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从头设计基于肽的蛋白质-蛋白质抑制剂(Pep-PPIs)的方法,通过映射 PP 界面的相互作用基序和物理化学过滤:以 p25-Cdk5 介导的神经退行性疾病为例。

De Novo Rational Design of Peptide-Based Protein-Protein Inhibitors (Pep-PPIs) Approach by Mapping the Interaction Motifs of the PP Interface and Physicochemical Filtration: A Case on p25-Cdk5-Mediated Neurodegenerative Diseases.

机构信息

Molecular Bio-Computation and Drug Design Research Group, School of Health Sciences, University of KwaZulu Natal, Westville Campus, Durban, South Africa.

School of Chemistry, Dalian University of Technology, Dalian, Liaoning, China.

出版信息

J Cell Biochem. 2024 Sep;125(9):e30633. doi: 10.1002/jcb.30633. Epub 2024 Aug 15.

DOI:10.1002/jcb.30633
PMID:39148280
Abstract

Protein-protein interactions, or PPIs, are a part of every biological activity and have been linked to a number of diseases, including cancer, infectious diseases, and neurological disorders. As such, targeting PPIs is considered a strategic and vital approach in the development of new medications. Nonetheless, the wide and flat contact interface makes it difficult to find small-molecule PP inhibitors. An alternative strategy would be to use the PPI interaction motifs as building blocks for the design of peptide-based inhibitors. Herein, we designed 12-mer peptide inhibitors to target p25-inducing-cyclin-dependent kinase (Cdk5) hyperregulation, a PPI that has been shown to perpetuate neuroinflammation, which is one of the major causal implications of neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease, and frontotemporal dementia. We generated a library of 5 062 500 peptide combination sequences (PCS) derived from the interaction motif of Cdk5/p25 PP interface. The 20 amino acids were differentiated into six groups, namely, hydrophobic (aliphatic), aromatic, basic, acidic, unique, and polar uncharged, on the basis of their physiochemical properties. To preserve the interaction motif necessary for ideal binding, de novo modeling of all possible peptide sequence substitutions was considered. A set of filters, backed by the Support Vector Machine (SVM) algorithm, was then used to create a shortlisted custom peptide library that met specific bioavailability, toxicity, and therapeutic relevance, leading to a refined library of 15 PCS. A greedy algorithm and coarse-grained force field were used to predict peptide structure and folding before subsequent modeling studies. Molecular docking was performed to estimate the relative binding affinities, and out of the top hits, Pep15 was subjected to molecular dynamics simulations and binding free-energy calculations in comparison to a known peptide inhibitor with experimental data (template peptide). Interestingly, the identified peptide through our protocol, Pep15, was found to show a significantly higher binding affinity than the reference template peptide (-48.10 ± 0.23 kcal/mol and -17.53 ± 0.27 kcal/mol, respectively). In comparison to the template peptide, Pep15 was found to possess a more compact and buried surface area, tighter binding landscape, and reduced conformational variability, leading to enhanced structural and kinetic stability of the Cdk5/p25 complex. Notably, both peptide inhibitors were found to have a minimal impact on the architectural integrity of the Cdk5/p25 secondary structure. Herein, we propose Pep15 as a novel and potentially disruptive peptide drug for Cdk5/p25-mediated neurodegenerative phenotypes that require further clinical investigation. The systematic protocol and findings of this report would serve as a valuable tool in the identification of critical PPI interface reactive residues, designing of analogs, and identification of more potent peptide-based PPI inhibitors.

摘要

蛋白质-蛋白质相互作用(PPIs)是每一种生物活性的一部分,与许多疾病有关,包括癌症、传染病和神经紊乱。因此,靶向 PPI 被认为是开发新药物的一种战略性和重要方法。然而,广泛而平坦的接触界面使得寻找小分子 PP 抑制剂变得困难。另一种策略是将 PPI 相互作用基序用作基于肽的抑制剂设计的构建块。在此,我们设计了 12 个氨基酸的肽抑制剂来靶向 p25 诱导的细胞周期蛋白依赖性激酶(Cdk5)过度调节,该 PPI 已被证明会持续引发神经炎症,这是阿尔茨海默病、帕金森病和额颞叶痴呆等神经退行性疾病的主要病因之一。我们生成了一个由 Cdk5/p25 PP 界面相互作用基序衍生的 5062500 个肽组合序列(PCS)文库。根据其理化性质,将 20 个氨基酸分为六个组,即疏水性(脂肪族)、芳香族、碱性、酸性、独特和极性非带电。为了保留理想结合所需的相互作用基序,考虑了对所有可能的肽序列取代进行从头建模。然后,使用一组基于支持向量机(SVM)算法的过滤器来创建符合特定生物利用度、毒性和治疗相关性的精选定制肽文库,从而得到 15 个 PCS 的精炼文库。使用贪婪算法和粗粒度力场来预测肽结构和折叠,然后进行后续建模研究。进行分子对接以估计相对结合亲和力,并且在 top hits 中,Pep15 进行了分子动力学模拟和结合自由能计算,与具有实验数据(模板肽)的已知肽抑制剂进行比较。有趣的是,通过我们的方案鉴定的肽,Pep15,被发现表现出比参考模板肽更高的结合亲和力(分别为-48.10 ± 0.23 kcal/mol 和-17.53 ± 0.27 kcal/mol)。与模板肽相比,Pep15 具有更紧凑和埋藏的表面积、更紧密的结合景观和更低的构象可变性,从而增强了 Cdk5/p25 复合物的结构和动力学稳定性。值得注意的是,两种肽抑制剂都被发现对 Cdk5/p25 二级结构的结构完整性几乎没有影响。在此,我们提出 Pep15 作为一种新型的、潜在的、用于 Cdk5/p25 介导的神经退行性表型的肽类药物,这需要进一步的临床研究。本报告中的系统方案和发现将成为识别关键 PPI 界面反应性残基、设计类似物和鉴定更有效的基于肽的 PPI 抑制剂的有价值的工具。

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