Article(id=1210517368313344995, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1210517366081975259, articleNumber=null, orderNo=null, doi=10.16438/j.0513-4870.2022-0769, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1655913600000, receivedDateStr=2022-06-23, revisedDate=1659628800000, revisedDateStr=2022-08-05, acceptedDate=null, acceptedDateStr=null, onlineDate=1766539430930, onlineDateStr=2025-12-24, pubDate=1668182400000, pubDateStr=2022-11-12, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1766539430930, onlineIssueDateStr=2025-12-24, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1766539430930, creator=13701087609, updateTime=1766539430930, updator=13701087609, issue=Issue{id=1210517366081975259, tenantId=1146029695717560320, journalId=1189982191388893191, year='2022', volume='57', issue='11', pageStart='3259', pageEnd='3450', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1766539430399, creator=13701087609, updateTime=1766539608198, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1210518111875363690, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1210517366081975259, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1210518111875363691, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1210517366081975259, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=3292, endPage=3300, ext={EN=ArticleExt(id=1210517369420641257, articleId=1210517368313344995, tenantId=1146029695717560320, journalId=1189982191388893191, language=EN, title=Application of native mass spectrometry in natural product research, columnId=1190335348648547107, journalTitle=Acta Pharmaceutica Sinica, columnName=Reviews, runingTitle=null, highlight=null, articleAbstract=

Natural products, rich in sources and diverse in structure, play an important role in drug discovery. It is a hotspot to identify efficiently bioactive components from complex mixtures in the field of natural product research. Traditional research methods, starting from extraction and isolation to structural elucidation, and finally to bioactivity assay, are generally time-consuming, laborious, and untargeted. Therefore, there is an urgent need to develop efficient screening methods for bioactive component discovery. Native mass spectrometry (native MS) is an underutilized method based on the interaction between target and ligand, which can rapidly discover potential active components in complex matrices. Due to the simple operation and the native state of the protein, the method is specific, efficient, and simple. Herein, we give a brief introduction of native MS and then review its recent application in natural product research, with its advantages and limitations, which would provide a reference for researchers in relevant fields.

, correspAuthors=Jia LIU, Yang YE, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2022 Acta Pharmaceutica Sinica. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Da-fu ZHU, Chun-ping TANG, Jia LIU, Yang YE), CN=ArticleExt(id=1210517370506965018, articleId=1210517368313344995, tenantId=1146029695717560320, journalId=1189982191388893191, language=CN, title=非变性质谱在天然产物研究中的应用, columnId=1190335349655180086, journalTitle=药学学报, columnName=综述, runingTitle=null, highlight=null, articleAbstract=

天然产物来源丰富、结构多样, 是新药研发的重要源泉。从复杂的天然产物基质中高效筛选出有生物活性的成分是天然产物研究的重要方向之一。传统的研究方法从提取分离开始, 然后进行结构鉴定和活性评价, 耗时长、工作量大且带有很大的随机性, 因此亟需发展高效的生物活性成分发现方法。非变性质谱(native MS) 是一种基于靶标和配体相互作用的研究方法, 能够根据两者之间的亲和力快速发现复杂基质中潜在的活性成分, 具有特异、高效、简便的特点。本文简单介绍了非变性质谱的基本原理和影响因素, 并综述了该方法近年来在天然产物研究领域中的应用, 讨论了该方法的优势与局限性, 以期为相关领域的研究者提供借鉴和参考。

, correspAuthors=刘佳, 叶阳, authorNote=null, correspAuthorsNote=
*刘佳, Tel: 86-21-58559563, E-mail: ;
叶阳, Tel: 86-21-50806600, E-mail:
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AUF: Affinity-ultrafiltration; SEC: Size exclusion chromatography; MagMASS: Magnetic microbead affinity selection screening , figureFileSmall=CszSX6zWfngT8N3+uTSdeg==, figureFileBig=t9Seh3vP04FeLNJa3NM6VA==, tableContent=null), ArticleFig(id=1210517374839681288, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210517368313344995, language=EN, label=null, caption=null, figureFileSmall=J2gZd80OGO+jkfeLintQLA==, figureFileBig=LuUnOKdqdwWqs95AF5Nscw==, tableContent=null), ArticleFig(id=1210517374948733198, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210517368313344995, language=CN, label=Figure 2, caption= Schematic of a native MS detection with an ESI source operated in positive mode , figureFileSmall=J2gZd80OGO+jkfeLintQLA==, figureFileBig=LuUnOKdqdwWqs95AF5Nscw==, tableContent=null), ArticleFig(id=1210517375036813590, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210517368313344995, language=EN, label=null, caption=null, figureFileSmall=Sx+Hg6yqUUly0BeQCjP4Gg==, figureFileBig=usjjGsX7/T/IkmTJfWUn4w==, tableContent=null), ArticleFig(id=1210517375137476892, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210517368313344995, language=CN, label=Figure 3, caption= Schematic diagram of native MS applied in the investigation of NPs , figureFileSmall=Sx+Hg6yqUUly0BeQCjP4Gg==, figureFileBig=usjjGsX7/T/IkmTJfWUn4w==, tableContent=null), ArticleFig(id=1210517375212974372, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210517368313344995, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
SampleTargetType of MSLigandRef.
Extract of Scutellaria baicalensisSARS-CoV-2 3CLproESI-FT-ICR-MSBaicalein, scutellarein, and ganhuangenin[33]
FlavonoidsBovine serum albuminESI-Q-IM-TOF-MSNaringenin[41]
Extract of Polycarpa aurataM. tuberculosis protein Rv1466ESI-FT-ICR-MSPolycarpine[42]
FlavonoidsHuman serum albuminESI-FT-ICR-MSTaxifolin, apigenin, diosmetin, naringenin, hesperetin, aromadendrin, daidzein[43]
FlavonoidsCu, Zn-superoxide dismutaseESI-Q-IM-TOF-MSQuercetin, rutin, naringin, quercetrin, isoquercitrin, hyperin, naringenin, narirutin, hesperitin, neohesperidin, and hesperidin[44]
Extract of Psydrax montigenaPlasmodium falciparum Rab11aESI-FT-ICR-MSArborside E[45]
Flavonoids and flavonoid glycosidesCytochrome-cESI-Q-TOF-MSTectoridin, hesperidin, daidzin, daidzein, 7-methoxy-4′-hydroxyisoflavone, 4′-methoxy-7-hydroxyisoflavone and 4′, 7-dimethoxyisoflavone[46]
Fragment library of natural productsPlasmodium falciparum 2′-deoxyuridine 5′-triphosphate nucleotidohydrolaseESI-FT-ICR-MSSecurinine, 4-α-hydroxy-allosecurinine, virosecurinine, allosecurinine, viroallosecurinine, norsecurinine, and securinine-N-oxide[47]
Extract of Leionema ellipticumBovine carbonic anhydrase IIESI-FT-ICR-MS6-(1S-Hydroxy-3-methylbutyl)-7-methoxy-2H-chromen-2-one[48]
Alkaloidsα1-Acid glycoproteinESI-IT-MSBerberine[49]
SaponinsCytochrome-cESI-Q-Trap-MSGinsenoside Rb1 and Rd[50]
), ArticleFig(id=1210517375363969326, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210517368313344995, language=CN, label=Table 1, caption=

Representative applications of native MS in the study of natural products (NPs)

, figureFileSmall=null, figureFileBig=null, tableContent=
SampleTargetType of MSLigandRef.
Extract of Scutellaria baicalensisSARS-CoV-2 3CLproESI-FT-ICR-MSBaicalein, scutellarein, and ganhuangenin[33]
FlavonoidsBovine serum albuminESI-Q-IM-TOF-MSNaringenin[41]
Extract of Polycarpa aurataM. tuberculosis protein Rv1466ESI-FT-ICR-MSPolycarpine[42]
FlavonoidsHuman serum albuminESI-FT-ICR-MSTaxifolin, apigenin, diosmetin, naringenin, hesperetin, aromadendrin, daidzein[43]
FlavonoidsCu, Zn-superoxide dismutaseESI-Q-IM-TOF-MSQuercetin, rutin, naringin, quercetrin, isoquercitrin, hyperin, naringenin, narirutin, hesperitin, neohesperidin, and hesperidin[44]
Extract of Psydrax montigenaPlasmodium falciparum Rab11aESI-FT-ICR-MSArborside E[45]
Flavonoids and flavonoid glycosidesCytochrome-cESI-Q-TOF-MSTectoridin, hesperidin, daidzin, daidzein, 7-methoxy-4′-hydroxyisoflavone, 4′-methoxy-7-hydroxyisoflavone and 4′, 7-dimethoxyisoflavone[46]
Fragment library of natural productsPlasmodium falciparum 2′-deoxyuridine 5′-triphosphate nucleotidohydrolaseESI-FT-ICR-MSSecurinine, 4-α-hydroxy-allosecurinine, virosecurinine, allosecurinine, viroallosecurinine, norsecurinine, and securinine-N-oxide[47]
Extract of Leionema ellipticumBovine carbonic anhydrase IIESI-FT-ICR-MS6-(1S-Hydroxy-3-methylbutyl)-7-methoxy-2H-chromen-2-one[48]
Alkaloidsα1-Acid glycoproteinESI-IT-MSBerberine[49]
SaponinsCytochrome-cESI-Q-Trap-MSGinsenoside Rb1 and Rd[50]
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非变性质谱在天然产物研究中的应用
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朱达夫 1, 2 , 唐春萍 2 , 刘佳 2, * , 叶阳 1, 2, *
药学学报 | 综述 2022,57(11): 3292-3300
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药学学报 | 综述 2022, 57(11): 3292-3300
非变性质谱在天然产物研究中的应用
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朱达夫1, 2, 唐春萍2, 刘佳2, * , 叶阳1, 2, *
作者信息
  • 1.上海科技大学生命科学与技术学院, 上海 201210
  • 2.中国科学院上海药物研究所, 上海 201203

通讯作者:

*刘佳, Tel: 86-21-58559563, E-mail: ;
叶阳, Tel: 86-21-50806600, E-mail:
Application of native mass spectrometry in natural product research
Da-fu ZHU1, 2, Chun-ping TANG2, Jia LIU2, * , Yang YE1, 2, *
Affiliations
  • 1. School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
  • 2. Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
出版时间: 2022-11-12 doi: 10.16438/j.0513-4870.2022-0769
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天然产物来源丰富、结构多样, 是新药研发的重要源泉。从复杂的天然产物基质中高效筛选出有生物活性的成分是天然产物研究的重要方向之一。传统的研究方法从提取分离开始, 然后进行结构鉴定和活性评价, 耗时长、工作量大且带有很大的随机性, 因此亟需发展高效的生物活性成分发现方法。非变性质谱(native MS) 是一种基于靶标和配体相互作用的研究方法, 能够根据两者之间的亲和力快速发现复杂基质中潜在的活性成分, 具有特异、高效、简便的特点。本文简单介绍了非变性质谱的基本原理和影响因素, 并综述了该方法近年来在天然产物研究领域中的应用, 讨论了该方法的优势与局限性, 以期为相关领域的研究者提供借鉴和参考。

非变性质谱  /  天然产物  /  生物亲和质谱  /  高通量筛选  /  分子间相互作用

Natural products, rich in sources and diverse in structure, play an important role in drug discovery. It is a hotspot to identify efficiently bioactive components from complex mixtures in the field of natural product research. Traditional research methods, starting from extraction and isolation to structural elucidation, and finally to bioactivity assay, are generally time-consuming, laborious, and untargeted. Therefore, there is an urgent need to develop efficient screening methods for bioactive component discovery. Native mass spectrometry (native MS) is an underutilized method based on the interaction between target and ligand, which can rapidly discover potential active components in complex matrices. Due to the simple operation and the native state of the protein, the method is specific, efficient, and simple. Herein, we give a brief introduction of native MS and then review its recent application in natural product research, with its advantages and limitations, which would provide a reference for researchers in relevant fields.

native MS  /  natural product  /  bio-affinity mass spectrometry  /  high-throughput screening  /  molecular interaction
朱达夫, 唐春萍, 刘佳, 叶阳. 非变性质谱在天然产物研究中的应用. 药学学报, 2022 , 57 (11) : 3292 -3300 . DOI: 10.16438/j.0513-4870.2022-0769
Da-fu ZHU, Chun-ping TANG, Jia LIU, Yang YE. Application of native mass spectrometry in natural product research[J]. Acta Pharmaceutica Sinica, 2022 , 57 (11) : 3292 -3300 . DOI: 10.16438/j.0513-4870.2022-0769
天然产物(natural products, NPs) 是新药研发的重要来源, 据统计, 从1981年至2019年美国食品药品管理局(FDA) 批准的药物中, 有49.2%的药物来自天然产物或与天然产物有关[1]。然而, 天然产物往往成分复杂, 传统的提取与分离曾是十分耗时的工作, 近年来, 随着色谱与波谱技术的发展, 涌现出了一批以液质联用(LC-MS) 为基础的研究策略, 如分子网络[2]、配体垂钓[3], 许多分离纯化和结构解析的工作已经可以在较短时间内完成。人们也逐渐将目光转向活性成分的靶向分离, 通过将每个分离阶段的各个馏分进行活性评估, 最终能够得到某种活性成分, 从而加速了天然产物的研究。由于绝大多数药物都是作用在蛋白质上从而发挥调节作用的, 因此, 建立基于靶标蛋白的天然产物研究策略具有重要的应用价值。
质谱(mass spectrometry, MS) 具有灵敏度高、样品用量少、分析速度快的优点, 在一次分析中可提供丰富的待测物信息, 常用于分子鉴定、元素分析等, 得益于软电离技术的出现, 质谱开始广泛应用于蛋白分析。非变性质谱(native MS) 的概念在二十世纪九十年代已经存在, “native MS”这一术语于2004年被提出[4]。通过电喷雾电离(electrospray ionization, ESI) 的软电离技术, 非变性质谱可以在气相中研究蛋白质在类似天然状态下的结构生物学问题, 如蛋白组装、大小形状、相互作用等[5], 自1991年首次使用质谱检测到非共价的受体-配体复合物以来[6], 非变性质谱在小分子药物和生物大分子的非共价相互作用研究中得到了众多关注。
然而由于早期质谱仪的分子量表征范围、分辨率等受限, 难以直接利用质谱进行大型靶标蛋白的配体筛选, 为此, 一种基于生物亲和质谱(bio-affinity mass spectrometry, BA-MS) 的间接筛选方法得到了发展, 如亲和超滤法(affinity-ultrafiltration, AUF)[7]、尺寸排阻色谱法(size exclusion chromatography, SEC)[8]、磁珠亲和筛选法(magnetic microbead affinity selection screening, MagMASS) [9]等(图 1)。该类方法基于受体-配体之间的亲和作用, 将靶标-配体复合物与未结合的配体分离, 然后再将结合在靶标上的配体解离下来进行分析, 相较于其他配体筛选策略, 该类方法具有样品消耗少、分析时间短、不需要放射性标签等优点[10, 11]。此外, 许多天然产物具有紫外/可见光发色团, 会对依赖荧光的高通量筛选方法产生干扰, 而这并不会干扰生物亲和质谱筛选。
近年来, 利用高分辨质谱仪中的各种碎片化手段以及离子淌度, 可以进一步获取蛋白质三维结构的信息, 如氨基酸序列、结合位点、亚基的鉴定等[12-16]。高性能的质谱仪也为直接分析大型蛋白和小分子的非共价相互作用以及直接进行蛋白靶标的配体筛选提供了可能。然而在天然产物研究中, 非变性质谱还没有得到广泛的应用。本文将综述非变性质谱技术在天然产物研究中的应用, 为天然活性成分的研究提供新的思路。
非变性质谱指的是不对蛋白进行变性处理, 检测完整蛋白质或复合物在气相状态下的空间构象及相互作用的结构质谱技术[17, 18]。为了能够尽可能保留完整蛋白质的天然构象, 该方法通常应用于纯化后的单一蛋白质或者复合物, 并且需要严格控制pH、温度、离子强度等条件。
在靶标-配体相互作用的研究中, 首先需要将靶标蛋白与配体混合在温和的缓冲溶液中, 潜在的活性分子与靶标蛋白由于亲和作用会形成靶标-配体复合物, 然后将分析物通过软电离从液相转移到气相后, 直接进行质谱检测。以ESI正离子模式为例, 喷针尖端的溶液在高压电场、流速和雾化气的推动下形成泰勒锥并发射喷雾, 在快速脱溶剂的过程中, 液滴表面电荷密度增大, 当斥力达到临界值时, 液滴进一步分散, 最终形成纳米级别的液滴使离子蒸发出来, 通过真空进入质量分析器, 进而获得靶标蛋白本身和靶标-配体复合物的质谱信号[19] (图 2)。
基于完整蛋白层面的分析, 自上而下质谱(top-down mass spectrometry, TDMS) 可以通过各种片段化方法获得完整蛋白水平下的序列和翻译后修饰信息, 结合变性和非变性条件, 可以获取多层次的结构信息[20]。相较于传统的“自下而上”的质谱分析, TDMS不需要预先酶解蛋白质, 但对质谱仪的性能以及数据分析提出了更高的要求[21]
目前, 对于非变性质谱中“非变性”的定义仍然不断被讨论。按照最理想的实验状态, 应该对待测蛋白在近似生理环境中进行检测, 而非变性质谱的检测需要将分析物从液相转移到气相, 因此无法满足严格“非变性”的定义。此外, 虽然已有证据表明蛋白或核酸等非挥发性生物分子可以在质谱检测的时间范围内在气相中维持天然结构和生物活性[22, 23], 但其空间构象与在溶液中一致程度还有待深入研究。因此, 非变性质谱中的“非变性”一词现在被普遍认为是指分析物在电离过程之前, 其所处的溶液属于近似生理环境, 从而维持溶液中生物分子的自然折叠状态。
目前, 研究者们已开发了多种基于生物亲和质谱的筛选方法, 如亲和超滤、磁珠吸附、细胞膜色谱等。这些方法都可以在一个复杂的混合体系中, 在维持蛋白空间结构不被破坏的前提下, 无标记地筛选出配体。它们均无偏向性, 可以同时筛选正构或者别构调节的配体。然而这些方法普遍在孵育后需要进行靶标和配体的分离, 再检测小分子配体, 因此在一定程度上会出现膜吸附、靶标失活等缺点, 导致漏失一些低亲和力的筛选结果。对于非变性质谱而言, 它没有分离过程, 可以筛选出低亲和力的配体, 同时, 它将筛选过程可视化, 可以在质谱上直接观察到靶标-配体复合物的峰, 是一种可以实现高效自动化分析的方法[24]
在相应的天然产物研究中, 小分子配体的结合亲和力、化学计量、结合位点是研究的重点。利用非变性质谱的方法, 配体的相对分子量可以通过计算游离靶标和靶标-配体复合物的质量差异来确定。平衡解离常数(Kd) 是评价亲和力的常用指标, 假设靶标和配体结合的比例在雾化前后相同且电离效率一致, 那么它可以简单地表达为Kd = [P][L]/[PL] (图 2), 其中[P]是游离蛋白的浓度, [L]是配体的浓度, [PL]是蛋白-配体复合物的浓度, 通过非变性质谱可以快速测定这一数值, 并对配体亲和力进行排名[25, 26], 而化学计量比可以通过观察质谱图上结合峰获得, 通过竞争性实验也可以快速判断化合物的结合位点和结合模式。相较于传统的天然产物研究方法, 非变性质谱能够直接定位到天然产物与蛋白靶标的作用位点, 其结构特点和结合模式可以为后续的药物化学、药理学等研究提供很好的参考。
在整个非变性质谱分析过程中, 保持靶标的天然状态以及分子间的相互作用对非变性质谱分析来说至关重要, 这需要保持温和的环境体系, 并且在不改变复合物或亚基结构的情况下进行脱溶剂, 因此需要充分优化实验条件以提高分析灵敏度和筛选覆盖率。
质谱检测的一个重要条件是分析物使用的溶剂系统与质谱仪具有兼容性, 通常情况下, 蛋白纯化后一般保存于非挥发性的缓冲溶液中, 然而质谱检测需要挥发性的溶剂系统如甲醇、乙腈、水等, 所以温和的挥发性缓冲溶液对于蛋白的质谱检测至关重要。具有质谱兼容性的常用挥发性缓冲液有碳酸氢铵溶液、醋酸铵溶液、三乙基乙酸铵溶液等。在分析过程中, 由于碳酸氢铵溶液产生的气泡可能会引起蛋白变性, 而醋酸铵溶液不产生气泡并且可以提高信号的稳定性, 所以目前主要使用醋酸铵水溶液来提供一个温和的溶液环境[27, 28]。有研究表明, 将醋酸铵浓度从10 mmol·L-1增加到500 mmol·L-1时, 测定获得的靶标-配体解离常数(Kd) 值具有超过50%的差异[29]。此外, 有研究表明在溶剂中加入亚毫摩尔浓度的碱金属醋酸盐可以显著降低电荷[30], 相反, 如果在其中添加间硝基苯甲醇(m-NBA) 将增加分析物的电荷数[31, 32]。这些结果表明, 缓冲盐种类或者添加剂对于蛋白在溶液中的稳定性具有不同的影响, 但目前关于不同缓冲液在进行靶标-天然产物相互作用尚缺乏系统的研究。
由于天然产物成分复杂, 各类化合物溶解度不同, 在研究中通常使用二甲亚砜(DMSO) 作为常规溶剂进行储备液的配制, 然而在本团队的前期研究中, 发现极低含量的DMSO (0.01%) 都会对整个质谱检测体系造成巨大的影响。相对而言, 蛋白对体系中甲醇和乙腈的含量具有一定的容忍度, 因此该研究选择了影响较小的甲醇作为配体的溶剂[33]。然而不同含量的有机溶剂会对蛋白构象产生一定的影响, 如pH为6.2时, 将乙腈含量从0%增加到50%会使光系统Ⅱ锰稳定蛋白(MSP) 的电荷态分布发生显著改变并且峰形变窄, 这表明了MSP发生了构象变化[34]。蛋白往往对溶液中的有机溶剂十分敏感, 这就要求在选择配体溶剂时既要考虑到化合物的溶解度, 又要考虑到蛋白的稳定性以及后续分析时与质谱的兼容性, 因此, 应预先比较和优化配体的溶剂, 以达到最佳的孵育和检测条件。
靶标和配体的浓度也会对研究结果造成不同的影响, 如果配体是混合物, 其浓度高于靶标, 配体与靶标之间的结合就有可能具有竞争性, 致使某些活性成分难以结合靶标; 相反, 如果配体浓度较低而且是低亲和力的化合物, 则在分析时可能观察不到结合物的峰。因此需要按照不同的实验目的和实验对象选择合适的靶标和配体的浓度。
非变性质谱需要使用特殊材料制作的喷针, 一般来说喷针尖端越细, 就越容易使复合物在电离过程中脱溶剂, 当喷针尖端达到亚微米级时, 可以显著降低加和离子的形成、提高信噪比、提高基质耐受性[35-38]。随着亚微米喷针以及纳升电喷雾的不断发展和优化, 其在蛋白复合物、核酸等生物大分子结构解析和生物样品分析中的应用研究有望进一步深入和拓展。
在对蛋白进行脱盐处理时, 超滤管的选择也十分重要, 如果截留量过大, 则有可能在不知觉的情况下改变了蛋白的浓度, 如果截留量过小, 则超滤效率会降低, 目前通常选择截留分子质量小于靶蛋白相对分子质量1/3的超滤管。此外, 孵育时间、温度、离心速度、离心时间和次数也需要一定的摸索和设计, 涡旋或者剧烈搅拌都有可能使蛋白变性, 同时, 蛋白也有可能受温度影响发生活性的改变, 这都将影响到最终的筛选结果。电离、脱溶剂和离子传输条件的优化对实验至关重要, 一方面用于这些步骤的能量和设置应尽可能的低, 以帮助保持复合物的天然构象; 另一方面又要足够高, 以确保通过仪器进行有效的电离和传输。此外, 选择合适的阳性对照将极大提高条件摸索的效率以及结果的可靠性。
在生物物理学研究领域中, 非变性质谱是一种强大的分析工具, 除了用于蛋白的结构表征以外, 可以通过直接分析靶标-配体复合物来研究非共价相互作用。自首次通过质谱观察到非共价的蛋白-配体复合物以来, 我国学者也开始利用质谱来测定生物大分子和小分子的复合物, 但在天然产物研究领域的应用出现较晚且早期多将非变性质谱归属于“电喷雾质谱”[39, 40]表 1[33, 41-50]列举了近年来使用非变性质谱研究天然产物的代表性实例, 其中以分子间相互作用的研究为主, 天然产物包括生物碱、黄酮、皂苷等, 使用的仪器多为傅里叶变换离子回旋共振质谱仪(Fourier transform ion cyclotron resonance mass spectrometry, FT-ICR-MS) 和四极杆飞行时间质谱仪(quadrupole-time of flight mass spectrometry, Q-TOF-MS)。
王献教授团队[51]针对细胞色素c (cytochrome-c, Cyt c) 的天然产物配体使用非变性质谱进行了一系列的研究, 在其前期探索中发现中药皂苷提取物人参皂苷Re、柴胡皂苷A、柴胡皂苷C与Cyt c结合能力大小依次为: 柴胡皂苷C > 人参皂苷Re > 柴胡皂苷A。为了进一步研究其中的分子间相互作用, 选择了4种糖和3种三萜来鉴定皂苷与Cyt c相互作用时的结合基团, 通过非变性质谱比较亲和力后, 发现皂苷中糖的部分是有效的结合基团, 而三萜部分对结合活性并没有影响, 柴胡皂苷-Cyt c复合物的结合常数与糖-Cyt c复合物的结合常数相近, 于是提出柴胡皂苷是通过糖基上的氢键与Cyt c结合的[52]。为了验证上述结论, 又挑选了7种黄酮或黄酮苷作为研究对象, 其中4', 7-二甲氧基异黄酮(4M7M) 无法与Cyt c形成复合物, 推测可能是因为4M7M的结构中缺少羟基, 使用非变性质谱测定的亲和力的高低也与计算机模拟得到的氢键能量的大小一致, 表明Cyt-c主要通过氢键与配体结合, 分子对接还预测了相互作用的位点, 但仍无法解释配体结构的差异对形成的非共价复合物稳定性的影响[46]。于是又利用不同结构的糖, 探索了配体的空间位阻和羟基数对结合亲和力的影响, 发现配体的官能团和空间结构对非共价蛋白复合物的稳定性密切相关[53]
许国旺教授团队[43]利用非变性质谱的方法研究了黄酮和人血清白蛋白(human serum albumin, HSA) 之间的非共价相互作用。多种结构相似的黄酮类化合物被用来揭示配体化学结构和HSA亲和力之间的关系, 非变性质谱分析的结果表明, 黄酮的羟基和双键的位置会影响其非共价相互作用, 而与已知有不同结合位点的药物—华法林和布洛芬进行竞争实验后, 发现芹菜素会与华法林竞争相同的位点, 相反, 芹菜素不会竞争布洛芬的结合位点, 反而与布洛芬出现了协同的结合作用, 最后通过圆二色谱和分子对接验证和补充了芹菜素和布洛芬的协同别构作用。
中国科学院长春应用化学研究所的团队[44]通过非变性质谱评价了不同的黄酮类化合物和铜锌超氧化物歧化酶(Cu, Zn-superoxide dismutase, SOD1) 之间的结合亲和力, 并利用了离子淌度质谱(ion mobility mass spectrometry, IM-MS) 的优势, 比较了同分异构体的亲和作用, 探索了小分子配体对靶标蛋白构象变化的影响。在其后续的研究中, 继续利用ESI-IM-MS研究了SOD1与其他类药物分子之间相互作用[54, 55]。离子淌度可以补充离子在气相中的结构信息, 深入了解小分子诱导的蛋白构象变化, 非变性质谱结合离子淌度的实验方法可以发挥各自的优势, 协同加强两者在生化领域的分析能力。
3CLpro是新型冠状病毒SARS-CoV-2复制的一个关键靶标蛋白, 在新冠的治疗方案中, 中医疗法有着良好的疗效, 其使用的药方中包含了大量清热解毒中药。本团队[56]在研究中获得黄芩素与3CLpro的共晶, 并利用非变性质谱快速评价了黄芩素、黄芩苷等潜在的3CLpro抑制剂对3CLpro的亲和作用, 其Kd与等温滴定量热法得出的结果一致, 充分体现了非变性质谱在分析效率上的优势。
Ronald J. Quinn教授团队[47]建立了一个由小分子天然产物组成的片段库, 通过ESI-FT-ICR-MS进行亲和力筛选, 发现了可以通过增强恶性疟原虫的2'-脱氧尿苷5'-三磷酸核苷水解酶(PfdUTPase) 活性从而抑制恶性疟原虫生殖的一叶萩碱相关的7个天然产物。在其进一步的研究中又通过非变性质谱筛选出了96个天然产物片段可以结合32个潜在的抗疟靶标, 其中79个片段分子具有体外抗疟活性[57]。基于片段的药物发现(fragment-based drug discovery, FBDD) 是药物研发中的一种高效策略, 它能够迅速发现有活性的片段分子, 通过对这些苗头片段的分析和改造, 可获得先导化合物。由于片段分子通常对靶标的亲和力较低, 因此非常适合使用灵敏的非变性质谱筛选方法, 这样的策略可以更有效地探索结构简单的片段分子的空间结构对结合的影响。
本团队使用非变性质谱建立了一种从清热解毒的中药粗提物中高效筛选出靶向SARS-CoV-2 3CLpro的天然产物分子的方法。为了避免提取物中高含量成分对低含量成分的掩盖而导致的假阴性, 本团队预先将6种中药的粗提物进行了初步分离, 建立了共96个组分的天然产物组分库, 并在此基础上通过非变性质谱的亲和筛选, 总共发现了包括黄芩素和黄芩苷在内的16个潜在的配体[33]。虽然从复杂的体系中筛选出活性成分依然具有挑战, 但随着更高性能的质谱仪的出现, 使用非变性质谱技术进行高通量筛选的工作已经积累了一些成功的实例文献, 相应的工作流程总结在图 3中。
尽管非变性质谱技术已被证明可用于分子间相互作用的研究, 但仍然存在诸多挑战。现在被广泛用于生物大分子表征和分子间相互作用的研究方法有: 等温滴定量热法(isothermal titration calorimetry, ITC)、核磁共振(nuclear magnetic resonance, NMR)、X射线(X-ray)、分子对接(docking)、微量热泳动(micro-scale thermophoresis, MST)、表面等离子共振(surface plasmon resonance, SPR)、差示扫描量热法(differential scanning calorimetry, DSC) 等, 而非变性质谱常常用于和以上方法互相验证, 通常不用作独立的方法, 并且目前仅在可溶性的蛋白和配体的相互作用研究中得到应用。
在高通量筛选中, 泛活性筛选干扰化合物(pan-assay interference compounds, PAINS) 是一个主要的问题。本团队在探索阶段也发现了如姜黄素或表没食子儿茶素没食子酸酯(epigallocatechin gallate, EGCG)[58]可以无差别地与靶标结合, 目前主要的策略使用变性的靶标蛋白或者其他非靶标蛋白进行对照来消除这类分子的干扰[59], 同时研究人员也总结了PAINS的化学特征, 并开发了相应的过滤器以在药物开发前期预警这类分子[60, 61]。此外, 在质谱上, 这类分子会和靶标蛋白形成多个结合峰, 虽然仍难以区分特异性结合和物理吸附, 但此类质谱结合模式提示在后续的活性筛选时, 这些化合物可能会出现假阳性结果。
由于电离效率、离子传输等的差异, 复杂的蛋白-配体复合物在不同的电喷雾条件下可能不稳定, 因此非变性质谱分析在定量研究上具有一定的难度。但随着更高性能的质谱仪的发展, 以及非变性质谱在蛋白质组学和结构生物学的更广泛的研究和应用[62], 期待将来能够实现相对更复杂的蛋白-配体复合物研究。
天然产物源于自然, 具有不易产生耐药性、毒副作用小、价格低廉等优点, 是传统医药领域的宝库, 从天然产物中发现先导化合物是新药研发的重要途径之一。由于天然产物成分复杂、结构多样、含量不一, 因此, 其活性成分的发现具有挑战性, 而多成分多靶点的研究和阐释更是非常困难。吗啡、青蒿素、紫杉醇、麻黄素、小檗碱、五味子甲素、延胡索乙素、芦丁、川芎嗪、甘草酸等活性化合物的发现提示需要将天然产物研究与现代科学技术紧密结合。
多年以来, 非变性质谱技术已经发展成为对蛋白质研究的一种灵敏、高效且经济的分析方法[63, 64], 这也为研究天然产物和生物大分子靶标的相互作用提供了新的思路。非变性质谱能够最大程度地保留靶标-配体的立体结构和作用力信息, 不仅可以利用已知靶标对混合物进行筛选, 也可以利用已知的天然产物分子对混合靶标进行研究, 甚至是研究这些活性物质对具有相互作用的蛋白之间的影响, 如PROTAC或分子胶水等, 进而深入探索活性分子可能发挥药效的机制。相信非变性质谱能够加快天然产物活性成分的发现, 启发研究者去发现其中的分子间相互作用, 或许在不久的将来, 随着基于质谱的方法不断开发和完善, 非变性质谱能成为天然产物研究以及药物发现的重要手段, 成为沟通化学与生物学的重要桥梁。
作者贡献: 朱达夫负责文献调研和文章撰写; 唐春萍负责文章修改; 刘佳负责写作指导和文章审阅; 叶阳负责课题监管与指导。
利益冲突: 本文中所有作者声明无任何利益冲突。
  • 国家自然科学基金资助项目(81803610)
  • 国家自然科学基金资助项目(21920102003)
  • 上海市科学技术委员会资助项目(20430780300)
  • 广东省重点领域研发计划项目(2020B0303070002)
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2022年第57卷第11期
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doi: 10.16438/j.0513-4870.2022-0769
  • 接收时间:2022-06-23
  • 首发时间:2025-12-24
  • 出版时间:2022-11-12
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  • 收稿日期:2022-06-23
  • 修回日期:2022-08-05
基金
国家自然科学基金资助项目(81803610)
国家自然科学基金资助项目(21920102003)
上海市科学技术委员会资助项目(20430780300)
广东省重点领域研发计划项目(2020B0303070002)
作者信息
    1.上海科技大学生命科学与技术学院, 上海 201210
    2.中国科学院上海药物研究所, 上海 201203

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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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