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Prediction of the Effect of Amino Acid Substitutions on the Secondary Structure of Proteins
Kadlec, Miroslav ; Vogel, Ivan (referee) ; Bendl, Jaroslav (advisor)
This thesis is focused on amino acid substitutions and their impact on protein secondary structure. The main aim is to prove, that although the protein sequence is frequently mutated during the evolution, protein secondary structure is more robust against changes. In this case, the elements of protein secondary structure stay almost unchanged although a significant number of substitutions is observed. The proof of this hypothesis was obtained by developed simulator of evolution which employs two well-estabilished predicting tools: PSIPRED for prediction of protein secondary structure and PhD-SNP for prediction of the effect of amino acid substitution on protein function. The results of the experiments are provided as graphs and their meainings is discussed.
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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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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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Prediction of the Effect of Amino Acid Substitutions on the Secondary Structure of Proteins
Kadlec, Miroslav ; Vogel, Ivan (referee) ; Bendl, Jaroslav (advisor)
This thesis is focused on amino acid substitutions and their impact on protein secondary structure. The main aim is to prove, that although the protein sequence is frequently mutated during the evolution, protein secondary structure is more robust against changes. In this case, the elements of protein secondary structure stay almost unchanged although a significant number of substitutions is observed. The proof of this hypothesis was obtained by developed simulator of evolution which employs two well-estabilished predicting tools: PSIPRED for prediction of protein secondary structure and PhD-SNP for prediction of the effect of amino acid substitution on protein function. The results of the experiments are provided as graphs and their meainings is discussed.
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