Computational drug design for neurosyphilis disease by targeting Phosphoglycerate Kinase in Treponema pallidum with enhanced binding affinity and reduced toxicity

利用计算机辅助药物设计,通过靶向梅毒螺旋体中的磷酸甘油酸激酶,提高结合亲和力并降低毒性,治疗神经梅毒

阅读:1

Abstract

Neurosyphilis, a severe neurological complication of syphilitic infection caused by the gram-negative spirochete Treponema pallidum poses significant challenges in treatment due to its irregular physiology and lack of efficacy in present therapeutic strategies. Here, we report a new approach to developing drug treatment that targets the enzyme phosphoglycerate kinase (PGK), an essential component of the T. pallidum glycolytic pathway. Therefore, a ligand was designed involving common neuroprotectant elements reported from literature by a computational drug design method, to increase their binding energy with lower toxicity. The calculated binding affinity of the designed ligand with PGK was analyzed by molecular docking to be - 116.68 kcal/mol. Also, interaction analysis predicted that there are 5 hydrophobic bonds and 3 hydrogen bonds present between the docked complex. Afterward, in-silico ADMET studies were conducted for the designed ligand that determined a strong pharmacological profile with good absorption, zero violation of Lipinski's rule, and non-toxic properties. DFT analysis further optimized the ligand with a HOMO/LOMO gap value of 0.01421 kcal/mol indicating higher reactivity and enhanced electronic interactions, improving ligand efficiency. Moreover, pharmacophore modeling confirmed the reactive nature of the ligand. Furthermore, MD simulations showed stability in the overall structure. The output shows that our optimized ligand has statistically better binding affinity than the currently used drug penicillin, with improved pharmacokinetic profiles. This work demonstrates the importance of ligand design for the discovery of new drugs to treat neurosyphilis.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。