Genome complexity: more on protein folding (Introduction)

by David Turell , Thursday, December 24, 2020, 22:38 (1216 days ago) @ David Turell

How does life search for the right folded protein for the right function to result?:

https://evolutionnews.org/2020/12/protein-folding-breakthrough-evolution-or-design/

"DeepMind is a leader in artificial intelligence (AI). Its geniuses managed to beat humans at the popular name Go using its AlphaGo algorithm. Its AI systems have now reached 90 percent success at predicting how a protein will fold.

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"A major challenge, however, is that the number of ways a protein could theoretically fold before settling into its final 3D structure is astronomical. In 1969 Cyrus Levinthal noted that it would take longer than the age of the known universe to enumerate all possible configurations of a typical protein by brute force calculation — Levinthal estimated 10^300 possible conformations for a typical protein. Yet in nature, proteins fold spontaneously, some within milliseconds — a dichotomy sometimes referred to as Levinthal’s paradox.

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"But we need to remember that this folding problem that has baffled humans for 50 years is solved rapidly in living cells at every moment. Levinthal noted that proteins routinely “fold spontaneously, some within milliseconds” inside the cell. A few need help from chaperones to find their native fold, but many go directly from 1D amino acid sequence to 3D functional protein.

"That’s not all. The cell has repair enzymes, too, that can dismantle improperly folded proteins and fix them or replace them if they are irreparable.

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"Returning to the protein folding problem, we have seen that the search space for protein folds is vast beyond comprehension, like an island as big as the universe. Observing cells routinely folding proteins quickly and accurately, one can conclude therefore that a mind was behind the information. That conclusion is certified by watching AI experts using their minds to reverse engineer protein folds. AI is not inventing sequences that will fold; it is trying to figure out how a given sequence will produce an observed functional fold. Inventing a fold de novo is the harder problem.

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"In short, DeepMind’s achievement is laudable, but the real prize goes to the designer of protein systems: their encoding in DNA, their translation in the ribosome, their spontaneous (sometimes chaperone-assisted) folding, their functions, their interactions, and their repair mechanisms. All those get perfect scores when not harmed by random mutations that degrade information. AI has not even begun to imitate those capabilities. Any higher scores through AI in the future will be attained by intelligent design, not evolution. The news only underscores the superior knowledge built into the molecular basis of life."

Comment: If this doesn't prove a designer did it, nothing will. Life is here because the proteins are expertly folded for coordinated function