Determination of mutation trend in proteins by means of translation probability between RNA codes and mutated amino acids

Predicting Crystallization Propensity of Proteins from Arabidopsis Thaliana

Background

Many studies have correlated characteristics of amino acids with crystallization propensity, as part of the effort to determine the factors that affect the propensity of protein crystallization. However, these characteristics are constant; that is, the encoded amino acid sequences have the same value for each type of amino acid. To overcome this inflexibility, three dynamic characteristics of amino acids and protein were introduced to analyze the crystallization propensity of proteins. Both logistic regression and neural network models were used to correlate each of two dynamic characteristics with the crystallization propensity of 301 proteins from Arabidopsis thaliana, and their results were compared with those obtained from each of 531 constant amino acid characteristics, which served as the benchmark.

Results

The neural network model was more powerful for predicting the crystallization propensity of proteins than the logistic regression model. Compared with the benchmark, the dynamic characteristics of amino acids provided good prediction results for the crystallization propensity, and the distribution probability gave the highest sensitivity. Using 90 % accuracy as a cutoff point, the predictable portion of A. thaliana portions was ranked, and the statistical analysis showed that the larger the predictable portion, the better the prediction.

Conclusions

These results demonstrate that dynamic characteristics have a certain relationship with the crystallization propensity, and they could be helpful for the prediction of protein crystallization, which may provide a theoretical concept for certain proteins before conducting experimental crystallization.

Electronic supplementary material

The online version of this article (doi:10.1186/s12575-015-0029-3) contains supplementary material, which is available to authorized users.

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.

Mutation trend of hemagglutinin of influenza A virus: a review from a computational mutation viewpoint

Since 1999 we have developed two computational mutation approaches to analyze the protein primary structure whose methodology and implications were reviewed in 2002. Our first approach is the calculation of predictable and unpredictable portions of amino-acid pairs in a protein, and the second is the calculation of amino-acid distribution rank in a protein. Both approaches provide quantitative measures to present a protein, which we have used to study a number of proteins with numerous mutations such as p53 proteins. More recently, we focussed our efforts on analyzing the proteins mutating frequently over time such as hemagglutinins of influenza A viruses. In this review we summarise our findings and their implications for hemagglutinin mutations in combination with some newly available data. Our approaches throw light on the true nature of genetic heterogeneity of influenza virus hemagglutinins; that is, the protein variability is highly relevant to its amino-acid construction. Using these approaches, we can monitor new mutations from influenza virus hemagglutinins and may predict their mutations in the future.