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Article(id=1308842606797353556, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1308842471539442166, articleNumber=null, orderNo=null, doi=10.16438/j.0513-4870.2025-1520, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1765987200000, receivedDateStr=2025-12-18, revisedDate=1773158400000, revisedDateStr=2026-03-11, acceptedDate=null, acceptedDateStr=null, onlineDate=1789981994580, onlineDateStr=2026-09-21, pubDate=1786464000000, pubDateStr=2026-08-12, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789981994580, onlineIssueDateStr=2026-09-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789981994580, creator=13701087609, updateTime=1789981994580, updator=13701087609, issue=Issue{id=1308842471539442166, tenantId=1146029695717560320, journalId=1189982191388893191, year='2026', volume='61', issue='8', pageStart='2821', pageEnd='3220', issueExtLink='null', onlineDate='null', pubDate='1786464000000', pubDateStr='2026-08-12', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789981962332, creator='13701087609', updateTime=1789984137222, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308851593773150481, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1308842471539442166, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308851593773150482, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1308842471539442166, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3085, endPage=3095, ext={EN=ArticleExt(id=1308842606990291541, articleId=1308842606797353556, tenantId=1146029695717560320, journalId=1189982191388893191, language=EN, title=Screening of hit compounds targeting the dynamic pocket of polypyrimidine tract-binding protein 1 (PTBP1) RBD3/4, columnId=1190335348761793317, journalTitle=Acta Pharmaceutica Sinica, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=

Polypyrimidine tract-binding protein 1 (PTBP1) is a critical regulatory target in cancers and neurological disorders. However, the development of its small-molecule inhibitors has been hampered by the lack of traditional druggable pockets on its RNA-binding domains. This study employed a computer-aided drug design (CADD) strategy focusing on a dynamic binding pocket mediated by the flexible linker3 within the PTBP1 RBD3/4 domain. A commercial library containing approximately 1.6 million compounds was subjected to structure-based virtual screening. Following initial filtering based on lead-likeness rules and subsequent molecular docking, 14 candidate compounds were synthesized. Their binding activities were experimentally validated using prokaryotically expressed and purified PTBP1-RBD3/4 recombinant protein via bio-layer interferometry (BLI). Six compounds with micromolar affinity were identified, among which C17414 exhibited the highest binding affinity (KD = 1.6 µmol·L-1). Molecular docking analysis revealed that C17414 efficiently occupies the target pocket through multiple interactions, including hydrogen bonds, salt bridges, π-π stacking and hydrophobic interactions. Systematic ADMET (absorption, distribution, metabolism, excretion and toxicity) property predictions indicated that C17414 and C7485 possess a favorable balance of lipophilicity, polar surface area and membrane permeability, suggesting promising drug-likeness and potential for central nervous system drug development. This work not only discovers a series of high-affinity PTBP1 inhibitor hits and validates the feasibility of targeting its dynamic pocket but also lays a solid foundation for developing novel therapies against PTBP1.

, authors=Yuan-hang WANG, Ying-yi LIU, Yi-lan HUANG, Mei-yi MAO, Ming-xing HUANG*, authorsList=Yuan-hang WANG, Ying-yi LIU, Yi-lan HUANG, Mei-yi MAO, Ming-xing HUANG, authorCompany=null, correspAuthors=Ming-xing HUANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2026 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, fund=null), CN=ArticleExt(id=1308842608080810592, articleId=1308842606797353556, tenantId=1146029695717560320, journalId=1189982191388893191, language=CN, title=靶向多聚嘧啶区结合蛋白1 (PTBP1) RBD3/4动态口袋的苗头化合物筛选, columnId=1190335348896011050, journalTitle=药学学报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=

多聚嘧啶区结合蛋白1 (polypyrimidine tract-binding protein 1, PTBP1) 是一个在神经系统疾病中具有重要调控作用的关键靶点。然而, 其RNA结合结构域(RNA binding domain, RBD) 表面缺乏典型的可成药口袋, 使得开发具有良好类药性的小分子抑制剂面临重大挑战。本研究采用计算机辅助药物设计策略, 聚焦于PTBP1 RBD3/4结构域中由柔性linker3介导的动态结合口袋, 对包含约160万个化合物的商业库进行虚拟筛选。通过类药性规则初筛和分子对接精筛, 合成了14个候选化合物, 并利用原核表达纯化的PTBP1-RBD3/4重组蛋白, 通过生物层干涉(bio-layer interferometry, BLI) 技术验证其结合活性。最终获得了6个具有微摩尔级别亲和力的苗头化合物, 其中C17414表现出最高的结合力(KD = 1.6 μmol·L-1)。分子对接提示C17414通过氢键、盐桥、π-π堆积及疏水作用等多种机制高效占据目标口袋。系统的ADMET性质预测进一步表明, C17414与C7485在脂溶性、极性表面积及透膜潜力之间取得了良好平衡, 具备优越的成药性, 特别是作为中枢神经系统药物的开发潜力。本研究不仅发现了一系列高亲和力的靶向PTBP1的苗头化合物, 验证了靶向其动态结合口袋的可行性, 也为开发针对PTBP1的相关疗法奠定了坚实的基础。

, authors=王远航, 刘樱怡, 黄艺岚, 毛美懿, 黄明星*, authorsList=王远航, 刘樱怡, 黄艺岚, 毛美懿, 黄明星, authorCompany=null, correspAuthors=黄明星, authorNote=

#

共同第一作者.

作者贡献:

王远航、刘樱怡共同完成文献调研、虚拟筛选方案设计、分子对接分析、数据整理及初稿撰写, 并对实验结果进行交叉验证; 黄艺岚负责PTBP1-RBD3/4重组蛋白的原核表达、纯化及SDS-PAGE分析; 毛美懿参与小分子化合物的ADMET性质预测、结构-活性关系分析及图表绘制; 黄明星提出研究思路, 指导整体实验设计, 监督项目实施, 并负责论文的最终修改与投稿。所有作者均参与论文讨论, 审阅并同意最终稿件。

, correspAuthorsNote=
黄明星, Tel: 18675866565, E-mail:
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tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1308842606797353556, language=CN, orderNo=4, keyword=分子对接), Keyword(id=1308842612115731082, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1308842606797353556, language=CN, orderNo=5, keyword=生物层干涉技术)], refs=[Reference(id=1308842613655040667, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1308842606797353556, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=1, rfOrder=0, authorNames=null, journalName=null, refType=null, unstructuredReference=Oberstrass FC, Auweter SD, Erat M, et al. 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Methods Enzymol, 2000, 326: 430-440., articleTitle=null, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1308842608294720097, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1308842606797353556, xref=null, ext=[AuthorCompanyExt(id=1308842608303108706, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1308842606797353556, companyId=1308842608294720097, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Biomedicine and Pharmacy, Guangdong University of Technology, Guangzhou 510006, China), AuthorCompanyExt(id=1308842608311497315, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1308842606797353556, companyId=1308842608294720097, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=广东工业大学生物医药学院, 广东 广州 510006)])], figs=[ArticleFig(id=1308842612220588683, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1308842606797353556, language=EN, label=null, caption=null, figureFileSmall=lqtTUSmDRFV+vik9mfoGrQ==, figureFileBig=rGYpxUI/xJ90rc4S5TsctQ==, tableContent=null), ArticleFig(id=1308842612296086156, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1308842606797353556, language=CN, label=Fig.1, caption=The chemical structures of six small-molecule inhibitors exhibiting high binding affinity for polypyrimidine tract binding protein 1 (PTBP1). C17414: 3-[5-Cyclopropyl-7-(difluoromethyl)-1, 2, 4 triazolo 1, 5-a pyrimidin-2-yl] propanoic acid; C15: N-[[3-(Phenylmethyl)-3H-imidazo[4, 5-b]pyridin-2-yl]methyl]acetamide; C18221: N-[[1-(Phenylmethyl)-1H-1, 3-benzodiazol-2-yl]methyl]acetamide; C2143: (2S, 3R)-N-(2-Methoxyethyl)-4-methyl-5-oxo-3-phenylmorpholine-2-carboxamide; C2577∶ 2-[[4-(2-Methylprop-2-en-1-yl)-5-phenyl-4H-1, 2, 4-triazol-3-yl]sulfanyl]acetic acid; C7485: N-[(Furan-2-yl)methyl]-2-[2-(methoxymethyl)-1H-1, 3-benzodiazol-1-yl]acetamide, figureFileSmall=lqtTUSmDRFV+vik9mfoGrQ==, figureFileBig=rGYpxUI/xJ90rc4S5TsctQ==, tableContent=null), ArticleFig(id=1308842612501607053, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1308842606797353556, language=EN, label=null, caption=null, figureFileSmall=x+1pYKvd44Myvl7gFusp3w==, figureFileBig=nCtE9nigWjRIossnA2k9Tg==, tableContent=null), ArticleFig(id=1308842612572910222, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1308842606797353556, language=CN, label=Fig.2, caption=Prokaryotic expression of the PTBP1 RBD3/4 domains. A: Plasmid map of the recombinant expression vector pQE80L-Avi-PTBP1. The diagram clearly indicates the N-terminal 6×His tag and the Avi tag (highlighted by a pink arrow); B: SDS-PAGE analysis of the induction and purification of the recombinant 6His-Avi-PTBP1 protein. Lane 1, Total protein from uninduced bacteria; Lane 2, Total bacterial protein after 6-hour induction with IPTG; Lane 3, Soluble supernatant after sonication; Lane 4, Recombinant protein purified by Ni-NTA affinity chromatography, figureFileSmall=x+1pYKvd44Myvl7gFusp3w==, figureFileBig=nCtE9nigWjRIossnA2k9Tg==, tableContent=null), ArticleFig(id=1308842612656796303, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1308842606797353556, language=EN, label=null, caption=null, figureFileSmall=wqh1jKoPDnUBos4Dbk9WfQ==, figureFileBig=wjfhXdSZHatkfhTc2tlDnA==, tableContent=null), ArticleFig(id=1308842612736488080, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1308842606797353556, language=CN, label=Fig.3, caption=Analysis of the interaction between PTBP1 and small-molecule inhibitors by bio-layer interferometry (BLI) A: C17414; B: C15; C: C18221; D: C2143, figureFileSmall=wqh1jKoPDnUBos4Dbk9WfQ==, figureFileBig=wjfhXdSZHatkfhTc2tlDnA==, tableContent=null), ArticleFig(id=1308842612807791249, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1308842606797353556, language=EN, label=null, caption=null, figureFileSmall=v2494IqshlcIZk4qu3DDHA==, figureFileBig=gmPjrf4fUlgO8otr1a+Pwg==, tableContent=null), ArticleFig(id=1308842612866511506, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1308842606797353556, language=CN, label=Fig.4, caption=Binding modes of the PTBP1 RBD3/4 domain with various ligands. A: Overall structure of the PTBP1 protein. The ligand binding site is indicated by a black square, with the ligand depicted as an orange stick-and-ball model; B: Close-up view of the ligand binding pocket, showing the three-dimensional binding conformation of the ligand (orange) within the protein surface (gray); C: Two-dimensional interaction diagram between PTBP1 and the ligand. Key intermolecular interactions are highlighted: Hydrogen bonds (green dashed lines), hydrophobic interactions (yellow dashed lines), and electrostatic interactions (red dashed lines). The amino acid residues involved in the interactions are labeled with their respective identifiers, figureFileSmall=v2494IqshlcIZk4qu3DDHA==, figureFileBig=gmPjrf4fUlgO8otr1a+Pwg==, tableContent=null), ArticleFig(id=1308842612929426067, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1308842606797353556, language=EN, label=null, caption=null, figureFileSmall=uIlA4tVMm2agz4opg3JOVg==, figureFileBig=YFUQhQuPTUaMlqiOlB5mXA==, tableContent=null), ArticleFig(id=1308842613013312148, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1308842606797353556, language=CN, label=Fig.5, caption=Structural overview of PTBP1. A: Schematic representation of PTBP1 highlighting the four RNA recognition motifs (RRMs) connected by flexible linkers. The nuclear localization signal (NLS) and the nuclear export signal (NES) are indicated; B: Detailed structure of individual RRMs, illustrating the βαββαβ topology. The four RRMs maintain the four-stranded β-sheet structure characteristic of canonical RRMs; C: Structural comparison focusing on RRM3 and RRM4, emphasizing their unique five-stranded β-sheet and the extensive interaction between them. The long loop connecting RRM3 and RRM4 is highlighted (linker3), which is crucial for their cooperative binding to RNA and the induction of RNA looping; D: The solvent interpolated charge surface of PTBPT1. The charge surface illustrates the electrostatic potential distribution across the protein, with blue indicating negative charges and red representing positive charges. RRM3 and RRM4 are depicted as closely interacting modules, forming a compact functional unit within the protein structure, figureFileSmall=uIlA4tVMm2agz4opg3JOVg==, figureFileBig=YFUQhQuPTUaMlqiOlB5mXA==, tableContent=null), ArticleFig(id=1308842613088809621, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1308842606797353556, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
NameMolecular weightLogPPSAH_acceptorH_donorDockScore
C17414282.2521.205 559.67961132.393
C15280.3321.290 845.63441131.056
C18221279.3442.282 535.72731130.928
C2143292.33-0.055 458.07461130.208
C2577289.3612.604 152.89661130.14
C7485299.3332.231 151.78751128.295
), ArticleFig(id=1308842613214638742, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1308842606797353556, language=CN, label=Tab.1, caption=

Characterization of high-affinity small-molecule inhibitors targeting PTBP1

, figureFileSmall=null, figureFileBig=null, tableContent=
NameMolecular weightLogPPSAH_acceptorH_donorDockScore
C17414282.2521.205 559.67961132.393
C15280.3321.290 845.63441131.056
C18221279.3442.282 535.72731130.928
C2143292.33-0.055 458.07461130.208
C2577289.3612.604 152.89661130.14
C7485299.3332.231 151.78751128.295
), ArticleFig(id=1308842613281747607, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1308842606797353556, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
NameC17414C15C18221C2143C2577C7485
Solubility-3.066-2.831-3.744-1.123-3.415-2.740
Solubility_level3.0003.0003.0004.0003.0003.000
BBB-0.769-0.650-0.134-1.298-0.285-0.809
BBB_level3.0003.0002.0003.0002.0003.000
CYP2D6-5.476-6.588-5.563-7.346-3.994-11.147
CYP2D6_applicability#MD12.27112.99614.26112.09113.21818.483
CYP2D6_applicability#MDpvalue1.63E-033.08E-041.36E-052.42E-031.81E-041.96E-10
Hepatotoxic-3.103-4.816-6.124-6.155-1.581-0.366
Hepatotoxic_applicability#MD10.84610.18210.24012.83215.17812.869
Hepatotoxic_applicability#MDpvalue9.99E-035.97E-025.21E-024.06E-069.93E-123.41E-06
PPB0.535-2.8224.164-4.469-0.5680.354
PPB_applicability#MD11.01511.16011.50613.57212.27012.505
PPB_applicability#MDpvalue0.4790.4040.2460.0010.0500.026
AlogP981.9631.3642.455-0.1892.9531.373
PSA_2D77.24757.98146.72068.62465.98668.204
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ADMET profiling of the six candidate compounds

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NameC17414C15C18221C2143C2577C7485
Solubility-3.066-2.831-3.744-1.123-3.415-2.740
Solubility_level3.0003.0003.0004.0003.0003.000
BBB-0.769-0.650-0.134-1.298-0.285-0.809
BBB_level3.0003.0002.0003.0002.0003.000
CYP2D6-5.476-6.588-5.563-7.346-3.994-11.147
CYP2D6_applicability#MD12.27112.99614.26112.09113.21818.483
CYP2D6_applicability#MDpvalue1.63E-033.08E-041.36E-052.42E-031.81E-041.96E-10
Hepatotoxic-3.103-4.816-6.124-6.155-1.581-0.366
Hepatotoxic_applicability#MD10.84610.18210.24012.83215.17812.869
Hepatotoxic_applicability#MDpvalue9.99E-035.97E-025.21E-024.06E-069.93E-123.41E-06
PPB0.535-2.8224.164-4.469-0.5680.354
PPB_applicability#MD11.01511.16011.50613.57212.27012.505
PPB_applicability#MDpvalue0.4790.4040.2460.0010.0500.026
AlogP981.9631.3642.455-0.1892.9531.373
PSA_2D77.24757.98146.72068.62465.98668.204
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CompoundChi2/DoFR2RmaxKD
C174141.148E-060.999 740.184 001.6E-06
C152.152E-060.952 840.158 178.6E-06
C18221010.121 651.2E-05
C21431.279E-3510.105 073.1E-05
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Binding kinetics characterization of four small-molecule inhibitors with PTBP1

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CompoundChi2/DoFR2RmaxKD
C174141.148E-060.999 740.184 001.6E-06
C152.152E-060.952 840.158 178.6E-06
C18221010.121 651.2E-05
C21431.279E-3510.105 073.1E-05
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靶向多聚嘧啶区结合蛋白1 (PTBP1) RBD3/4动态口袋的苗头化合物筛选
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王远航 , 刘樱怡 , 黄艺岚 , 毛美懿 , 黄明星 *
药学学报 | 研究论文 2026,61(8): 3085-3095
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药学学报 |研究论文 2026 , 61 (8) : 3085 -3095
靶向多聚嘧啶区结合蛋白1 (PTBP1) RBD3/4动态口袋的苗头化合物筛选
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王远航, 刘樱怡, 黄艺岚, 毛美懿, 黄明星*
作者信息
  • 广东工业大学生物医药学院, 广东 广州 510006
通讯作者:
黄明星, Tel: 18675866565, E-mail:
作者简介:

#

共同第一作者.

作者贡献:

王远航、刘樱怡共同完成文献调研、虚拟筛选方案设计、分子对接分析、数据整理及初稿撰写, 并对实验结果进行交叉验证; 黄艺岚负责PTBP1-RBD3/4重组蛋白的原核表达、纯化及SDS-PAGE分析; 毛美懿参与小分子化合物的ADMET性质预测、结构-活性关系分析及图表绘制; 黄明星提出研究思路, 指导整体实验设计, 监督项目实施, 并负责论文的最终修改与投稿。所有作者均参与论文讨论, 审阅并同意最终稿件。

Screening of hit compounds targeting the dynamic pocket of polypyrimidine tract-binding protein 1 (PTBP1) RBD3/4
Yuan-hang WANG, Ying-yi LIU, Yi-lan HUANG, Mei-yi MAO, Ming-xing HUANG*
Affiliations
  • School of Biomedicine and Pharmacy, Guangdong University of Technology, Guangzhou 510006, China
出版时间: 2026-08-12 doi: 10.16438/j.0513-4870.2025-1520
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摘要
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多聚嘧啶区结合蛋白1 (polypyrimidine tract-binding protein 1, PTBP1) 是一个在神经系统疾病中具有重要调控作用的关键靶点。然而, 其RNA结合结构域(RNA binding domain, RBD) 表面缺乏典型的可成药口袋, 使得开发具有良好类药性的小分子抑制剂面临重大挑战。本研究采用计算机辅助药物设计策略, 聚焦于PTBP1 RBD3/4结构域中由柔性linker3介导的动态结合口袋, 对包含约160万个化合物的商业库进行虚拟筛选。通过类药性规则初筛和分子对接精筛, 合成了14个候选化合物, 并利用原核表达纯化的PTBP1-RBD3/4重组蛋白, 通过生物层干涉(bio-layer interferometry, BLI) 技术验证其结合活性。最终获得了6个具有微摩尔级别亲和力的苗头化合物, 其中C17414表现出最高的结合力(KD = 1.6 μmol·L-1)。分子对接提示C17414通过氢键、盐桥、π-π堆积及疏水作用等多种机制高效占据目标口袋。系统的ADMET性质预测进一步表明, C17414与C7485在脂溶性、极性表面积及透膜潜力之间取得了良好平衡, 具备优越的成药性, 特别是作为中枢神经系统药物的开发潜力。本研究不仅发现了一系列高亲和力的靶向PTBP1的苗头化合物, 验证了靶向其动态结合口袋的可行性, 也为开发针对PTBP1的相关疗法奠定了坚实的基础。

多聚嘧啶区结合蛋白1  /  计算机辅助药物设计  /  虚拟筛选  /  分子对接  /  生物层干涉技术

Polypyrimidine tract-binding protein 1 (PTBP1) is a critical regulatory target in cancers and neurological disorders. However, the development of its small-molecule inhibitors has been hampered by the lack of traditional druggable pockets on its RNA-binding domains. This study employed a computer-aided drug design (CADD) strategy focusing on a dynamic binding pocket mediated by the flexible linker3 within the PTBP1 RBD3/4 domain. A commercial library containing approximately 1.6 million compounds was subjected to structure-based virtual screening. Following initial filtering based on lead-likeness rules and subsequent molecular docking, 14 candidate compounds were synthesized. Their binding activities were experimentally validated using prokaryotically expressed and purified PTBP1-RBD3/4 recombinant protein via bio-layer interferometry (BLI). Six compounds with micromolar affinity were identified, among which C17414 exhibited the highest binding affinity (KD = 1.6 µmol·L-1). Molecular docking analysis revealed that C17414 efficiently occupies the target pocket through multiple interactions, including hydrogen bonds, salt bridges, π-π stacking and hydrophobic interactions. Systematic ADMET (absorption, distribution, metabolism, excretion and toxicity) property predictions indicated that C17414 and C7485 possess a favorable balance of lipophilicity, polar surface area and membrane permeability, suggesting promising drug-likeness and potential for central nervous system drug development. This work not only discovers a series of high-affinity PTBP1 inhibitor hits and validates the feasibility of targeting its dynamic pocket but also lays a solid foundation for developing novel therapies against PTBP1.

polypyrimidine tract-binding protein 1  /  computer-aided drug design  /  virtual screening  /  molecular docking  /  bio-layer interferometry
王远航, 刘樱怡, 黄艺岚, 毛美懿, 黄明星. 靶向多聚嘧啶区结合蛋白1 (PTBP1) RBD3/4动态口袋的苗头化合物筛选. 药学学报, 2026 , 61 (8) : 3085 -3095 . DOI: 10.16438/j.0513-4870.2025-1520
Yuan-hang WANG, Ying-yi LIU, Yi-lan HUANG, Mei-yi MAO, Ming-xing HUANG. Screening of hit compounds targeting the dynamic pocket of polypyrimidine tract-binding protein 1 (PTBP1) RBD3/4[J]. Acta Pharmaceutica Sinica, 2026 , 61 (8) : 3085 -3095 . DOI: 10.16438/j.0513-4870.2025-1520
正文
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多聚嘧啶区结合蛋白1 (polypyrimidine tract binding protein 1, PTBP1) 是RNA结合蛋白家族中的关键成员, 其分子结构包含4个RNA识别基序(RNA recognition motif, RRM), 亦常被称为RNA结合结构域(RNA binding domain, RBD), 这些结构域是其执行功能的基础[1, 2]。PTBP1通过这4个RRM在转录后调控中发挥核心作用, 包括调控pre-mRNA的选择性剪接、mRNA稳定性、多聚腺苷酸化及IRES介导的翻译起始等过程[2-5]。其中, RRM1、RRM2和RRM3具有经典的四链β片层结构, 主要负责结合嵌入于茎环等结构化RNA元件中的短嘧啶序列; 而RRM4则具有独特的结构延伸, 形成五链β片层, 使其能够识别更长且更柔性的单链嘧啶区[1]。尤为重要的是, RRM3和RRM4彼此间通过一段较长的柔性连接区(linker3) 发生广泛的相互作用, 这一结构特征使PTBP1能够同时结合相距较远的嘧啶序列, 并诱导RNA成环, 从而有效抑制选择性剪接事件[1, 6]。
近年来研究表明, PTBP1在多种肿瘤细胞(如胶质瘤、肝癌和乳腺癌) 中异常高表达, 通过调控特定剪接异构体及原癌基因的表达, 促进肿瘤细胞的增殖、侵袭与转移[5, 7-9]。然而, PTBP1在中枢神经系统(central nervous system, CNS) 中的功能尤为突出, 已成为神经发育与神经退行性疾病研究的核心靶点之一。研究发现, PTBP1在非神经元细胞(如星形胶质细胞) 中高表达, 通过抑制神经元特异性外显子的剪接以维持前体细胞状态; 而在神经元分化过程中, PTBP1表达显著下调, 从而允许神经元特异性剪接程序的激活, 推动神经元的成熟与功能建立[10-12]。更重要的是, PTBP1的异常表达或功能失调已被广泛关联于多种中枢神经系统疾病, 包括阿尔茨海默病、帕金森病、肌萎缩侧索硬化症及自闭症谱系障碍等[13, 14]。近期突破性研究进一步表明, 通过基因编辑工具或反义寡核苷酸在体内抑制PTBP1表达, 可高效诱导星形胶质细胞直接转分化为功能性神经元, 并在脑缺血、帕金森病等动物模型中显著改善运动与认知功能[15-17]。这些成果不仅验证了PTBP1作为神经再生治疗靶点的可行性, 也凸显了开发可穿透血脑屏障的小分子PTBP1抑制剂在CNS疾病干预中的巨大潜力。
尽管PTBP1在肿瘤与神经系统疾病中均展现出重要的调控功能, 并被视为极具潜力的治疗靶点, 但针对该蛋白的小分子抑制剂开发仍面临重大挑战。主要困难在于其RNA结合域表面通常较为平滑, 缺乏典型的“可成药”口袋[1, 6, 18]。目前公开报道的高活性、高选择性PTBP1小分子抑制剂仍十分有限, 严重制约了对其生物学功能的深入探索及靶向治疗的临床转化。
虽然传统小分子抑制剂的开发因PTBP1的RRM结构域缺乏经典静态口袋而进展缓慢, 但近年来多种创新策略也取得了一定的成效。①变构多肽抑制剂: Schmeing等[19]基于RRM1中独特的瞬态α3螺旋, 设计出可变构抑制PTBP1-RNA相互作用的订书肽, 并通过晶体结构(PDB: 8bwf) 证实其作用模式, 为靶向动态蛋白界面提供了直接范例; ②基因层面干预: 利用CasRx/sgRNA系统在体抑制PTBP1表达, 已被证实能在脑缺血模型中将星形胶质细胞转分化为功能性神经元, 并改善记忆功能, 从疾病模型层面验证了PTBP1作为治疗靶点的有效性[20]; ③天然产物的调控作用: 如芹菜素、南蛇藤茎提取物等物质也被发现可下调PTBP1表达, 但其作用通常较为间接[21, 22]。上述进展共同印证了靶向PTBP1的治疗潜力, 同时也凸显出开发高亲和力、可口服、能穿透血脑屏障的小分子抑制剂仍是当前亟待满足的关键需求, 对推动CNS疗法转化至关重要。
值得关注的是, 近期结构生物学研究发现, PTBP1的RBD3/4结构域在溶液中具有显著的构象动态性, 其连接区域(linker3) 的柔性可能暴露出潜在的小分子结合位点[6]。这一“动态口袋”在静态晶体结构中不可见, 但在生理条件下可能周期性开放, 为小分子干预提供机会窗口。鉴于本研究旨在开发用于中枢神经系统疾病的PTBP1靶向药物, 本研究特别关注化合物的类药性是否符合CNS药物的设计准则。因此, 在虚拟筛选初期, 作者采用适用于中枢神经系统药物的简化版里宾斯基三规则(rule of three, RO3), 优先保留分子质量≤ 300 Da、脂水分配系数(partition coefficient, LogP) ≤ 3、氢键供体≤ 3、氢键受体≤ 6、极性表面积(polar surface area, PSA) ≤ 60 Å2的化合物。该筛选策略有助于富集具备良好血脑屏障穿透能力、适度脂溶性与低分子复杂度的苗头分子, 从而提高后续先导化合物向CNS药物转化的成功率。
本研究采用计算与实验相结合的整合性策略, 旨在筛选和鉴定靶向PTBP1 RBD3/4结构域的高亲和力小分子抑制剂。首先, 通过同源建模与分子对接虚拟筛选大规模化合物库; 随后, 利用原核表达系统获得高纯度PTBP1-RBD3/4重组蛋白; 最后, 通过生物层干涉技术(bio-layer interferometry, BLI) 验证小分子与靶蛋白的结合亲和力, 并对苗头化合物进行系统的ADMET性质预测。该研究不仅为PTBP1靶向药物开发提供了具有前景的苗头化合物, 也为针对动态RNA结合蛋白的抑制剂设计提供了新的思路与方法。
结果
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1　基于CADD的PTBP1抑制剂筛选
本研究采用计算机辅助药物设计方法对PTBP1抑制剂进行虚拟筛选。从ChemDiv公司获取的虚拟筛选库包含约160万个化合物, 首先通过Pipeline Pilot软件基于中枢神经系统药物RO3规则进行初步筛选, 获得符合条件(分子质量≤ 300 Da、LogP ≤ 3、氢键供体≤ 3、氢键受体≤ 6、PSA ≤ 60 Å2) 的化合物共59 607个。
随后, 采用Discovery Studio软件中的LibDock模块对上述约6万个化合物进行分子对接分析。选取对接打分排名前1 000的分子, 进一步利用RDKit和CSD Python包分别计算蛋白-配体界面未满足氢键数量及扭转角几何异常值。设定筛选阈值为N_unsats ≤ 1且N_unusual_torsion ≤ 1, 以降低假阳性并优化候选化合物选择。
在此基础上, 进一步排除预测溶解度较低及在结合构象中存在未满足氢键的分子, 随后通过基于Tanimoto相似性的聚类分析降低配体结构冗余。最终对聚类后保留的62个分子在PyMol中进行人工检查, 评估其相互作用模式和几何构型合理性; 经综合评估, 选定28个化合物通过商业合成服务进行合成, 最终得到14个化合物。后续实验验证发现6个化合物与PTBP1具有较高亲和力, 其化学结构如图1所示。
2　高亲和力苗头化合物的结构和性质分析
对筛选获得的6个高亲和力PTBP1抑制剂进行系统的结构与性质分析, 其基本理化参数见表1。
C17414母核为[1,2,4]三唑并[1, 5-a]嘧啶双环系统。嘧啶环5位连接环丙基, 该环状结构限制了键的旋转, 可能有助于稳定特定构象; 同时, 其疏水性有助于填充蛋白质的疏水口袋; 7位二氟甲基的引入有助于提高代谢稳定性与脂溶性; 三唑环2位延伸的丙酸侧链提供羧酸基团, 赋予分子酸性特征。该结构兼具亲水与疏水区域, 整体LogP为1.21, PSA为59.68 Å2, 表现出适宜的脂溶性与透膜潜力。
C15母核为咪唑并[4, 5-b]吡啶结构。母核3位N原子连接苄基, 增强疏水性与刚性; 2位甲胺侧链的氮原子被乙酰化形成酰胺键。分子中氮原子与羰基氧可作为氢键受体, 整体LogP为1.29, PSA为45.63 Å2, 显示良好的脂溶性, 有利于穿越血脑屏障。
C18221结构母核为苯并咪唑环, 与C15类似, 但苄基连接于咪唑1号氮原子。2位为N-((苯并[d]咪唑-2-基)甲基)乙酰胺结构, 酰胺与仲胺基团可形成氢键。其LogP为2.28, PSA为35.73 Å2, 较高的脂溶性提示其具备良好的中枢渗透潜力。
C2143为(2S, 3R)-3-苯基取代吗啉-2-甲酰胺衍生物, 具有明确立体构型。结构中含有内酰胺环与苯环, 分别提供极性区域与疏水锚点; 2位连接甲氧乙基酰胺侧链, 含醚键与酰胺键, 可作为氢键供体/受体。该分子LogP为-0.06, PSA为58.07 Å2, 亲水性较强, 可能影响其被动跨膜渗透。
C2577母核为1, 2, 4-三氮唑环, 5位连接苯基, 4位取代为2-甲基烯丙基。3位经硫醚键连接羧甲基, 使其在生理条件下以阴离子形式存在。该结构兼具疏水区域与强亲水性羧基, LogP为2.60, PSA为52.90 Å2, 较高的脂溶性与离子化特性可能影响其体内分布行为。
C7485母核为苯并咪唑环, 1号氮原子通过乙酰连接臂延伸至N-(呋喃-2-基甲基)酰胺侧链, 2位为甲氧甲基取代。酰胺基团与呋喃环氧原子提供极性作用位点, 苯环部分贡献疏水性。其LogP为2.23, PSA为51.79 Å2, 适中的脂溶性与极性表面积支持其作为中枢神经系统药物的开发潜力。
3　候选化合物的ADMET性质与成药性分析
对6种候选化合物进行系统的ADMET (吸收、分布、代谢、排泄和毒性) 特性预测, 以评估其成药潜力, 关键结果汇总于表2。
3.1　吸收与溶解度特性
所有化合物的预测溶解度等级(solubility_level) 为3或4级(对应LogS范围为-4~-1)。其中C2143 (LogS = -1.12) 溶解度最佳, 而C18221 (LogS = -3.74) 溶解度较差, 可能需要通过制剂策略改善溶出性能。
脂溶性与极性表面积分析显示, C17414 (AlogP98 = 1.96, PSA_2D = 77.2 Å2) 与C7485 (AlogP98 = 1.37, PSA_2D = 68.2 Å2) 处于类药化合物的理想区间(AlogP98 0~3, PSA_2D 60~140 Å2), 兼具良好膜渗透性与溶解性, 符合BCS Ⅱ类特征。相比之下, C2143因亲水性强(AlogP98 = -0.19) 可能导致被动渗透不足。
3.2　分布与代谢特性
血浆蛋白结合率(plasma protein binding, PPB) 分析表明, C2143表现为低结合(PPB = -4.47), 可能提高游离药物浓度; C7485为中等结合(PPB = 0.35), 需关注其有效血药浓度。
肝毒性风险评估显示, C2577与C15具有潜在肝毒性风险(评分分别为-1.58与-4.82)。其中C15显示较强的CYP2D6抑制潜力(P = 0.000 3), 提示可能存在代谢性药物相互作用。
血脑屏障(blood-brain barrier, BBB) 穿透分析表明, C18221与C2577具有较高的中枢暴露潜力(BBB评分分别为-0.13与-0.29), 这对靶向中枢系统的药物开发具有积极意义, 但需关注潜在神经毒性。
3.3　开发挑战与优化方向
基于上述分析, 各候选化合物的主要优化方向如下: C17414: 综合性质均衡, 需关注其CYP2D6抑制潜力(P = 0.001 6); C15: 肝毒性风险显著, 建议优先开展体外代谢稳定性评估; C18221: 溶解性差, 可考虑引入亲水基团或采用无定形制剂; C2143: 亲水性强, 限制吸收, 可设计前药改善生物利用度; C2577: 高脂溶性(AlogP98 = 2.95) 可能引起组织蓄积; C7485: 综合性质较优, 需在体内模型中验证其蛋白结合与代谢特性。
4　结构-ADMET性质关系分析
为进一步指导先导化合物的结构优化, 本研究结合各化合物的化学结构特征, 对其ADMET性质进行了深入的构效关系分析:
C17414结构中包含环丙基、二氟甲基及末端羧基。环丙基的刚性疏水结构有助于填充蛋白质疏水口袋, 提升结合亲和力, 但也可能增加组织蓄积倾向; 二氟甲基的引入显著增强了代谢稳定性与脂溶性, 但其强电负性可能导致与CYP450酶的非预期相互作用, 与预测的CYP2D6抑制潜力相符; 末端羧基虽有利于形成盐桥和氢键, 但也可能导致在生理pH下电离, 影响血脑屏障穿透效率。优化建议: 可考虑将羧基酯化前药化, 或替换为其他极性基团以平衡溶解性与透膜性。
C15与C18221均含有苯并咪唑/咪唑并吡啶母核与苄基疏水尾部。苄基的存在显著提升了LogP, 有利于BBB穿透, 但也可能增加CYP介导的代谢风险(如C15的肝毒性预测)。酰胺键的引入增强了极性, 但C18221中苯并咪唑母核的平面刚性可能导致溶解性下降。优化建议: 可对苄基进行氟化或引入小极性基团以调节脂溶性, 或对母核进行修饰以改善溶解性。
C2143具有独特的立体构型与较多氢键受体, 导致其亲水性较强(LogP ≈ -0.06), 预测溶解度最佳但BBB穿透能力较弱。吗啉环与苯环的共存体现了极性/疏水平衡的设计, 但目前结构更偏向极性。优化建议: 可在苯环引入适度疏水取代基, 或通过前药策略掩蔽极性基团, 提升膜渗透性。
C2577含三氮唑与羧基, 离子化倾向强, 且疏水部分较多(LogP ≈ 2.60), 导致其处于高脂溶性与高极性矛盾状态, 可能影响体内分布。优化建议: 可考虑将羧基替换为中性等排体, 或调整疏水尾部长度以降低LogP至更理想范围(< 3)。
C7485结构相对均衡, 苯并咪唑与呋喃环提供适中的疏水与极性特征, 使其在AlogP98与PSA_2D参数上均处于类药区间。其较优的ADMET综合表现提示该骨架具有较好的可优化性。优化建议: 可进一步探索呋喃环的生物电子等排体, 以调节代谢稳定性与毒性风险。
5　重组PTBP1-RBD3/4蛋白的可溶性表达与纯化
为获得PTBP1的RBD3/4结构域蛋白, 本研究构建了pQE80L-Avi-PTBP1重组表达质粒, 其结构示意图如图2A所示。该质粒在目标序列N端融合了6×His标签(用于Ni-NTA亲和纯化) 与Avi标签(用于生物素化)。
将验证正确的重组质粒转化至AVB101表达菌株, 在37 ℃条件下通过1 mmol·L-1 IPTG诱导表达6 h, 同时加入50 μmol·L-1生物素促进蛋白生物素化。SDS-PAGE分析显示(图2B), 诱导后菌体(泳道2) 在预期分子质量处出现明显蛋白条带, 而未诱导对照组(泳道1) 无此条带, 表明目标蛋白成功表达。
超声裂解后, 目标蛋白主要存在于上清中(泳道3), 显示其以可溶形式表达。经Ni-NTA 6FF SefinoseTM树脂纯化, 获得单一、清晰的蛋白条带(泳道4), 无可见杂蛋白或降解产物, 表明获得了高纯度重组蛋白。Bradford法定量显示, 每毫升菌液可获得约60 μg重组蛋白。上述结果证实, 作者成功建立了PTBP1-RBD3/4结构域的原核表达与纯化体系, 为后续功能研究提供了高质量蛋白材料。
6　苗头化合物与PTBP1的结合亲和力测定
采用BLI技术测定了候选小分子抑制剂与PTBP1蛋白相互作用的动力学参数, 从中筛选出亲和力最高的4个分子进行深入分析, 其动力学参数列于表3。
如表3所示, C17414和C15表现出最强的结合亲和力, 平衡解离常数(equilibrium dissociation constant, KD) 分别为1.6×10-6和8.6×10-6 mol·L-1, 达到微摩尔级别。C18221与C2143的亲和力依次减弱, KD值分别为1.2×10-5和3.1×10-5 mol·L-1。所有拟合曲线的R2值均接近1, 且Chi2/DoF值极低, 表明结合模型可靠、数据质量良好。
结合传感图谱(图3) 进一步分析, C17414 (图3A) 与C15 (图3B) 均表现出快速结合与快速解离的特征, 符合小分子化合物与靶标之间通过非共价作用结合的典型动力学模式。C18221 (图3C) 与C2143 (图3D) 的结合响应相对较低, 与二者KD值较大的结果一致。
7　小分子抑制剂与PTBP1的结合模式分析
分子对接结果表明, 4种高亲和力小分子抑制剂均能有效结合于PTBP1的RBD3/4结构域活性口袋(图4A)。以结合亲和力最高的C17414为例, 其与靶标通过多种分子间相互作用协同实现高效结合(图4B、C)。
极性相互作用在初始识别中起关键作用: C17414的羧酸基团与Lys108和Arg192分别形成强氢键(1.67 Å) 和盐桥; 同时, Asn101的HD22氢原子与抑制剂嘧啶环N4原子形成常规氢键(1.87 Å), 有效稳定了氮杂环的空间取向。
芳香系统间的互补作用显著增强结合强度: His181的咪唑环与抑制剂氟原子(F17) 通过碳氢键(2.33 Å) 结合, 而Phe107的芳香环与C17414的嘧啶环形成双向π-π堆积(距离5.55~5.98 Å, θ角22.9~79.3°), 通过垂直堆叠最大化疏水接触。
空间匹配通过卤素键进一步优化: 抑制剂的氟原子(F18) 与Arg106的主链氧形成卤素键(3.69 Å), 精确调控了结合位点的几何构型。
疏水作用在结合后期发挥重要作用: C17414的甲基环丙烷和烷基链通过范德华力与Val29 (4.89 Å)、Leu104 (4.25 Å) 等疏水残基结合, 有效填充蛋白质的疏水口袋。氟原子(F17/F18) 还通过疏水作用与Arg106和Arg192的烷基侧链结合(3.79~4.67 Å), 体现了氟原子的多功能性。
此外, 次级作用力进一步巩固结合稳定性: Ser102的氧孤对电子与抑制剂的π轨道形成π-孤对电子作用(2.75 Å), 而酰胺-π堆积(Arg106/Phe107与嘧啶环, 3.82~4.60 Å) 提供了多中心协同效应。
综上, C17414通过极性作用、芳香堆积、疏水填充和卤素键等多机制协同, 实现了与PTBP1的高效结合。这一详细的相互作用机制为后续药物优化(如氟原子替换、烷基链延长等) 提供了关键结构依据。
讨论
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本研究综合运用计算机辅助药物设计与实验验证, 成功筛选并鉴定出以C17414为代表的高亲和力PTBP1小分子抑制剂, 并系统阐明了其结合机制与成药潜力。
首先, 通过针对PTBP1 RBD3/4结构域中由linker3柔性介导产生的独特结合口袋进行虚拟筛选, 有效规避了该蛋白的RNA结合域表面通常平滑、缺乏经典成药口袋的挑战。分子对接结果表明, C17414等先导化合物能通过形成氢键、盐桥、π-π堆积、卤素键及疏水作用等多种非共价相互作用, 高效占据该口袋, 其中羧酸基团与Lys108和Arg192的极性相互作用尤为关键。这从结构基础上解释了C17414在BLI实验中表现出最高亲和力的原因, 也为基于结构的后续优化提供了明确方向。
其次, ADMET预测揭示了各苗头化合物的优势和潜在缺陷。综合来看, C17414与C7485在脂溶性、极性表面积及预测吸收特性之间取得了最佳平衡, 最具开发为中枢神经系统药物的潜力。值得注意的是, 尽管C18221与C2577显示出较高的血脑屏障穿透倾向, 但其较差的溶解度或潜在的代谢毒性风险构成了主要开发瓶颈。提示在后续优化中需针对不同化合物的特性采取差异化策略, 例如通过前药设计改善C2143的渗透性, 或通过结构修饰降低C15的肝毒性风险。
此外, 本研究建立的高效原核表达与纯化体系, 成功获得了高纯度、经生物素化的PTBP1-RBD3/4蛋白, 为BLI等生物物理实验提供了可靠的材料基础。BLI结合动力学结果与分子对接预测的高度一致性, 不仅验证了基于该动态口袋进行虚拟筛选策略的有效性, 也强烈提示该口袋是一个具有潜力的、可被小分子占据的位点, 为其后续作为功能抑制位点的研究提供了结构基础。
然而, 本研究仍存在一定局限性。目前的研究集中于RBD3/4结构域, 而PTBP1作为多结构域蛋白, 其各结构域间的协同作用及小分子对全蛋白构象的潜在影响尚不明确。此外, 尽管ADMET预测为候选化合物的初步筛选提供了有价值的指导, 但需要指出的是, 计算预测存在一定的局限性。例如, 文中提示的C15与C2577的潜在肝毒性风险, 以及C17414与C7485对CYP2D6的抑制潜力, 均基于计算模型。这些预测结果需要更可靠的体外实验加以验证。综上所述, 尽管PTBP1是一个重要的疾病相关靶点, 但其缺乏经典静态口袋的特性严重阻碍了小分子抑制剂的开发。本研究聚焦于中枢神经系统疾病治疗需求, 独辟蹊径, 靶向由柔性linker3介导的动态结合口袋, 并采用CNS药物导向的筛选标准。本研究不仅旨在发现新型苗头化合物, 更在于从概念上验证“靶向RNA结合蛋白动态界面”这一创新策略的可行性, 为同类“难成药”靶点的CNS药物开发提供了新的研究策略。
实验部分
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1　材料
本实验所使用的AVB101菌株购自GeneCopoeiaTM, 其为大肠杆菌B株(基因型: hsdR、lon11、su1A1)。该菌株携带一个pACYC184质粒, 该质粒上含有IPTG诱导型birA基因(pBirAcm), 可实现生物素连接酶的过表达。该菌株因其生长特性稳定, 且缺乏可降解重组蛋白的OmpT与Lon蛋白酶, 故被推荐用于蛋白质表达实验。
实验中使用的pQE80L载体购自QIAGEN, 其特点包括含有T5启动子、lac操纵子及一个N端6His标签。此外, 该载体还带有顺式抑制元件, 可表达lacIq基因产物, 在IPTG诱导前可抑制蛋白表达, 从而无需额外引入携带该基因的质粒。因此, 该载体特别适用于表达可能对大肠杆菌产生毒性作用的基因产物。
2　基因序列与蛋白结构
人源PTBP1基因序列检索自NCBI数据库。鉴于PTBP1基因存在8个转录变体及6种蛋白异构体, 本研究选用MANA (Matched Annotation from the NCBI and EMBL-EBI) 推荐的转录本NM_002 819.5, 其编码的蛋白质由557个氨基酸组成(NP_002 810.1)。
由于目前尚未见PTBP1蛋白全长的三维晶体结构报道, 本研究采用AlphaFold蛋白质结构数据库所提供的预测结构(AF-P26599-F1-v6) (图5A)。此外, PTBP1的RNA结合结构域RBD1~4的三维结构已有实验数据支持, 相关结构信息来源于Swiss-Model数据库。具体而言, RBD1、RBD2及RBD3/4与CUCUCU RNA形成的复合物结构分别对应PDB编号2ad9、2adb与2adc[1]; 而溶液中处于自由状态的RBD3/4结构则选用2evz[6]。
通常, PTBP1的RBD1、RBD2及RBD3/4结构域表面较为平滑, 缺乏明显的药物结合口袋。然而, 在RBD3/4结构中, 连接RBD3与RBD4的linker3具有一定柔性, 使其在溶液中可呈现多种动态构象(图5C)。其中部分构象(如2evz-19) 能够暴露内部残基, 形成较深的药物结合口袋, 为小分子抑制剂的开发提供了结构基础。
3　基于商业化合物库的虚拟筛选
本研究所用的小分子化合物库来源于ChemDiv公司, 共包含约160万种化合物。利用Pipeline Pilot软件, 按照适用于中枢神经系统药物的RO3规则对该库进行初步筛选。RO3规则的核心标准包括: 分子质量≤ 300 Da、LogP ≤ 3、氢键供体数≤ 3; 补充标准为: 氢键受体数≤ 6、PSA ≤ 60 Å2。筛选所得符合RO3规则的化合物将用于后续分子对接研究。最终用于湿实验验证的小分子化合物亦由ChemDiv公司负责合成。
4　基于活性口袋的分子对接筛选
本研究采用Discovery Studio软件中的LibDock模块进行初步分子对接筛选[23]。首先, 使用其Macromolecules/Prepared Protein程序对PTBP1结构域RBD3/4 (PDB: 2evz-19) 进行处理, 包括去除水分子、补全不完整残基、统一蛋白构象及添加氢原子等步骤。随后, 通过Receptor-Ligand Interactions/Define and Edit Binding Site/From Receptor Cavities程序识别潜在的活性口袋。在所有候选位点中, 选择RBD3/RBD4的交接界面与linker3区域共同形成的结合口袋作为对接位点, 并删除其他位点。该口袋中心坐标为(1.4, -13.3, -31.5), 半径为12.4 Å。选择该位点主要基于两方面原因: 其一, 该口袋在结构中最为深大; 其二, 该区域与RBD3/4同RNA的结合位点重叠, 在此处结合的化合物有望有效干扰PTBP1功能。为提升筛选效率, 将Max Hits to Save参数设置为10, 以限制每个配体保留的对接构象数量, 从而缩短计算时间。
5　PTBP1结构域RBD3/4的原核表达
本研究采用位点特异性生物素化与蛋白诱导同步的策略, 实现PTBP1蛋白C端200个氨基酸(残基358~557, 包含RBD3和RBD4结构域) 在原核系统中的表达[24]。选用AVB101大肠杆菌菌株及pQE80L表达载体进行实验操作。
首先, 采用NovoPro平台的ExpOptimizer工具对目标序列进行密码子优化(https://www.novopro.cn/tools/codon-optimization.htmL), 并在优化过程中规避BamH Ⅰ与Hind Ⅲ酶切位点。随后, 在目标序列N端引入Avi标签(序列: GLNDIFEAQKIEWHE)[25]。由生工生物工程(上海) 股份有限公司合成经BamH Ⅰ/Hind Ⅲ酶切位点修饰的重组cDNA片段, 并将其克隆至pUC-SP载体中, 获得pUC-SP-Avi-PTBP1重组质粒。将该重组质粒与pQE80L载体分别经BamH Ⅰ和Hind Ⅲ双酶切后, 通过凝胶电泳回收线性化载体与目的片段, 使用T4 DNA连接酶于16 ℃连接过夜, 构建pQE80L-Avi-PTBP1重组表达质粒。
将构建好的重组质粒转化至DH5α感受态细胞, 通过氨苄青霉素抗性筛选阳性克隆, 并经菌落PCR及测序验证序列正确性。验证正确的质粒最终转化至AVB101表达菌株, 通过氨苄青霉素/氯霉素双抗性筛选及菌落PCR确认重组菌株构建成功。菌落PCR反应程序为: 94 ℃预变性5 min; 随后进行35个循环(94 ℃ 30 s, 58 ℃ 30 s, 72 ℃ 1 min); 最后72 ℃延伸10 min。反应使用TaKaRa公司的EmeraldAmp MAX PCR Master Mix (2×Premix) 并搭配特异性引物(正向: 5′-CGAGGCCCTTTCGTCTTC-3′; 反向: 5′-GGATCTA TCAACAGGAGTCC-3′) 进行。
6　重组蛋白的诱导表达与纯化
将含有pQE80L-Avi-PTBP1质粒的AVB101表达菌株单克隆接种于10 mL LB培养基(含100 mg·L-1氨苄青霉素与25 mg·L-1氯霉素), 于37 ℃、150 r·min-1振荡培养过夜。次日, 将过夜培养物按1∶20比例转接至150 mL新鲜LB培养基中, 在相同条件下继续培养至OD600 ≈ 0.7。随后在菌液中加入100 mmol·L-1的IPTG与5 mmol·L-1的生物素(溶于pH 8.3的10 mmol·L-1 Bicine缓冲液) 各1.5 mL, 于37 ℃、150 r·min-1诱导表达6 h。分别于诱导起始与结束时采集1 mL菌液样品, 经12 000 ×g离心2 min收集菌体, 重悬于100 μL 1×SDS-PAGE上样缓冲液, 用于后续电泳分析。
诱导结束后, 菌体经12 000 ×g离心2 min收集, 重悬于40 mL非变性裂解缓冲液(50 mmol·L-1 NaH2PO4、300 mmol·L-1 NaCl、10 mmol·L-1咪唑, pH 8.0)。菌悬液依次经液氮速冻与冰水浴融化, 进行两次冻融循环, 随后于冰水浴中使用φ3 mm探头进行超声破碎(功率500 W, 超声10 s, 间歇20 s, 共12循环)。裂解液于4 ℃、12 000 ×g离心10 min, 收集上清作为可溶性蛋白样品。
采用预装1 mL Ni-NTA 6FF SefinoseTM树脂的重力柱(生工生物工程股份有限公司) 对重组6His-Avi-PTBP1蛋白进行固定化金属亲和层析纯化。首先, 使用2 mL结合缓冲液(组成同裂解缓冲液) 平衡层析柱, 随后上样10 mL可溶性蛋白上清。依次使用5 mL洗涤缓冲液(50 mmol·L-1 NaH2PO4、300 mmol·L-1 NaCl、60 mmol·L-1咪唑, pH 8.0) 洗涤4次, 直至A280恢复基线, 最后以5 mL洗脱缓冲液(50 mmol·L-1 NaH2PO4、300 mmol·L-1 NaCl、250 mmol·L-1咪唑, pH 8.0) 洗脱目标蛋白。
蛋白浓度的测定采用Bradford法, 以BSA作为标准品。蛋白分子质量与纯度通过SDS-PAGE进行分析, 使用含0.1% SDS的12%聚丙烯酰胺凝胶进行电泳, 并以0.25%考马斯亮蓝R-250染色显示蛋白条带。
7　利用BLI分析结合动力学
采用Sartorius Octet Red-96生物层干涉系统及Octet SSA (超级链霉亲和素) 生物传感器进行动力学分析。小分子化合物首先用DMSO配制成4 mmol·L-1储备液, 随后使用PBST缓冲液(PBS + 0.02% Tween 20, pH 7.4) 稀释至20 μmol·L-1工作浓度, 此时DMSO终浓度为0.5%。进一步用含0.5% DMSO的PBST缓冲液将化合物稀释为20、10、5、2.5、1.25和0.625 μmol·L-1的浓度梯度。将纯化的重组6His-Avi-PTBP1蛋白用PBS缓冲液稀释至25~30 μg·mL-1作为分析样品。蛋白固定实验前, 先将SSA传感器在PBST中预平衡10 min; 蛋白固定程序包括: 基线平衡60 s, 蛋白加载600 s, 以及后续的基线平衡120 s。动力学测试实验前, 先将固化了蛋白的SSA传感器在PBST (含0.5%的DMSO) 中预平衡10 min; 小分子化合物与蛋白相互作用的动力学测试在30 ℃条件下进行, 信号采集频率设定为2 Hz。实验流程依次为: 基线平衡60 s, 结合反应90 s, 解离过程120 s。
8　ADMET性质预测模型
本研究使用BIOVIA Discovery Studio软件中的“ADMET Descriptors”模块, 对候选化合物的关键药代动力学与毒性性质进行预测。该模块基于定量构效关系模型, 其预测模型使用大量经实验验证的化合物数据集进行训练和验证。
8.1　肝毒性预测模型
该模型为一个二分类模型, 用于预测化合物是否具有引起人类肝毒性的潜力。其训练集包含对多种已知肝毒性化合物的结构-活性数据。模型通过计算分子描述符(如AlogP、分子极性表面积、特定子结构片段等) 进行分类预测, 并输出预测评分。评分为负数表示肝毒性风险较低, 正数表示风险较高。模型的内部验证准确度(cross validation accuracy) 为≈ 75%。
8.2　CYP2D6抑制预测模型
该模型预测化合物是否为细胞色素P450 2D6同工酶的抑制剂。CYP2D6是参与多种药物代谢的关键酶, 其抑制可能导致严重的药物-药物相互作用。模型同样为二分类模型, 基于已知的CYP2D6抑制剂和非抑制剂数据集构建。输出的P值反映了化合物属于抑制剂类别的概率, P值越小(通常 < 0.05), 表明其为CYP2D6抑制剂的统计学可能性越高。
8.3　血脑屏障穿透与血浆蛋白结合模型
这些模型基于回归或分类算法, 分别预测化合物穿越血脑屏障的能力(共4级, 2级表示穿透性好) 及与人类血浆蛋白的结合程度(以结合率预测值表示)。
9　统计学分析
本研究对所有实验数据进行了系统的统计分析。采用均值±标准差表示数据的集中趋势与离散程度。所有实验均设3次重复, 以确保结果的可靠性。使用统计分析系统(SAS, 8.0版) 进行数据处理与假设检验。组间比较采用适当统计方法, 以P < 0.05为显著性水平, 判断差异是否具有统计学意义。

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2026年第61卷第8期
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doi: 10.16438/j.0513-4870.2025-1520
  • 接收时间：2025-12-18
  • 首发时间：2026-09-21
  • 出版时间：2026-08-12
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  • 收稿日期：2025-12-18
  • 修回日期：2026-03-11
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    广东工业大学生物医药学院, 广东 广州 510006

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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
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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