# Yale University computer science professor takes a look at protein formation probabilities

When I was in graduate school, we studied a book called “Mirror Worlds”, authored by famous computer science professor David Gelernter at Yale University. This week, I noticed that Dr. Gelernter had written an article in the prestigious Claremont Review of Books. In his article, he applies his knowledge of computer science to the problem of the origin of life.

Evolution, if it is going to work at all, has to explain the problem of how the basic building blocks of life – proteins – can emerge from non-living matter. It turns out that the problem of the origin of life is essentially a problem of information – of code. If the components of proteins are ordered properly, then the sequence folds up into a protein that has biological function. If the sequence is not good, then just like computer code, it won’t run.

Here’s Dr. Gelernter to explain:

How to make proteins is our first question. Proteins are chains: linear sequences of atom-groups, each bonded to the next. A protein molecule is based on a chain of amino acids; 150 elements is a “modest-sized” chain; the average is 250. Each link is chosen, ordinarily, from one of 20 amino acids. A chain of amino acids is a polypeptide—“peptide” being the type of chemical bond that joins one amino acid to the next. But this chain is only the starting point: chemical forces among the links make parts of the chain twist themselves into helices; others straighten out, and then, sometimes, jackknife repeatedly, like a carpenter’s rule, into flat sheets. Then the whole assemblage folds itself up like a complex sheet of origami paper. And the actual 3-D shape of the resulting molecule is (as I have said) important.

Imagine a 150-element protein as a chain of 150 beads, each bead chosen from 20 varieties. But: only certain chains will work. Only certain bead combinations will form themselves into stable, useful, well-shaped proteins.

So how hard is it to build a useful, well-shaped protein? Can you throw a bunch of amino acids together and assume that you will get something good? Or must you choose each element of the chain with painstaking care? It happens to be very hard to choose the right beads.

Gelernter decides to spot the Darwinist a random sequence of 150 elements. Now the task the Darwinist is to use random mutation to arrive at a sequence of 150 links that has biological function.

[W]hat are the chances that a random 150-link sequence will create such a protein? Nonsense sequences are essentially random. Mutations are random. Make random changes to a random sequence and you get another random sequence. So, close your eyes, make 150 random choices from your 20 bead boxes and string up your beads in the order in which you chose them. What are the odds that you will come up with a useful new protein?

[…]The total count of possible 150-link chains, where each link is chosen separately from 20 amino acids, is 20150. In other words, many. 20150 roughly equals 10195, and there are only 1080 atoms in the universe.

What proportion of these many polypeptides are useful proteins? Douglas Axe did a series of experiments to estimate how many 150-long chains are capable of stable folds—of reaching the final step in the protein-creation process (the folding) and of holding their shapes long enough to be useful. (Axe is a distinguished biologist with five-star breeding: he was a graduate student at Caltech, then joined the Centre for Protein Engineering at Cambridge. The biologists whose work Meyer discusses are mainly first-rate Establishment scientists.) He estimated that, of all 150-link amino acid sequences, 1 in 1074 will be capable of folding into a stable protein. To say that your chances are 1 in 1074 is no different, in practice, from saying that they are zero. It’s not surprising that your chances of hitting a stable protein that performs some useful function, and might therefore play a part in evolution, are even smaller. Axe puts them at 1 in 1077.

In other words: immense is so big, and tiny is so small, that neo-Darwinian evolution is—so far—a dead loss. Try to mutate your way from 150 links of gibberish to a working, useful protein and you are guaranteed to fail. Try it with ten mutations, a thousand, a million—you fail. The odds bury you. It can’t be done.

Keep in mind that you need many, many proteins in order to have even a simple living cell. (And that’s not even considering the problem of organizing the proteins into a system).

So, if you’re a naturalist, then your only resources to explain the origin of life are chance and mutation. As Dr. Gelernter shows, naturalistic explanations won’t work to solve even part of the problem. Not even with a long period of time.  Not even if you use the entire universe as one big primordial soup, and keep trying sequences for the history of the universe. It just isn’t possible to arrive at sequences that have biological function in the time available, using the resources available. The only viable explanation is that there is a computer scientist who wrote the code without using trial and error. Something that ordinary software engineers like myself and Dr. Gelernter do all the time. We know what kind of cause is adequate to explain functioning code.

## One thought on “Yale University computer science professor takes a look at protein formation probabilities”

1. Yep, they continue to suppress the truth in unrighteousness. Every day they discover more of how exquisitely God made his universe, yet they live in denial of it to justify their sins.

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