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.
Wu
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Computational Mutation Project, DreamSciTech Consulting, Shenzhen, Guangdong Province, China. hongguanglishibahao@yahoo.com
Yan
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D000595:Amino Acid Sequence; D000596:Amino Acids / Q000235:genetics; D003198:Computer Simulation; D006388:Hemagglutinins / Q000235:genetics*; D053124:Influenza A Virus, H5N1 Subtype / Q000235:genetics*; D016296:Mutagenesis; D009154:Mutation; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D015202:Protein Engineering / Q000379:methods*
D000596:Amino Acids; D006388:Hemagglutinins