Proteinski motivi i klasifikacija u proteinske familije

master's thesis

• Title (english): Protein motifs and classification into protein families
• Author: Dolores Horvat
• Mentor: Pavle Goldstein (mentor)
• Committee member: Pavle Goldstein (predsjednik povjerenstva)
• Committee member: Goran Muić (član povjerenstva)
• Committee member: Ozren Perše (član povjerenstva)
• Committee member: Tina Bosner (član povjerenstva)
• Granter: University of Zagreb; Faculty of Science; (Department of Mathematics); Zagreb
• Defense date and country: 2023-03-03, Croatia
• Scientific / art field, discipline and subdiscipline: NATURAL SCIENCES; Mathematics

Abstract

Ovaj diplomski rad proučava problem klasifikacije proteina u proteinske familije. Proteini se klasificiraju identifikacijom kratkog niza aminokiselina kojeg sadrže, a zovemo ga motivom. Motiv je ostao djelomično očuvan u evoluciji te se može povezati s prepoznatljivim dijelom u strukturi proteina koji izvršava određenu funkciju.Za razliku od tipičnih pretraživača motiva koji promatraju sličnost potencijalnih motiva sa zadanim motivom, u ovom radu se opisuje i testira metoda koja promatra njihovu međusobnu sličnost. Metoda je zamišljena da djeluje u kombinaciji s nekim pretraživačem motiva pružajući mu tako svoj pristup u traženju odgovora. Uz pomoć opisa aminokiselina numeričkim vektorima, problem se smješta u euklidski prostor gdje se umjesto sličnosti promatra udaljenost. Pretpostavka je da se motivi, pronađeni u proteinima koji biološki pripadaju promatranoj familiji, grupiraju. Cilj metode pronaći je optimalnu kuglu koja će obuhvatiti što veći broj pravih odgovora uz što manje pogrešnih. Metoda je testirana na dvanaest biljnih proteoma u čijim proteinima se tražilo karakteristične motive VQ proteinske familije. U većini slučajeva, metoda se pokazala uspješnom što je ujedno i potvrdilo pretpostavku o grupiranju

Abstract (english)

This thesis studies the protein family classification problem. Proteins are classified by identifying a short amino acids sequence contained within them. This short sequence is called a motif. During evolution, the motif has remained partly conserved and can be associated with the recognizable part of the protein structure that performs a distinct function. Unlike typical motif scanners that observe the similarity of potential motifs with a given motif, this thesis describes and tests the method that considers pairwise similarity. The method works in combination with another motif scanner by providing additional criteria for motif identification. By describing amino acids using numerical vectors the problem is placed in Euclidean space where distance is considered instead of similarity. The assumption is that the motifs, found in proteins that belong biologically to the observed family, will group. The goal of this method is to find the optimal ball that will contain as many correct hits and as few wrong ones as possible. The method was tested on twelve plant proteomes whose proteins were searched for containing characteristic motifs of the VQ protein family. In most of the cases, the method proved to be successful which in turn confirmed true positives clustering assumption.

• Keywords: aminokiseline; pretraživača motiva; numerički vektori; VQ proteinska familija; biljni proteomi
• Keywords (english): amino acids; motive scanners; numerical vectors; VQ protein family; plant proteomes
• Language: croatian
• URN:NBN: urn:nbn:hr:217:465395
• Study programme: Title: Mathematical Statistics; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra matematike (magistar/magistra matematike)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2023-04-04 14:58:12