique to verify the selective interactions of other proteins computationally predicted to be unique binders of our RNA of interest or of the control. Finally, we validated the protocol by western blot via the detection of TDP-43 with an appropriate antibody. 
This protocol will allow the study of the protein partners of an RNA of interest in near-to-physiological conditions, helping uncover unique and unpredicted protein-RNA interactions.

Here, we present an optimized in vitro method to uncover, quantify, and validate protein interactors of specific RNA sequences, using total protein extract from human cells, streptavidin beads coated with biotinylated RNA, and mass spectrometry analysis.

使用短生物素化RNA对RNA结合蛋白进行优化的定量下拉分析

Protein-RNA interactions regulate gene expression and cellular functions at transcriptional and post-transcriptional levels. For this reason, identifying ...

A Protocol for Computer-Based Protein Structure and Function Prediction

Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the understanding of their biological role. Although experimental methods can provide detailed information for a small fraction of these proteins, computational modeling is needed for the majority of protein molecules which are experimentally uncharacterized. The I-TASSER server is an on-line workbench for high-resolution modeling of protein structure and function. Given a protein sequence, a typical output from the I-TASSER server includes secondary structure prediction, predicted solvent accessibility of each residue, homologous template proteins detected by threading and structure alignments, up to five full-length tertiary structural models, and structure-based functional annotations for enzyme classification, Gene Ontology terms and protein-ligand binding sites. All the predictions are tagged with a confidence score which tells how accurate the predictions are without knowing the experimental data. To facilitate the special requests of end users, the server provides channels to accept user-specified inter-residue distance and contact maps to interactively change the I-TASSER modeling; it also allows users to specify any proteins as template, or to exclude any template proteins during the structure assembly simulations. The structural information could be collected by the users based on experimental evidences or biological insights with the purpose of improving the quality of I-TASSER predictions. The server was evaluated as the best programs for protein structure and function predictions in the recent community-wide CASP experiments. There are currently &gt;20,000 registered scientists from over 100 countries who are using the on-line I-TASSER server.

Guidelines for computer based structural and functional characterization of protein using the I-TASSER pipeline is described. Starting from query protein sequence, 3D models are generated using multiple threading alignments and iterative structural assembly simulations. Functional inferences are thereafter drawn based on matches to proteins with known structure and functions.

基于计算机的蛋白质结构与功能预测议定书

Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the ...

生物学

Interactome-Seq: A Protocol for Domainome Library Construction, Validation and Selection by Phage Display and Next Generation Sequencing

Folding reporters are proteins with easily identifiable phenotypes, such as antibiotic resistance, whose folding and function is compromised when fused to poorly folding proteins or random open reading frames. We have developed a strategy where, by using TEM-1 &#946;-lactamase (the enzyme conferring ampicillin resistance) on a genomic scale, we can select collections of correctly folded protein domains from the coding portion of the DNA of any intronless genome. The protein fragments obtained by this approach, the so called &#34;domainome&#34;, will be well expressed and soluble, making them suitable for structural/functional studies.
By cloning and displaying the &#34;domainome&#34; directly in a phage display system, we have showed that it is possible to select specific protein domains with the desired binding properties (e.g., to other proteins or to antibodies), thus providing essential experimental information for gene annotation or antigen identification.
The identification of the most enriched clones in a selected polyclonal population can be achieved by using novel next-generation sequencing technologies (NGS). For these reasons, we introduce deep sequencing analysis of the library itself and the selection outputs to provide complete information on diversity, abundance and precise mapping of each of the selected fragment. The protocols presented here show the key steps for library construction, characterization, and validation.

The protocols described allow the construction, characterization and selection (against the target of choice) of a &#34;domainome&#34; library made from any DNA source. This is achieved by a research pipeline that combines different technologies: phage display, a folding reporter and next generation sequencing with a web tool for data analysis.

Interactome: Domainome 图书馆建设、噬菌体展示和下一代测序的验证与选择协议

Folding reporters are proteins with easily identifiable phenotypes, such as antibiotic resistance, whose folding and function is compromised when fused to ...

An Integrated Approach for Microprotein Identification and Sequence Analysis

Next-generation sequencing (NGS) has propelled the field of genomics forward and produced whole genome sequences for numerous animal species and model organisms. However, despite this wealth of sequence information, comprehensive gene annotation efforts have proven challenging, especially for small proteins. Notably, conventional protein annotation methods were designed to intentionally exclude putative proteins encoded by short open reading frames (sORFs) less than 300 nucleotides in length to filter out the exponentially higher number of spurious noncoding sORFs throughout the genome. As a result, hundreds of functional small proteins called microproteins (&#60;100 amino acids in length) have been incorrectly classified as noncoding RNAs or overlooked entirely.
Here we provide a detailed protocol to leverage free, publicly available bioinformatic tools to query genomic regions for microprotein-coding potential based on evolutionary conservation. Specifically, we provide step-by-step instructions on how to examine sequence conservation and coding potential using Phylogenetic Codon Substitution Frequencies (PhyloCSF) on the user-friendly University of California Santa Cruz (UCSC) Genome Browser. Additionally, we detail steps to efficiently generate multiple species alignments of identified microprotein sequences to visualize amino acid sequence conservation and recommend resources to analyze microprotein characteristics, including predicted domain structures. These powerful tools can be used to help identify putative microprotein-coding sequences in noncanonical genomic regions or to rule out the presence of a conserved coding sequence with translational potential in a noncoding transcript of interest.

The protocol described here provides detailed instructions on how to analyze genomic regions of interest for microprotein-coding potential using PhyloCSF on the user-friendly UCSC Genome Browser. Additionally, several tools and resources are recommended to further investigate sequence characteristics of identified microproteins to gain insight into their putative functions.

一种用于微蛋白鉴定和序列分析的综合方法

Next-generation sequencing (NGS) has propelled the field of genomics forward and produced whole genome sequences for numerous animal species and model ...