Prediction of mutations initiated by internal power in H3N2 hemagglutinins of influenza a virus from north america

Probably the best way to predict mutations is to find the cause for mutations, by which the cause-mutation relationship can be built. However, many causes which have resulted in mutations in the past might not leave any trace due to the changes in environments. As well, the current proteins may not be sensitive to the causes, which led to mutations in the past, because of evolution. Thus we might have recorded many mutations, but few of their corresponding causes, and it would be difficult to establish the one-to-one cause-mutation relationship. However, the internal power engineering mutations within a protein would exist, of which randomness should play an important role. Since 1999, we have developed three methods to quantify the randomness within a protein by which we can build a cause-mutation relationship because we can classify the occurrence and non-occurrence of mutation as unity and zero, and transfer this relationship into the classification problem, which can be solved using logistic regression. Recently, we used the logistic regression to predict the mutation positions in H5N1 hemagglutinins from influenza A virus, and applied the amino-acid mutating probability to predict the would-be-mutated amino acids at predicted positions as the concept-initiated study. However, we still need to conduct many proof-of-concept studies to test whether this cause-mutation relationship is independent of protein subtypes, whether the logistic regression is powerful enough, etc. In this study, we attempted to use the logistic regression to predict the mutation positions in H3N2 hemagglutinins of influenza A virus from North America to answer the questions in the proof-of-concept stage.