In a protein primary structure, a type of amino acids is usually clustered together rather than homogenously distributes. What advantage of such distribution and how to measure different distributions are rarely addressed. In the current communication, we apply the distribution probability to investigate the mutations/variants from monoamine oxidases A and B. The results reveal the rationale of clustering distribution of amino acids in the full-length sequence and in functional regions of the monoamine oxidases, and how the mutations/variants affect the distribution probability. Thus, we can quantitatively measure and compare not only the amino-acid distributions across different monoamine oxidases, but also the effect of mutations/variants on the enzymatic function.
The natural disasters caused by extreme precipitations significantly have increased around the world. In the US, wild fires and flooding appear to increase year-by-year. The extreme precipitations are attributed to the climate change. Over recent years we have successfully used the random walk to simulate the global warming in many places around the world. This encourages us to use the random walk model to simulate the precipitations. In this investigation, we used the random walk model to simulate the precipitations in 50 US state capitals for the 20th century. The results show that the random walk can approximately simulate the precipitation in these cities in both 1/–1 and decimal forms. Thus the results suggest the random mechanism is one of causes for unusual precipitations over recent years.
The successful prediction of mutations and recombination is perhaps the most important step for the preparedness against any emerging diseases. Our group had developed a model for the prediction of possible mutations in H5N1 hemagglutinin from influenza A virus. However, much more effort is needed to enhance the predictability. In this study, we include the RNA STOP codon from hemagglutinin for prediction because influenza A virus is a RNA virus and mutations occurred in RNA STOP codon. A total of 429 H5N1 hemagglutinins of influenza A viruses are used in a logistic regression model because it can include the interaction of two independent variables for comparison between the predictions including RNA STOP codons and the predictions without RNA STOP codons. The results demonstrated that the predictions with RNA STOP codons are better than the predictions without RNA STOP codons. Thus, the inclusion of RNA STOP codons does enhance the predictability.
The mathematical modeling is an important development in biological computation. Over years, our research group has been using a system of differential equations to study the evolutionary process of nine of ten proteins from influenza A virus. In this study, we take a close look at our approach with polymerase basic protein 2 (PB2) from influenza A virus as an example in order to have a whole picture on the strength and weakness of our approach. For this purpose, firstly we used the amino-acid predictability to convert 2430 PB2s into the predictable portions, which were plotted against time in x. y coordinates, secondly we used the analytical solution of a system of differential equations to fit the curves with respect to PB2 subtypes, and obtained model parameters, and the statistical tests were for the goodness-of-fit and halflife of PB2 evolution, and finally, we discussed the strong and weak points in this approach. In this manner, we can refine the approach using differential equations to study the evolution of proteins and move on.
Virus evolution is important because it generates new mutations, which can be harmful to humans. Because the evolution is a process along the time course, and many mathematical tools describe the phenomenon along the time course, thus it is possible to apply a mathematical tool to a virus evolution. Neuraminidase is one of two surface proteins in influenza A virus playing an important role influenza transmission, thus it is important to model its evolution. Yet, a protein sequence should be converted into numerical values in order to be workable in mathematical tools, and a driving force for evolution should be defined. In this study, first we use the amino-acid pair predictability as a measure of driving force for evolution to convert 3828 neuraminidases sampled from 1956 to 2008 into numerical values; second we use a system of differential equations to describe the mutation neuraminidases; and third we use the analytical solution to fit the evolution of neuraminidases. The results show a promising and encouraging trajectory of evolution of neuraminidases along the time course.
The current COVID-19 pandemic continues with its new variants, whose mutations are unpredictable. Thus, how to predict mutations in viruses has profound meanings for vaccine and drug development as well as prevention measures. Currently the documented mutations in SARS-CoV-2 are not abundant yet, especially for making phylogenetic tree, it would be useful and easy to use the virus data with abundant mutations such as influenza A virus to build predictive model. In this study, a neural network with feedforward backpropagation algorithm is employed to predict the probabilistically possible mutation positions and mutated amino acids in hemagglutinins from Eurasia H1 influenza A virus. The study demonstrates an encouraging result and suggests the possibility to continue working along this research line.
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