通过TRADD-TRAF2复合物界面评估筛选CVD的肽计量学。

In silico pharmacology Pub Date : 2023-10-27 eCollection Date: 2023-01-01 DOI:10.1007/s40203-023-00166-0
A Manikandan, S Jeevitha, Laharika Vusa
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引用次数: 0

摘要

本研究的主要目的是筛选和开发治疗动脉粥样硬化(AS)的类肽药物,动脉粥样硬化是一种心血管疾病。从TRADD(肿瘤坏死因子受体1型相关死亡结构域蛋白)和TRAF2(TNF受体相关因子2)复合物的蛋白质-蛋白质相互作用界面获得拟肽。TRADD-TRAF2相互作用在AS发病机制中至关重要,因为它有助于激活NF-κB的一系列信号转导子。从概念上讲,触发的NF-κB使诱导型一氧化氮合酶(iNOS)合成大量一氧化氮(NO),这促进了AS的进展。检测的TRADD-TRAF2复合物(PDB ID:1F3V)及其相互作用细节显示,TRADD的序列范围W11-G165与TRAF2高度相互作用。选择W11-G165的序列范围用于在硅胶中设计和制备抑制肽。所选择的序列用丙氨酸扫描法突变以具有一系列抑制肽。在不同的计算机工具的帮助下,前三个,即MIP11-25L、MIP131-143h和MIP149-164m-肽显示出与TRAF2的关键残基的最佳相互作用。因此,这三种肽被用于使用肽模拟物虚拟筛选工具pepMMsMIMIC生成肽模拟物。通过使用分子对接工具和MD(分子动力学)模拟,鉴定并检索了大约600种拟肽,以进行进一步筛选。应用密度泛函理论(DFT)和ADMET预测来验证筛选的拟肽药物的可药用性。在结果中,结合能值为-9.6千卡/摩尔和- 9.1 kcal/mol被筛选为最佳,并被建议用于进一步的临床前研究。
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Peptidomimetics for CVD screened via TRADD-TRAF2 complex interface assessments.

The main aim of this study is to screen and develop Peptidomimetics to treat atherosclerosis (AS) which is a Cardio Vascular Disease (CVD). Peptidomimetics were obtained from the protein-protein interaction interface of TRADD (Tumor necrosis factor receptor type 1-associated DEATH domain protein) and TRAF2 (TNF receptor-associated factor 2) complex. TRADD-TRAF2 interaction is critical in AS pathogenesis since it assists a series of signal transducers that activate NF-κB. Conceptually, the triggered NF-κB makes an extensive amount of nitric oxide (NO) synthesized by inducible nitric oxide synthase (iNOS), which boons the progress of AS. The examined TRADD-TRAF2 complex (PDB ID: 1F3V) and its interaction details revealed that the sequence range W11-G165 of TRADD highly interacts with TRAF2. The sequence range W11-G165 was selected for the design and preparation of the inhibitory peptide in silico. The selected sequence was mutated with the alanine scanning method to have a range of inhibitory peptides. With the help of different in silico tools, the top three, namely MIP11-25 L, MIP131-143 h, and MIP149-164 m peptides showed the best interaction with the critical residues of TRAF2. Thus, these three peptides were used for generating peptidomimetics using pepMMsMIMIC, a peptidomimetics virtual screening tool. Around 600 peptidomimetics were identified & and retrieved for further screening by employing molecular docking tools and MD (Molecular Dynamics) simulations. Density Functional Theory (DFT) and ADMET predictions were applied to validate the screened peptidomimetics druggability. In the results, peptidomimic compounds MMs03918858 and MMs03927281 with binding energy values of -9.6 kcal/mol and - 9.1 kcal/mol respectively were screened as the best and are proposed for further pre-clinical studies.

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