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基于 FMO 的达芦那韦类似物设计作为 HIV-1 蛋白酶抑制剂。

FMO-guided design of darunavir analogs as HIV-1 protease inhibitors.

机构信息

Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, 10330, Thailand.

Center of Excellence in Structural and Computational Biology, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.

出版信息

Sci Rep. 2024 Feb 13;14(1):3639. doi: 10.1038/s41598-024-53940-1.

Abstract

The prevalence of HIV-1 infection continues to pose a significant global public health issue, highlighting the need for antiretroviral drugs that target viral proteins to reduce viral replication. One such target is HIV-1 protease (PR), responsible for cleaving viral polyproteins, leading to the maturation of viral proteins. While darunavir (DRV) is a potent HIV-1 PR inhibitor, drug resistance can arise due to mutations in HIV-1 PR. To address this issue, we developed a novel approach using the fragment molecular orbital (FMO) method and structure-based drug design to create DRV analogs. Using combinatorial programming, we generated novel analogs freely accessible via an on-the-cloud mode implemented in Google Colab, Combined Analog generator Tool (CAT). The designed analogs underwent cascade screening through molecular docking with HIV-1 PR wild-type and major mutations at the active site. Molecular dynamics (MD) simulations confirmed the assess ligand binding and susceptibility of screened designed analogs. Our findings indicate that the three designed analogs guided by FMO, 19-0-14-3, 19-8-10-0, and 19-8-14-3, are superior to DRV and have the potential to serve as efficient PR inhibitors. These findings demonstrate the effectiveness of our approach and its potential to be used in further studies for developing new antiretroviral drugs.

摘要

HIV-1 感染的流行继续构成重大的全球公共卫生问题,凸显了需要针对病毒蛋白的抗逆转录病毒药物来降低病毒复制。HIV-1 蛋白酶 (PR) 就是这样一个目标,它负责切割病毒多蛋白,导致病毒蛋白的成熟。虽然达芦那韦 (DRV) 是一种有效的 HIV-1 PR 抑制剂,但由于 HIV-1 PR 中的突变,可能会出现耐药性。为了解决这个问题,我们使用片段分子轨道 (FMO) 方法和基于结构的药物设计开发了一种新方法来创建 DRV 类似物。使用组合编程,我们通过在 Google Colab 中实现的云端模式(即组合模拟生成器工具 (CAT))生成了新型类似物。设计的类似物通过分子对接与 HIV-1 PR 野生型和主要突变进行级联筛选在活性部位。分子动力学 (MD) 模拟证实了评估配体结合和筛选设计类似物的敏感性。我们的研究结果表明,由 FMO 指导的三种设计类似物 19-0-14-3、19-8-10-0 和 19-8-14-3,优于 DRV,有可能成为有效的 PR 抑制剂。这些发现表明了我们方法的有效性及其在进一步开发新的抗逆转录病毒药物的研究中的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b9d/10864397/041dcd45c511/41598_2024_53940_Fig1_HTML.jpg

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