A road map of cellular protein homeostasis

Profiling human protein degradome delineates cellular responses to proteasomal inhibition and reveals a feedback mechanism in regulating proteasome homeostasis

Global change in protein turnover (protein degradome) constitutes a central part of cellular responses to intrinsic or extrinsic stimuli. However, profiling protein degradome remains technically challenging. Recently, inhibition of the proteasome, e.g., by using bortezomib (BTZ), has emerged as a major chemotherapeutic strategy for treating multiple myeloma and other human malignancies, but systematic understanding of the mechanisms for BTZ drug action and tumor drug resistance is yet to be achieved. Here we developed and applied a dual-fluorescence-based Protein Turnover Assay (ProTA) to quantitatively profile global changes in human protein degradome upon BTZ-induced proteasomal inhibition. ProTA and subsequent network analyses delineate potential molecular basis for BTZ action and tumor drug resistance in BTZ chemotherapy. Finally, combined use of BTZ with drugs targeting the ProTA-identified key genes or pathways in BTZ action reduced BTZ resistance in multiple myeloma cells. Remarkably, BTZ stabilizes proteasome subunit PSMC1 and proteasome assembly factor PSMD10, suggesting a previously under-appreciated mechanism for regulating proteasome homeostasis. Therefore, ProTA is a novel tool for profiling human protein degradome to elucidate potential mechanisms of drug action and resistance, which might facilitate therapeutic development targeting proteostasis to treat human disorders.

SProtP: a web server to recognize those short-lived proteins based on sequence-derived features in human cells

Protein turnover metabolism plays important roles in cell cycle progression, signal transduction, and differentiation. Those proteins with short half-lives are involved in various regulatory processes. To better understand the regulation of cell process, it is important to study the key sequence-derived factors affecting short-lived protein degradation. Until now, most of protein half-lives are still unknown due to the difficulties of traditional experimental methods in measuring protein half-lives in human cells. To investigate the molecular determinants that affect short-lived proteins, a computational method was proposed in this work to recognize short-lived proteins based on sequence-derived features in human cells. In this study, we have systematically analyzed many features that perhaps correlated with short-lived protein degradation. It is found that a large fraction of proteins with signal peptides and transmembrane regions in human cells are of short half-lives. We have constructed an SVM-based classifier to recognize short-lived proteins, due to the fact that short-lived proteins play pivotal roles in the control of various cellular processes. By employing the SVM model on human dataset, we achieved 80.8% average sensitivity and 79.8% average specificity, respectively, on ten testing dataset (TE1-TE10). We also obtained 89.9%, 99% and 83.9% of average accuracy on an independent validation datasets iTE1, iTE2 and iTE3 respectively. The approach proposed in this paper provides a valuable alternative for recognizing the short-lived proteins in human cells, and is more accurate than the traditional N-end rule. Furthermore, the web server SProtP (http://reprod.njmu.edu.cn/sprotp) has been developed and is freely available for users.