The Application of MD Simulation to Lead Identification, Vaccine Design, and Structural Studies in Combat against Leishmaniasis - A Review.

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-01-01 DOI:10.2174/1389557523666230901105231
Saravanan Vijayakumar, Lukkani Laxman Kumar, Subhomoi Borkotoky, Ayaluru Murali
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Abstract

Drug discovery, vaccine design, and protein interaction studies are rapidly moving toward the routine use of molecular dynamics simulations (MDS) and related methods. As a result of MDS, it is possible to gain insights into the dynamics and function of identified drug targets, antibody-antigen interactions, potential vaccine candidates, intrinsically disordered proteins, and essential proteins. The MDS appears to be used in all possible ways in combating diseases such as cancer, however, it has not been well documented as to how effectively it is applied to infectious diseases such as Leishmaniasis. As a result, this review aims to survey the application of MDS in combating leishmaniasis. We have systematically collected articles that illustrate the implementation of MDS in drug discovery, vaccine development, and structural studies related to Leishmaniasis. Of all the articles reviewed, we identified that only a limited number of studies focused on the development of vaccines against Leishmaniasis through MDS. Also, the PCA and FEL studies were not carried out in most of the studies. These two were globally accepted utilities to understand the conformational changes and hence it is recommended that this analysis should be taken up in similar approaches in the future.

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将 MD 模拟应用于抗击利什曼病的先导物鉴定、疫苗设计和结构研究 - 综述。
药物发现、疫苗设计和蛋白质相互作用研究正迅速朝着常规使用分子动力学模拟(MDS)和相关方法的方向发展。通过分子动力学模拟,可以深入了解已确定的药物靶点、抗体-抗原相互作用、潜在候选疫苗、内在无序蛋白和必需蛋白的动力学和功能。MDS似乎被用于以各种可能的方式对抗癌症等疾病,然而,它在利什曼病等传染病中的应用效果如何,还没有很好的记录。因此,本综述旨在调查 MDS 在防治利什曼病方面的应用情况。我们系统地收集了一些文章,这些文章说明了在与利什曼病有关的药物发现、疫苗开发和结构研究中应用 MDS 的情况。我们发现,在所有审查过的文章中,只有少数研究侧重于通过 MDS 开发利什曼病疫苗。此外,大多数研究都没有进行 PCA 和 FEL 研究。这两项研究是全球公认的了解构象变化的实用工具,因此建议今后在类似的方法中采用这两项分析。
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CiteScore
7.20
自引率
4.30%
发文量
567
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