Evoluční strategie v úloze predikce vlivu aminokyselinových mutací na stabilitu proteinů

This thesis is focused on predicting the impact of amino acid substitution on protein stability. The main goal is to create a consensual predictor that uses the outputs of chosen existing tools in order to improve accuracy of prediction. The optimal consensus of theese tools was designed using evolution strategies in three variants: 1/5 success rule, self-adaptation variant and the CMA-ES method. Then, the quality of calculated weight vectors was tested on the independent dataset. Although the highest prediction performance was attained by self-adaptation method, the differences between all three variants were not significant. Compared to the individual tools, the predictions provided by consensual methods were generally more accurate - the self-adaptation variant imporved the Pearson's corelation coeficient of the predictions by 0,057 on the training dataset. On the testing dataset, the improvement of designed method was smaller (0,040). Relatively low improvement of prediction performance (both on the training and the testing dataset) were caused by the fact, that for some records of testing dataset, some individual tools vere not able to provide their results. When omitting these records, consensual method improved the Pearson's corelations coeficient by 0,118.

Amino acid mutations, amino acid substitutions, protein stability, evolution strategy, protein predictions.