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Human powered electric sources
Naller, Ivan ; Škoda, Jan (referee) ; Baxant, Petr (advisor)
The aim of this work is to map sources of electricity-driven man. It is divided into three parts, the first of which deals with the muscles of the human body and essential components of the diet. The second part includes a search created prototypes of generators from small to big achievements. The last part is focused on the performance that can be achieved in a given individual and the associated energy demands of humanity. After the experiment, it is apparent that the average power of man is in the range of 150 to 300 W. On the basis of the data obtained it is possible to use the energy generated man (in gyms, fitness centers ....) for lighting, or simple applications such as water heating etc.
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Prediction of Protein Solubility
Marušiak, Martin ; Martínek, Tomáš (referee) ; Hon, Jiří (advisor)
Protein solubility is closely related to the usability of proteins in industrial use and research. The successful prediction of solubility would therefore lead to a significant saving of financial resources. This work presents new solubility predictor Solpex based on machine learning that achieved better performance on independent test set than any comparable solubility prediction tool. The predictor implementation was preceded by a study of the biological nature of solubility, evaluation of existing solubility prediction approaches, datasets building, many experiments with novel features and selection of the best features for the predictor. As the most important step in machine learning is the datasets building, this work mainly benefits from own rigorous processing of the main source of solubility data - the TargetTrack database.
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Detection of Correlated Mutations
Ižák, Tomáš ; Bendl, Jaroslav (referee) ; Martínek, Tomáš (advisor)
Tato práce zkoumá existující možnosti a metody detekce korelovaných mutací v proteinech. Práce začíná teoretickým úvodem do zkoumané problematiky. Využití informací o korelovaných mutacích je především při predikci terciální struktury proteinu či hledání oblastí s významnou funkcí. Dále následuje přehled v současnosti používaných metod detekce a jejich výhody a nevýhody. V této práci jsou zkoumány zejména metody založené na statistice (například Pearsonově korelačním koeficientu nebo Pearsonově chi^2 testu), informační teorii (Mutual information - MI) a pravděpodobnosti (ELSC nebo Spidermonkey). Dále jsou popsány nejdůležitější nástroje s informací o tom, které metody používají a jakým způsobem. Také je diskutována možnost návrhu optimálního algoritmu. Jako optimální z hlediska úspěšnosti detekce je doporučeno využít více zmíněných metod. Také je doporučeno při detekci využít fyzikálně-chemických vlastností aminokyselin. V praktické části byla vyvinuta metoda využívající fyzikálně-chemických vlastností aminokyselin a fylogenetických stromů. Výsledky detekce byly porovnány s nástroji CAPS, CRASP a CMAT.
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Protein Classification Techniques
Dekrét, Lukáš ; Zendulka, Jaroslav (referee) ; Burgetová, Ivana (advisor)
Main goal of classifying proteins into families is to understand structural, functional and evolutionary relationships between individual proteins, which are not easily deducible from available data. Since the structure and function of proteins are closely related, determination of function is mainly based on structural properties, that can be obtained relatively easily with current resources. Protein classification is also used in development of special medicines, in the diagnosis of clinical diseases or in personalized healthcare, which means a lot of investment in it. I created a new hierarchical tool for protein classification that achieves better results than some existing solutions. The implementation of the tool was preceded by acquaintance with the properties of proteins, examination of existing classification approaches, creation of an extensive data set, realizing experiments and selection of the final classifiers of the hierarchical tool.
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Development of Meta-Server for Prediction of Mutations Effects on Protein Function
Lisák, Peter ; Burgetová, Ivana (referee) ; Jaša, Petr (advisor)
This bachelor thesis deals with analysis of genomic data, more specifically prediction of effects of mutations on protein function using a protein sequence or tertiary structure. The theoretical introduction describes the basics of genetics and bioinformatics and is followed by description of selected prediction tools such as SIFT, MAPP and AUTO-MUTE. A unified interface for work with different tools is proposed in the thesis. The meta-server interface allows running a computation and collecting results from one site. meta-server combines results of implemented tools and provides a consensual prediction, which is expected to be more accurate than the results from individual tools. Finally, testing of meta-server on the real data and comparisons of predictions with the experimentally obtained results are presented.
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Acceleration of Algorithms for Clustering of Tunnels in Proteins
Jaroš, Marta ; Vašíček, Zdeněk (referee) ; Martínek, Tomáš (advisor)
This thesis deals with the clustering of tunnels in data obtained from the protein molecular dynamics simulation. This process is very computationaly intensive and it has been a challenge for scientific communities. The goal is to find such an algorithm with optimal time and space complexity ratio. The research of clustering algorithms, work with huge highdimensional datasets, visualisation and cluster-comparing methods are discussed. The thesis provides a proposal of the solution of this problem using the Twister Tries algorithm. The implementation details are analysed and the testing results of the solution quality and space complexity are provided. The goal of the thesis was to prove that we could achieve the same results with a stochastic algorithm - Twister Tries , as with an exact algorithm ( average-linkage ). This assumption was not confirmed confidently. Another finding of the hashing functions analysis shows that we could obtain the same results of hashing with a low dimensional hashing function but in much better computational time.
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Functional Annotation of Nucleotide Polymorphism Using Evolution Strategy
Šalanda, Ondřej ; Martínek, Tomáš (referee) ; Bendl, Jaroslav (advisor)
This thesis brings a new approach to the prediction of the the effect of amino acid substitution. The main goal is to create a new meta-tool, which combines evaluations of eight already implemented prediction tools. The use of weighted consensus over those tools should lead to better accuracy and versatility of prediction. The novelty of developed tool lies in involving evolution strategy with experimentally defined parameters as a way to determine the best weight distribution. At the end, a complex comparison and evaluation of results is given.
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Bioinformatics Tool for Protein Structure Prediction
Plaga, Michal ; Burgetová, Ivana (referee) ; Martínek, Tomáš (advisor)
The goal of this thesis is test and comparation of the offline tools for prediction of protein structure and creation of metaprediktor, which allows the user to select the appropriate tool, according to given parameters. Testing tool is based on a dataset of proteins, which is based on the SCOP database and it is trying to be as balanced as possible to include proteins from different families and thus could best evaluate individual tools. The results of this thesis are requirements of metaprediktor and also which data and settings can be allowed and processed and how it will be implemented.
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Protein Structure Prediction
Tuček, Jaroslav ; Martínek, Tomáš (referee) ; Burgetová, Ivana (advisor)
This work describes the three dimensional structure of protein molecules and biological databases used to store information about this structure or its hierarchical classification. Current methods of computational structure prediction are overviewed with an emphasis on comparative modeling. This particular method is also implemented in a proof-of-concept program and finally, the implementation is analysed.
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Large-Scale Analysis of the Ligand Transport and Docking inside of the Protein Tunels
Ježík, Andrej ; Martínek, Tomáš (referee) ; Musil, Miloš (advisor)
This thesis discusses large-scale analysis of the ligand transport and docking inside of the protein tunnels. Protein-ligand interactions are involved in processes such as cell signalling, transport, metabolism, regulation, gene expression, and enzyme activity. To understand the interaction between these molecules is vitally important for the research for new pharmaceuticals. The procedure of protein-ligand docking involves the following steps: (i) finding the structures of proteins (receptors) and ligands, (ii) identifying ligand binding sites, (iii) considering receptor/ligand flexibility, and (iv) computing interaction energy between the receptor and the ligand. Additional functionality will be implemented to allow CaverWeb to test a complete set of pre-processed drug ligands on a protein, in an effort to enhance the efficiency of the procedure for large sets of ligands, which will allow a much smoother workflow.
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