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Title:Inhibicija samo-replikacije proteinskih fibrilov
Authors:ID Curk, Samo (Author)
ID Gosak, Marko (Mentor) More about this mentor... New window
ID Šarić, Anđela (Comentor)
Files:.pdf MAG_Curk_Samo_2018.pdf (10,23 MB)
MD5: 170CFAC6FDD65D387BCBA7547A28D181
PID: 20.500.12556/dkum/51caf013-b6cf-4dea-a484-79039d4ef842
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FNM - Faculty of Natural Sciences and Mathematics
Abstract:Proteinski fibrili nastanejo z agregacijo delno zvitih proteinov in so odgovorni ali pomembno vplivajo na mnogo hudih človeških bolezni, kot sta Alzeimerjeva ali diabetes tipa II. Samo-replikacija fibrilov je proces, v katerem obstoječi proteinski fibrili katalizirajo nastanek novih fibrilov na način, da ponudijo vezavno površino, kjer se proteini lažje srečajo in agregirajo. To prispeva k naglem in eksponentnem napredovanju bolezni. V magistrski nalogi predstavimo Monte Carlo simulacije računalniškega modela agregacije, v katerega vpeljemo inhibitorje. To so delci, ki se lahko vežejo na površino fibrilov in tako inhibirajo oziroma upočasnijo proces samo-replikacije. Takšen način inhibicije se izkaže za zelo učinkovit, ampak zaradi odbojne interakcije med delci naletimo tudi na pojav makromolekularnega gnečenja, ki povzroči, da se pri določeni pokritosti površine s proteini hitrost samo-replikacije poveča. Edinstven deskriptor hitrosti replikacije najdemo v povprečni velikosti skupka na površino vezanih proteinov, ki nosi informacijo o celotni porazdelitvi agregacijskih skupkov. Predstavimo teorije, ki uspešno razložijo vse značilnosti opažanega obnašanja. S pomočjo mrežnega modela napovemo, katere interakcije med delci na površini imajo največji inhibicijski potencial.
Keywords:agregacija amiloidov, samo-sestavljanje proteinov, inhibicija, ra\v cunalni\v ske simulacije, metoda Monte Carlo, nukleacijski mehanizem, gne\v cenje makromolekul, krajina proste energije, statisti\v cna mehanika, mehka snov, fizikalna kemija
Place of publishing:Maribor
Publisher:[S. Curk]
Year of publishing:2018
PID:20.500.12556/DKUM-72364 New window
UDC:577.3(043.2)
COBISS.SI-ID:24130568 New window
NUK URN:URN:SI:UM:DK:KAMTFQ19
Publication date in DKUM:20.11.2018
Views:1314
Downloads:170
Metadata:XML DC-XML DC-RDF
Categories:FNM
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:20.09.2018

Secondary language

Language:English
Title:Inhibition of self-replication of protein fibrils
Abstract:Protein fibrils are formed by a process called amyloid aggregation and are implicated in many debilitating human diseases such as Alzheimer's or Type II Diabetes. Self-replication of fibrils is a process by which existing protein fibrils catalyse the formation of new fibrils by offering a surface on which proteins can bind, and therefore facilitate aggregation. This leads to exponential growth of fibril mass and fast propagation of amyloid diseases. In this thesis, we present simulations of a minimal but fairly complex computational model of aggregation with added inhibitory particles that can bind to the fibril surface. It turns out the mechanism of inhibition where inhibitors compete with proteins for the surface is very promising. However, we also find a manifestation of a macromolecular crowding effect which actually promotes self-replication at given protein coverage of the fibril surface. We find a unique descriptor for the rate of replication in the average protein aggregate size. We present theories that successfully explain all characteristics of observed simulation behaviour. By employing a lattice model, we predict which inter-particle interactions on the fibril surface have the largest inhibitory potential.
Keywords:Amyloid Aggregation, Self-assembly, Protein Fibrils, Inhibition, Course-grained Simulation, Monte Carlo Method, Nucleation Mechanism, Macromolecular Crowding, Free Energy Landscape, Statistical Mechanics, Soft Matter, Physical Chemistry


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