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机器学习在固有无序蛋白质中因磷酸化而导致的微妙构象变化。

Machine Learning Subtle Conformational Change due to Phosphorylation in Intrinsically Disordered Proteins.

机构信息

Tata Institute of Fundamental Research, Hyderabad 500046, India.

出版信息

J Phys Chem B. 2023 Nov 9;127(44):9433-9449. doi: 10.1021/acs.jpcb.3c05136. Epub 2023 Oct 31.

Abstract

Phosphorylation of intrinsically disordered proteins/regions (IDPs/IDRs) has a profound effect in biological functions such as cell signaling, protein folding or unfolding, and long-range allosteric effects. However, here we focus on two IDPs, namely 83-residue IDR transcription factor Ash1 and 92-residue long N-terminal region of CDK inhibitor Sic1 protein, found in , for which experimental measurements of average conformational properties, namely, radius of gyration and structure factor, indicate negligible changes upon phosphorylation. Here, we show that a judicious dissection of conformational ensemble via combination of unsupervised machine learning and extensive molecular dynamics (MD) trajectories can highlight key differences and similarities among the phosphorylated and wild-type IDP. In particular, we develop Markov state model (MSM) using the latent-space dimensions of an autoencoder, trained using multi-microsecond long MD simulation trajectories. Examination of structural changes among the states, prior to and upon phosphorylation, captured several similarities and differences in their backbone contact maps, secondary structure, and torsion angles. Hydrogen bonding analysis revealed that phosphorylation not only increases the number of hydrogen bonds but also switches the pattern of hydrogen bonding between the backbone and side chain atoms with the phosphorylated residues. We also observe that although phosphorylation introduces salt bridges, there is a loss of the cation-π interaction. Phosphorylation also improved the probability for long-range hydrophobic contacts and also enhanced interaction with water molecules and improved the local structure of water as evident from the geometric order parameters. The observations on these machine-learnt states gave important insights, as it would otherwise be difficult to determine experimentally which is important, if we were to understand the role of phosphorylation of IDPs in their biological functions.

摘要

磷酸化无序蛋白质/区域(IDPs/IDRs)对细胞信号转导、蛋白质折叠或展开以及远程别构效应等生物功能有深远影响。然而,这里我们关注的是两种 IDP,即 83 个残基的 IDR 转录因子 Ash1 和 92 个残基长的 CDK 抑制剂 Sic1 蛋白的 N 端区域,在 中发现了它们,对其平均构象特性(即回转半径和结构因子)的实验测量表明,磷酸化后几乎没有变化。在这里,我们通过无监督机器学习和广泛的分子动力学(MD)轨迹的组合,展示了对构象集合的巧妙剖析,可以突出磷酸化和野生型 IDP 之间的关键差异和相似之处。特别是,我们使用自动编码器的潜在空间维度开发了马尔可夫状态模型(MSM),该自动编码器使用多微秒长的 MD 模拟轨迹进行训练。在磷酸化前后,检查状态之间的结构变化,捕捉到它们的骨架接触图、二级结构和扭转角之间的一些相似和不同。氢键分析表明,磷酸化不仅增加了氢键的数量,而且还改变了磷酸化残基与骨架和侧链原子之间的氢键模式。我们还观察到,尽管磷酸化引入了盐桥,但阳离子-π 相互作用会丢失。磷酸化还提高了远程疏水性接触的概率,并与水分子相互作用增强,改善了局部结构,这从几何有序参数中可以明显看出。这些基于机器学习的状态的观察提供了重要的见解,如果要了解 IDP 磷酸化在其生物学功能中的作用,否则从实验上很难确定哪些是重要的。

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