Viruses are unique organisms that lack the protein machinery necessary for its propagation (like polymerase) yet possess other proteins that facilitate its propagation (like host cell anchoring proteins). This study explores seven different frameworks to assist rapid visualization of proteins that are common to viruses residing in a given host. The proposed frameworks rely only on protein sequence information. It was found that the sequence similarity-based framework with an associated profile hidden Markov model was a better tool to assist visualization of proteins common to a given host than other proposed frameworks based only on amino acid composition or other amino acid properties. The lack of knowledge of profile hidden Markov models for many protein structures limit the utility of the proposed protein sequence similarity-based framework. The study concludes with an attempt to extrapolate the utility of the proposed framework to predict viruses that may pose potential human health risks.

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Protein – Protein interactions (PPIs) are thought to be conserved between species, although this has not been systematically investigated. This problem was explored in Escherichia coli from experimental data in Treponema pallidum by predicting PPIs, focusing on protein domains of little or unknown function. The comparison of T. pallidum to a model organism such as E. coli can not only reveal additional data about T. pallidum but also reveals how E. coli is similar to this distantly related, obligate parasite. A set of novel T. pallidum interactions, enriched for proteins of unknown function, were the basis of over 23,000 predicted homologous E. coli protein-protein and domain-domain interactions. Utilizing computational methods of protein analysis to define identity cross-species comparisons, this work shows that T. pallidum is nearly 61% similar to E. coli by orthologous groups (OG), demonstrating that what we knew of T. pallidum can be applied to E. coli. Observed binary interactions of that same pool of OGs result in only 4.3% shared T. pallidum interactions. Assigning function to proteins of unknown function leads to a greater understanding of how individual proteins relate to the larger interactome, the whole of interactions within a cell.

Tato práce se zabývá vyhledáváním homologních enzymů v proteinových databázích, jejímž cílem je navrhnout nástroj poskytující takové vyhledávání. Čtenář se seznámí se základní teorií týkající se proteinů, enzymů, homologie, ale také s existujícími nástroji pro vyhledávání homologních proteinů a enzymů. Dále je popsáno ohodnocení nalezených existujících nástrojů pro vyhledávání homologních enzymů. Pro potřeby vyhodnocení byla vytvořena datová sada spolu s algoritmem pro vyhodnocení vyýsledků jednotlivých nástrojů. Další částí práce je návrh a implementace nové metody pro vyhledávání homologních enzymů společně s jejím vyhodnocením. Jsou popsány dva algoritmy (One-by-One a MSA) pro vyhledávání homologních enzymů, jejichž porovnání ukazuje, že MSA algoritmus je zanedbatelně lepší z hlediska přesnosti než One-by-One algoritmus zatímco z hlediska rychlosti vítězí One-by-One algoritmus.

No description available.

The work is focused on the area of the proteins and their domains. It also briefly describes gathering methods of the protein´s structure at the various levels of the hierarchy. This is followed by examining of existing tools for protein´s domains prediction and databases consisting of domain´s information. In the next part of the work selected representatives of prediction methods are introduced.  These methods work with the information about the internal structure of the molecule or the amino acid sequence. The appropriate chapter outlines applied procedure of domains´ boundaries prediction. The prediction is derived from the primary structure of the protein, using a neural network  The implemented procedure and its possibility of further development in the related thesis are introduced at the conclusion of this work.