Motif alignment and protein secondary structure

master's thesis

• Title (croatian): Usklađivanje motiva i sekundarna struktura proteina
• Author: Ivan Čutura
• Mentor: Pavle Goldstein (mentor)
• Committee member: Željka Milin Šipuš (predsjednik povjerenstva)
• Committee member: Josip Tambača (član povjerenstva)
• Committee member: Bojan Basrak (član povjerenstva)
• Committee member: Pavle Goldstein (član povjerenstva)
• Granter: University of Zagreb; Faculty of Science; (Department of Mathematics); Zagreb
• Defense date and country: 2024-07-16, Croatia
• Scientific / art field, discipline and subdiscipline: NATURAL SCIENCES; Mathematics

Abstract

This thesis is concerned with the classification of protein motifs based on their secondary structure. A protein motif is a short sequence of amino acids that has remained partially conserved throughout evolution and can be associated with a recognizable part of the protein structure performing a distinct function. The goal is to improve the accuracy of motif scanning procedures by detecting as many significant motifs as possible (true positives) while minimizing the number of wrong assignments (false positives). To enhance accuracy, we employ an approach based on pairwise similarity, examining not just the similarity to the query but also the mutual similarity among protein motifs. 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 protein motifs sharing the same secondary structure annotation will group together and be located within a sphere. By finding this sphere, we obtain points such that each pair is “sufficiently” close to one another. However, we encounter two key challenges: the annotation for protein secondary structure is not reliable, and we lack a characteristic query. We addressed these challenges to the best of our ability, but due to unreliable annotation, we couldn't fully test the method. We showed that true positives do, in fact, cluster and could be found using this method. However, for this approach to be fully effective, we need more accurate annotation and a better characteristic query.

Abstract (croatian)

Ovaj rad bavi se klasifikacijom proteinskih motiva na temelju njihove sekundarnosti struktura. Proteinski motiv je kratki niz aminokiselina koji je djelomično ostao sačuvan tijekom evolucije i može se povezati s prepoznatljivim dijelom proteina struktura koja obavlja posebnu funkciju. Cilj je poboljšati točnost motiva procedure skeniranja otkrivanjem što više značajnih motiva (pravi pozitivi) dok minimizira broj pogrešnih dodjela (lažno pozitivnih). Kako bismo povećali točnost, koristimo pristup koji se temelji na ispitivanju sličnosti u paru ne samo sličnost s upitom nego i međusobna sličnost među proteinskim motivima. Po opisujući aminokiseline pomoću numeričkih vektora, problem se postavlja u euklidski prostor gdje se u obzir uzima udaljenost umjesto sličnosti. Pretpostavka je da proteinski motivi koji dijele istu napomenu sekundarne strukture grupirat će se zajedno i nalaziti unutar sfera. Pronalaženjem ove sfere dobivamo takve točke da je svaki par “dovoljno” blizu jedan drugome. Međutim, susrećemo se s dva ključna izazova: napomenom za sekundarnu strukturu proteina nije pouzdan, a nedostaje nam karakterističan upit. Ovim izazovima smo se obratili najbolje što smo mogli, ali zbog nepouzdane bilješke nismo mogli u potpunosti testirati metodu. Pokazali smo da se prave pozitive zapravo grupiraju i mogu se pronaći ovom metodom. Međutim, da bi ovaj pristup bio potpuno učinkovit, potrebna nam je preciznija anotacija i a bolji karakteristični upit.

• Keywords: sequence of amino acids; pairwise similarity; Euclidean space
• Language: english
• URN:NBN: urn:nbn:hr:217:103963
• Study programme: Title: Biomedical Mathematics; 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: 2024-07-25 09:20:08