At the Cold Spring Harbor Meeting on 'Molecular Chaperones and the Heat Shock Response' in May 1996, Susan Lindquist presented evidence that a chaperone of yeast termed Hsp104, which her group had been investigating for several years, is able to dissolve protein aggregates (Glover, J.R., Lindquist, S., 1998. Hsp104, Hsp70, and Hsp40: a novel chaperone system that rescues previously aggregated proteins. Cell 94, 73-82). Among many of the participants this news stimulated reactions reaching from decided skepticism to utter disbelief because protein aggregation was widely considered to be an irreversible process. Several years and publications later, it is undeniable that Susan had been right. Hsp104 is an ATP dependent molecular machine that-in cooperation with Hsp70 and Hsp40-extracts polypeptide chains from protein aggregates and facilitates their refolding, although the molecular details of this process are still poorly understood. Meanwhile, close homologues of Hsp104 have been identified in bacteria (ClpB), in mitochondria (Hsp78), and in the cytosol of plants (Hsp101), but intriguingly not in the cytosol of animal cells (Mosser, D.D., Ho, S., Glover, J.R., 2004. Saccharomyces cerevisiae Hsp104 enhances the chaperone capacity of human cells and inhibits heat stress-induced proapoptotic signaling. Biochemistry 43, 8107-8115). Observations that Hsp104 plays an essential role in the maintenance of yeast prions (see review by James Shorter in this issue) have attracted even more attention to the molecular mechanism of this ATP dependent chaperone (Chernoff, Y.O., Lindquist, S.L., Ono, B., Inge-Vechtomov, S.G., Liebman, S.W., 1995. Role of the chaperone protein Hsp104 in propagation of the yeast prion-like factor [PSI+]. Science 268, 880-884).
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.jsb.2006.02.004 | DOI Listing |
J Chem Inf Model
January 2025
Institute of Chemistry, Technische Universität Berlin, Straße des 17. Juni 135, Berlin 10623, Germany.
Machine learning (ML) is a powerful tool for the automated data analysis of molecular dynamics (MD) simulations. Recent studies showed that ML models can be used to identify protein-ligand unbinding pathways and understand the underlying mechanism. To expedite the examination of MD simulations, we constructed PathInHydro, a set of supervised ML models capable of automatically assigning unbinding pathways for the dissociation of gas molecules from [NiFe] hydrogenases, using the unbinding trajectories of CO and H from [NiFe] hydrogenase as a training set.
View Article and Find Full Text PDFEClinicalMedicine
December 2024
Department of Pathology and Genetics, Laboratory of Cancer Medical Science, Hokuto Hospital, Obihiro, Hokkaido, Japan.
Background: Pancreatic cancer is highly aggressive and has a low survival rate primarily due to late-stage diagnosis and the lack of effective early detection methods. We introduce here a novel, noninvasive urinary extracellular vesicle miRNA-based assay for the detection of pancreatic cancer from early to late stages.
Methods: From September 2019 to July 2023, Urine samples were collected from patients with pancreatic cancer (n = 153) from five distinct sites (Hokuto Hospital, Kawasaki Medical School Hospital, National Cancer Center Hospital, Kagoshima University Hospital, and Kumagaya General Hospital) and non-cancer participants (n = 309) from two separate sites (Hokuto Hospital and Omiya City Clinic).
Natl Sci Rev
January 2025
Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China.
The high thermopower of ionic thermoelectric (-TE) materials holds promise for miniaturized waste-heat recovery devices and thermal sensors. However, progress is hampered by laborious trial-and-error experimentations, which lack theoretical underpinning. Herein, by introducing the simplified molecular-input line-entry system, we have addressed the challenge posed by the inconsistency of -TE material types, and present a machine learning model that evaluates the Seebeck coefficient with an of 0.
View Article and Find Full Text PDFExp Biol Med (Maywood)
January 2025
Department of Pediatric Surgery, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease with a poor prognosis. Its non-specific clinical symptoms make accurate prediction of disease progression challenging. This study aimed to develop molecular-level prognostic models to personalize treatment strategies for IPF patients.
View Article and Find Full Text PDFFront Immunol
January 2025
Department of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
Background: Sepsis is an uncontrolled reaction to infection that causes severe organ dysfunction and is a primary cause of ARDS. Patients suffering both sepsis and ARDS have a poor prognosis and high mortality. However, the mechanisms behind their simultaneous occurrence are unclear.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!