Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influenza A virus

Quantifying Mutational Response to Track the Evolution of SARS-CoV-2 Spike Variants: Introducing a Statistical-Mechanics-Guided Machine Learning Method

The emergence of SARS-CoV-2 and its variants that critically affect global public health requires characterization of mutations and their evolutionary pattern from specific Variants of Interest (VOIs) to Variants of Concern (VOCs). Leveraging the concept of equilibrium statistical mechanics, we introduce a new responsive quantity defined as "Mutational Response Function (MRF)" aptly quantifying domain-wise average entropy-fluctuation in the spike glycoprotein sequence of SARS-CoV-2 based on its evolutionary database. As the evolution transits from a specific variant to VOC, we find that the evolutionary crossover is accompanied by a dramatic change in MRF, upholding the characteristic of a dynamic phase transition. With this entropic information, we have developed an ancestral-based machine learning method that helps predict future domain-specific mutations. The feedforward binary classification model pinpoints possible residues prone to future mutations that have implications for enhanced fusogenicity and pathogenicity of the virus. We believe such MRF analyses followed by a statistical mechanics augmented ML approach could help track different evolutionary stages of such species and identify a critical evolutionary transition that is alarming.

Searching of predictors to predict pH optimum of cellulases

The optimal working conditions for enzymes are very much elegant, and their determination is often through experimental approach, which generally is costly and time-consuming. Therefore, it is important to develop methods to use as simple as possible information to predict the optimal working condition for enzymes. Cellulase is a very important enzyme widely used in industries. In this study, we attempted to use a 20-1 feedforward backpropagation neural network to screen 24 amino acid properties related to the primary structure of cellulases as predictors to predict the pH optimum in cellulases. The results show that some predictors can predict the pH, especially amino acid distribution probability.

Evidence obtained from ANOVA to reason cross-species infection and cross-subtype mutation in neuraminidases of influenza A viruses

The current pandemic of A/H1N1 influenza raises a serious question on cross-species infection and cross-subtype mutation because our previous focus on possible influenza pandemic laid on H5N1 subtype and the cross-species infection between avian and human. In this study, we analyse 3874 neuraminidases from influenza A viruses using anova to answer the question of if there is barrier between species and between subtypes. The results show that there is no cross-species barrier in some species, and the intra-species variation is larger than the inter-species variation in some species hosting the viruses, and the cross-subtype mutation is possible because there is no cross-subtype barrier in some subtypes and the intra-subtype variation is larger than the inter-subtype variation in some subtypes. These results highlight the state of barrier of influenza A virus, which can help us understand the current pandemic and manufacture more effective vaccines.

Mutation patterns in human α-galactosidase A

A way to study the mutation pattern is to convert a 20-letter protein sequence into a scalar protein sequence, because the 20-letter protein sequence is neither vector nor scalar while a promising way to study patterns is in numerical domain. In this study, we use the amino-acid pair predictability to convert α-galactosidase A with its 137 mutations into scalar sequences, and analyse which amino-acid pairs are more sensitive to mutation. Our results show that the unpredictable amino-acid pairs are more sensitive to mutation, and the mutation trend is to narrow the difference between predicted and actual frequency of amino-acid pairs.

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.

Quantitative relationship between mutated amino-acid sequence of human copper-transporting ATPases and their related diseases

Copper-transporting ATPase 1 and 2 (ATP7A and ATP7B) are two highly homologous P-type copper ATPase exporters. Mutations in ATP7A can lead to Menkes disease which is an X-linked disorder of copper deficiency. Mutations in ATP7B can cause Wilson disease which is an autosomal recessive disorder of copper toxicity. In this study, we attempt to build a quantitative relationship between mutated ATPase and Menkes/Wilson disease. First, we use the amino-acid distribution probability as a measure to quantify the difference in ATPase before and after mutation. Second, we use the cross-impact analysis to define the quantitative relationship between mutant ATPase protein and Menkes/Wilson disease, and compute various probabilities. Finally, we use the Bayesian equation to determine the probability that Menkes/Wilson disease is diagnosed under a mutation. The results show (i) the vast majority of mutations lead to the amino-acid distribution probability increase in mutant ATP7As and decrease in ATP7Bs, and (ii) the probability that a mutation causes Menkes/Wilson disease is about nine tenth. Thus we provide a way to use the descriptively probabilistic method to couple the mutation with its clinical outcome after quantifying mutations in proteins.