Intelligent Design

Protein Folding and Evolution

Spread the love

Proteins consist of hundreds of amino acids attached to each other like train cars, and when they fold up they consistently find the same three dimensional shape. Like a necklace that magically falls into the same shape every time it is dropped onto a table, the consistency of protein folding once seemed like a paradox. For there is an astronomical number of shapes the protein could possibly take on. How does it find the same one so consistently, and so quickly? The answer has interesting implications for evolution.  Read more

3 Replies to “Protein Folding and Evolution

  1. 1
    bornagain77 says:

    🙂 Do Not Try This At Home 🙂 Very Funny Dr. Hunter

    Here are a few notes on the sheer wonder of protein folding itself:

    Instead of us just looking at the probability of finding a single ‘simple’ protein molecule by chance, (a solar system full of blind men solving the Rubik’s Cube simultaneously (Hoyle), let’s also look at the complexity which goes into crafting the shape of just one protein molecule. Complexity will give us a better indication if a protein molecule is indeed the handi-work of an infinitely powerful Creator.

    In the year 2000 IBM announced the development of a new super-computer, called Blue Gene, which was 500 times faster than any supercomputer built up until that time. It took 4-5 years to build. Blue Gene stands about six feet high, and occupies a floor space of 40 feet by 40 feet. It cost $100 million to build. It was built specifically to better enable computer simulations of molecular biology. The computer performs one quadrillion (one million billion) computations per second. Despite its speed, it was estimated to take one entire year for it to analyze the mechanism by which JUST ONE “simple” protein will fold onto itself from its one-dimensional starting point to its final three-dimensional shape.

    “Blue Gene’s final product, due in four or five years, will be able to “fold” a protein made of 300 amino acids, but that job will take an entire year of full-time computing.” Paul Horn, senior vice president of IBM research, September 21, 2000

    Networking a few hundred thousand computers together has reduced the time to a few weeks for simulating the folding of a single protein molecule:

    A Few Hundred Thousand Computers vs. A Single Protein Molecule – video

    As well, despite some very optimistic claims, it seems future ‘quantum computers’ will not fair much better in finding functional proteins in sequence space than even a idealized ‘material’ supercomputer of today can do:

    The Limits of Quantum Computers – March 2008
    Excerpt: “Quantum computers would be exceptionally fast at a few specific tasks, but it appears that for most problems they would outclass today’s computers only modestly. This realization may lead to a new fundamental physical principle”

    The Limits of Quantum Computers – Scott Aaronson – 2007
    Excerpt: In the popular imagination, quantum computers would be almost magical devices, able to “solve impossible problems in an instant” by trying exponentially many solutions in parallel. In this talk, I’ll describe four results in quantum computing theory that directly challenge this view.,,, Second I’ll show that in the “black box” or “oracle” model that we know how to analyze, quantum computers could not solve NP-complete problems in polynomial time, even with the help of nonuniform “quantum advice states”,,,

    Here is Scott Aaronson’s blog in which refutes recent claims that P=NP (Of note: if P were found to equal NP, then a million dollar prize would be awarded to the mathematician who provided the proof that NP problems could be solved in polynomial time):

    Excerpt: Quantum computers are not known to be able to solve NP-complete problems in polynomial time.

    Protein folding is found to be a ‘intractable NP-complete problem’ by several different methods. Thus protein folding will not be able to take advantage of any advances in speed that quantum computation may offer to any other problems of computation that may be solved in polynomial time:

    Combinatorial Algorithms for Protein Folding in Lattice
    Models: A Survey of Mathematical Results – 2009
    Excerpt: Protein Folding: Computational Complexity
    NP-completeness: from 10^300 to 2 Amino Acid Types
    NP-completeness: Protein Folding in Ad-Hoc Models
    NP-completeness: Protein Folding in the HP-Model

    Another factor severely complicating man’s ability to properly mimic protein folding is that, much contrary to evolutionary thought, many proteins fold differently in different ‘molecular’ situations:

    The Gene Myth, Part II – August 2010
    Excerpt: the rate at which a protein is synthesized, which depends on factors internal and external to the cell, affects the order in which its different portions fold. So even with the same sequence a given protein can have different shapes and functions. Furthermore, many proteins have no intrinsic shape, taking on different roles in different molecular contexts. So even though genes specify protein sequences they have only a tenuous influence over their functions.

    Also of interest to the extreme difficultly man has in computing the folding of a protein within any reasonable amount of time, it seems water itself, (H2O), was ‘designed’ with protein folding in mind:

    Protein Folding: One Picture Per Millisecond Illuminates The Process – 2008
    Excerpt: The RUB-chemists initiated the folding process and then monitored the course of events. It turned out that within less than ten milliseconds, the motions of the water network were altered as well as the protein itself being restructured. “These two processes practically take place simultaneously“, Prof. Havenith-Newen states, “they are strongly correlated.“ These observations support the yet controversial suggestion that water plays a fundamental role in protein folding, and thus in protein function, and does not stay passive.

    Water Is ‘Designer Fluid’ That Helps Proteins Change Shape – 2008
    Excerpt: “When bound to proteins, water molecules participate in a carefully choreographed ballet that permits the proteins to fold into their functional, native states. This delicate dance is essential to life.”

    ,,,, As Frank Turek would say, ‘I Don’t Have Enough Faith To Be An Atheist’ 🙂

  2. 2
    kairosfocus says:


    Money excerpt from Dr Hunter’s post:


    >> while the protein folding paradox is resolved, there is a nagging feeling. Yes, the protein overcomes its entropy barrier—it all works just fine. But it works just fine only because a very special amino acid sequence was specified. That amino acid sequence is just as astronomically rare as the three dimensional structure that the unfolded protein was able to find. So from where did this amino acid sequence come?

    The string of amino acids that make up a protein comes from the cell’s translating machine called the ribosome. The ribosome takes as input a string of nucleotides and produces as output a string of amino acids. The translation is done according to the genetic code.

    And from where did the string of nucleotides come? It came from the DNA. A massive protein copying machine slides along an opened section of DNA and copies a gene.

    And from where did the DNA gene come? According to evolution it evolved, but it is here that we find another entropy barrier. Just as the folding protein is confronted with an astronomical number of structures, so too the DNA gene is confronted with its own nightmare of choices . . . .

    A typical gene has something like a thousand nucleotides. Given that there are four different types of nucleotides, this means there are 4^1000 different sequences that could make up the gene. This is equal to a 1 followed by about 600 zeros—a big number. That’s more than the number of nano seconds since the Big Bang—by about 10^574 (a 1 followed by 574 zeros).

    Finding the right gene sequence to get a particular job done in the cell would make finding a needle in a haystack seem easy. The problem is so difficult that we haven’t yet figured out the answer, but it would be a 1 in 10^100++ long shot. Do not try this at home.

    It is a huge entropy barrier, but it has some important differences compared to the protein folding entropy barrier we saw above. First, this gene entropy barrier is due to the large number of possible DNA nucleotide sequences whereas the protein folding entropy barrier was due to the large number of possible protein shapes.

    And the protein folding entropy barrier was overcome by those fortuitously coordinated chemical interactions, which as we saw traced back to the amino acid sequence, which in turn traced back to the gene sequence.

    But the gene entropy barrier has no such convenient explanation. Genes don’t come together via prearranged, specified information to overcome all odds. >>

    Muy interesante . . .

  3. 3
    bornagain77 says:

    previous money quote from Dr. Hunter,

    How Proteins Evolved – Cornelius Hunter – December 2010
    Excerpt: Comparing ATP binding with the incredible feats of hemoglobin, for example, is like comparing a tricycle with a jet airplane. And even the one in 10^12 shot, though it pales in comparison to the odds of constructing a more useful protein machine, is no small barrier. If that is what is required to even achieve simple ATP binding, then evolution would need to be incessantly running unsuccessful trials. The machinery to construct, use and benefit from a potential protein product would have to be in place, while failure after failure results. Evolution would make Thomas Edison appear lazy, running millions of trials after millions of trials before finding even the tiniest of function.

Leave a Reply