The bioinformatics of nucleotide sequence coding for proteins requiring metal coenzymes and proteins embedded with metals

All metallo-proteins need post-translation metal incorporation. In fact, the isotope ratio of Fe, Cu, and Zn in physiology and oncology have emerged as an important tool. The nickel containing F430 is the prosthetic group of the enzyme methyl coenzyme M reductase which catalyzes the release of methane in the final step of methano-genesis, a prime energy metabolism candidate for life exploration space mission in the solar system. The 3.5 Gyr early life sulfite reductase as a life switch energy metabolism had Fe-Mo clusters. The nitrogenase for nitrogen fixation 3 billion years ago had Mo. The early life arsenite oxidase needed for anoxygenic photosynthesis energy metabolism 2.8 billion years ago had Mo and Fe. The selection pressure in metal incorporation inside a protein would be quantifiable in terms of the related nucleotide sequence complexity with fractal dimension and entropy values. Simulation model showed that the studied metal-required energy metabolism sequences had at least ten times more selection pressure relatively in comparison to the horizontal transferred sequences in Mealybug, guided by the outcome histogram of the correlation R-sq values. The metal energy metabolism sequence group was compared to the circadian clock KaiC sequence group using magnesium atomic level bond shifting mechanism in the protein, and the simulation model would suggest a much higher selection pressure for the energy life switch sequence group. The possibility of using Kepler 444 as an example of ancient life in Galaxy with the associated exoplanets has been proposed and is further discussed in this report. Examples of arsenic metal bonding shift probed by Synchrotron-based X-ray spectroscopy data and Zn controlled FOXP2 regulated pathways in human and chimp brain studied tissue samples are studied in relationship to the sequence bioinformatics. The analysis results suggest that relatively large metal bonding shift amount is associated with low probability correlation R-sq outcome in the bioinformatics simulation.

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