蛋白激酶小分子抑制剂选择性及其JAK2激酶调控机制的计算化学研究
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摘要
基于物理学原理的计算化学,其方法和技术可广泛应用于生物分子体系的模拟研究。在蛋白质功能和结构的研究中,可用于蛋白质三维结构的同源建模,蛋白质-蛋白质复合物的分子对接,和分子动力学模拟蛋白的柔性及功能分析。在计算机辅助药物设计中,基于结构的药物虚拟筛选已经被证明是非常有效的方法。在本论文的研究工作中,我们针对两个很有意义的生物学问题开发了相应的计算方法并开展了研究,一是开发了一种基于物理学原理的预测激酶抑制剂选择性的算法；二是设计了一套基于结构信息的从头预测蛋白质-蛋白质复合物的对接策略。
如何合理设计具有选择性的激酶小分子抑制剂是激酶药物开发中一个具有挑战性但是很重要的问题。我们开发的算法主要包括以下步骤：1.选择具有代表性的处于活性构象的激酶晶体结构；2.用比较对接的方法构建配体受体复合物；3.在配体存在下搜索结合位点的氨基酸构象；4.用MM-GB/SA的打分函数评估最终的配体和受体之间的结合自由能。我们首先回顾性地评价了这种算法在预测17个常见的激酶小分子抑制剂和143个激酶选择性的表现。两个相关性分析参数,排名前20%的富集因子(EF20)平均值2.81和预测指数(PI)平均值0.35表明我们的算法能够预测一个激酶小分子抑制剂的选择性,尤其是对有选择性好的抑制剂具有更好的预测能力。然后我们将这种算法应用到化疗药物米托蒽醌(MX)可能结合的激酶靶点中。根据预测结果,我们取排名前五位的激酶进行体外活性检测。结果表明米托蒽醌对PIM1激酶具有低纳摩尔级的抑制活性。同时,我们发现米托蒽醌也能在肿瘤细胞中抑制PIM1底物的磷酸化。我们进一步解析了米托蒽醌和PIM1的复合物晶体结构,从而揭示了米托蒽醌抗癌机制的一种新的结构基础和作用机制。虽然米托蒽醌最初被认为是通过嵌入DNA和抑制Ⅱ型拓扑异构酶起作用的,我们的研究结果第一次表明米托蒽醌还可能通过抑制PIM1激酶发挥其抗癌疗效和特异性。
蛋白激酶在细胞内行使功能时是被严格调控的,包括激酶自身二聚化或寡聚化,其它结构域或蛋白的相互作用。例如JAK2激酶结构域(JH1结构域)就受到相邻的假激酶结构域(JH2结构域)的调控,但是具体两个结构域之间的作用机理依然不清楚。我们首先采用MuInf,一种能够鉴定蛋白扭转角相关性位点的算法来预测JAK2假激酶结构域和激酶结构域可能的变构调节位点,然后将这些变构调节位点作为蛋白质与蛋白质相互作用的界面来构建起始模型,随后我们用蛋白质对接和分子动力学模拟来更合理地修正我们的结构模型。我们构建的JAK2复合物模型揭示了JAK2假激酶结构域自我抑制的可能的一种作用机制,它通过重要的疏水性作用和广泛的静电相互作用将激酶结构域中的活化茎环限制在非活性状态,同时也阻碍αC螺旋的转动。进一步的分子动力学模拟表明在大多数骨髓增生性肿瘤患者中发现的JAK2的一个获得性突变(V617F),可能通过减弱假激酶结构域的这种抑制作用来激动JAK2。重要的是我们用的假激酶结构域在结合界面上的氨基酸的突变实验证实,这些重要氨基酸的突变确实能够激活JAK2激酶活性,并能促进用这些突变体转化的BaF3/EpoR细胞增殖。尽管还可能存在其它的假激酶结构域介导的JAK2激酶的调控机制,我们构建的JAK2假激酶和激酶复合物模型提供了一种合理的假说,这有助于进一步设计实验来阐明假激酶结构域在正常和病理中的JAK2的调控机理。同时我们这种逐级蛋白对接的策略能够更广泛地应用到研究其它蛋白质-蛋白质相互作用的体系。
The theory of computational chemisty is on the basis of physics, and the theoretical computational technologies can be widely used to model and simulate the biomolecule systems. In the protein function and structure study, the computational approaches can provide a series of tools, including the homology modeling for protein three-dimensional structure prediction, the protein-protein docking algorithm for complex structures prediction, and the molecular dynamics (MD) simulations for protein flexibility and function analysis. In the field of computational drug design, the structure-based virtual screening has been widely applied. In this thesis, two efficient computational protocols were developed to address two interesting biological questions, individually. One is a physics-based approach to computationally modeling the kinase inhibitor selectivity profile. The other is a novel informatics-guided protein-protein docking strategy for ab initio modeling protein complex structures.
The rational design of selective kinase inhibitors is a great challenge in the kinase drug discovery. We described a physics-based approach to computationally modeling the kinase inhibitor selectivity profile. Briefly, our computational modeling procedure consists the following steps:1. Selecting the representative kinase crystal structure in an active conformation;2. Predicting the ligand binding pose using comparative docking;3. Sampling the binding site conformation with the presence of the docked ligand;4. Scoring the resulted complex structure using the MM-GB/SA scoring function. We retrospectively assessed this protocol by computing the binding profiles of17well-known kinase inhibitors against143kinases. The averaged EF20value of2.81and the averaged PI value of0.35indicated a reasonable prediction performance of our computational approach in ranking order of the kinase targets for a given inhibitor. This automatic workflow allowed rapid evaluation of compounds of interest against the entire structural kinome. Next, we predicted the binding profile of the chemotherapy drug mitoxantrone (MX), and chose the predicted top five kinase targets for in vitro kinase assays. Remarkably, mitoxantrone was shown to possess low nanomolar inhibitory activity against PIM1kinase and to inhibit the PIM1-mediated subtrates phosphorylation in cancer-cells. We further determined the crystal complex structure of PIM1bound with mitoxantrone, which revealed the structural and mechanistic basis for a novel mode of PIM1inhibition. Although mitoxantrone's mechanism of action had been originally thought to act through DNA intercalation and type Ⅱ topoisomerase inhibition, we provided for the first time evidence that PIM1kinase inhibition may contribute to mitoxantrone's therapeutic efficacy and specificity.
The function of protein kinases are highly regulated in the cell by their dimerization, supramolecular assemblies, other regulatory domains or interacting proteins. For example, the pseudokinase domain (JH2) of Janus Kinase2(JAK2) regulates the activity of its adjacent kinase domain (JH1) althought the exact molecular mechanisms are not fully understood. We first applied MuInf algorithm, one method can identify protein sites exhibited correlated torsional motions, to predict the potential protein interface. The primary models from MutInf predicted interfaces were refined by a hierarchical protein-protein docking and molecular dynamics simulations refinement procedure to systematically improve the quality of these complex models. We applied this novel strategy to model the pseudokinase domain-kinase domain complex structure of the JAK2. Based on this model, a detailed JAK2JH2-mediated auto-inhibition mechanism was proposed, where JH2traped the activation loop of JH1in an inactive conformation and blocked the movement of kinase a C helix through critical hydrophobic contacts and extensive electrostatic interactions. These stabilizing interactions were less favorable in JAK2-V617F, a gain-of-function mutation within JH2was found in the majority of patients with myeloproliterative neoplasms. Notably, several predicted binding interfacial residues in JH2were confirmed to hyperactivate JAK2kinase activity in site-directed mutagenesis and BaF3/EpoR cell transformation studies. Although there might exist other JH2-mediated mechanisms to control JH1, our JH1-JH2structural model represented a verifiable working hypothesis for further experimental studies to elucidate the role of JH2in regulating JAK2in both normal and pathological settings. This step-wise computational strategy we devised may be easily adopted for studying novel protein-protein interactions in a general manner.
如何合理设计具有选择性的激酶小分子抑制剂是激酶药物开发中一个具有挑战性但是很重要的问题。我们开发的算法主要包括以下步骤：1.选择具有代表性的处于活性构象的激酶晶体结构；2.用比较对接的方法构建配体受体复合物；3.在配体存在下搜索结合位点的氨基酸构象；4.用MM-GB/SA的打分函数评估最终的配体和受体之间的结合自由能。我们首先回顾性地评价了这种算法在预测17个常见的激酶小分子抑制剂和143个激酶选择性的表现。两个相关性分析参数,排名前20%的富集因子(EF20)平均值2.81和预测指数(PI)平均值0.35表明我们的算法能够预测一个激酶小分子抑制剂的选择性,尤其是对有选择性好的抑制剂具有更好的预测能力。然后我们将这种算法应用到化疗药物米托蒽醌(MX)可能结合的激酶靶点中。根据预测结果,我们取排名前五位的激酶进行体外活性检测。结果表明米托蒽醌对PIM1激酶具有低纳摩尔级的抑制活性。同时,我们发现米托蒽醌也能在肿瘤细胞中抑制PIM1底物的磷酸化。我们进一步解析了米托蒽醌和PIM1的复合物晶体结构,从而揭示了米托蒽醌抗癌机制的一种新的结构基础和作用机制。虽然米托蒽醌最初被认为是通过嵌入DNA和抑制Ⅱ型拓扑异构酶起作用的,我们的研究结果第一次表明米托蒽醌还可能通过抑制PIM1激酶发挥其抗癌疗效和特异性。
蛋白激酶在细胞内行使功能时是被严格调控的,包括激酶自身二聚化或寡聚化,其它结构域或蛋白的相互作用。例如JAK2激酶结构域(JH1结构域)就受到相邻的假激酶结构域(JH2结构域)的调控,但是具体两个结构域之间的作用机理依然不清楚。我们首先采用MuInf,一种能够鉴定蛋白扭转角相关性位点的算法来预测JAK2假激酶结构域和激酶结构域可能的变构调节位点,然后将这些变构调节位点作为蛋白质与蛋白质相互作用的界面来构建起始模型,随后我们用蛋白质对接和分子动力学模拟来更合理地修正我们的结构模型。我们构建的JAK2复合物模型揭示了JAK2假激酶结构域自我抑制的可能的一种作用机制,它通过重要的疏水性作用和广泛的静电相互作用将激酶结构域中的活化茎环限制在非活性状态,同时也阻碍αC螺旋的转动。进一步的分子动力学模拟表明在大多数骨髓增生性肿瘤患者中发现的JAK2的一个获得性突变(V617F),可能通过减弱假激酶结构域的这种抑制作用来激动JAK2。重要的是我们用的假激酶结构域在结合界面上的氨基酸的突变实验证实,这些重要氨基酸的突变确实能够激活JAK2激酶活性,并能促进用这些突变体转化的BaF3/EpoR细胞增殖。尽管还可能存在其它的假激酶结构域介导的JAK2激酶的调控机制,我们构建的JAK2假激酶和激酶复合物模型提供了一种合理的假说,这有助于进一步设计实验来阐明假激酶结构域在正常和病理中的JAK2的调控机理。同时我们这种逐级蛋白对接的策略能够更广泛地应用到研究其它蛋白质-蛋白质相互作用的体系。
The theory of computational chemisty is on the basis of physics, and the theoretical computational technologies can be widely used to model and simulate the biomolecule systems. In the protein function and structure study, the computational approaches can provide a series of tools, including the homology modeling for protein three-dimensional structure prediction, the protein-protein docking algorithm for complex structures prediction, and the molecular dynamics (MD) simulations for protein flexibility and function analysis. In the field of computational drug design, the structure-based virtual screening has been widely applied. In this thesis, two efficient computational protocols were developed to address two interesting biological questions, individually. One is a physics-based approach to computationally modeling the kinase inhibitor selectivity profile. The other is a novel informatics-guided protein-protein docking strategy for ab initio modeling protein complex structures.
The rational design of selective kinase inhibitors is a great challenge in the kinase drug discovery. We described a physics-based approach to computationally modeling the kinase inhibitor selectivity profile. Briefly, our computational modeling procedure consists the following steps:1. Selecting the representative kinase crystal structure in an active conformation;2. Predicting the ligand binding pose using comparative docking;3. Sampling the binding site conformation with the presence of the docked ligand;4. Scoring the resulted complex structure using the MM-GB/SA scoring function. We retrospectively assessed this protocol by computing the binding profiles of17well-known kinase inhibitors against143kinases. The averaged EF20value of2.81and the averaged PI value of0.35indicated a reasonable prediction performance of our computational approach in ranking order of the kinase targets for a given inhibitor. This automatic workflow allowed rapid evaluation of compounds of interest against the entire structural kinome. Next, we predicted the binding profile of the chemotherapy drug mitoxantrone (MX), and chose the predicted top five kinase targets for in vitro kinase assays. Remarkably, mitoxantrone was shown to possess low nanomolar inhibitory activity against PIM1kinase and to inhibit the PIM1-mediated subtrates phosphorylation in cancer-cells. We further determined the crystal complex structure of PIM1bound with mitoxantrone, which revealed the structural and mechanistic basis for a novel mode of PIM1inhibition. Although mitoxantrone's mechanism of action had been originally thought to act through DNA intercalation and type Ⅱ topoisomerase inhibition, we provided for the first time evidence that PIM1kinase inhibition may contribute to mitoxantrone's therapeutic efficacy and specificity.
The function of protein kinases are highly regulated in the cell by their dimerization, supramolecular assemblies, other regulatory domains or interacting proteins. For example, the pseudokinase domain (JH2) of Janus Kinase2(JAK2) regulates the activity of its adjacent kinase domain (JH1) althought the exact molecular mechanisms are not fully understood. We first applied MuInf algorithm, one method can identify protein sites exhibited correlated torsional motions, to predict the potential protein interface. The primary models from MutInf predicted interfaces were refined by a hierarchical protein-protein docking and molecular dynamics simulations refinement procedure to systematically improve the quality of these complex models. We applied this novel strategy to model the pseudokinase domain-kinase domain complex structure of the JAK2. Based on this model, a detailed JAK2JH2-mediated auto-inhibition mechanism was proposed, where JH2traped the activation loop of JH1in an inactive conformation and blocked the movement of kinase a C helix through critical hydrophobic contacts and extensive electrostatic interactions. These stabilizing interactions were less favorable in JAK2-V617F, a gain-of-function mutation within JH2was found in the majority of patients with myeloproliterative neoplasms. Notably, several predicted binding interfacial residues in JH2were confirmed to hyperactivate JAK2kinase activity in site-directed mutagenesis and BaF3/EpoR cell transformation studies. Although there might exist other JH2-mediated mechanisms to control JH1, our JH1-JH2structural model represented a verifiable working hypothesis for further experimental studies to elucidate the role of JH2in regulating JAK2in both normal and pathological settings. This step-wise computational strategy we devised may be easily adopted for studying novel protein-protein interactions in a general manner.
引文
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