인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Rhomboid intramembrane proteases (RIPs) are a family of serine hydrolases distinguished by their membrane-embedded active sites and proposed involvement in metabolic and neurodegenerative diseases as well as cancer. Despite the ubiquity of these enzymes in all of kingdoms of life, our knowledge of the enzymatic functions of RIPs is still quite limited. Consequently, the development of suitable substrate-based activity assays for these proteases has proved challenging due to the lack of known substrates. Activity-based protein profiling (ABPP) represents an alternative approach for studying the activity of these enzymes; in these assays, a small-molecule probe is used to engage the active enzyme by binding covalently to its active site. Here, we present our progress on the development of ABPP assays for the human RIPs. We expressed all five human RIPs (hRHBDL1, hRHBDL2, hRHBDL3, hRHBDL4, and hPARL), along with their inactive mutants, in HEK293T cells. We then screened a library of small molecule probes, including fluorophosphonates, β-lactams, and benzoxazinones, for their ability to engage the active enzymes, but not their inactive mutants, in a complex proteome. Probe labeling was visualized by performing the azide-alkyne Huisgen cycloaddition reaction with a functionalized rhodamine on probe-treated proteome followed by gel electrophoresis (SDS-PAGE). Through these efforts, we have identified activity-based probes for several of the human RIPs and observed differences in probe engagement in lysate versus intact cells. Our findings provide encouraging precedent for the development of suitable ABPP assays for each of the human RIPs as well as insight into the types of chemical scaffolds that could be used to generate inhibitors for these enzymes. This work has been supported through a grant from the National Institute of General Medical Sciences (R15GM146210).
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.