这项工作是由赠款从威康信托基金会和BBSRC到IG的支持。中空玻璃是一种惠康高级研究员。

1，产生和SILAC标记的细胞株传代注意：必须避免在传代实验样品进行分析和制备的各个阶段中使用胰蛋白酶-EDTA作为胰蛋白酶可能包含未标记的氨基酸，这会导致样品的不完整的标签。 <ol><li>以制备瓶SILAC媒体的添加0.5毫升等分适当SILAC标记的精氨酸（84 mg / ml的PBS中）和赖氨酸（146毫克/毫升在PBS中），以500毫升的瓶子中（含有精氨酸/赖氨酸自由DMEM中L-谷氨酰胺）。 </li><li>接着，加入50 mL透析胎牛血清和5 ml青霉素/链霉素的500毫升瓶装的。 注意：HEK 293T细胞（ATCC）应维持在DMEM培养基缺乏精氨酸和赖氨酸和补充光（R0K0），中（R6K4）或重（R10K8）氨基酸，透析的胎牛血清（10 kDa的截止），以及青霉素/链霉素。细胞必须维持在介质的最低5细胞分裂，以确保完全标签。在大多数情况下，细胞可以容易地标记在≥2周。 </li><li>分裂细胞传代，细胞应该从单层击中烧瓶脱落。替代方案包括使用细胞刮刀或通过使用无酶，PBS系的细胞解离缓冲液。 注：为了节省媒体，细胞应在传代T25烧瓶和较大的手机号码仅前已实验产生的。 </li><li> 24小时转染细胞之前，种子3.5×10 6 SILAC标记的细胞变成一个10 平方厘米的菜被调查每个实验条件。 </li></ol>标记的细胞用质粒pEGFP-融合构建体的2。转<ol><li>从细胞中取出介质，并用9ml无抗生素的DMEM SILAC（轻型，中型或重型）纸张取代。 注：抗生素不应该加入到培养基，因为它们可能具有的基于脂质体的转染试剂的效率干扰。 </li><li>准备相重为10微克合适的质粒混合（epEGFP-C1的pEGFP-eIF4A-I的pEGFP-eIF4A-Ⅱ）在500μL无抗生素SILAC DMEM中，用500μl不含抗生素的SILAC含10混在一微升转染试剂（ 例如 ，脂质体2000）。吹打向上和向下几次混合反应彻底。 </li><li>孵育反应混合物在室温下搅拌20分钟，然后加滴加至细胞单层。从侧面轻轻摇动平板到另一边。 </li><li>培养转染的细胞在37℃和10％的CO 2为24小时。 注意：如果感兴趣的结果在任何明显毒性的蛋白质的表达，那么它可能有必要降低质粒转染的量和/或随后的表​​达时期的持续时间。 </li></ol> 3，收获细胞裂解液<ol><li>收获细胞，用细胞刮刀菜入冰冷的PBS中。通过离心分离，在220×g离心收集细胞，476，C 5分钟。在10ml的冰冷的PBS洗涤细胞进一步三倍。 </li><li>重悬细胞沉淀在200微升细胞溶解缓冲液含有新加入的蛋白酶抑制剂混合物III在5微升1倍浓度和RNA酶鸡尾酒（可选）（10毫米的Tris·Cl /液pH7.5，150 mM氯化钠，0.5毫摩尔EDTA，0.5％NP40）每毫升。 注意：对于具有已知核酸结合活性的蛋白质，这可能是必要的核酸酶加至沉淀之前的裂解液。在核酸酶被添加的情况下，样品应在冰上温育在冰上30分钟，用移液每隔10分钟。从样品和分析通过琼脂糖凝胶电泳的一小部分中提取总核酸可以测试核酸酶的有效性。 </li><li>离心样品以13,000×g离心，4℃，10分钟保留上清为可溶的细胞裂解物。 </li><li>细胞裂解物的浓度应通过BCA测定根据制造商的说明进行评估。 </li><li>用裂解缓冲液含有蛋白酶抑制剂混合物III正常化蛋白浓度在500微升的最终体积。 </li><li>调整音量，以1毫升加入500微升的稀释缓冲液含有蛋白酶抑制剂混合物III在最终1倍浓度（10毫米的Tris·Cl /液pH7.5，150 mM氯化钠，0.5 mM EDTA）中。样本的50微升等分应予保留，作为样本输入和裂解液放在冰上，同时制备抗GFP珠（ 例如 GFP陷阱）。 注：通常收益率在最后1ml样品之间1-3.5毫克蛋白质的不同而不同。虽然上述缓冲器适合于许多感兴趣的蛋白质，对于其他人可能需要修改缓冲器组件，以确保诱饵蛋白可溶化并保持蛋白质 - 蛋白质相互作用。可能的改变包括缓冲剂（磷酸盐，HEPES），盐浓度（150-500毫米），洗涤剂的选择，或其它添加剂。 </li></ol> 4，绑定到抗-GFP珠<ol><li>短暂涡旋振荡珠浆，以重悬珠。用200μl枪头年底切断，25微升的每个样品珠转移到一个新的管。 注：用户应准备珠为一个单一的SILAC实验作为mastermix以减少样品到样品的变化。 </li><li>对于每25微升珠浆，以2,700×g离心5分钟加20倍体积的稀释液（每75微升浆1,500微升），以及离心机的珠子。接着，在20倍体积的稀释缓冲液洗珠进一步的2倍。 </li><li>新增每25微升珠浆100微升稀释缓冲液。用200μl前端与端部切断，转移85微升此悬浮浆液对每个从步骤3.6的SILAC标记的样品。 </li><li>孵育在旋转器上有孔玻璃珠的样品在4℃下2小时。 </li></ol> 5，洗涤，洗脱，并在样品的制备为MS分析<ol><li>离心样品在2,700×g离心，46，C 5分钟。将50μl的上清液应保留作为未结合的样品，用弃去上清液的剩余部分。 </li><li> 1毫升稀释缓冲液应加入到每个管中，以重悬珠和样品离心2700×g离心，4℃5分钟。上清液应该被丢弃。这应进行两次。 </li><li>洗脱蛋白从珠通过加入50微升2×SDS上样缓冲液，并在95℃下加热10分钟。离心2700×g离心2分钟沉淀的珠子在4℃下</li><li>保留上清液在预润滑管，在那里它可以被再保存于-80℃直至需要。提交给质谱设备，混合标记的样品1时01分01秒（ 例如 ，10微升每个），并提交混合样品。 注意：此时样品可通过免疫印迹，银染或其他手段来测试已知/未知约束力的合作伙伴的交互进行测试。一个例子给出在图URE 2显示一个已知的相互作用伴侣的特异性结合- eIF4G通过免疫印迹，和现在的下拉样本中的目标蛋白银染色条带的出现，而不是对照样品。 </li><li>提交样品进行LC-MS/MS分析。 注意：一旦确信感兴趣的标签蛋白被成功结合的互动合作伙伴，每个标记的样品等体积（轻型，中型和重型）相结合，并提交LC-MS/MS分析。这是通常的提交30μl总的IP样本进行分析。这将需要混合10μl的光的标记的样品与10微升培养基中标记的和10μl重标记的样品，得到30μl的总。 </li></ol> 6数据分析I：了解结果，并去除低自信识别标志注：由蛋白质组的Discoverer软件返回的列标题的列表如表1衍射的。ferent软件（ 例如 ，MSQuant，MaxQuant），但会返回不同的标题，其中只有一个子集所需要的分析，这些都是常见的各种软件包。数据应该总是包含每个蛋白的登录号识别的，比之比较每个样本比率（光与介质的光比重，中与重等），确定独特的肽的数量，以及某些形式的假阳性率或可靠性指示。 <ol><li>去之前通过数据，首先将原始数据复制到一个新的电子表格。从这个表格中删除除给予入藏号的所有列，独特的肽数目，比较样品，比值变化，以及蛋白质的描述比。如果副本进行实验，这些应合并成一个单一的Excel文件，每个实验上出现一个单独的标签。 注：低信心数据包括确定只有一个独特的肽的蛋白质，而那些瓦特这里的量化是不可能的。 </li><li>使用过人之处&#39;排序&#39;功能由肽的数量的订购数据和删除的条目缺少一个以上的肽的蛋白质。然后，通过比排序和删除，缺乏SILAC率（无法量化的蛋白质）的蛋白质。 </li><li>转换SILAC比率 ​​用下式记录2值：&#39;=日志（SILAC比率 ​​，2）“，其中”SILAC比率​​&#39;是取代的小区标识符。 注意：作为一个SILAC比率 ​​导致蛋白质表示丰度的降低被限制为值0和1之间的任何数据时，通常以一个SILAC比转换到一个日志2 SILAC比，因为这意味着蛋白质既增加或减少一个样本被表示对数尺度，其中2或-2表示4倍的增加或减少的丰富和3或-3的9倍的增加或减少分别在丰盈。下面的这个转换，数据应该适合嘎ussian分布围绕0日志2 SILAC比率 ​​居中。这方面的一个例子是在图3中 。 </li><li>在Excel文件中创建新列，并计算日志2 SILAC比所有样本/模拟列。要转换的模拟/样品比对样本/模拟比率，用公式：“= 1/ratio” </li></ol> 7数据分析二：选择高可信度相互作用的进一步研究<ol><li>开放GRAPHPAD棱镜，选择New&gt;数据表格和图表...选择从窗口左侧的列表&#39;列&#39;，并选择输入/导入数据&gt;进入副本值，堆积成列选项。按“创建”。 </li><li>选择并从Excel电子表格复制一个给定的日志2采样/模拟SILAC比列到新的棱镜文件。 </li><li>单击下拉“插入”菜单，选择“新建分析”。在列分析选择&#39;频率分布“。保持默认选项，请单击“确定”。 注意：此步骤生成的直方图表示确定在一个给定比率的蛋白质的数量。本应形成高斯分布。 </li><li>在“结果”文件夹中的新的“直方图”部分已经生成。选择频率分布部分。 </li><li>单击下拉“插入”菜单，并选择新的分析&gt; XY&gt;非线性回归（曲线拟合）。点击“高斯分布”，然后单击确定。 注意：该步骤适合的曲线以在步骤7.3中产生的频度分布数据，产生随后可以用来计算阈值显着性值。 </li><li>在出现的结果窗口中，均值和标准差给出。通过加入1.96标准偏差的平均值生成的阈值。 注意：这些值表示的均值和高斯区的标准偏差ribution，而不是总数据集。 1.96个标准差会放置一个阈值的95％可信限（P≤0.05）。 2.58 SD将使99％，3.3 SD 99.9％。该阈值应分别确定各副本的实验，因为它可能会有所不同。 </li></ol> 8数据分析三：合并数据集副本注：有信心在确定合作伙伴的互动典型SILAC下拉实验最好应进行三次，用中型和重型标记的样品转换为重复控制的媒体对结果有任何影响之一。一种蛋白质，其显示为交互中的至少两个的三个实验中，用&#39;switched&#39;介质样品理想地表示其中的一个，是一个高可信度的相互作用。 <ol><li>返回到Excel文件，创建一个新的选项卡称为“组合拳”。标签列中的“加入”，并复制从每个所有登录号ØF中的各个实验进入这个单柱。 </li><li>选择“数据”选项卡，然后在“删除重复项”选项。 </li><li>创建用于SILAC比率，并且从每个实验比率变化以及对蛋白质名称/描述柱列。 </li><li>在描述选项卡中，使用VLOOKUP公式来收集每个登录号的说明。拖动公式向下的栏填入蛋白质描述。该VLOOKUP公式为：“= VLOOKUP（AccessionNo，WheretoLook，ColumnsAccross，假）”，其中：AccessionNo是$ AX X是行号WheretoLook是“实验选项卡名称”$ A $ 2：。！$ Y $ Z其中Y和Z是右下方一块数据在电子表格上被引用。ColumnsAccross是列从加入民横跨数BER在A列中所需的价值所在。 注：例如，如果登录号在A列，和感兴趣的数据位于列E这里的值是5（A列数为1）。作为登录号被汇集在步骤b中，它是最有可能VLOOKUP将不会获得从一个实验所需的所有名字。它返回N / A，修改公式，从依次在每个实验中得到说明，直到所有的描述获得。 </li><li>为了突出合并数据集在相互作用的蛋白质，单独选择每个比列，然后单击主页选项卡上，其次是条件格式&gt;突出显示单元格规则&gt;更多规则。 </li><li>选择风格“经典”。包含“单元格的值&#39;格式只有细胞，”大于或等于“。在文件名框中，键入1.96的标准偏差值，然后单击“确定”。 </li><li>评估的比例变化为每良性互动。如果减去％VARiability将下降一个&#39;打&#39;低于阈值，这应该谨慎对待。 注：此为个别比率为每肽，它们一起组成了一个蛋白质的SILAC率如何都保持一致，并以百分比表示。当该比率的变化是考虑到和蛋白质提供了高于阈值的SILAC比率，这个蛋白表示命中。如果该比率的变化给出一个范围，低于阈值时，“命中”可以代表一种污染物。 </li><li>重复此为每个结果的列和整个实验比较。在两个或两个以上的实验确定突出显示蛋白质代表高置信相互作用。 注：根据用于分配蛋白质登录号，在不同的实验中的蛋白质可能是下一个不同的，甚至是多个登记号标识的数据库上。检查此，以确保蛋白质被不小心遗漏是很重要的。 </li&gt; </ol>

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J., Dedhar, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mapping+the+integrin-linked+kinase+interactome+using+SILAC.">Mapping the integrin-linked kinase interactome using SILAC.</a> J Proteome Res. 7, 1740-1749 (2008).</li><li>Mittler, G., Butter, F., Mann, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+SILAC-based+DNA+protein+interaction+screen+that+identifies+candidate+binding+proteins+to+functional+DNA+elements.">A SILAC-based DNA protein interaction screen that identifies candidate binding proteins to functional DNA elements.</a> Genome research. 19, 284-293 (2009).</li><li>Selbach, M., Mann, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein+interaction+screening+by+quantitative+immunoprecipitation+combined+with+knockdown+QUICK.">Protein interaction screening by quantitative immunoprecipitation combined with knockdown QUICK.</a> Nat Methods. 3, 981-983 (2006).</li><li>Boulon, S., 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=Establishment+of+a+protein+frequency+library+and+its+application+in+the+reliable+identification+of+specific+protein+interaction+partners.">Establishment of a protein frequency library and its application in the reliable identification of specific protein interaction partners.</a> Mol Cell Proteomics. 9, 861-879 (2010).</li><li>Wang, X., Huang, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identifying+dynamic+interactors+of+protein+complexes+by+quantitative+mass+spectrometry.">Identifying dynamic interactors of protein complexes by quantitative mass spectrometry.</a> Mol Cell Proteomics. 7, 46-57 (2008).</li><li>Fang, L., 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=Characterization+of+the+human+COP9+signalosome+complex+using+affinity+purification+and+mass+spectrometry.">Characterization of the human COP9 signalosome complex using affinity purification and mass spectrometry.</a> J Proteome Res. 7, 4914-4925 (2008).</li></ol>

作者宣称，他们有没有竞争的财务权益。

这里所描述的SILAC下拉战略是一个非常敏感和强大的新型检测手段的蛋白质：蛋白质相互作用，进而允许改变的结合模式的利益密切相关的样本之间的快速和简单的歧视。在这个例子中该技术被用来研究蛋白质：该eIF4AI和eIF4AII蛋白6蛋白的相互作用。据作者所知，这是文献利用SILAC蛋白质组学的效用研究eIF4A这两种亚型的细胞相互作用组的第一个研究。 如上面所描述的方法使用GFP-标记和抗GFP珠9，10，因此，可能需要修改以使这种方法可用于感兴趣的特定蛋白质，例如该标记是否被放置在N-或C-末端的蛋白质。如果可能的话，蛋白质印迹或功能试验应被执行以检测已知蛋白质相互作用伴侣的结合。应的蛋白不耐受融合与GFP标记，其它的标记或下拉策略已经被同时使用其它标记施加到SILAC下拉（FLAG 11，生物素12，链球菌（自己的数据，未发表）），或者通过使用第一抗体与蛋白的兴趣，其中siRNA敲除靶蛋白提供了一个控制样品13。这样的实验已经在文献中其它地方进行了描述，但简单地说，步骤1和步骤5.4-8将如上适用，步骤2-5.3修改以适合所选择的使用等于蛋白质输入中所描述的表达/下拉系统步骤2.4-2.5。作为量化阶段允许非特异性结合的蛋白，以在分析级被移除时，建议以省略预温育与对照珠，或高盐冲洗，以保持低亲和力的蛋白质：蛋白质相互作用与感兴趣的蛋白。核酸酶马为y包含或根据特定实验的具体细节在此省略了协议。例如：在本方法中使用的蛋白是RNA解旋酶，RNA酶鸡尾酒被列入协议，除去通过核糖核酸（步骤3.2）介导的间接的相互作用。在某些情况下，但是，也可以受益于并行运行试验有和无核酸酶的识别核酸依赖的相互作用。 在这个协议中，“中”和在重复实验“重”样品的交换建议控制为变异通过在SILAC培养基或细胞生长的差异引入。另一种控制涉及所有三个（&#39;光&#39;，&#39;中&#39;和&#39;重&#39;）媒体复制实验的顺序切换。虽然这种方法有其潜在的更为严格，但它增加了分析的复杂性，因为在至少一个重复，目的蛋白质瓦特生病产生的“光”标记的细胞，所以它是必要的&#39;轻&#39;的样品（环境污染物如角蛋白），以及那些当绑定到一种蛋白质，只是丰富了“光”的样品在一贯的蛋白质鉴定区分兴趣。 而使用量化数据的允许特定的非特异性相互作用，通过使用阈值判别，难免有些真正的相互作用可能被丢弃。上面的方法是一种简单，快速的方法来识别蛋白质：蛋白质相互作用，可以由研究人员没有以往的经验与质谱，或大蛋白的分析可以很容易地尝试：蛋白质相互作用数据集。对于大多数的用途，这是绰绰有余的识别感兴趣的新蛋白质。进一步的修改，以这种方式来帮助减少这种数据丢失，在文献中其它地方描述的，并且包括使用PR的其中对于特定的一组实验参数（细胞株，胎圈基质，缓冲液条件）已知污染蛋白质otein频率文库可以被排除10,14。然而，这取决于具体的实验参数，可能需要执行一系列的控制实验来产生珠蛋白组，因此，这样可以提高实验的两个的费用和复杂性。可从www.peptracker.co.uk网站14上这一技术的进一步信息。 还应当指出的是，协议上述涉及混合不同地标记的样品在免疫过程结束（称为混合纯化后 - MAP SILAC实验）。这样做是因为蛋白质：蛋白质相互作用发生在一给定的平衡15。但应注意的是，其他团体已经联合在该协议与样品孵育描述这张地图SILAC方法前向下拉（纯化后混合- PAM SILAC），用于不同的时间长度（20分钟至2小时，已在文献中使用）15，16。根据如何迅速的蛋白质比下降朝向1:1，因此能够定性地调查的结合亲和力，并确定蛋白质作为稳定的或动态的相互作用蛋白15。 总之，SILAC下拉代表一个非常强大的识别蛋白与感兴趣的特定蛋白质相互作用，在生理学相关的设定的装置。该技术可以很容易地适用于许多不同的纯化策略，允许其应用到感兴趣的任何给定的蛋白质。结果的量化大大简化了识别真正的相互作用，并且允许使用以除去非特异性结合严格缓冲液条件放松，从而保持低亲和力相互作用。如最多三个样品可以在上面进行比较策略，该技术具有在比较中蛋白质的差异不同蛋白同种型，突变体蛋白，或药理学抑制剂的效果之间的结合清楚的长处。作为整个凝胶切片进行了分析，而不是单个波段即污点通过考马斯，确定在高置信度的蛋白质的数量通常比那些在一个标准的GST / TAP-下拉确定更高，实验者偏压在选择感兴趣的蛋白被删除。该技术因此相比，毫不逊色与鉴定新的蛋白质相互作用（酵母双杂交，GST /他或TAP下拉）使用其他常用的技巧。

在了解蛋白质功能的一个重要步骤是识别相关的蛋白质相互作用的。如该蛋白是未知有许多技术可用，每个都有自己的优点和缺点。这些包括酵母双杂交系统中，使用重组蛋白的下拉测定法，以及串联亲和纯化或TAP-标记1，2。 更近期除了这些技术是目的蛋白质的亲和纯化的自相关的哺乳动物细胞系的结合，随后定量质谱法使用的氨基酸稳定同位素标记的细胞培养（SILAC）3。这个拥有在细胞中的定位和翻译后修饰的酵母双杂交方法的优点是不被干扰，以及超过传统优势TAP-标签，因为它是一个定量而非定性的方法，允许用户意图dily区分非特异性相互作用的蛋白质和污染物，从特异性结合宿主因素。此外，作为一个示例，通常分析的整体，而不是作为单独的蛋白质条带，感兴趣的蛋白质没有通过类似地迁移的蛋白质在凝胶上，它们也不是通常需要存在于足够水平的染色后是可见的，从而屏蔽自信地鉴定蛋白质4的数量增加。 为了演示这种技术，与之密切相关的真核翻译起始因子的GFP融合eIF4AI和eIF4AII的份额超过90％的氨基酸同一性被SILAC-免疫定量蛋白质组学研究。人类eIF4AI和II分别克隆到pEGFP-C1，以形成其中的GFP融合到eIF4A的N-末端的融合蛋白。为了避免需要用于产生稳定细胞系的瞬时转染来提供这些构建以稳定同位素标记的293T细胞。 图1）。 SILAC免疫沉淀使之不仅直接相互作用也低亲和力或蛋白质复合物4间接的相互作用的鉴定。使用这个系统，eIF4AI和Ⅱ免疫沉淀使重现性好，有信心识别的主要结合伴侣eIF4G（异构体的I / II和III）5，以及与eIF4E的间接的相互作用，以及eIF3复合体的许多组件。

<table border="0" cellpadding="0" cellspacing="0" width="1191">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:271px;">Dulbecco&#39;s Modified Eagle Medium &#160;(DMEM)</td>
			<td style="width:87px;">Dundee Cell Products</td>
			<td style="width:88px;">LM010</td>
			<td style="width:745px;">DMEM lacking Arginine and Lysine. (and containing L-glutamine)</td>
		</tr>
		<tr height="21">
			<td height="42" rowspan="2" style="height:42px;width:271px;">Dialysed FBS (10kDa cutoff) 500ml</td>
			<td rowspan="2" style="width:87px;">Dundee Cell Products</td>
			<td rowspan="2" style="width:88px;">DS1003</td>
			<td rowspan="2" style="width:745px;"></td>
		</tr>
		<tr height="21">
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:271px;">Arginine (R0) 25g</td>
			<td style="width:87px;">Sigma-Aldrich</td>
			<td style="width:88px;">A8094</td>
			<td style="width:745px;">Resuspend 0.5g in 6ml PBS, filter sterilize, and freeze in 0.5ml aliquots (gives a 84mg/ml stock)</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:271px;">Arginine (R6) 0.5g</td>
			<td style="width:87px;">Cambridge Isotope Labs</td>
			<td style="width:88px;">CLM-2265</td>
			<td style="width:745px;">Resuspend 0.5g in 6ml PBS, filter sterilize, and freeze in 0.5ml aliquots (gives a 84mg/ml stock)</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:271px;">Arginine (R10) 0.5g</td>
			<td style="width:87px;">Cambridge Isotope Labs</td>
			<td style="width:88px;">CNLM-539</td>
			<td style="width:745px;">Resuspend 0.5g in 6ml PBS, filter sterilize, and freeze in 0.5ml aliquots (gives a 84mg/ml stock)</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:271px;">Lysine (K0) 25g</td>
			<td style="width:87px;">Sigma-Aldrich</td>
			<td style="width:88px;">L8662</td>
			<td style="width:745px;">Resuspend 0.5g in 3.5ml PBS, filter sterilize, and freeze in 0.5ml aliquots (gives a 146mg/ml stock)</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:271px;">Lysine (K4) 0.5g</td>
			<td style="width:87px;">Cambridge Isotope Labs</td>
			<td style="width:88px;">DLM-2640</td>
			<td style="width:745px;">Resuspend 0.5g in 3.5ml PBS, filter sterilize, and freeze in 0.5ml aliquots (gives a 146mg/ml stock)</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:271px;">Lysine (K8) 0.5g</td>
			<td style="width:87px;">Cambridge Isotope Labs</td>
			<td style="width:88px;">CNLM-291</td>
			<td style="width:745px;">Resuspend 0.5g in 3.5ml PBS, filter sterilize, and freeze in 0.5ml aliquots (gives a 146mg/ml stock)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:271px;">Penicillin-Streptomycin, Liquid. 100ml</td>
			<td style="width:87px;">Gibco</td>
			<td style="width:88px;">15140-122</td>
			<td style="width:745px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:271px;">Lipofectamine 2000 transfection reagent</td>
			<td style="width:87px;">Invitrogen</td>
			<td style="width:88px;">11668-027</td>
			<td style="width:745px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:271px;">GFP-trap Agarose (500&#956;l resin)</td>
			<td style="width:87px;">Chromotek</td>
			<td style="width:88px;">gta-20</td>
			<td style="width:745px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:271px;">5x SDS Sample loading buffer</td>
			<td style="width:87px;">Fisher Scientific</td>
			<td style="width:88px;">PN39000</td>
			<td style="width:745px;">The use of purchased rather than homemade sample buffer is recommended to minimize keratin contamination.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:271px;">Protease inhibitor cocktail set III</td>
			<td style="width:87px;">Calbiochem</td>
			<td style="width:88px;">539134</td>
			<td style="width:745px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:271px;">1.7ml&#160; prelubricated tubes</td>
			<td style="width:87px;">Costar</td>
			<td style="width:88px;">3207</td>
			<td style="width:745px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:271px;">BCA protein assay kit (1L)</td>
			<td style="width:87px;">Pierce</td>
			<td style="width:88px;">23225</td>
			<td style="width:745px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:271px;">Tris (Trizma)</td>
			<td style="width:87px;">Sigma-Aldrich</td>
			<td>T1503</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:271px;">Ethylenediaminetetraacetic acid (EDTA)</td>
			<td style="width:87px;">Sigma-Aldrich</td>
			<td>E6758</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:271px;">Sodium Chloride</td>
			<td style="width:87px;">Sigma-Aldrich</td>
			<td>S3014</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:271px;">Rnase cocktail</td>
			<td style="width:87px;">Ambion</td>
			<td>AM2286&#160;</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:271px;">NP-40 alternative</td>
			<td style="width:87px;">Millipore</td>
			<td align="right">492016</td>
			<td></td>
		</tr>
	</tbody>
</table>

identification of protein interaction partners in mammalian cells using silac-immunoprecipitation quantitative proteomics

定量蛋白质组学结合免疫亲和纯化，SILAC免疫沉淀，代表了新型蛋白质的发现的有力手段：蛋白质相互作用。通过使蛋白质丰度的控制和测试样品中的准确的相对定量，真相互作用可容易地从实验的污染物区分开来。低亲和力相互作用可以通过使用不太严格的缓冲液条件予以保留，并保持易于识别。这个协议讨论的组织培养细胞与亲和稳定同位素标记的氨基酸，转染和免疫沉淀的标签标记的蛋白质的兴趣，其次是制备以提交质谱设施。该协议随后将讨论如何分析和解释从质谱仪，以确定细胞伙伴感兴趣的蛋白相互作用返回的数据。作为一个例子该技术被应用到鉴定者TIFY蛋白结合到真核细胞翻译起始因子：eIF4AI和eIF4AII。

在一个典型的SILAC下拉实验，确定蛋白（&gt; 90％）的绝大多数代表的污染物以及蛋白质非特异性结合到亲和基质，这一点在图2B中 ，即使当洗涤协议除去大部分细胞质污染物如GAPDH（ 图2A）。然而，非特异性结合的蛋白质在正常分布的集群允许蛋白质特异性结合到感兴趣的蛋白质加以区别，因为这些有较高的采样/模拟比值比背景。虽然污染物理论上簇周围的0日志2 SILAC比，这不一定的情况下，这样的一个例子是在图3的可能原因包括细胞的不完善SILAC标记，装载不等量或溶解物的浓度上在抗GFP珠，珠纯化或混合不均过程中意外丢失ING样品的纯化方法3的端部。然而，假设数据是基于从正态分布污染物的平均值的阈值标准偏差进行分析，在该数据的定心轻微位移应不影响结果的质量。 当比较的两个相关蛋白质之间的蛋白质相互作用的差异，其中感兴趣1蛋白的产生在细胞内水平高于第二（无论是由于变化的转染效率，或所述蛋白质或mRNA的固有特性），可能会出现类似的情况。一些变化中表达（ 例如 ， 图2A）可以用于通过分析SILAC比率 ​​对于这两个样品进行校正。在这个例子中这将是GFP-eIF4AI和GFP-eIF4AII样品。通过分析4AI/4AII SILAC比率如第7所讨论的，它是可以识别的蛋白质，其结合同种型之间显著而变化。 <p级=“jove_content”&gt;在图4中，在所进行的实验复制品之一确定的eIF4A结合蛋白的表示示出了起始因子复合物这一协议来实现的覆盖范围。最高的比率是典型的观察与eIF4G，直接绑定到eIF4A，对eIF4E的，其中结合eIF4G在现场远离eIF4A结合位点比率较低。观察到的eIF3复合物的成员比率较低。然而，这清楚地表明，在实验令人满意地识别eIF4AI和II的直接和间接的结合伴侣。正如人们从他们的高序列一致性6，可以预期的蛋白质：蛋白质相互作用出现了两种异构体之间在很大程度上保存在本实验系统7，8。的选择的一些相互作用的蛋白质的列在表2中 ，示出的数据格式。 <table border="0" cellpadding="0" cellsp约占新交=“0”&gt; <tbody><tr><td> 列标题 </td><td> 描述 </td></tr><tr><td> 加入 </td><td> 显示该序列的登录号 </td></tr><tr><td>覆盖</td><td>包括的鉴定的肽的蛋白序列的比例</td></tr><tr><td> ♯PSM </td><td>肽谱匹配</td></tr><tr><td> ♯肽 </td><td> 确定了一个独特的蛋白质的肽的总数 </td></tr><tr><td> ♯的AA </td><td>在氨基酸的蛋白质的长度</td></tr><tr><td>兆瓦（大） </td><td>分子量在道尔顿的蛋白质。不包括修改</td></tr><tr><td>钙。 PI </td><td>蛋白质的理论等电点</td></tr><tr><td>得分</td><td>总得分的蛋白质（表示所逐张的总和idual肽分数）。所需的意义确切的分数将实验之间有所不同。 A MS工厂将通常适用5％的假阳性率阈值。 </td></tr><tr><td>序列</td><td>构成蛋白质的氨基酸序列</td></tr><tr><td> 比 </td><td> 肽命名的标记的样品中的相对强度，相对于第二标记的样品 </td></tr><tr><td>比计数</td><td>这被用来计算给定的蛋白质比肽比的数</td></tr><tr><td> 率变异性（％） </td><td> 用来计算给定的蛋白质比单个肽比的变异性 </td></tr><tr><td> 描述 </td><td> 蛋白质的名称 </td></tr></tbody></table> 表1从蛋白质组发现者报告标准列标题。而有用的信息可以从所有这些列的获得，对于那些本分析的关键以粗体显示。 <table border="0" cellpadding="0" cellspacing="0" width="1057"><tbody><tr><td> 加入 </td><td> 肽 </td><td> 4AI/Mock </td><td> 4AII/Mock </td><td> 名称 </td><td> SILAC分析 </td></tr><tr><td> A8K7F6 </td><td> 21 </td><td> 100 </td><td> 1 </td><td> eIF4AI </td><td rowspan="2"> “诱饵”蛋白</td></tr><tr><td> Q14240 </td><td> 22 </td><td> 0.01 </td><td> 90.855 </td><td> eIF4AII </td></tr><tr><td> G5E9S1 </td><td> 25 </td><td> 47.575 </td><td> 30.53 </td><td> eIF4GI </td><td rowspan="3">相互作用的蛋白质</td></tr><tr><td> Q59GJ0 </td><td> 5 </td><td> 11.778 </td><td> 10.619 </td><td> eIF4GII60; </td></tr><tr><td> P06730 </td><td> 3 </td><td> 7.22 </td><td> 7.57 </td><td> eIF4E的</td></tr><tr><td> Q5T6W5 </td><td> 4 </td><td> 0.685 </td><td> 0.646 </td><td> hnRNPK </td><td rowspan="2">非特异性结合的污染物</td></tr><tr><td> P62805 </td><td> 1 </td><td> 0.531 </td><td> 0.498 </td><td>组蛋白H4 </td></tr><tr><td> H6VRG2 </td><td> 18 </td><td> - </td><td> - </td><td>角蛋白1 </td><td rowspan="2">环境污染物</td></tr><tr><td> P35527 </td><td> 11 </td><td> 0.01 </td><td> 0.01 </td><td>角蛋白细胞骨架1 9 </td></tr></tbody></table> 表2。典型数据从一个SILAC免疫实验。给予示例数据为利益/诱饵蛋白（肽高，高的比率），蛋白质与利益（高/低肽，高比）的蛋白质相互作用，非特异性结合蛋白质（高/低PEPTIDE中，比率低于截止 - 本实验0.96）以及环境污染物（通常是高的肽，低于阈值的负比率/）。 <img alt="图1" fo:content-width="5in" fo:src="/files/ftp_upload/51656/51656fig1highres.jpg" src="/files/ftp_upload/51656/51656fig1.jpg" /> 图1的试验方案，第一细胞生长在媒体缺乏精氨酸和赖氨酸和用稳定同位素标记的精氨酸和赖氨酸取代的2周（1）。 （2）将细胞接种到10cm 2的菜肴和瞬时转染质粒编码绿色荧光蛋白（模拟）或GFP融合蛋白（样品）。 （3）将细胞裂解并GFP或GFP融合蛋白从细胞裂解物免疫沉淀。 （4）样本进行组合以1:1的比例和提交LC-MS/MS分析。然后，数据分析，以除去低置信蛋白的identifications并选择对应于真正的相互作用蛋白浓缩蛋白的水平。 <img alt="图2" fo:content-width="5in" fo:src="/files/ftp_upload/51656/51656fig2highres.jpg" src="/files/ftp_upload/51656/51656fig2.jpg" /> 图2。确认合适的免疫沉淀的条件。 A）细胞裂解液的Western blot分析，以及从免疫沉淀的非粘结和粘结部分证实了表达和目的蛋白的免疫沉淀。针对GAPDH的Western印迹证实了非相互作用蛋白并进一步印迹耗尽eIF4A的一个已知的相互作用伙伴证实蛋白质结合的权益。B的蛋白质）在不知道目的蛋白质的相互作用伙伴的成功的免疫沉淀，银染色的凝胶可确认相互作用蛋白的免疫沉淀。在这银染<html>编辑凝胶带的GFP和GFP-eIF4AI/II是清楚的，只有在GFP-4AI/II-bound车道，而不是在GFP控制车道带的正确大小eIF4G迁移存在。 <img alt="图3" fo:content-width="5in" fo:src="/files/ftp_upload/51656/51656fig3highres.jpg" src="/files/ftp_upload/51656/51656fig3.jpg" /> 图3。代表性的成果。直方图显示蛋白质的比率从（A）GFP-eIF4AI或（B）GFP-eIF4AII下拉一个重复的分布。在1.96的标准偏差截止标有虚线。相互作用的蛋白质常污染物分布范围以外的距离〜（B）中的（A）0.25和1，和〜0.3-1.5明 ​​显。 les/ftp_upload/51656/51656fig4.jpg“/&gt; 图4。从SILAC实验知识产权的单个副本标识起始因子复杂的成员。蛋白在绿色是用于下拉相互作用蛋白感兴趣的蛋白质是由红变灰，根据白登录与红色的SILAC IP 2比SILAC是最丰富的蛋白质的分析，和白色作为1.96的SD截止。蛋白质灰色阴影并没有确定这一分析。

Watch this Scientific Journal Video about Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics at JoVE.com

Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics

SILAC免疫实验指用于发现新蛋白的有力手段：蛋白质相互作用。通过使蛋白质丰度的控制和测试样品，真相互作用的精确相对定量可以很容易地从实验的污染物区分开来，并且低亲和力相互作用，通过使用较少的严格的缓冲条件下保存。

的蛋白质相互作用的合作伙伴在哺乳动物细胞中使用SILAC-免疫定量蛋白质组学鉴定

Quantitative proteomics combined with immuno-affinity purification, SILAC immunoprecipitation, represent a powerful means for the discovery of novel ...

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Research

JoVE Journal

Biology

31.3K 次观看.  University of Cambridge.  SILAC免疫实验指用于发现新蛋白的有力手段：蛋白质相互作用。通过使蛋白质丰度的控制和测试样品，真相互作用的精确相对定量可以很容易地从实验的污染物区分开来，并且低亲和力相互作用，通过使用较少的严格的缓冲条件下保存。 

视频: Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics

Identification of protein complexes with quantitative proteomics in S. cerevisiae

Lipids are the building blocks of cellular membranes that function as barriers and in compartmentalization of cellular processes, and recently, as important intracellular signalling molecules. However, unlike proteins, lipids are small hydrophobic molecules that traffic primarily by poorly described nonvesicular routes, which are hypothesized to occur at membrane contact sites (MCSs). MCSs are regions where the endoplasmic reticulum (ER) makes direct physical contact with a partnering organelle, e.g., plasma membrane (PM). The ER portion of ER-PM MCSs is enriched in lipid-synthesizing enzymes, suggesting that lipid synthesis is directed to these sites and implying that MCSs are important for lipid traffic. Yeast is an ideal model to study ER-PM MCSs because of their abundance, with over 1000 contacts per cell, and their conserved nature in all eukaryotes. Uncovering the proteins that constitute MCSs is critical to understanding how lipids traffic is accomplished in cells, and how they act as signaling molecules. We have found that an ER called Scs2p localize to ER-PM MCSs and is important for their formation. We are focused on uncovering the molecular partners of Scs2p. Identification of protein complexes traditionally relies on first resolving purified protein samples by gel electrophoresis, followed by in-gel digestion of protein bands and analysis of peptides by mass spectrometry. This often limits the study to a small subset of proteins. Also, protein complexes are exposed to denaturing or non-physiological conditions during the procedure. To circumvent these problems, we have implemented a large-scale quantitative proteomics technique to extract unbiased and quantified data. We use stable isotope labeling with amino acids in cell culture (SILAC) to incorporate staple isotope nuclei in proteins in an untagged control strain. Equal volumes of tagged culture and untagged, SILAC-labeled culture are mixed together and lysed by grinding in liquid nitrogen. We then carry out an affinity purification procedure to pull down protein complexes. Finally, we precipitate the protein sample, which is ready for analysis by high-performance liquid chromatography/ tandem mass spectrometry. Most importantly, proteins in the control strain are labeled by the heavy isotope and will produce a mass/ charge shift that can be quantified against the unlabeled proteins in the bait strain. Therefore, contaminants, or unspecific binding can be easily eliminated. By using this approach, we have identified several novel proteins that localize to ER-PM MCSs. Here we present a detailed description of our approach. 

Here we describe a new quantitative proteomics technique for identifying protein complexes in Saccharomyces cerevisiae. In this study, we have used the SILAC method together with affinity purification followed by tandem mass spectrometry to identify with high specificity the binding partners of an ER protein, Scs2p.

鉴定与定量蛋白质组学在酿酒酵母中的蛋白质复合物

Lipids are the building blocks of cellular membranes that function as barriers and in compartmentalization of cellular processes, and recently, as ...

科研

生物学

Bimolecular Fluorescence Complementation (BiFC) Assay for Protein-Protein Interaction in Onion Cells Using the Helios Gene Gun

Investigation of gene function in diverse organisms relies on knowledge of how the gene products interact with each other in their normal cellular environment. The Bimolecular Fluorescence Complementation (BiFC) Assay1 allows researchers to visualize protein-protein interactions in living cells and has become an essential research tool. This assay is based on the facilitated association of two fragments of a fluorescent protein (GFP) that are each fused to a potential interacting protein partner. The interaction of the two protein partners would facilitate the association of the N-terminal and C-terminal fragment of GFP, leading to fluorescence. For plant researchers, onion epidermal cells are an ideal experimental system for conducting the BiFC assay because of the ease in obtaining and preparing onion tissues and the direct visualization of fluorescence with minimal background fluorescence. The Helios Gene Gun (BioRad) is commonly used for bombarding plasmid DNA into onion cells. We demonstrate the use of Helios Gene Gun to introduce plasmid constructs for two interacting Arabidopsis thaliana transcription factors, SEUSS (SEU) and LEUNIG HOMOLOG (LUH)2 and the visualization of their interactions mediated by BiFC in onion epidermal cells.

Bimolecular Fluorescence Complementation BiFC Assay for Protein-Protein Interaction in Onion Cells Using the Helios Gene Gun

This article illustrates how to properly use the BioRad Helios Gene Gun to introduce plasmid DNA into onion epidermal cells and how to test for protein-protein interactions in onion cells based on the principle of Bimolecular Fluorescence Complementation (BiFC)

双分子荧光互补（附设）检测蛋白质相互作用的洋葱细胞使用的Helios基因枪

Investigation of gene function in diverse organisms relies on knowledge of how the gene products interact with each other in their normal cellular ...

Identification of Protein Interacting Partners Using Tandem Affinity Purification

A critical and often limiting step in understanding the function of host and viral proteins is the identification of interacting cellular or viral protein partners. There are many approaches that allow the identification of interacting partners, including the yeast two hybrid system, as well as pull down assays using recombinant proteins and immunoprecipitation of endogenous proteins followed by mass spectrometry identification1. Recent studies have highlighted the utility of double-affinity tag mediated purification, coupled with two specific elution steps in the identification of interacting proteins. This approach, termed Tandem Affinity Purification (TAP), was initially used in yeast2,3 but more recently has been adapted to use in mammalian cells4-8.
As proof-of-concept we have established a tandem affinity purification (TAP) method using the well-characterized eukaryotic translation initiation factor eIF4E9,10.The cellular translation factor eIF4E is a critical component of the cellular eIF4F complex involved in cap-dependent translation initiation10. The TAP tag used in the current study is composed of two Protein G units and a streptavidin binding peptide separated by a Tobacco Etch Virus (TEV) protease cleavage sequence. The TAP tag used in the current study is composed of two Protein G units and a streptavidin binding peptide separated by a Tobacco Etch Virus (TEV) protease cleavage sequence8. To forgo the need for the generation of clonal cell lines, we developed a rapid system that relies on the expression of the TAP-tagged bait protein from an episomally maintained plasmid based on pMEP4 (Invitrogen). Expression of tagged murine eIF4E from this plasmid was controlled using the cadmium chloride inducible metallothionein promoter.
Lysis of the expressing cells and subsequent affinity purification via binding to rabbit IgG agarose, TEV protease cleavage, binding to streptavidin linked agarose and subsequent biotin elution identified numerous proteins apparently specific to the eIF4E pull-down (when compared to control cell lines expressing the TAP tag alone). The identities of the proteins were obtained by excision of the bands from 1D SDS-PAGE and subsequent tandem mass spectrometry. The identified components included the known eIF4E binding proteins eIF4G and 4EBP-1. In addition, other components of the eIF4F complex, of which eIF4E is a component were identified, namely eIF4A and Poly-A binding protein. The ability to identify not only known direct binding partners as well as secondary interacting proteins, further highlights the utility of this approach in the characterization of proteins of unknown function.

Tandem affinity purification is a robust approach for the identification of protein binding partners. As proof of concept, this methodology was applied to the well-characterized translation initiation factor eIF4E to co-precipitate the host cell factors involved in translation initiation. This method is easily adapted to any cellular or viral protein.

鉴定蛋白质相互作用的合作伙伴使用串联亲和纯化

A critical and often limiting step in understanding the function of host and viral proteins is the identification of interacting cellular or viral protein ...

Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

Among methods to study protein-protein interaction inside cells, Bimolecular Fluorescence Complementation (BiFC) is relatively simple and sensitive. BiFC is based on the production of fluorescence using two non-fluorescent fragments of a fluorescent protein (Venus, a Yellow Fluorescent Protein variant, is used here). Non-fluorescent Venus fragments (VN and VC) are fused to two interacting proteins (in this case, AKAP-Lbc and PDE4D3), yielding fluorescence due to VN-AKAP-Lbc-VC-PDE4D3 interaction and the formation of a functional fluorescent protein inside cells.
BiFC provides information on the subcellular localization of protein complexes and the strength of protein interactions based on fluorescence intensity. However, BiFC analysis using microscopy to quantify the strength of protein-protein interaction is time-consuming and somewhat subjective due to heterogeneity in protein expression and interaction. By coupling flow cytometric analysis with BiFC methodology, the fluorescent BiFC protein-protein interaction signal can be accurately measured for a large quantity of cells in a short time. Here, we demonstrate an application of this methodology to map regions in PDE4D3 that are required for the interaction with AKAP-Lbc. This high throughput methodology can be applied to screening factors that regulate protein-protein interaction.

Flow cytometric analysis of Bimolecular Fluorescence Complementation provides a high throughput quantitative method to study protein-protein interaction. This methodology can be applied to mapping protein binding sites and for screening factors that regulate protein-protein interaction.

双分子荧光互补流式细胞仪分析：高通量的定量方法研究蛋白质 - 蛋白质相互作用

Among methods to study protein-protein interaction inside cells, Bimolecular Fluorescence Complementation (BiFC) is relatively simple and sensitive. BiFC ...

siRNA Screening to Identify Ubiquitin and Ubiquitin-like System Regulators of Biological Pathways in Cultured Mammalian Cells

Post-translational modification of proteins with ubiquitin and ubiquitin-like molecules (UBLs) is emerging as a dynamic cellular signaling network that regulates diverse biological pathways including the hypoxia response, proteostasis, the DNA damage response and transcription.&#160; To better understand how UBLs regulate pathways relevant to human disease, we have compiled a human siRNA &#8220;ubiquitome&#8221; library consisting of 1,186 siRNA duplex pools targeting all known and predicted components of UBL system pathways. This library can be screened against a range of cell lines expressing reporters of diverse biological pathways to determine which UBL components act as positive or negative regulators of the pathway in question.&#160; Here, we describe a protocol utilizing this library to identify ubiquitome-regulators of the HIF1A-mediated cellular response to hypoxia using a transcription-based luciferase reporter.&#160; An initial assay development stage is performed to establish suitable screening parameters of the cell line before performing the screen in three stages: primary, secondary and tertiary/deconvolution screening. &#160;The use of targeted over whole genome siRNA libraries is becoming increasingly popular as it offers the advantage of reporting only on members of the pathway with which the investigators are most interested.&#160; Despite inherent limitations of siRNA screening, in particular false-positives caused by siRNA off-target effects, the identification of genuine novel regulators of the pathways in question outweigh these shortcomings, which can be overcome by performing a series of carefully undertaken control experiments.

Here, we describe a methodology to perform a targeted siRNA &#8220;ubiquitome&#8221; screen to identify novel ubiquitin and ubiquitin-like regulators of the HIF1A-mediated cellular response to hypoxia.&#160; This can be adapted to any biological pathway where a robust read out of reporter activity is available.

siRNA的筛选识别泛素和泛素样系统压力表生物学途径在培养的哺乳动物细胞

Post-translational modification of proteins with ubiquitin and ubiquitin-like molecules (UBLs) is emerging as a dynamic cellular signaling network that ...

Identifying Protein-protein Interaction Sites Using Peptide Arrays

Protein-protein interactions mediate most of the processes in the living cell and control homeostasis of the organism. Impaired protein interactions may result in disease, making protein interactions important drug targets. It is thus highly important to understand these interactions at the molecular level. Protein interactions are studied using a variety of techniques ranging from cellular and biochemical assays to quantitative biophysical assays, and these may be performed either with full-length proteins, with protein domains or with peptides. Peptides serve as excellent tools to study protein interactions since peptides can be easily synthesized and allow the focusing on specific interaction sites. Peptide arrays enable the identification of the interaction sites between two proteins as well as screening for peptides that bind the target protein for therapeutic purposes. They also allow high throughput SAR studies. For identification of binding sites, a typical peptide array usually contains partly overlapping 10-20 residues peptides derived from the full sequences of one or more partner proteins of the desired target protein. Screening the array for binding the target protein reveals the binding peptides, corresponding to the binding sites in the partner proteins, in an easy and fast method using only small amount of protein.
In this article we describe a protocol for screening peptide arrays for mapping the interaction sites between a target protein and its partners. The peptide array is designed based on the sequences of the partner proteins taking into account their secondary structures. The arrays used in this protocol were Celluspots arrays prepared by INTAVIS Bioanalytical Instruments. The array is blocked to prevent unspecific binding and then incubated with the studied protein. Detection using an antibody reveals the binding peptides corresponding to the specific interaction sites between the proteins.

Peptide array screening is a high throughput assay for identifying protein-protein interaction sites. This allows mapping multiple interactions of a target protein and can serve as a method for identifying sites for inhibitors that target a protein. Here we describe a protocol for screening and analyzing peptide arrays.

鉴定蛋白质相互作用的网站使用多肽阵列

Protein-protein interactions mediate most of the processes in the living cell and control homeostasis of the organism. Impaired protein interactions may ...

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein&#8217;s function, regulation and localization often depend on its interactions with other proteins. Here, we describe a protocol for the yeast protein-fragment complementation assay (PCA), a powerful method to detect direct and proximal associations between proteins in living cells. The interaction between two proteins, each fused to a dihydrofolate reductase (DHFR) protein fragment, translates into growth of yeast strains in presence of the drug methotrexate (MTX). Differential fitness, resulting from different amounts of reconstituted DHFR enzyme, can be quantified on high-density colony arrays, allowing to differentiate interacting from non-interacting bait-prey pairs. The high-throughput protocol presented here is performed using a robotic platform that parallelizes mating of bait and prey strains carrying complementary DHFR-fragment fusion proteins and the survival assay on MTX. This protocol allows to systematically test for thousands of protein-protein interactions (PPIs) involving bait proteins of interest and offers several advantages over other PPI detection assays, including the study of proteins expressed from their endogenous promoters without the need for modifying protein localization and for the assembly of complex reporter constructs.

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells

Proteins interact with each other and these interactions determine in a large part their functions. Protein interaction partners can be identified at high-throughput in vivo using a yeast fitness assay based on the dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA).

全基因组蛋白质相互作用通过蛋白质片段互补法（PCA）的活细胞筛选

Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein&#8217;s function, regulation and localization often ...

Direct Protein Delivery to Mammalian Cells Using Cell-permeable Cys2-His2 Zinc-finger Domains

Due to their modularity and ability to be reprogrammed to recognize a wide range of DNA sequences, Cys2-His2 zinc-finger DNA-binding domains have emerged as useful tools for targeted genome engineering. Like many other DNA-binding proteins, zinc-fingers also possess the innate ability to cross cell membranes. We recently demonstrated that this intrinsic cell-permeability could be leveraged for intracellular protein delivery. Genetic fusion of zinc-finger motifs leads to efficient transport of protein and enzyme cargo into a broad range of mammalian cell types. Unlike other protein transduction technologies, delivery via zinc-finger domains does not inhibit enzyme activity and leads to high levels of cytosolic delivery. Here a detailed step-by-step protocol is presented for the implementation of zinc-finger technology for protein delivery into mammalian cells. Key steps for achieving high levels of intracellular zinc-finger-mediated delivery are highlighted and strategies for maximizing the performance of this system are discussed.

Direct Protein Delivery to Mammalian Cells Using Cell-permeable Cys2-His2 Zinc-finger Domains

Zinc-finger domains are intrinsically cell-permeable and capable of mediating protein delivery into a broad range of mammalian cell types. Here, a detailed step-by-step protocol for implementing zinc-finger technology for intracellular protein delivery is presented.

蛋白质直接送货到哺乳动物细胞利用细胞渗透半胱氨酸<sub&gt; 2</sub&gt; -His<sub&gt; 2</sub&gt;锌指域

Due to their modularity and ability to be reprogrammed to recognize a wide range of DNA sequences, Cys2-His2 zinc-finger DNA-binding domains have emerged ...

Identification of Intracellular Signaling Events Induced in Viable Cells by Interaction with Neighboring Cells Undergoing Apoptotic Cell Death

Cells dying by apoptosis, also referred to as regulated cell death, acquire multiple new activities that enable them to influence the function of adjacent live cells. Vital activities, such as survival, proliferation, growth, and differentiation, are among the many cellular functions modulated by apoptotic cells. The ability to recognize and respond to apoptotic cells appears to be a universal feature of all cells, regardless of lineage or organ of origination. However, the diversity and complexity of the response to apoptotic cells mandates that great care be taken in dissecting the signaling events and pathways responsible for any particular outcome. In particular, one must distinguish among the multiple mechanisms by which apoptotic cells can influence intracellular signaling pathways within viable responder cells, including: receptor-mediated recognition of the apoptotic cell, release of soluble mediators by the apoptotic cell, and/or engagement of the phagocytic machinery. Here, we provide a protocol for identifying intracellular signaling events that are induced in viable responder cells following their exposure to apoptotic cells. A major advantage of the protocol lies in the attention it pays to dissection of the mechanism by which apoptotic cells modulate signaling events within responding cells. While the protocol is specific for a conditionally immortalized mouse kidney proximal tubular cell line (BU.MPT cells), it is easily adapted to cell lines that are non-epithelial in origin and/or derived from organs other than the kidney. The use of dead cells as a stimulus introduces several unique factors that can hinder the detection of intracellular signaling events. These problems, as well as strategies to minimize or circumvent them, are discussed within the protocol. Application of this protocol should aid our expanding knowledge of the broad influence that dead or dying cells exert on their live neighbors, both in health and in disease.

Here, we present a protocol for the determination of intracellular signaling events induced in viable cells by physical interaction with adjacent dead or dying cells. The protocol focuses on signaling events induced by receptor-mediated recognition of the dead cells, as opposed to their phagocytic uptake or release of soluble mediators.

的细胞内信号与相邻小区发生细胞凋亡通过互动活细胞诱导活动标识

Cells dying by apoptosis, also referred to as regulated cell death, acquire multiple new activities that enable them to influence the function of adjacent ...

A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions

Filopodia are dynamic, finger-like cellular protrusions associated with migration and cell-cell communication. In order to better understand the complex signaling mechanisms underlying filopodial initiation, elongation and subsequent stabilization or retraction, it is crucial to determine the spatio-temporal protein activity in these dynamic structures. To analyze protein function in filopodia, we recently developed a semi-automated tracking algorithm that adapts to filopodial shape-changes, thus allowing parallel analysis of protrusion dynamics and relative protein concentration along the whole filopodial length. Here, we present a detailed step-by-step protocol for optimized cell handling, image acquisition and software analysis. We further provide instructions for the use of optional features during image analysis and data representation, as well as troubleshooting guidelines for all critical steps along the way. Finally, we also include a comparison of the described image analysis software with other programs available for filopodia quantification. Together, the presented protocol provides a framework for accurate analysis of protein dynamics in filopodial protrusions using image analysis software.

We present a software solution for semi-automated tracking of relative protein concentration along the length of dynamic cellular protrusions.

软件辅助跟踪动态细胞突变中蛋白质浓度的图形用户界面

Filopodia are dynamic, finger-like cellular protrusions associated with migration and cell-cell communication. In order to better understand the complex ...