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Predictor of the Effect of Amino Acid Substitutions on Protein Function
Musil, Miloš ; Martínek, Tomáš (referee) ; Bendl, Jaroslav (advisor)
This thesis discusses the issue of predicting of the effect of amino acid substitutions on protein funkcion, based on phylogenetic analysis method, inspired by tool MAPP. Significant number of genetic diseases is caused by nonsynonymous SNPs manifested as single point mutations on the protein level. The ability to identify deleterious substitutions could be useful for protein engineering to test whether the proposed mutations do not damage protein function same as for targeting disease causing harmful mutations. However the experimental validation is costly and the need of predictive computation methods has risen. This thesis describes desing and implementation of a new in silico predictor based on the principles of evolutionary analysis and dissimilarity between original and substituting amino acid physico-chemical properties. Developed algorithm was tested on four datasets with 74,192 mutations from 16,256 sequences in total. The predictor yields up to 72 % accuracy and in the comparison with the most existing tools, it is substantially less time consuming. In order to achieve the highest possible efficiency, the optimization process was focused on selection of the most suitable (a) third-party software for calculation of a multiple sequence alignment, (b) overall decision threshold and (c) a set of physico-chemical properties.
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Predicting the Effect of Amino Acid Substitutions on Protein Function Using MAPP Method
Pelikán, Ondřej ; Vogel, Ivan (referee) ; Bendl, Jaroslav (advisor)
This thesis discusses the issue of predicting the effect of amino acid substitutions on protein function using MAPP method. This method requires the multiple sequence alignment and phylogenetic tree constructed by third-party tools. Main goal of this thesis is to find the combination of suitable tools and their parameters to generate the inputs of MAPP method on the basis of analysis on one massively mutated protein. Then, the MAPP method is tested with chosen combination of parameters and tools on two large independent datasets and consequently is compared with the other tools focused on prediction of the effect of mutations. Apart from this the web interface for the MAPP method was created. This interface simplifies the use of the method since the user need not to install any tools or set any parameters.
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Predicting the Effect of Amino Acid Substitutions on Protein Function Using MAPP Method
Pelikán, Ondřej ; Vogel, Ivan (referee) ; Bendl, Jaroslav (advisor)
This thesis discusses the issue of predicting the effect of amino acid substitutions on protein function using MAPP method. This method requires the multiple sequence alignment and phylogenetic tree constructed by third-party tools. Main goal of this thesis is to find the combination of suitable tools and their parameters to generate the inputs of MAPP method on the basis of analysis on one massively mutated protein. Then, the MAPP method is tested with chosen combination of parameters and tools on two large independent datasets and consequently is compared with the other tools focused on prediction of the effect of mutations. Apart from this the web interface for the MAPP method was created. This interface simplifies the use of the method since the user need not to install any tools or set any parameters.
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|
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Predictor of the Effect of Amino Acid Substitutions on Protein Function
Musil, Miloš ; Martínek, Tomáš (referee) ; Bendl, Jaroslav (advisor)
This thesis discusses the issue of predicting of the effect of amino acid substitutions on protein funkcion, based on phylogenetic analysis method, inspired by tool MAPP. Significant number of genetic diseases is caused by nonsynonymous SNPs manifested as single point mutations on the protein level. The ability to identify deleterious substitutions could be useful for protein engineering to test whether the proposed mutations do not damage protein function same as for targeting disease causing harmful mutations. However the experimental validation is costly and the need of predictive computation methods has risen. This thesis describes desing and implementation of a new in silico predictor based on the principles of evolutionary analysis and dissimilarity between original and substituting amino acid physico-chemical properties. Developed algorithm was tested on four datasets with 74,192 mutations from 16,256 sequences in total. The predictor yields up to 72 % accuracy and in the comparison with the most existing tools, it is substantially less time consuming. In order to achieve the highest possible efficiency, the optimization process was focused on selection of the most suitable (a) third-party software for calculation of a multiple sequence alignment, (b) overall decision threshold and (c) a set of physico-chemical properties.
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