Prediction of Mutation Positions in H5N1 Neuraminidases From Influenza A Virus by Means of Neural Network

Abstract

Quantification of mutation capacity within a protein could be a way to model the mutation relationship not only because history might not leave many cues on the causes for mutations but also the evolved protein might no longer be subject to previous mutation causes. Randomness should play a constant role in engineering mutations in proteins because randomness suggests the maximal probability of occurrence by which a protein would be constructed with the least time and energy to meet the speed of rapidly changing environments. Since 1999, we have developed three approaches for quantifying of randomness of protein by which each amino acid has three numeric values. In this study, we model our three random numeric values in each amino acid with occurrence and non-occurrence of mutation, which are classified as unity and zero, using a 3-6-1 feedforward backpropagation neural network to predict the mutation positions in H5N1 neuraminidases. The results show that the neural network can capture the mutation relationship as measured by prediction sensitivity, specificity, and total correct rate. With the help of translation probability between RNA codes and mutated amino acids, we predict the would-be-mutated amino acids at predicted mutation positions.

Searching of Predictors to Predict pH Optimum of Cellulases

“…Actually, all these pieces of information is to use a particular number to replace a certain amino acid in proteins; naturally, each amino acid should have a fixed value using these pieces of information. On the other hand, we have developed a measure, which results in different value for the same type of amino acid [26][27][28][29][30] based on occupancy of subpopulations and partitions [31] according to the following equation:…”

Predictors for Predicting Temperature Optimum in Beta-Glucosidases

“…Each type of amino acids has its distribution probability as example shown in Table 2. However, the same type of amino acids can have different values in different proteins according to their real distribution pattern along protein sequence [34][35][36][37][38].…”

Possibility of cross-species/subtype reassortments in influenza A viruses

“…As mentioned in the Introduction, the one-way ANOVA deals with decimal data rather than alphabets that represent amino acids in proteins, thus all NS proteins must be converted into numbers, and the amino-acid pair predictability has been used to do so. [13][14][15][16][17][18][19][20][21][22][23][24][68][69][70][71][72][73] Taking the ABB86874 NS2 protein (strain A/ swine/Ontario/57561/03[H1N1]) as an example, it has 121 amino acids, which construct 120 adjacent amino-acid pairs. This NS2 protein has 15 leucines, "L", and the appearance of amino-acid pair LL should be twice according to the permutation (15/121 × 14/120 × 120 = 1.74).…”