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通过消减基因组分析鉴定新的药物靶点及其通过分子对接和分子动力学模拟得到的抑制剂。

New drug target identification in by subtractive genome analysis and their inhibitors through molecular docking and molecular dynamics simulations.

作者信息

Alotaibi Bader S, Ajmal Amar, Hakami Mohammed Ageeli, Mahmood Arif, Wadood Abdul, Hu Junjian

机构信息

Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Al-Quwayiyah, Shaqra Univesity, Riyadh, Saudi Arabia.

Department of Biochemistry, Computational Medicinal Chemistry Laboratory, UCSS, Abdul Wali Khan University, Mardan, Pakistan.

出版信息

Heliyon. 2023 Jun 26;9(7):e17650. doi: 10.1016/j.heliyon.2023.e17650. eCollection 2023 Jul.

Abstract

is a rod shape, Gram-negative bacterium that causes sepsis (with a greater than 50% mortality rate), necrotizing fasciitis, gastroenteritis, skin, and soft tissue infection, wound infection, peritonitis, meningitis, pneumonia, keratitis, and arthritis. Based on pathogenicity is categorized into three biotypes. Type 1 and type 3 cause diseases in humans while biotype 2 causes diseases in eel and fish. Due to indiscriminate use of antibiotics has developed resistance to many antibiotics so curing is dramatically a challenge. is resistant to cefazolin, streptomycin, tetracycline, aztreonam, tobramycin, cefepime, and gentamycin. Subtractive genome analysis is the most effective method for drug target identification. The method is based on the subtraction of homologous proteins from both pathogen and host. By this process set of proteins present only in the pathogen and perform essential functions in the pathogen can be identified. The entire proteome of strain ATCC 27562 was reduced step by step to a single protein predicted as the drug target. AlphaFold2 is one of the applications of deep learning algorithms in biomedicine and is correctly considered the game changer in the field of structural biology. Accuracy and speed are the major strength of AlphaFold2. In the PDB database, the crystal structure of the predicted drug target was not present, therefore the Colab notebook was used to predict the 3D structure by the AlphaFold2, and subsequently, the predicted model was validated. Potent inhibitors against the new target were predicted by virtual screening and molecular docking study. The most stable compound ZINC01318774 tightly attaches to the binding pocket of bisphosphoglycerate-independent phosphoglycerate mutase. The time-dependent molecular dynamics simulation revealed compound ZINC01318774 was superior as compared to the standard drug tetracycline in terms of stability. The availability of strain ATCC 27562 has allowed identification of drug target which will provide a base for the discovery of specific therapeutic targets against

摘要

是一种杆状革兰氏阴性菌,可导致败血症(死亡率超过50%)、坏死性筋膜炎、肠胃炎、皮肤和软组织感染、伤口感染、腹膜炎、脑膜炎、肺炎、角膜炎和关节炎。基于致病性可分为三种生物型。1型和3型导致人类疾病,而生物型2导致鳗鱼和鱼类疾病。由于抗生素的滥用,它已对许多抗生素产生耐药性,因此治疗极具挑战性。它对头孢唑林、链霉素、四环素、氨曲南、妥布霉素、头孢吡肟和庆大霉素耐药。消减基因组分析是药物靶点鉴定的最有效方法。该方法基于从病原体和宿主中减去同源蛋白质。通过这个过程,可以鉴定出仅存在于病原体中并在病原体中发挥基本功能的一组蛋白质。菌株ATCC 27562的整个蛋白质组逐步减少到一个预测为药物靶点的单一蛋白质。AlphaFold2是深度学习算法在生物医学中的应用之一,被正确地认为是结构生物学领域的游戏规则改变者。准确性和速度是AlphaFold2的主要优势。在蛋白质数据银行(PDB)数据库中,不存在预测药物靶点的晶体结构,因此使用Colab笔记本通过AlphaFold2预测三维结构,随后对预测模型进行验证。通过虚拟筛选和分子对接研究预测了针对新靶点的强效抑制剂。最稳定的化合物ZINC01318774紧密附着于不依赖双磷酸甘油酸的磷酸甘油酸变位酶的结合口袋。时间依赖性分子动力学模拟显示,化合物ZINC01318774在稳定性方面优于标准药物四环素。菌株ATCC 27562的可得性使得能够鉴定药物靶点,这将为发现针对……的特异性治疗靶点提供基础

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39b9/10336522/8128c609a849/gr1.jpg

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