利用结构生物信息学方法促进药物研发
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摘要
结构生物信息学是生物信息学中关注在原子和哑细胞空间尺寸上结构信息表述、存储、获取和分析的一个分支。近年来,世界各国对结构生物信息学在药物研发中的应用越来越重视。因为结构生物信息学几乎可以应用到药物发现中的各个环节例如靶标评估、虚拟筛选和先导化合物优化等。本论文的绪论部分综述了结构生物信息学的研究框架,给出了相关的实用数据库和工具,阐述了结构生物信息学方法在药物研发中的重要作用。我的研究工作主要围绕利用结构生物信息学方法促进药物研发这一主题展开,进行了以下3个相对独立的研究：
1.GPCR药效团模型产生的新方法研究
G蛋白偶联受体(GPCR)具有保守的七次跨膜结构域,是药物治疗中最有前景的靶点蛋白。利用药效团技术可以加快基于GPCR的药物研发,但是现有药效团模型只适用于那些有晶体结构或配体信息的少数GPCR,仍有大部分GPCR缺乏对应的药效团模型以用来进行先导化合物的设计和发现。因此如何对这些GPCR产生药效团模型是这个领域的难点和热点。本研究旨在解决上述难题,通过研究现有方法的优缺点,在充分挖掘和利用所有GPCR结构数据的基础上,开发出一种全新的药效团产生方法Pharm-Map-Pick。我们的方法主要包括三步：第1步,构建一个关键氨基酸和药效团特征的关联数据库；第2步,根据确定的氨基酸位置,将药效团特征映射到每一个GPCR;第3步,优选药效团特征来构建最终的药效团模型。研究结果表明该方法既不需要晶体结构也无需配体信息就可以对整个GPCR家族产生药效团模型。这些模型不仅能准确预测GPCR-配体的结合模式,而且具有很高的富集因子,可以直接用于虚拟筛选。我们还构建了世界上首个包含人体内所有GPCR的药效团数据库。这种方法以及产生的药效团模型将极大地促进GPCR的基础研究和药物研发。
2.基因组水平预测阿司匹林的作用靶点
除了发挥抗炎,解热和镇痛等治疗作用,阿司匹林还用于预防心血管疾病以及各种类型的癌症。尽管很多文献已经表明阿司匹林确实可以预防心血管疾病和各种类型的癌症,但是阿司匹林众多疗效背后的分子机制现在还不是完全清楚。阿司匹林的多种疗效不能只归功于一种蛋白靶点,更有可能的是涉及到多个靶标和分子通路。因此,系统地识别阿司匹林的潜在分子靶标可以帮助我们理解阿司匹林的多种疗效和可能的副作用。在本研究中,我们联合结构生物信息学和系统生物学的方法来预测和分析阿司匹林在全基因组范围内的潜在靶标和分子通路。首先利用蛋白质局部结构检测和比对工具加上分子对接以及结合自由能的计算我们识别出了21个阿司匹林的潜在靶标。对这些靶标进行的进一步系统生物学分析,我们发现了阿司匹林参与的一些分子通路例如血管内皮生长因子、促分裂原活化蛋白激酶、JNK/P38、Fc epsilon RI和花生四烯酸代谢等。基于这些发现,我们构建了阿司匹林的靶点-细胞效应相互作用网络。这个网络可以很好地解释阿司匹林具有的抗炎症以及预防心血管疾病和癌症等多种医学疗效。随着这部分论文的完成,我们已经建立了识别小分子药物作用靶标和分子通路的通用平台。可以用来寻找其他小分子的分子靶标。
3.识别和比较人类PPI界面上的可药性结合口袋
识别和比较在人类PPI界面上的可药性结合口袋,为发现潜在药物新靶标提供重要结构基础。PPIs是生物体内最复杂、最具多样性和最具调控重要性的一类相互作用。最近十年,PPIs已经成为新颖治疗中具有吸引力的分子靶标。本研究我们识别出了1731个存在人类PPI复合物结合界面上的可药性结合位点。通过对所有人类PPI复合物结合界面的可药性结合口袋的系统识别以及相关特征研究,产生了很多以前未知的有趣发现：1.在有结合界面的蛋白之中有接近一半的蛋白是具有可药性结合口袋;2.具有可药性结合口袋的蛋白的功能大都集中在各类疾病的分子通路上：3.较整个结合界面而言,可药性结合口袋的所有疏水性氨基酸的出现频率都增加了,疏水性的增加有利于小分子的结合；
4.同聚体和异聚体蛋白相互作用的差异,同聚体相互作用的蛋白双方同时具有可药性结合口袋的比例远大于异聚体相互作用；5.构建了一个可药性结合口袋的相似性网络,可以用来指导我们设计多靶标药物。这些发现对于研发靶向相互作用的药物以及多靶标药物都有实际意义。
Structural Bioinformatics, as a branch of bioinformatics, is concerned about the expression, storage, access and analysis of the structural information at atomic and sub-cellular space size. In recent years, researchers have paid more attention to the applications of structural bioinformatics in drug development. Structural bioinformatics can be applied to almost all aspects of drug discovery, such as the evaluation of target assessment, virtual screening and lead optimization. In this paper, we reviewed the research framework of structural bioinformatics, given the practical databases and tools for structural bioinformatics and elaborated the important role of structural bioinformatics in drug discovery. My research mainly focused on the use of structural bioinformatics methods to promote drug discovery and development. The following three relatively independent works were carried out:
1. Research on the new method of generating pharmacophore model for GPCRs
G-protein coupled receptors (GPCRs) are the most prominent therapeutic target family characterized by seven conserved transmembrane (7TM) α-helical fold. With the aim to speed up the process of GPCR-based drug discovery, by studying the advantages and disadvantages of the existing methods and data mining from GPCR structures, we developed a new method, Pharm-Map-Pick, which can rapidly generate pharmacophore models for GPCR family. Our method mainly comprises three steps:constructing a library for key residues and pharmacophore features revealed from complex structures, mapping these pharmacophore features to a given GPCR and picking appropriate features to generate a final pharmacophore model. The results show that our method neither require a crystal structure nor ligand information. These models can accurately predict the binding mode of GPCR-ligand and has a high enrichment factor, thus can be directly used for virtual screening. We also built a world's first database contains pharmacophore modes for all human GPCRs. This method and the resulting pharmacophore models will greatly contribute to the GPCR-based basic research and drug discovery.
2. Genome-wide Prediction of Targets for Aspirin
Besides to anti-inflammatory, analgesic, and anti-pyretic properties, aspirin is used for the prevention of cardiovascular disease and various types of cancer. The multiple activities of aspirin cannot be attributed wholly to a single target and likely involve several molecular targets and pathways. In this study, we combined structural bioinformatics and systems biology approaches to predict potential targets and molecular pathways of aspirin in genome-wide. Firstly, using the tool of protein local structure detection and comparison together with molecular docking and binding free energy calculations, we identified21potential targets of aspirin. Further systems biology analysis of these targets, we found that aspirin participates in multiple molecular pathways, such as vascular endothelial growth factor, mitogen-activated protein kinase, JNK/p38, Fc epsilon RI and arachidonic acid metabolism. Based on these findings, we constructed a target of aspirin-cell effect interaction network. This network can explain a variety of medical efficacy of aspirin, which is used for anti-inflammatory as well as the prevention of cardiovascular disease and cancer. With the finish of this work, we have established a common platform to identify targets for small-molecule drugs. It can be used to find molecular targets for other small molecules.
3. Identification and comparison of the druggable pocket at the interface of human PPI
In the last decade, PPIs have become the attractive molecular target for novel therapy. In this study, we identified1731druggable pocket at the interface of human PPI. Identification and comparison of these druggable pockets resulted in a lot of previously unknown interesting findings:1. Nearly half of the proteins have druggable pockets at the interface of human PPI;2. These proteins are enriched in the molecular pathways of various diseases;3. Compared to the protein binding interface, the frequencies of hydrophobic amino acids in the druggable pockets are increased;4. The partners of homo-PPI tend to have druggable pockets simultaneously. However, it is exactly the opposite for the partners of hetero-PPI;5. The druggable pocket similarity network built here can be used to guide the design of multi-target drugs. These findings have practical significance for discovery of the drug targeting protein interactions as well as the multi-target drugs.
1.GPCR药效团模型产生的新方法研究
G蛋白偶联受体(GPCR)具有保守的七次跨膜结构域,是药物治疗中最有前景的靶点蛋白。利用药效团技术可以加快基于GPCR的药物研发,但是现有药效团模型只适用于那些有晶体结构或配体信息的少数GPCR,仍有大部分GPCR缺乏对应的药效团模型以用来进行先导化合物的设计和发现。因此如何对这些GPCR产生药效团模型是这个领域的难点和热点。本研究旨在解决上述难题,通过研究现有方法的优缺点,在充分挖掘和利用所有GPCR结构数据的基础上,开发出一种全新的药效团产生方法Pharm-Map-Pick。我们的方法主要包括三步：第1步,构建一个关键氨基酸和药效团特征的关联数据库；第2步,根据确定的氨基酸位置,将药效团特征映射到每一个GPCR;第3步,优选药效团特征来构建最终的药效团模型。研究结果表明该方法既不需要晶体结构也无需配体信息就可以对整个GPCR家族产生药效团模型。这些模型不仅能准确预测GPCR-配体的结合模式,而且具有很高的富集因子,可以直接用于虚拟筛选。我们还构建了世界上首个包含人体内所有GPCR的药效团数据库。这种方法以及产生的药效团模型将极大地促进GPCR的基础研究和药物研发。
2.基因组水平预测阿司匹林的作用靶点
除了发挥抗炎,解热和镇痛等治疗作用,阿司匹林还用于预防心血管疾病以及各种类型的癌症。尽管很多文献已经表明阿司匹林确实可以预防心血管疾病和各种类型的癌症,但是阿司匹林众多疗效背后的分子机制现在还不是完全清楚。阿司匹林的多种疗效不能只归功于一种蛋白靶点,更有可能的是涉及到多个靶标和分子通路。因此,系统地识别阿司匹林的潜在分子靶标可以帮助我们理解阿司匹林的多种疗效和可能的副作用。在本研究中,我们联合结构生物信息学和系统生物学的方法来预测和分析阿司匹林在全基因组范围内的潜在靶标和分子通路。首先利用蛋白质局部结构检测和比对工具加上分子对接以及结合自由能的计算我们识别出了21个阿司匹林的潜在靶标。对这些靶标进行的进一步系统生物学分析,我们发现了阿司匹林参与的一些分子通路例如血管内皮生长因子、促分裂原活化蛋白激酶、JNK/P38、Fc epsilon RI和花生四烯酸代谢等。基于这些发现,我们构建了阿司匹林的靶点-细胞效应相互作用网络。这个网络可以很好地解释阿司匹林具有的抗炎症以及预防心血管疾病和癌症等多种医学疗效。随着这部分论文的完成,我们已经建立了识别小分子药物作用靶标和分子通路的通用平台。可以用来寻找其他小分子的分子靶标。
3.识别和比较人类PPI界面上的可药性结合口袋
识别和比较在人类PPI界面上的可药性结合口袋,为发现潜在药物新靶标提供重要结构基础。PPIs是生物体内最复杂、最具多样性和最具调控重要性的一类相互作用。最近十年,PPIs已经成为新颖治疗中具有吸引力的分子靶标。本研究我们识别出了1731个存在人类PPI复合物结合界面上的可药性结合位点。通过对所有人类PPI复合物结合界面的可药性结合口袋的系统识别以及相关特征研究,产生了很多以前未知的有趣发现：1.在有结合界面的蛋白之中有接近一半的蛋白是具有可药性结合口袋;2.具有可药性结合口袋的蛋白的功能大都集中在各类疾病的分子通路上：3.较整个结合界面而言,可药性结合口袋的所有疏水性氨基酸的出现频率都增加了,疏水性的增加有利于小分子的结合；
4.同聚体和异聚体蛋白相互作用的差异,同聚体相互作用的蛋白双方同时具有可药性结合口袋的比例远大于异聚体相互作用；5.构建了一个可药性结合口袋的相似性网络,可以用来指导我们设计多靶标药物。这些发现对于研发靶向相互作用的药物以及多靶标药物都有实际意义。
Structural Bioinformatics, as a branch of bioinformatics, is concerned about the expression, storage, access and analysis of the structural information at atomic and sub-cellular space size. In recent years, researchers have paid more attention to the applications of structural bioinformatics in drug development. Structural bioinformatics can be applied to almost all aspects of drug discovery, such as the evaluation of target assessment, virtual screening and lead optimization. In this paper, we reviewed the research framework of structural bioinformatics, given the practical databases and tools for structural bioinformatics and elaborated the important role of structural bioinformatics in drug discovery. My research mainly focused on the use of structural bioinformatics methods to promote drug discovery and development. The following three relatively independent works were carried out:
1. Research on the new method of generating pharmacophore model for GPCRs
G-protein coupled receptors (GPCRs) are the most prominent therapeutic target family characterized by seven conserved transmembrane (7TM) α-helical fold. With the aim to speed up the process of GPCR-based drug discovery, by studying the advantages and disadvantages of the existing methods and data mining from GPCR structures, we developed a new method, Pharm-Map-Pick, which can rapidly generate pharmacophore models for GPCR family. Our method mainly comprises three steps:constructing a library for key residues and pharmacophore features revealed from complex structures, mapping these pharmacophore features to a given GPCR and picking appropriate features to generate a final pharmacophore model. The results show that our method neither require a crystal structure nor ligand information. These models can accurately predict the binding mode of GPCR-ligand and has a high enrichment factor, thus can be directly used for virtual screening. We also built a world's first database contains pharmacophore modes for all human GPCRs. This method and the resulting pharmacophore models will greatly contribute to the GPCR-based basic research and drug discovery.
2. Genome-wide Prediction of Targets for Aspirin
Besides to anti-inflammatory, analgesic, and anti-pyretic properties, aspirin is used for the prevention of cardiovascular disease and various types of cancer. The multiple activities of aspirin cannot be attributed wholly to a single target and likely involve several molecular targets and pathways. In this study, we combined structural bioinformatics and systems biology approaches to predict potential targets and molecular pathways of aspirin in genome-wide. Firstly, using the tool of protein local structure detection and comparison together with molecular docking and binding free energy calculations, we identified21potential targets of aspirin. Further systems biology analysis of these targets, we found that aspirin participates in multiple molecular pathways, such as vascular endothelial growth factor, mitogen-activated protein kinase, JNK/p38, Fc epsilon RI and arachidonic acid metabolism. Based on these findings, we constructed a target of aspirin-cell effect interaction network. This network can explain a variety of medical efficacy of aspirin, which is used for anti-inflammatory as well as the prevention of cardiovascular disease and cancer. With the finish of this work, we have established a common platform to identify targets for small-molecule drugs. It can be used to find molecular targets for other small molecules.
3. Identification and comparison of the druggable pocket at the interface of human PPI
In the last decade, PPIs have become the attractive molecular target for novel therapy. In this study, we identified1731druggable pocket at the interface of human PPI. Identification and comparison of these druggable pockets resulted in a lot of previously unknown interesting findings:1. Nearly half of the proteins have druggable pockets at the interface of human PPI;2. These proteins are enriched in the molecular pathways of various diseases;3. Compared to the protein binding interface, the frequencies of hydrophobic amino acids in the druggable pockets are increased;4. The partners of homo-PPI tend to have druggable pockets simultaneously. However, it is exactly the opposite for the partners of hetero-PPI;5. The druggable pocket similarity network built here can be used to guide the design of multi-target drugs. These findings have practical significance for discovery of the drug targeting protein interactions as well as the multi-target drugs.
引文
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