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Long-Range Changes in Neurolysin Dynamics Upon Inhibitor Binding

机译:抑制剂结合后神经蛋白动力学的远程变化

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Crystal structures of neurolysin, a zinc metallopeptidase, do not show a significant conformational change upon the binding of an allosteric inhibitor. Neurolysin has a deep channel where it hydrolyzes a short neuropeptide neurotensin to create inactive fragments and thus controls its level in the tissue. Neurolysin is of interest as a therapeutic target since changes in neurotensin level have been implicated in cardiovascular disorders, neurological disorders, and cancer, and inhibitors of neurolysin have been developed. An understanding of the dynamical and structural differences between apo and inhibitor-bound neurolysin will aid in further design of potent inhibitors and activators. For this purpose, we performed several molecular dynamics (MD) simulations for both apo and inhibitor-bound neurolysin. A machine learning method (Linear Discriminant Analysis) is applied to reveal differences between the apo and inhibitor-bound ensembles in an automated way, and large differences are observed on residues that are far from both the active site and the inhibitor binding site. The effects of inhibitor binding on the collective motions of neurolysin are extensively analyzed and compared using both Principal Component Analysis and Elastic Network Model calculations. We find that inhibitor binding induces additional low frequency motions that are not observed in the apo form. ENM also reveals changes in inter- and intradomain communication upon binding. Furthermore, differences are observed in the inhibitor-bound neurolysin contact network that are far from the active site, revealing long-range allosteric behavior. This study also provides insight into the allosteric modulation of other neuropeptidases with similar folds.
机译:锌金属肽酶,锌金属肽酶的晶体结构不显示颠覆抑制剂的结合作用的显着构象变化。神经胶质素具有深的通道,在那里它水解短的神经肽神经腺苷以产生无活性片段,从而控制其在组织中的水平。神经溶素作为治疗靶标的感兴趣,因为神经调素水平的变化涉及心血管障碍,神经紊乱和癌症,并且已经开发了神经蛋白的抑制剂。理解APO和抑制剂的神经胶质素之间的动态和结构差异将有助于进一步设计有效抑制剂和活化剂。为此目的,我们对APO和抑制剂结合的神经蛋白进行了几种分子动力学(MD)模拟。应用机器学习方法(线性判别分析)以揭示以自动化方式揭示APO和抑制剂的含量差异,并且在远离活性位点和抑制剂结合位点的残基上观察到大的差异。利用主成分分析和弹性网络模型计算,广泛分析并比较了抑制剂对神经溶血素集体运动的影响。我们发现抑制剂结合诱导在APO形式中未观察到的额外低频运动。 enm还揭示了在结合时涉及和发布室内通信的变化。此外,在远离活性部位的抑制剂结合的神经胶质蛋白接触网络中观察到差异,揭示了远程变形行为。本研究还提供了对具有相似折叠的其他神经肽酶的变构调节的洞察力。

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