Estimation of amino acid pairs sensitive to variants in human phenylalanine hydroxylase protein by means of a random approach

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.

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

In this study, we estimate the translation probability to amino acid from RNA codon. With the determined 183 translation probabilities and amino-acid composition of eight highly mutated proteins, we construct the theoretical distributions of mutated amino acids in these proteins and then compare them with their actual distributions affected by mutations. Thereafter we trace the pattern of translation probabilities from RNA codons to mutated amino acids of 1053 point missense mutations. Finally, we statistically conclude that the natural mutation trend goes along the theoretical translation probability.

Timing of mutation in hemagglutinins from influenza A virus by means of unpredictable portion of amino-acid pair and fast Fourier transform

In this study, we calculate the unpredictable portion of amino-acid pairs, which has been developed by us over the last several years, of 1201 hemagglutinins from influenza A viruses dated from 1918 to 2004 in order to compare them with respect to subtypes, species, and years. After noticing the fluctuations of unpredictable portion along the time course, we use the fast Fourier transform to find the mutation periodicity of hemagglutinins. Then we estimate our position at the current cycle of hemagglutinin evolutionary process to determine how many years remain before the next outbreak of influenza and bird flu. Finally, we use the trend line and channel to outlook the hemagglutinins for the next half a century. As our study covers almost all the full-length amino-acid sequences of hemagglutinins from various influenza A viruses, the conclusion will be valid for years until the number of hemagglutinins in protein databank will be significantly increased.

Reasoning of spike glycoproteins being more vulnerable to mutations among 158 coronavirus proteins from different species

In this study, we used the probabilistic models developed by us over the last several years to analyze 158 proteins from coronaviruses in order to determine which protein is more vulnerable to mutations. The results provide three lines of evidence suggesting that the spike glycoprotein is different from the other coronavirus proteins: (1) the spike glycoprotein is more sensitive to mutations, this is the current state of the spike glycoprotein, (2) the spike glycoprotein has undergone more mutations in the past, this is the history of spike glycoprotein, and (3) the spike glycoprotein has a bigger potential towards future mutations, this is the future of spike glycoprotein. Furthermore, this study gives a clue on the species susceptibility regarding different proteins.

Figure Predictable and unpredictable portions in coronavirus proteins. The data are presented as median with interquartile range. * the predictable and unpredictable portions in spike glycoprotein group are statistically different from any other protein groups at p<0.05 level, except for hemagglutinin-esterase precursor group. # the predictable and unpredictable portions in spike glycoprotein group are statistically different from hemagglutinin-esterase precursor, membrane protein and nucleocapsid protein groups at p<0.05 level. † the predictable and unpredictable portions in spike glycoprotein group are statistically different from hemagglutinin-esterase precursor, and membrane protein groups at p<0.05 level.

Determination of sensitive positions to mutations in human p53 protein

Over last several years, we demonstrated that the mutations are more likely to occur at randomly unpredictable amino acid pairs in a protein. We therefore can in principle predict the amino acid pairs sensitive to the future mutations in a protein. However, we still need to predict the positions at which the sensitive amino acid pairs are located in a protein. In this study, we use a probabilistic approach to analyze the effect of 191 mutations in human p53 protein and can approximately estimate the sensitive positions to mutations in human p53 protein.

Potential targets for anti-SARS drugs in the structural proteins from SARS related coronavirus

This is a further study on the severe acute respiratory syndrome (SARS) using the probabilistic models. The purpose was to define the potential targets for anti-SARS drugs in the structural proteins from human SARS related coronavirus (SARS-CoV) while knowing little about the functional sites and possible mutations in these proteins. From a probabilistic viewpoint, we can theoretically select the amino acid pairs as potential candidates for anti-SARS drugs. These candidates have a greater chance of colliding with anti-SARS drugs, are more likely to link with the protein functions and are less vulnerable to mutations.

Fate of 130 hemagglutinins from different influenza A viruses

In this study, we use our probabilistic models to analyze 130 hemagglutinins from different influenza A virus in order to gain the insight into their fate. The results provide three lines of evidence regarding the H5, H6, and H9 hemagglutinins: (i) the H5 hemagglutinins are more sensitive to mutations, this is the current state of the H5, H6, and H9 hemagglutinins; (ii) the H5 hemagglutinins had experienced more mutations in the past, this is the history of the H5, H6, and H9 hemagglutinins; and (iii) the H6 hemagglutinins has a bigger potential towards future mutations, this is the future of the H5, H6, and H9 hemagglutinins. Furthermore, this study gives two clues on the mutation tendency that is a degeneration process and the species susceptibility that is the chickens and ducks.

Prediction of amino acid pairs sensitive to mutations in the spike protein from SARS related coronavirus

In this study, we analyzed the amino acid pairs affected by mutations in two spike proteins from human coronavirus strains 229E and OC43 by means of random analysis in order to gain some insight into the possible mutations in the spike protein from SARS-CoV. The results demonstrate that the randomly unpredictable amino acid pairs are more sensitive to the mutations. The larger is the difference between actual and predicted frequencies, the higher is the chance of mutation occurring. The effect induced by mutations is to reduce the difference between actual and predicted frequencies. The amino acid pairs whose actual frequencies are larger than their predicted frequencies are more likely to be targeted by mutations, whereas the amino acid pairs whose actual frequencies are smaller than their predicted frequencies are more likely to be formed after mutations. These findings are identical to our several recent studies, i.e. the mutations represent a process of degeneration inducing human diseases.

Analysis of amino acid pairs sensitive to variants in human collagen alpha5(IV) chain precursor by means of a random approach

In this data-based theoretical analysis, we use the random approach to analyze the amino acid pairs in 5(IV) chain precursor (CA54) in order to determine which amino acid pairs are more sensitive to 151 variants from missense mutant human CA54 protein. The rationale of this study is based on our hypothesis and previous findings that harmful variance is more likely to occur at randomly unpredictable amino acid pair position rather than at randomly predictable positions. This is reasonable to argue as randomly predictable amino acid pairs are less likely to be deliberately evolved, whereas randomly unpredictable amino acid pairs are probably deliberately evolved in connection with protein function. The results show that all 151 variants occurred at randomly unpredictable amino acid pairs and the chance of a variant occurring is markedly higher in randomly unpredictable amino acid pairs than in predictable pairs. Thus, randomly unpredictable amino acid pairs are more sensitive to variance in human CA54. The results also suggest that the human CA54 protein has a natural tendency towards variants.