Predicting Km values of beta-glucosidases using cellobiose as substrate.

The Michaelis-Menten constant Km is a very important parameter to relate enzyme with its substrate in enzymatic reaction. Although Km can be experimentally determined, the Km values are not easily available in literature. With rapid increase of newly designed enzymes, we face the shortage of parameters related to enzymatic reactions. The beta-glucosidase is a crucial enzyme for cellulose hydrolysis and cellobiose is one of its substrates. In this study, we attempt to develop models to predict Km with cellobiose as substrates using information about primary structure of beta-glucosidase. The results show that the 20-1 feedforward backpropagation neural network using the amino-acid distribution probability as predictor works best for prediction of Km values.

Prediction of optimal pH and temperature of cellulases using neural network.

Cellulase is an important enzyme widely used in various industries, and now in fermentation of biomass into biofuels. Enzymatic function of cellulase is closely related to pH, temperature, substrate concentration, etc. For newly found cellulase, it would be more cost-effective to predict its optimal pH and temperature before conducting the costly experiments. In this study, we used a 20-2 feedforward backpropagation neural network to build the relationship between information obtained from primary structure of cellulase with optimal pH and temperature to predict the optimal pH and temperature in cellulases. The results show that the amino-acid distribution probability representing the primary structure of cellulase can predict both optimal pH and temperature, whereas various properties of amino acids related to the primary structure cannot do so.

Fitting evolutionary process of influenza A virus nucleoproteins using analytical solution of system of differential equations.

Very recently we explored the possibility of using differential equations to describe the evolution of proteins. In this study we used the amino-acid pair predictability to quantify 1709 nucleoproteins of influenza A viruses isolated from 1918 to 2008 to represent their evolutionary process, thereafter we used the analytical solution of system of differential equations to fit the evolution of the nucleoprotein family. The results showed that the analytical solution could fit the nucleoprotein evolution and the obtained parameters were useful for timing of future mutations. Our approach provided a way to quantitatively analyze protein dynamics and evolution.

Trends in global warming and evolution of matrix protein 2 family from influenza A virus.

The global warming is an important factor affecting the biological evolution, and the influenza is an important disease that threatens humans with possible epidemics or pandemics. In this study, we attempted to analyze the trends in global warming and evolution of matrix protein 2 family from influenza A virus, because this protein is a target of anti-flu drug, and its mutation would have significant effect on the resistance to anti-flu drugs. The evolution of matrix protein 2 of influenza A virus from 1959 to 2008 was defined using the unpredictable portion of amino-acid pair predictability. Then the trend in this evolution was compared with the trend in the global temperature, the temperature in north and south hemispheres, and the temperature in influenza A virus sampling site, and species carrying influenza A virus. The results showed the similar trends in global warming and in evolution of M2 proteins although we could not correlate them at this stage of study. The study suggested the potential impact of global warming on the evolution of proteins from influenza A virus.

Rationale for cross-species infection and cross-subtype mutation in hemagglutinins from influenza A virus.

The current H1N1 swine pandemic raises the question why the cross-species infection and cross-subtype mutation are so easy. In this study, we use ANOVA to analyze all influenza A virus hemagglutinins available in database in order to answer this question. The results show that there are differences between species and between subtypes in most cases, but the intra-subtype/species variation is generally larger than the inter-subtype/species variation, which provides the rational for cross-species infection and cross-subtype mutation.

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.

Analysis of distributions of amino acids in the primary structure of tumor suppressor p53 family according to the random mechanism.

It is well known that the evolutionary process leads to the majority of amino acids clustering in some regions rather than being homogenously distributed along a protein. Among numerous factors affecting the evolutionary process is chance, whose impact therefore should be present in a protein primary structure. The issue of how to measure the random distribution of amino acids in a primary structure is of importance for the understanding of protein structure and functions. In this study, we use the random principle as a tool to analyze and compare the distributions of amino acids in the primary structure of the p53 protein family. The results, for example, show that the amino acids are distributed more randomly in mouse p53 and less randomly in common tree shrew p53, the distribution ranks of amino acids are relatively lower in the functional regions (about 0.5 on average) than in the whole sequences (about 1.2 on average) except for mouse p53. From the probabilistic distribution view, the composition of human p53 is relatively stable in the functional regions rather than in the whole sequence, which may suggest one of the potential effects on the mutations inducing human cancers. In general, we can use the distribution probability to present quantitatively a type of distribution of amino acids in a protein, to compare quantitatively the magnitude of clusters between different proteins and to track the effect of chance on the evolutionary process.