Vladimir Muronetz

Prof. Dr. Vladimir Muronetz

Belozersky Insitute, Moscow State University

Research Focus & Metrics

Academic Career Length

24Years (2003-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 July 2017 in International Journal of Biological Macromolecules
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Journal Article
Closed access
VMVladimir Muronetz
Vladimir Muronetz
KBKseniya V. Barinova
Kseniya V. Barinova
YSYulia Y. Stroylova
Yulia Y. Stroylova
PSPavel Semenyuk
Pavel Semenyuk
ESElena V. Schmalhausen
Elena V. Schmalhausen

7 October 2015 in Macromolecular Bioscience
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Journal Article
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SSSvetlana A. Sorokina
Svetlana A. Sorokina
YSYulia Yu. Stroylova
Yulia Yu. Stroylova
ZSZinaida Shifrina
Zinaida Shifrina
VMVladimir Muronetz
Vladimir Muronetz

18 August 2017 in Biochemistry (Moscow)
Verified
Journal Article
Closed access
PDF file
VMVladimir Muronetz
Vladimir Muronetz
AMAleksandra Melnikova
Aleksandra Melnikova
ZSZ. N. Seferbekova
Z. N. Seferbekova
KBK. V. Barinova
K. V. Barinova
ESE. V. Schmalhausen
E. V. Schmalhausen
This review considers the interrelation between different types of protein glycation, glycolysis, and the development of amyloid neurodegenerative diseases. The primary focus is on the role of the...

1 June 2003 in Journal of Chromatography B
Verified
Journal Article
Closed access
VMVladimir Muronetz
Vladimir Muronetz
TKTimo Korpela
Timo Korpela

12 October 2020 in Molecules
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Journal Article
Open access
PDF file
VMVladimir Muronetz
Vladimir Muronetz
KBKseniya Barinova
Kseniya Barinova
SKSofia Kudryavtseva
Sofia Kudryavtseva
MMMaria Medvedeva
Maria Medvedeva
AMAleksandra Melnikova
Aleksandra Melnikova
Matej SovaMatej Sova
Matej Sova
This review presents the main properties of hydroxycinnamic acid (HCA) derivatives and their potential application as agents for the prevention and treatment of neurodegenerative diseases. It is partially focused on the successful use of these compounds as inhibitors of amyloidogenic transformation of proteins. Firstly, the prerequisites for the emergence of interest in HCA derivatives, including natural compounds, are described. A separate section is devoted to synthesis and properties of HCA derivatives. Then, the results of molecular modeling of HCA derivatives with prion protein as well as with α-synuclein fibrils are summarized, followed by detailed analysis of the experiments on the effect of natural and synthetic HCA derivatives, as well as structurally similar phenylacetic and benzoic acid derivatives, on the pathological transformation of prion protein and α-synuclein. The ability of HCA derivatives to prevent amyloid transformation of some amyloidogenic proteins, and their presence not only in food products but also as natural metabolites in human blood and tissues, makes them promising for the prevention and treatment of neurodegenerative diseases of amyloid nature.

Verified
Journal Article
Open access
PDF file
PSPavel Semenyuk
Pavel Semenyuk
VMVladimir Muronetz
Vladimir Muronetz
Interaction of proteins with charged macromolecules is involved in many processes in cells. Firstly, there are many naturally occurred charged polymers such as DNA and RNA, polyphosphates, sulfated glycosaminoglycans, etc., as well as pronouncedly charged proteins such as histones or actin. Electrostatic interactions are also important for “generic” proteins, which are not generally considered as polyanions or polycations. Finally, protein behavior can be altered due to post-translational modifications such as phosphorylation, sulfation, and glycation, which change a local charge of the protein region. Herein we review molecular modeling for the investigation of such interactions, from model polyanions and polycations to unfolded proteins. We will show that electrostatic interactions are ubiquitous, and molecular dynamics simulations provide an outstanding opportunity to look inside binding and reveal the contribution of electrostatic interactions. Since a molecular dynamics simulation is only a model, we will comprehensively consider its relationship with the experimental data.