Obaseki Ikponwmosa, Ndolo Chioma C, Adedeji Ayodeji A, Popoola Hannah O, Kravats Andrea N
Department of Chemistry & Biochemistry, Miami University, Oxford, OH 45056, USA.
Department of Chemistry & Biochemistry, Miami University, Oxford, OH 45056, USA; Cell, Molecular, and Structural Biology Graduate Program, Miami University, Oxford, OH 45056, USA.
Trends Pharmacol Sci. 2025 May;46(5):453-467. doi: 10.1016/j.tips.2025.03.004. Epub 2025 Apr 18.
Binding immunoglobulin protein (BiP) and glucose-regulated protein 94 (Grp94) are endoplasmic reticulum (ER)-localized molecular chaperones that ensure proper protein folding and maintain protein homeostasis. However, overexpression of these chaperones during ER stress can contribute to disease progression in numerous pathologies. Although these chaperones represent promising therapeutic targets, their inhibition has been challenged by gaps in understanding of targetable chaperone features and their complex biology. To overcome these challenges, a new assay has been developed to selectively target BiP, and compounds that exploit subtle conformational changes of Grp94 have been designed. This review summarizes recent advances in elucidating structural and functional dynamics of BiP and Grp94. We explore leveraging this information to develop novel therapeutic interventions. Finally, given the recent advances in computing, we discuss how machine learning methods can be used to accelerate drug discovery efforts.
结合免疫球蛋白蛋白(BiP)和葡萄糖调节蛋白94(Grp94)是内质网(ER)定位的分子伴侣,可确保蛋白质正确折叠并维持蛋白质稳态。然而,在内质网应激期间这些伴侣蛋白的过度表达可导致多种病理状态下的疾病进展。尽管这些伴侣蛋白是很有前景的治疗靶点,但对可靶向伴侣蛋白特征及其复杂生物学的理解存在差距,这对它们的抑制提出了挑战。为了克服这些挑战,已开发出一种新的检测方法来选择性靶向BiP,并设计了利用Grp94细微构象变化的化合物。本综述总结了在阐明BiP和Grp94的结构和功能动力学方面的最新进展。我们探索利用这些信息来开发新的治疗干预措施。最后,鉴于计算方面的最新进展,我们讨论如何使用机器学习方法来加速药物发现工作。