这项工作得到了国家自然科学基金（82004031）和四川省科技计划（2022NSFSC1303）的支持。我们非常感谢成都中医药大学中药创新研究所的孙佳怡在蛋白质印迹方面的帮助。

1. 下载 xanthatin 和 Keap1 的结构
<ol><li>打开 PubChem 数据库 （https://pubchem.ncbi.nlm.nih.gov/），输入 xanthatin（小分子），然后按 搜索并单击 第一个结果。单击“ 下载 ”，然后单击“2D结构”下的“ 保存 ”，以.sdf格式保存化合物。</li>
	<li>下载蛋白质的晶体结构。<ol><li>打开 UniProt 数据库 （https://www.uniprot.org/），输入 Keap1 并点击搜索。单击左列中“流行生物”下的“ 人类 ”，然后单击右列中的“ 名为 Q14145 的第一个条目 ”。</li>
		<li>单击左列函数下的 Structure ，找到 PDB ID 为 2FLU 的结构，然后单击 RCSB-PDB。单击“ 下载文件 ”并选择 “PDB 格式 ”以下载 Keap1 蛋白的晶体结构。</li>
	</ol></li>
</ol>2. 分子对接
<ol><li>在桌面上新建一个名为 molecular docking 的文件夹，并将下载的 xanthatin（小分子）和 Keap1（蛋白质）结构保存在此文件夹中。 注意：文件夹名称和路径必须为英文。</li>
	<li>打开 Maestro 软件，点击文件，选择更改工作目录，点击桌面，双击选择新创建的分子对接文件夹，点击选择选项。</li>
	<li>小分子加工<ol><li>单击 “文件 并导入结构选项”，单击 “桌面”，双击“分子对接”文件夹，单击“ xanthatin结构文件”，然后单击“ 打开 ”以导入小分子文件。</li>
		<li>单击软件右上角 的任务 ，然后选择 LigPrep 选项。使用工作区中的结构;其余参数为默认参数，单击“ 运行 ”以执行小分子处理。</li>
	</ol></li>
	<li>蛋白质结构加工<ol><li>单击“ 文件”和“导入结构 ”选项，单击 “桌面”，然后双击“ 分子对接 ”文件夹。单击“ Keap1 蛋白质结构 ”文件，然后单击“ 打开 ”以导入蛋白质结构文件。</li>
		<li>单击软件右上角 的“任务 ”，然后选择“ 蛋白质制备向导 ”选项。选中 “从基团中删除超过 5 Å 的水”框 ，并输入 pH 值为 7.4+/-0.0。单击“ 预处理”，单击“ 优化” ，然后单击“优化”>“删除 Waters >最小化”。</li>
		<li>上一个任务完成后，单击下一个任务。单击 “任务” ，然后选择“ 站点地图 ”选项，对所有参数使用默认设置，然后单击“ 运行”。</li>
		<li>单击“ 文件”和“导入结构 ”选项，单击 “桌面”，双击分子对接文件夹，然后打开名为“sitemap_1”的文件夹。单击名为 Sitemap_1_out.maegz 的第一个文件，然后单击“ 打开”。</li>
		<li>双击圆点以在工作区中选择sitemap_1_protein，然后单击 Sitemap_1_site_1 以选择它。单击 “任务 ”，然后选择“ 受体网格生成 ”选项。 注意：站点地图预测的蛋白质位点根据评分进行排名，得分最高的位点排在第一位。</li>
		<li>在 receptor 下选择 Entry 选项，其余参数不变。点击蛋白质结构中的 小白球 ，生成默认大小的对接盒，并将作业名称更改为 glide-grid_2FLU。单击 “运行”。</li>
	</ol></li>
	<li>分子对接<ol><li>单击 “任务” ，然后选择“ 配体对接 ”选项。从文件中选择 Receptor Grid As ，然后单击 Browse。单击 “桌面”，双击“分子对接”文件夹，双击滑动grid_2FLU文件，单击“Glide-grid_2FLU.zip文件”，然后单击“ 打开”。</li>
		<li>单击“ Use Ligands from files”>浏览>桌面。双击分子对接文件夹，双击ligprep_1文件，单击Ligprep_1-out.maegz文件，然后单击 打开。</li>
		<li>单击 “设置 ”，然后选择 “精度为XP（额外精度） ”选项，将作业名称更改为“glide-dock_XP_2FLU”，然后单击“ 运行”。</li>
	</ol></li>
	<li>查看对接结果<ol><li>单击“ 文件”和“导入结构 ”选项，单击“ 桌面”，然后双击“ 分子对接 ”文件夹。双击 glide-dock_XP_2FLU 文件，单击 glide-dock_XP_1_pv.maegz 文件，然后单击“ 打开”。</li>
		<li>双击圆点以在工作区中选择配体，然后单击以选择sitemap_1_protein。单击 “表”，滑向最右侧，然后在“停靠分数”下查看分数。</li>
	</ol></li>
	<li>2D 结果的可视化<ol><li>双击圆点以在工作区中选择配体，然后单击以同时选择配体sitemap_1_protein。单击 Ligand Interaction > View ，然后选中 LID 图例 框。</li>
		<li>单击 “文件”，选择“ 保存屏幕截图”，在宽度处输入 6000，取消选中 “透明背景”框，然后单击“ 确定”。单击 “桌面”，将文件命名为 2D-xanthatin-2FLU，然后保存图像。</li>
	</ol></li>
	<li>3D结果的可视化<ol><li>双击圆点以在工作区中选择配体，然后单击以同时选择配体sitemap_1_protein。</li>
		<li>在“快速选择”中单击“ L ”以选择小分子，然后单击“ 样式”。单击样式中 的第二行 以更改小分子键的大小，然后单击颜色原子以更改小分子颜色。</li>
		<li>选择图中的小分子，单击鼠标右键，选择 扩展选择，然后选择 4 Å。单击“在样式中 应用标签 ”以显示标签。</li>
		<li>单击样式中的第二行以更改氨基酸键的大小，然后单击 “颜色原子 ”以更改氨基酸颜色。</li>
		<li>单击 “下一步”>“反转”，按住控制键并单击“样式”下的第一个眼睛位置，以仅显示带有标签的氨基酸。</li>
		<li>单击“ 设置色带>样式 ”以将蛋白质显示为色带样式，然后单击“色带”下的“ 编辑色带 ”以更改色带颜色。</li>
		<li>单击屏幕右下角的 交互切换 ，单击鼠标右键并取消选中联系人/冲突。</li>
		<li>在任务中搜索“测量”，选择 “距离”，然后单击屏幕中由黄色虚线（氢键）连接的两个原子，以测量氢键的长度。</li>
		<li>按住鼠标滚轮旋转蛋白质，将蛋白质调整到屏幕的中心，并确保每个氨基酸标签都可见。</li>
		<li>单击 “工作区”>“将图像另存为 ”，然后单击桌面上的。默认情况下将图像保存为600dpi，将文件命名为3D-xanthatin-2FLU，选择文件类型为tiff格式，然后保存图像。</li>
	</ol></li>
</ol>3. 细胞培养和CETSA样品制备
<ol><li>细胞培养<ol><li>将 5 x 106 RAW264.7 细胞/mL 加入 100 mm 培养皿中，并与 6 mL 含有 10% FBS 和 1% 抗生素 - 抗真菌溶液的 DMEM 培养基在 37 °C、95% 湿度和 5% CO2 下孵育。准备三个培养皿，当细胞达到80%汇合度时进行实验。</li>
	</ol></li>
	<li>CETSA样品制备<ol><li>吸出旧培养基，向每个培养皿中加入 2 mL 加热至室温的磷酸盐缓冲盐水 （PBS），洗涤 2 次。</li>
		<li>向每个细胞培养皿中加入 2 mL PBS，混合细胞，并将细胞收集到两个 1.5 mL 微量离心管中。在室温下以377× g 离心3分钟。</li>
		<li>弃去上清液，用含有 1% 广谱蛋白酶抑制剂混合物的 2 mL PBS 重悬细胞。分装到单独的微量离心管中，每个离心管中含有 1 mL 细胞悬液。</li>
		<li>将微量离心管在液氮中冷冻至白色固体，然后立即在37°C水浴中解冻并重复3次。在4°C下以12,000× g 离心10分钟。</li>
		<li>取两个微量离心管，一个含有100μMxanthatin，另一个含有等体积的DMSO，并向每个管中加入450μL离心上清液，在37°C孵育30分钟。</li>
		<li>将来自两个微量离心管的上清液加入体积为 60 μL/管的 14 个 PCR 管中，并在不同温度（45 °C、48 °C、51 °C、54 °C、57 °C、60 °C 和 63 °C）下同时加热管 3 分钟，每个温度下有两个 PCR 管，用于 DMSO 和 xanthatin 组， 分别。</li>
		<li>对于未加热的组，从两个微量离心管中的每一个中取出上清液，并加入14个PCR管中，7个用于DMSO组，7个用于黄素组，每个体积为60μL。不要在这里加热样品。</li>
		<li>在室温下冷却加热组的管子。将所有管（加热和未加热）在4°C下以12,000× g 离心10分钟，并从每个管中取出48μL上清液到新的1.5mL微量离心管中。向每个试管中加入12μL SDS-PAGE蛋白上样缓冲液，涡旋混合充分，并在100°C的水浴中加热5分钟。 注意：制备的CETSA样品必须储存在-80°C，并在1周内通过蛋白质印迹进行测试。</li>
	</ol></li>
</ol>4. 蛋白质印迹
<ol><li>SDS-PAGE凝胶的制备<ol><li>用超纯水冲洗一块长玻璃板和一块短玻璃板。将板放入夹紧槽中，然后放在透明板上。加入超纯水并进行泄漏检查 10 分钟。</li>
		<li>将 8 mL 下凝胶溶液和 8 mL 下凝胶缓冲液加入锥形瓶中，轻轻摇动混合。向凝胶溶液中加入 160 μL 凝血促进剂并充分混合。 注意： 下层粘合剂的体积不应超过夹紧槽上的绿线。</li>
		<li>从板中排出超纯水，并用滤纸吸干。加入准备好的下凝胶溶液。加入 2 mL 异丙醇，等待 20 分钟凝固。</li>
		<li>取 2 mL 上凝胶溶液和 2 mL 上凝胶缓冲液加入锥形瓶中，轻轻摇动瓶进行混合。</li>
		<li>从凝胶层中取出异丙醇，并用滤纸吸干多余的异丙醇。</li>
		<li>将 40 μL 凝血促进剂加入混合的上凝胶溶液中，充分混合，然后在长板和短玻璃板之间加入并插入 1.5 mm 15 孔梳子，等待 20 分钟。 注意： 梳子应垂直于上层插入，梳子和溶液之间没有气泡。</li>
	</ol></li>
	<li>电泳<ol><li>向烧杯中加入1 L超纯水，加入一袋SDS-PAGE电泳缓冲液粉末，用玻璃棒充分搅拌，并将制备的凝胶加入电泳单元中。将设备放入盒子中，然后拉出梳子。</li>
		<li>在室温下解冻标记物和 CETSA 样品，涡旋并下降。根据预定顺序，向第一个孔中加入 2.5 μL 标记物，向剩余的每个孔中加入 20 μL CETSA 样品。 注：根据步骤3.2.6中描述的温度处理，从45°C到63°C依次加入CETSA样品;首先加入DMSO组，然后加入xanthatin组（图1）。</li>
		<li>将剩余的电泳溶液加入电泳单元中，盖上盖子，插入电极，正极对正极，负极对负极。</li>
		<li>打开电源开关并将电压开关调整到 80 V 以开始电泳。</li>
	</ol></li>
	<li>转移到膜上<ol><li>将 850 mL 超纯水、100 mL 无水乙醇和 50 mL 快速转印缓冲液加入烧杯中，充分搅拌。准备泡沫板、塑料刀、镊子、托盘和膜转移装置。</li>
		<li>根据凝胶的大小切开PVDF膜，做标记，然后将其浸泡在甲醇中30秒以使其活化。</li>
		<li>将转移溶液倒入托盘中，放置并打开转移夹，并放置海绵垫和三层转移滤纸。</li>
		<li>停止电泳，然后打开电泳盒的盖子。取电泳装置，倒出电泳液，取出板。将板放在泡沫板上，用塑料刀轻轻撬开，然后切开凝胶，以清楚地看到蛋白质标记物和靶蛋白。</li>
		<li>用镊子握住标记的一侧，在装有跨膜溶液的托盘中洗涤，然后将其平放在滤纸上。将活化的 PVDF 膜浸入转印溶液中，用镊子握住标记面，然后面朝下覆盖凝胶。 注意： PVDF 膜标记的一侧是正面，请注意在放置过程中不会产生气泡。</li>
		<li>将两层转移滤纸和一层海绵垫放在PVDF膜上，盖住并夹紧转移夹，然后放入转移罐中。 注意： 传输夹的黑色到传输插槽的黑色。</li>
		<li>将剩余的跨膜溶液加入跨膜盒中，盖上盖子，阳性转阳性，阴性转阴性。</li>
		<li>将电流调节为 400 mA，时间调整为 30 分钟，然后按 Run 开始在室温下转移膜。</li>
	</ol></li>
	<li>阻塞<ol><li>向玻璃瓶中加入 2 L 超纯水，加入一袋 TBS 粉末，然后加入 2 mL 吐温制成 TBST 溶液。称量BSA粉末，用TBST制备5%BSA溶液，并向培养箱中加入6mL BSA溶液。</li>
		<li>停止膜转移，然后打开转移夹。夹住 PVDF 膜的标记侧并将其放入培养箱中。将培养箱放在振荡器上，并在室温下块1.5小时。</li>
	</ol></li>
	<li>一抗的孵育<ol><li>用 5% BSA 溶液以 1：1000 制备一抗（详见 材料表 ），在培养箱中回收 BSA 溶液，并向培养箱中加入 6 mL 一抗。将培养箱放入装有冰袋的泡沫盒中，并在摇摇杯上放置过夜。</li>
	</ol></li>
	<li>二抗的孵育<ol><li>第二天在培养箱中收集一抗，然后加入 6 mL TBST 溶液，并将其放在振荡器上洗涤 5 分钟。</li>
		<li>倒出洗净的TBST溶液，加入新鲜的洗涤;重复 5 次，并用 5% BSA 溶液以 1：5000 制备二抗。</li>
		<li>倒出 TBST 溶液，向孵育箱中加入 6 mL 二抗。将培养箱放在振荡器上，在室温下孵育1.5小时。</li>
		<li>收集二抗（详见 材料表 ），然后加入 6 mL TBST 溶液，并将其置于振荡器上洗涤 5 分钟。倒出洗净的TBST溶液，加入新鲜的洗涤;重复 5 次。</li>
	</ol></li>
	<li>露出条带<ol><li>在最后一次洗涤结束时保留 TBST 溶液。取等体积的化学发光试剂 A 和 B，摇匀，充分混合。将溶液避光。</li>
		<li>打开凝胶成像系统，点击“ 样本”，检查校准，等待校准完成，然后点击“ 标记”，检查校准。</li>
		<li>用镊子拾取条带的标记侧，并完全浸入化学发光试剂溶液中。夹住标记的一侧，将条带正面朝下放入成像仪中，合上成像仪的盖子，单击 曝光，并将曝光时间设置为 1 秒。 注意：开发商必须完全覆盖条带，并且开发应在黑暗的环境中进行。</li>
		<li>单击 “保存”，为图像命名，然后将其另存为.tif文件。分析蛋白质印迹的光密度。将DMSO组各温度的灰度值与45 °C的灰度值进行比较，将xanthatin组各温度的灰度值与xanthatin在45 °C的灰度值进行比较。</li>
	</ol></li>
</ol>

验证黄素-Keap1 与 Docking 和 CETSA 的相互作用

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作者没有什么可透露的。

疾病靶点的确定与药物的发现和开发密切相关27.通过精确靶向特定靶点，可以开发候选药物以更有效地治疗特定疾病，同时最大限度地减少与药物相关的副作用28,29。最常用的靶标是蛋白质靶标30。然而，由于细胞内蛋白质的广泛多样性，特殊蛋白质靶标的鉴定带来了巨大的挑战30,31<sup class="...

蛋白质是生物体中非常重要的大分子，在细胞内具有多种独特的功能，例如膜组成、细胞骨架形成、酶活性、运输、细胞信号传导以及参与细胞内和细胞外机制 1,2,3。蛋白质主要通过与多种分子的特异性相互作用来表现出其生物学功能，包括其他蛋白质、核酸、小分子配体和金属离子 1,4。配体是与生物体中的蛋白质特异性结合的小分子化合物。蛋白质和配体之间的相互作用发生在蛋白质上的特定位点，称为结合位点，也称为结合口袋5。在药物化学研究中，重点在于识别与疾病明显相关的关键蛋白质，这些蛋白质是药物的靶标6。因此，深入了解蛋白质和配体之间的结合位点对于促进药物发现、设计和研究至关重要7,8。
分子对接是一种广泛使用的用于研究蛋白质-配体结合的计算工具，它利用蛋白质和配体的三维结构来探索它们在形成稳定复合物时的主要结合模式和亲和力9,10,11。分子对接技术的应用起源于1970年代。基于锁键配对原理，利用分子对接软件的算法，通过分析对接结果，可以确定化合物与分子靶标之间的相互作用。这种方法可以预测化合物和靶分子的活性结合位点。因此，它有助于识别配体-受体相互作用的最佳结合构象（此处称为结合模型），这对于理解这些分子结合的机制至关重要 12,13,14,15。虽然分子对接提供了有价值的基于计算机的配体-受体相互作用预测，但重要的是要注意这些是初步发现。因此，进一步的实验验证对于确认这些相互作用至关重要。
细胞热位移测定 （CETSA） 最初由 Pär Nordlund 的研究团队于 2013 年提出，是一种验证药物-靶蛋白相互作用的方法。该技术专门测试药物结合诱导的靶蛋白的热稳定性，为确认分子相互作用提供了一种实用的方法16,17,18。这种方法基于配体结合在靶蛋白内启动热位移的基本原理，适用于各种生物样品，包括细胞裂解物、完整活细胞和组织19,20。CETSA 通过检测由于配体结合导致的蛋白质热力学稳定性并将观察到的表型反应与目标化合物联系起来，支持完整细胞中小分子的直接靶向参与21,22。在源自 CETSA 的各种方法中，Western Blot-CETSA （WB-CETSA） 被认为是一种经典方法。在使用 CETSA 方法制备样品后，使用蛋白质印迹分析来检测靶蛋白热稳定性的变化。这使得可以精确测定细胞系统内的药物-蛋白质相互作用17,23。
Xanthatin 是从植物 Xanthium L. 中分离出的一种生物活性化合物，具有抗炎等特性，在中医中已用于治疗鼻窦炎和关节炎等疾病24,25。Kelch 样 ECH 相关蛋白 1 （Keap1） 是基于 Cullin3 的 Cullin-RING E3 泛素连接酶多亚基蛋白复合物的组成部分，也是细胞内氧化还原稳态的重要调节因子，它通过调节细胞内氧化还原状态来影响炎症反应的强度和持续时间26。在这项研究中，我们首先利用分子对接来研究xanthatin（小分子）与Keap1蛋白之间的相互作用，旨在预测它们的结合模式。随后，我们采用 CETSA 方法通过评估 xanthatin 对 Keap1 蛋白热稳定性的影响来验证这种相互作用。

<table><tbody><tr><td>0.45 &amp;mu;m Polyvinylidene fluoride membrane</td><td>Millipore</td><td>PR05509</td><td></td></tr><tr><td>Anhydrous ethanol</td><td>Chron chemicals</td><td>64-17-5</td><td></td></tr><tr><td>Bovine serum albumin</td><td>BioFroxx</td><td>4240GR100</td><td></td></tr><tr><td>Broad-spectrum protease inhibitor mixtures</td><td>Boster Biological Technology Co., Ltd</td><td>AR1193</td><td></td></tr><tr><td>DMSO</td><td>Boster Biological Technology Co., Ltd</td><td>PYG0040</td><td></td></tr><tr><td>Enhanced chemiluminescence reagent</td><td>Beyotime Biotechnology Co., Ltd</td><td>P0018S</td><td></td></tr><tr><td>GAPDH antibody</td><td>ProteinTech Group Co., Ltd</td><td>10494-1-AP</td><td></td></tr><tr><td>Gel Imaging Instrument</td><td>E-BLOT</td><td>Touch Imager Pro</td><td></td></tr><tr><td>Gradient PCR instrument</td><td>Biometra TADVANCED</td><td>Biometra Tadvanced 96SG</td><td></td></tr><tr><td>High-speed freezing centrifuge</td><td>Beckman Coulter</td><td>Allegra X-30R</td><td></td></tr><tr><td>Horseradish peroxidase-conjugated affiniPure goat antibody</td><td>ProteinTech Group Co., Ltd</td><td>SA00001-2</td><td></td></tr><tr><td>Isopropyl alcohol&amp;nbsp;</td><td>Chron chemicals</td><td>67-63-0</td><td></td></tr><tr><td>Keap1 antibody</td><td>Zen BioScience Co., Ltd</td><td>R26935</td><td></td></tr><tr><td>Metal bath</td><td>Analytik Jena</td><td>TSC</td><td></td></tr><tr><td>Methanol</td><td>Chron chemicals</td><td>67-56-1</td><td></td></tr><tr><td>Ncmblot rapid transfer buffer (20&amp;times;)</td><td>NCM Biotech Co., Ltd</td><td>WB4600</td><td></td></tr><tr><td>Omni-Easy OneStep PAGE gel fast preparation kie</td><td>Epizyme Biotech Co., Ltd</td><td>PG212</td><td></td></tr><tr><td>Phosphate buffer saline</td><td>Boster Biological Technology Co., Ltd</td><td>PYG0021</td><td></td></tr><tr><td>Prestained Color Protein Marker</td><td>Biosharp&amp;nbsp;</td><td>BL741A</td><td></td></tr><tr><td>Protein Blotting Electrophoresis System</td><td>Bio-Rad</td><td>MiniPROTEAN&amp;Ograve;Tetra Cell</td><td></td></tr><tr><td>RAW264.7 cell</td><td>Beyotime Biotechnology Co., Ltd</td><td>C7505</td><td></td></tr><tr><td>RAW264.7 cell-specific medium</td><td>Procell Life Science&amp;amp;Technology Co., Ltd</td><td>CM-0597</td><td></td></tr><tr><td>SDS-PAGE protein loading buffer</td><td>Boster Biological Technology Co., Ltd</td><td>AR1112-10</td><td></td></tr><tr><td>SDS-PAGE running buffer powder</td><td>Servicebio</td><td>G2018</td><td></td></tr><tr><td>Tris buffered saline powder</td><td>Servicebio</td><td>G0001</td><td></td></tr><tr><td>Tween 20</td><td>BioFroxx</td><td>1247ML100</td><td></td></tr><tr><td>Water bath</td><td>Memmert</td><td>WNE10</td><td></td></tr><tr><td>Water purifier</td><td>Millipore</td><td>Milli- IQ 7005</td><td></td></tr><tr><td>Xanthatin</td><td>ChemConst Biotechnology Co., Ltd</td><td>CONST210706</td><td></td></tr></tbody></table>

protein target prediction and validation of small molecule compound

蛋白质是人体生理学的基础，其靶点在研究和药物开发中至关重要。关键蛋白质靶标的识别和验证已成为药物开发不可或缺的一部分。分子对接是一种广泛用于研究蛋白质-配体结合的计算工具，特别是在药物和蛋白质靶标相互作用的背景下。为了进行结合的实验验证并直接获得药物与其靶标的结合，使用细胞热位移测定 （CETSA） 方法。本研究旨在将分子对接与CETSA相结合，以预测和验证药物与重要蛋白质靶点之间的相互作用。具体来说，我们通过分子对接分析预测了xanthatin和Keap1蛋白之间的相互作用及其结合模式，然后使用CETSA法验证了该相互作用。结果表明，黄素可以与Keap1蛋白的特定氨基酸残基建立氢键，降低Keap1蛋白的热稳定性，表明黄素可以直接与Keap1蛋白相互作用。

分子对接分析预测了黄素与Keap1蛋白之间的相互作用。 图 2 显示了 xanthatin 与 Keap1 蛋白的氨基酸残基 Gly-367 和 Val-606 之间形成氢键，Gly-367 的氢键长度为 2.17 Å，Val-606 的氢键长度为 2.13 Å。此外，计算出的对接分数为 -5.69 kcal/mol 表明 xanthatin 和 Keap1 蛋白之间具有良好的结合亲和力。
CETSA方法显示，黄素结合改变了Keap1蛋白的热稳定性，证实了黄素与Keap1?...

Watch this Scientific Journal Video about Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA at JoVE.com

作者聚焦：通过分子对接和 CETSA 简化蛋白质靶标预测和验证

这里使用的实验展示了一种分子对接结合细胞热位移测定的方法，以预测和验证小分子和蛋白质靶标之间的相互作用。

小分子化合物的蛋白质靶点预测与验证

Proteins are fundamental to human physiology, with their targets being crucial in research and drug development. The identification and validation of ...

我们的研究重点是蛋白质靶点、小分子化合物的预测和验证。这里使用的实验展示了一种分子对接方法与类似的热位移测定相结合，以预测和验证小分子和蛋白质靶标之间的相互作用。CETSA的这些步骤很复杂，操作过程中的任何步骤错误都会对结果产生很大影响。我们的协议将预测与验证相结合，有效降低了时间消耗和经济成本。此外，实验仪器和材料很容易选择使用。最后，CETSA样品易于在测试中制备，保证了样品的可重复性。

protein-target-prediction-and-validation-of-small-molecule-compound

Research

JoVE Journal

Medicine

Protein Target Prediction and Validation of Small Molecule Compound

2.2K 次观看.  Chengdu University of Traditional Chinese Medicine. 我们的研究重点是蛋白质靶点、小分子化合物的预测和验证。这里使用的实验展示了一种分子对接方法与类似的热位移测定相结合，以预测和验证小分子和蛋白质靶标之间的相互作用。CETSA的这些步骤很复杂，操作过程中的任何步骤错误都会对结果产生很大影响。我们的协议将预测与验证相结合，有效降低了时间消耗和经济成本。此外，实验仪器和材料很容易选择使用?...

视频: 作者聚焦：通过分子对接和 CETSA 简化蛋白质靶标预测和验证

Identification of Small Molecule-binding Proteins in a Native Cellular Environment by Live-cell Photoaffinity Labeling

Identifying the molecular target(s) of small molecules is a challenging but necessary step towards understanding their mechanism of action. While several target identification methods have been developed and used to successfully elucidate the binding proteins of a variety of small molecules, these techniques have drawbacks that make them unsuitable for detecting certain types of small molecule-target interactions. In particular, non-covalent interactions that depend on native cellular conditions, such as those of membrane proteins whose structures may be perturbed upon cell lysis, are often not amenable to affinity-based target identification methods. Here, we demonstrate a method wherein a probe containing a photolabile group is used to covalently crosslink to the small molecule binding protein within the environment of the live cell, allowing the detection and isolation of the target protein without the need for maintenance of the interaction after cell lysis. This technique is a valuable tool for studying biologically interesting small molecules with unknown mechanisms, both in the context of basic biology as well as drug discovery.

We describe here a method for identification of small molecule-binding proteins using photoaffinity labeling. The advantage of this technique is that binding and covalent labeling of the target proteins occurs within the live cellular environment, removing the risk of disrupting native protein structure and binding conditions upon cell lysis.

小分子结合蛋白的鉴定在天然细胞环境由活细胞光亲和标记

Identifying the molecular target(s) of small molecules is a challenging but necessary step towards understanding their mechanism of action. While several ...

科研

生物化学

Method for Identifying Small Molecule Inhibitors of the Protein-protein Interaction Between HCN1 and TRIP8b

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are expressed ubiquitously throughout the brain, where they function to regulate the excitability of neurons. The subcellular distribution of these channels in pyramidal neurons of hippocampal area CA1 is regulated by tetratricopeptide repeat-containing Rab8b interacting protein (TRIP8b), an auxiliary subunit. Genetic knockout of HCN pore forming subunits or TRIP8b, both lead to an increase in antidepressant-like behavior, suggesting that limiting the function of HCN channels may be useful as a treatment for Major Depressive Disorder (MDD). Despite significant therapeutic interest, HCN channels are also expressed in the heart, where they regulate rhythmicity. To circumvent off-target issues associated with blocking cardiac HCN channels, our lab has recently proposed targeting the protein-protein interaction between HCN and TRIP8b in order to specifically disrupt HCN channel function in the brain. TRIP8b binds to HCN pore forming subunits at two distinct interaction sites, although here the focus is on the interaction between the tetratricopeptide repeat (TPR) domains of TRIP8b and the C terminal tail of HCN1. In this protocol, an expanded description of a method for purifying TRIP8b and executing a high throughput screen to identify small molecule inhibitors of the interaction between HCN and TRIP8b, is described. The method for high throughput screening utilizes a Fluorescence Polarization (FP) -based assay to monitor the binding of a large TRIP8b fragment to a fluorophore-tagged eleven amino acid peptide corresponding to the HCN1 C terminal tail. This method allows &#39;hit&#39; compounds to be identified based on the change in the polarization of emitted light. Validation assays are then performed to ensure that &#39;hit&#39; compounds are not artifactual.

The interaction between HCN channels and their auxiliary subunit has been identified as a therapeutic target in Major Depressive Disorder. Here, a fluorescence polarization-based method for identifying small molecule inhibitors of this protein-protein interaction, is presented.

方法确定HCN1和TRIP8b之间蛋白 - 蛋白相互作用的小分子抑制剂

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are expressed ubiquitously throughout the brain, where they function to regulate the ...

A Semi-Quantitative Drug Affinity Responsive Target Stability (DARTS) assay for studying Rapamycin/mTOR interaction

Drug Affinity Responsive Target Stability (DARTS) is a robust method for detection of novel small molecule protein targets. It can be used to verify known small molecule-protein interactions and to find potential protein targets for natural products. Compared with other methods, DARTS uses native, unmodified, small molecules and is simple and easy to operate. In this study, we further enhanced the data analysis capabilities of the DARTS experiment by monitoring the changes in protein stability and estimating the affinity of protein-ligand interactions. The protein-ligand interactions can be plotted into two curves: a proteolytic curve and a dose-dependence curve. We have used the mTOR-rapamycin interaction as an exemplary case for establishment of our protocol. From the proteolytic curve we saw that the proteolysis of mTOR by pronase was inhibited by the presence of rapamycin. The dose-dependency curve allowed us to estimate the binding affinity of rapamycin and mTOR. This method is likely to be a powerful and simple method for accurately identifying novel target proteins and for the optimization of drug target engagement.

A Semi-Quantitative Drug Affinity Responsive Target Stability DARTS assay for studying Rapamycin/mTOR interaction

In this study, we enhanced the data analysis capabilities of the DARTS experiment by monitoring the changes in protein stability and estimating the affinity of protein-ligand interactions. The interactions can be plotted into two curves: a proteolytic curve and a dose-dependence curve. We have used mTOR-rapamycin interaction as an exemplary case.

用于研究拉帕霉素/mTOR相互作用的半定量药物亲和反应目标稳定性 （DARTS） 测定

Drug Affinity Responsive Target Stability (DARTS) is a robust method for detection of novel small molecule protein targets. It can be used to verify known ...

High-Throughput Cellular Profiling of Targeted Protein Degradation Compounds Using HiBiT CRISPR Cell Lines

Targeted protein degradation compounds, including molecular glues or proteolysis targeting chimeras, are an exciting new therapeutic modality in small molecule drug discovery. This class of compounds induces protein degradation by bringing into proximity the target protein and the E3 ligase machinery proteins required to ubiquitinate and ultimately degrade the target protein through the ubiquitin-proteasomal pathway (UPP). Profiling of target protein degradation in a high-throughput fashion, however, remains highly challenging given the complexity of cellular pathways required to achieve degradation. Here we present a protocol and screening strategy based on the use of CRISPR/Cas9 endogenous tagging of target proteins with the 11 amino acid HiBiT tag which complements with high affinity to the LgBiT protein, to produce a luminescent protein. These CRISPR targeted cell lines with endogenous tags can be used to measure compound induced degradation in either real-time, kinetic live cell or endpoint lytic modes by monitoring luminescent signal using a luminescent plate-based reader. Here we outline the recommended screening protocols for the different formats, and&#160;also describe the calculation of key degradation parameters of rate, Dmax, DC50, Dmax50, as well as multiplexing with cell viability assays. These approaches enable rapid discovery and triaging of early stage compounds while maintaining endogenous expression and regulation of target proteins in relevant cellular backgrounds, allowing for efficient optimization of lead therapeutic compounds.

This protocol describes the quantitative luminescent detection of protein degradation kinetics in living cells that have been engineered using CRISPR/Cas9 to express antibody free endogenous protein detection tag fused to a target protein. Detailed instructions for calculating and obtaining quantitative degradation parameters, rate, Dmax, DC50, and Dmax50 are included.

使用 HiBiT CRISPR 细胞系对靶向蛋白质降解化合物进行高通量细胞分析

Targeted protein degradation compounds, including molecular glues or proteolysis targeting chimeras, are an exciting new therapeutic modality in small ...

癌症研究

A Rapid and Quantitative Fluorimetric Method for Protein-Targeting Small Molecule Drug Screening

We demonstrate a new drug screening method for determining the binding affinity of small drug molecules to a target protein by forming fluorescent gold nanoclusters (Au NCs) within the drug-loaded protein, based on the differential fluorescence signal emitted by the Au NCs. Albumin proteins such as human serum albumin (HSA) and bovine serum albumin (BSA) are selected as the model proteins. Four small molecular drugs (e.g., ibuprofen, warfarin, phenytoin, and sulfanilamide) of different binding affinities to the albumin proteins are tested. It was found that the formation rate of fluorescent Au NCs inside the drug loaded albumin protein under denaturing conditions (i.e., 60 &#176;C or in the presence of urea) is slower than that formed in the pristine protein (without drugs). Moreover, the fluorescent intensity of the as-formed NCs is found to be inversely correlated to the binding affinities of these drugs to the albumin proteins. Particularly, the higher the drug-protein binding affinity, the slower the rate of Au NCs formation, and thus a lower fluorescence intensity of the resultant Au NCs is observed. The fluorescence intensity of the resultant Au NCs therefore provides a simple measure of the relative binding strength of different drugs tested. This method is also extendable to measure the specific drug-protein binding constant (KD) by simply varying the drug content preloaded in the protein at a fixed protein concentration. The measured results match well with the values obtained using other prestige but more complicated methods.

A protocol for small molecular drug screening based on in-situ synthesis of ultrasmall fluorescent gold nanoclusters (Au NCs) using drug-loaded protein as template is presented. This method is simple to determine the binding affinity of drugs to a target protein by a visible fluorescent signal emitted from the protein-templated Au NCs.

一种快速定量荧光方法蛋白靶向小分子药物筛选

We demonstrate a new drug screening method for determining the binding affinity of small drug molecules to a target protein by forming fluorescent gold ...

生物工程

<meta charset="utf8"></head><body>预测蛋白质-蛋白质结合界面的氨基酸偏好

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Many protein-protein interactions involve the binding of short protein segments to peptide-binding domains. Usually, such interactions require the recognition of linear motifs with variable conservation. The combination of highly conserved and more variable regions in the same ligands often contributes to the multispecificity of binding, a common property of enzymes and cell signaling proteins. Characterization of amino acid preferences of peptide-binding domains is important for the design of mediators of protein-protein interactions (PPIs). Computational methods are an efficient alternative to the often costly and cumbersome experimental techniques, enabling the design of potential mediators that can be later validated in downstream experiments. Here, we described a methodology using the Pepspec application of the Rosetta molecular modeling package to predict the amino acid preferences of peptide-binding domains. This methodology is useful when the structure of the receptor protein and the nature of the peptide ligand are both known or can be inferred. The methodology starts with a well-characterized anchor from the ligand, which is extended by randomly adding amino acid residues. The binding affinity of peptides generated this way is then evaluated by flexible-backbone peptide docking in order to select the peptides with the best predicted binding scores. These peptides are then used to calculate amino acid preferences and to optionally compute a position-weight matrix (PWM) that can be used in further studies. To illustrate the application of this methodology, we used the interaction between subunits of human interferon regulatory factor 5 (IRF5), previously known to be multispecific but globally guided by a short conserved motif called pLxIS. The estimated amino acid preferences were consistent with previous knowledge about the IRF5 binding surface. Positions occupied by phosphorylatable serine residues exhibited a high frequency of aspartate and glutamate, likely because their negatively charged side chains are similar to phosphoserine.

<meta charset="utf8"></head><body>作者聚焦:一种破译多特异性蛋白质-蛋白质相互作用中氨基酸偏好的计算方法

We describe a methodology based on sequence diversification to estimate the amino acid preferences of multispecific binding sites in protein-protein interactions (PPIs). In this strategy, thousands of potential peptide ligands are generated and screened in silico, thus overcoming some limitations of available experimental methods.

参与蛋白质-蛋白质相互作用的潜在多特异性肽结合结构域的氨基酸偏好的计算预测

Many protein-protein interactions involve the binding of short protein segments to peptide-binding domains. Usually, such interactions require the ...

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

<meta charset="utf8"></head><body>定量-不可逆栓系:高效筛选亲电配体

Covalent Fragment Screening Using the Quantitative Irreversible Tethering Assay

Compounds that form covalent bonds with specific target proteins offer a variety of advantages&#160;as chemical probes and therapeutic agents. Most commonly, mildly reactive, electrophilic small molecules are employed to form covalent bonds with select cysteine side chains in specific proteins. Electrophile-first approaches of ligand discovery, whereby a library of electrophilic small molecules are screened against a protein target, have become popular as they avoid the need for time-consuming downstream installation of an electrophilic warhead. Such screening is complicated, however, as electrophilic ligands can exhibit a wide range of different rates of spontaneous reaction with cysteines. Quantitative-irreversible tethering (qIT) offers a fluorescence-based method for hit identification and development that normalizes data for these differences in intrinsic compound reactivity. Rates of reaction of individual compounds with a target protein are determined and compared to compound reactivity with the unstructured tripeptide glutathione (this being a proxy for spontaneous compound reaction), enabling the identification of compounds that preferentially react with the protein of interest. This methodology has been successfully applied to identify selective covalent fragments against several drug targets, including SARS-CoV-2 main protease, cyclin-dependent kinase 2, and RAP27A. Here, we demonstrate the application of qIT to a target protein to generate a quantitative and robust data set, allowing prioritization of hit ligands for future development.

<meta charset="utf8"></head><body>使用定量不可逆栓系测定法进行共价片段筛选

The quantitative irreversible tethering (qIT) assay is a fluorescence-based method of identifying covalent fragments that selectively engage with a target protein. Here, a detailed protocol is outlined to describe the qIT assay, with example results included.

使用定量不可逆栓系测定法进行共价片段筛选

Compounds that form covalent bonds with specific target proteins offer a variety of advantages&#160;as chemical probes and therapeutic agents. Most ...

小分子化合物的蛋白质靶点预测与验证

副本

摘要

探索更多视频

此视频中的章节

小分子结合蛋白的鉴定在天然细胞环境由活细胞光亲和标记

方法确定HCN1和TRIP8b之间蛋白 - 蛋白相互作用的小分子抑制剂

用于研究拉帕霉素/mTOR相互作用的半定量药物亲和反应目标稳定性（DARTS）测定

使用 HiBiT CRISPR 细胞系对靶向蛋白质降解化合物进行高通量细胞分析

一种快速定量荧光方法蛋白靶向小分子药物筛选

参与蛋白质-蛋白质相互作用的潜在多特异性肽结合结构域的氨基酸偏好的计算预测

参与蛋白质-蛋白质相互作用的潜在多特异性肽结合结构域的氨基酸偏好的计算预测

参与蛋白质-蛋白质相互作用的潜在多特异性肽结合结构域的氨基酸偏好的计算预测

基于计算机的蛋白质结构与功能预测议定书

使用定量不可逆栓系测定法进行共价片段筛选

小分子化合物的蛋白质靶点预测与验证

副本

摘要

探索更多视频

此视频中的章节

小分子结合蛋白的鉴定在天然细胞环境由活细胞光亲和标记

方法确定HCN1和TRIP8b之间蛋白 - 蛋白相互作用的小分子抑制剂

用于研究拉帕霉素/mTOR相互作用的半定量药物亲和反应目标稳定性 （DARTS） 测定

使用 HiBiT CRISPR 细胞系对靶向蛋白质降解化合物进行高通量细胞分析

一种快速定量荧光方法蛋白靶向小分子药物筛选

参与蛋白质-蛋白质相互作用的潜在多特异性肽结合结构域的氨基酸偏好的计算预测

参与蛋白质-蛋白质相互作用的潜在多特异性肽结合结构域的氨基酸偏好的计算预测

参与蛋白质-蛋白质相互作用的潜在多特异性肽结合结构域的氨基酸偏好的计算预测

基于计算机的蛋白质结构与功能预测议定书

使用定量不可逆栓系测定法进行共价片段筛选

用于研究拉帕霉素/mTOR相互作用的半定量药物亲和反应目标稳定性（DARTS）测定