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Modern methods for protein secondary structure prediction and their comparison
Kraus, Ondřej ; Novotný, Marian (advisor) ; Pleskot, Roman (referee)
Today, there are several protein secondary structure predictors; most of them use algorithms such as hidden Markov models or artificial neural networks. Therefore I will introduce them to a reader in my thesis. I will explain their principles, as well as their advantages and disadvantages. The majority of contemporary predictors have accuracy 70%-80% for prediction of three types of protein secondary structure. However these results are only approximate, due to different testing methodology. Therefore the user should get familiar with the method and its testing methodology in detail at first. Key-words: protein structure prediction, hidden Markov model, artificial neural network, nearest neighbour, protein secondary structure
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Bioinformatic methods of detection of protein coevolution
Pařízková, Hana ; Schneider, Bohdan (advisor) ; Hampl, Vladimír (referee)
The term coevolution describes the situation when two or more species or biomole- cules reciprocally affect each others' evolution. On the protein level, it is thought to be the main mechanism ensuring correct folding, interactions and function of a protein, and it can be observed both on the level of interacting protein families and individual amino acid residues. Coevolution studies have been proved to be a powerful tool for prediction of protein structure, function, interaction partners, etc. In this thesis, different algorithms used for detection of protein coevolution are described, as well as their applications and limitations. Keywords: coevolution, protein family, protein structure prediction, interac- tion partners, correlated mutations, mirrortree, mutual information, direct cou- pling analysis
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Modern methods for protein secondary structure prediction and their comparison
Kraus, Ondřej ; Novotný, Marian (advisor) ; Pleskot, Roman (referee)
Today, there are several protein secondary structure predictors; most of them use algorithms such as hidden Markov models or artificial neural networks. Therefore I will introduce them to a reader in my thesis. I will explain their principles, as well as their advantages and disadvantages. The majority of contemporary predictors have accuracy 70%-80% for prediction of three types of protein secondary structure. However these results are only approximate, due to different testing methodology. Therefore the user should get familiar with the method and its testing methodology in detail at first. Key-words: protein structure prediction, hidden Markov model, artificial neural network, nearest neighbour, protein secondary structure
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