20.500.12556/DKUM-95508 Evolucijska specializacija encimov in informacijska kompleksnost: kvantitativna analiza encimov ß-laktamaz Evolutionary specialization of enzymes and informational complexity: a quantitative analysis of ß-lactamases V magistrskem delu kvantitativno proučujem povezavo med evolucijsko specializacijo encimov in informacijsko kompleksnostjo na primeru treh encimov iz družine β-laktamaz (β-laktamaza I, RTEM β-laktamaza, PCI β-laktamaza). Z vidika teorije informacije izračunam Shannonovo entropijo, informacijo in informacijsko kompleksnost iz primarne strukture encimov, pri čemer obravnavam kodone kot mikrostanja in aminokisline kot makrostanja. V teoretični analizi razvijem kinetični model encimske reakcije (E + S ⇌ ES → EP → E + P) in numerično določim maksimalno produkcijo entropije oz. hitrosti encimske reakcije v programskem okolju Berkeley Madonna. Rezultati pokažejo različne informacijske profile in različne vrednosti produkcije entropije. Encim PCI β-laktamaza izstopa z najvišjo informacijsko kompleksnostjo, medtem ko je za encim značilna najnižja produkcija entropije. Encim β-laktamaza I dosega najvišjo produkcijo entropije, zmerno kompleksnost in največjo evolucijsko razdaljo od skupnega prednika, RTEM pa vmesne vrednosti. Primerjava encimov kaže na obratno razmerje med informacijsko kompleksnostjo in katalitsko učinkovitostjo. Opažena korelacija med večjo evolucijsko razdaljo in večjo produkcijo entropije podpira tezo, da daljši evolucijski pritisk optimizira katalizo. Interpretacijo utemeljim v okviru načela maksimalne produkcije entropije (MEP) in razpravljam o omejitvah modela (predpostavka enakih verjetnosti kodonov, zanemarjanje višjih strukturnih ravni). In this thesis, we investigate the relationship between the evolutionary specialization of enzymes and their complexity, using three enzymes from the β-lactamase family (β-lactamase I, RTEM β-lactamase, and PCI β-lactamase) as case studies. From the perspective of information theory, we calculate the Shannon information entropy, information, and informational complexity from the primary structure of the enzymes, treating codons as microstates and amino acids as macrostates. In theoretical analysis, we develop a kinetic model of the enzymatic reaction (E + S ⇌ ES → EP → E + P) and numerically determine the rate of maximal entropy production of the enzymatic reaction by using the Berkeley Madonna software. The results reveal distinct informational profiles and varying rates of entropy production. The enzyme PCI β-lactamase shows the highest informational complexity, while exhibiting the lowest maximal entropy production rate. In contrast, β-lactamase I shows the highest maximal entropy production rate, moderate complexity, and the greatest evolutionary distance from the common ancestor. RTEM β-lactamase exhibits intermediate values in all aspects. The comparison of the calculated results suggests an inverse relationship between informational complexity and catalytic efficiency: a more “streamlined” informational structure does not necessarily imply higher functional efficiency. The observed correlation between greater evolutionary distance and higher entropy production rate supports the hypothesis that prolonged evolutionary pressure optimizes catalysis. The observed correlation between evolutionary distance and entropy production supports the hypothesis that evolutionary pressure optimizes catalysis. This interpretation is grounded in the framework of the Maximum Entropy Production (MEP) principle. encimska kinetika informacijska kompleksnost Shannonova entropija evolucijska razdalja β-laktamaze načelo maksimalne produkcije entropije. enzyme kinetics informational complexity Shannon entropy evolutionary distance β-lactamases maximum entropy production principle. true true false [J. Žnidarič] Slovenski jezik Angleški jezik Magistrsko delo/naloga 2025-09-23 10:44:14 2025-10-15 14:21:47 2025-10-16 09:30:35 0000-00-00 00:00:00 2025 0 Maribor 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 536.75(043.2) 253339139 MAG_Znidaric_Jan_2025.pdf MAG_Znidaric_Jan_2025.pdf 1 11BAD04BA6807EC372FA384BE5F9854E 62e405edfacb45e85f9f91a8b97e584d10ed324f3ea6ca3cb13872a4ac72b06f 2ce8f151-9c98-11f0-bb41-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=199319 Fakulteta za naravoslovje in matematiko 0 0 0