Prediction of possible mutations in H5N1 hemagglutitins of influenza A virus by means of logistic regression

Quantitative relationship between mutated amino-acid sequence of human copper-transporting ATPases and their related diseases

Copper-transporting ATPase 1 and 2 (ATP7A and ATP7B) are two highly homologous P-type copper ATPase exporters. Mutations in ATP7A can lead to Menkes disease which is an X-linked disorder of copper deficiency. Mutations in ATP7B can cause Wilson disease which is an autosomal recessive disorder of copper toxicity. In this study, we attempt to build a quantitative relationship between mutated ATPase and Menkes/Wilson disease. First, we use the amino-acid distribution probability as a measure to quantify the difference in ATPase before and after mutation. Second, we use the cross-impact analysis to define the quantitative relationship between mutant ATPase protein and Menkes/Wilson disease, and compute various probabilities. Finally, we use the Bayesian equation to determine the probability that Menkes/Wilson disease is diagnosed under a mutation. The results show (i) the vast majority of mutations lead to the amino-acid distribution probability increase in mutant ATP7As and decrease in ATP7Bs, and (ii) the probability that a mutation causes Menkes/Wilson disease is about nine tenth. Thus we provide a way to use the descriptively probabilistic method to couple the mutation with its clinical outcome after quantifying mutations in proteins.

Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influenza A virus

This is the continuation of our studies on the prediction of mutation engineered by randomness in proteins from influenza A virus. In our previous studies, we have demonstrated that randomness plays a role in engineering mutations because the measures of randomness in protein are different before and after mutations. Thus we built a cause-mutation relationship to count the mutation engineered by randomness, and conducted several concept-initiated studies to predict the mutations in proteins from influenza A virus, which demonstrated the possibility of prediction of mutations along this line of thought. On the other hand, these concept-initiated studies indicate the directions forwards the enhancement of predictability, of which we need to use the neural network instead of logistic regression that was used in those concept-initiated studies to enhance the predictability. In this proof-of-concept study, we attempt to apply the neural network to modeling the cause-mutation relationship to predict the possible mutation positions, and then we use the amino acid mutating probability to predict the would-be-mutated amino acids at predicted positions. The results confirm the possibility of use of internal cause-mutation relationship with neural network model to predict the mutation positions and use of amino acid mutating probability to predict the would-be-mutated amino acids.

Prediction of mutations engineered by randomness in H5N1 neuraminidases from influenza A virus

In this proof-of-concept study, we attempt to determine whether the cause-mutation relationship defined by randomness is protein dependent by predicting mutations in H5N1 neuraminidases from influenza A virus, because we have recently conducted several concept-initiated studies on the prediction of mutations in hemagglutinins from influenza A virus. In our concept-initiated studies, we defined the randomness as a cause for mutation, upon which we built a cause-mutation relationship, which is then switched into the classification problem because the occurrence and non-occurrence of mutations can be classified as unity and zero. Thereafter, we used the logistic regression and neural network to solve this classification problem to predict the mutation positions in hemagglutinins, and then used the amino acid mutating probability to predict the would-be-mutated amino acids. As the previous results were promising, we extend this approach to other proteins, such as H5N1 neuraminidase in this study, and further address various issues raised during the development of this approach. The result of this study confirms that we can use this cause-mutation relationship to predict the mutations in H5N1 neuraminidases.