hrvatski jezikClear Cookie - decide language by browser settings

Signatures of conformational stability and oxidation resistance in proteomes of pathogenic bacteria

Vidović, Anita; Supek, Fran; Nikolić, Andrea; Kriško, Anita (2014) Signatures of conformational stability and oxidation resistance in proteomes of pathogenic bacteria. Cell Reports, 7 (5). pp. 1393-1400. ISSN 2211-1247

PDF - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (4MB) | Preview


Protein oxidation is known to compromise vital cellular functions. Therefore, invading pathogenic bacteria must resist damage inflicted by host defenses via reactive oxygen species. Using comparative genomics and experimental approaches, we provide multiple lines of evidence that proteins from pathogenic bacteria have acquired resistance to oxidative stress by an increased conformational stability. Representative pathogens exhibited higher survival upon HSP90 inhibition and a less-oxidation-prone proteome. A proteome signature of the 46 pathogenic bacteria encompasses 14 physicochemical features related to increasing protein conformational stability. By purifying ten representative proteins, we demonstrate in vitro that proteins with a pathogen-like signature are more resistant to oxidative stress as a consequence of their increased conformational stability. A compositional signature of the pathogens’ proteomes allowed the design of protein fragments more resilient to both unfolding and carbonylation, validating the relationship between conformational stability and oxidability with implications for synthetic biology and antimicrobial strategies.

Item Type: Article
Uncontrolled Keywords: protein oxidation resistance; conformational stability; protein carbonylation; machine learning; pathogenic bacteria; comparative genomics
Subjects: NATURAL SCIENCES > Biology > Biochemistry and Molecular Biology
NATURAL SCIENCES > Biology > Microbiology
BIOTECHNICAL SCIENCES > Biotechnology > Bioinformatics
Divisions: Division of Electronics
Project titleProject leaderProject codeProject type
Strojno učenje prediktivnih modela u računalnoj biologiji[136501] Tomislav Šmuc098-0000000-3168MZOS
Learning from Massive, Incompletely annotated, and Structured Data – MAESTRATomislav Šmuc612944FP7
Depositing User: Fran Supek
Date Deposited: 14 Jul 2014 15:42
DOI: 10.1016/j.celrep.2014.04.057

Actions (login required)

View Item View Item


Downloads per month over past year

Increase Font
Decrease Font
Dyslexic Font