The origin of new genes

6. GENE GROWTH
6.1  Aunt Adoption
6.2  Gene regulation
6.3  The Leapfrog Protein
6.4  Adoption  
6.4.1 Why is adoption absolutely necessary for evolution?
     1. Darwin’s most primitive eye needs genes
6.4.2 Functional adoption and ‘function-acquiring mutations
     1. Dead genes
     2. The advantageous mutation
     3. Free mutation
     4. The Valley of Dead Genes
     5. A hidden path?  The distinction between different genes
     6. The distinction between different genes
     7. The Leapfrog protein is an essential protein which could not have evolved
     8. The greater part of the genes does not vary at all!
     9. Essential genes can differ greatly between non-related species
    10. The Evolution Mountain Range
    11. A bone to pick with E. coli
6.4.3 Metabolic adoption
6.5  Conclusions

In the previous chapter, we heard from the proponents of evolution themselves how macro-evolution theoretically would occur. Their words have seriously called the role of Master Crook Mutation into question, but there are a few people who are still not yet completely convinced. That is why I will try, in this chapter, to permanently settle the issue of Master Crook Mutation’s role as the (sole) cause of macro-evolution. That will immediately lay the foundation for dealing with his family members in chapter 7.

In order to have an idea of what we are discussing and to prevent unrealistic theoreticising and philosophising, we need to take a look at the way in which genes work together, and I will give a specific example of a robot protein. Afterwards, I will elaborate on the need for gene growth and adoption, because these are conditions of macro-evolution.

This is the most important, but also the most extensive and probably the most difficult chapter in the first part of the book. It might be helpful for the reader to read the summary which can be found in the Quick Tour in Chapter 18, in order to better understand the general line of this material.

6.1 Aunt Adoption
Do we know of a mechanism (like for instance natural variation) which takes care of gene growth? We will be applying ourselves to this question in this and the next chapter. This question is important, because the evolution theory says that there is (or has been) an increase in complexity. The ‘more highly developed’ animals originated from ‘less highly developed’ ancestors.^[1] In the end, we are all descended from unicellular organisms. Bacteria have only one chromosome which has much less DNA than any other creature. How did this growth in DNA get started? No, that isn’t put right. How did gene growth get started? Where did the hundreds of genes come from that code for organs which were not there at first? If a species has 10,000 genes, what happens to make it 10,001 at some point in time, at finally 11,000?
The question is actually bigger than that: how did macro-evolution get started? But it is clear that gene growth ^[2] is absolutely necessary for macro-evolution, because the number of genes varies greatly from species to species. If evolution took place between species with a different number of genes, somehow, from their common ancestor onwards, growth or increase in genes must have taken place.

This means that we can already come to a conclusion about Master Crook Mutation’s potential uncle: whatever his name is, whoever he is, he is married to Aunt Adoption! But to understand who Aunt Adoption is, we need a better understanding of the way in which genes and proteins work.

6.2 Gene-regulation
Genes are not just pieces of DNA that code for a protein, and they also don’t spend all day just making proteins. Nothing happens until the moment they are needed. Transcription, or the translation of the code to the model (mRNA) that makes the protein, has to be turned on or off. The production of proteins has to be able to be sped up, or slowed down. Certain genes only have to work in specific organs. In the skin, no insulin, which is necessary to maintain the blood sugar level, needs to be made, for example. In short: genes need to be regulated. This happens through operators, promotors and repressors. A gene which codes for a protein needs other, different genes to work with, which regulate it.

Below you can see an example of such co-operation of genes (called metabolism). Here you see the genes for lactose-metabolism in bacteria^[3] during which lactose is broken down into the two sugars glucose and galactose. These serve as ‘food’.

It is important to now study the figures below, before reading further.

Figure (a).  The order of the functional elements on the DNA
blue, i:		The code of a repressor gene; when this piece of DNA is translated, a Turn-Off-This-Gene protein is made
purple:		Transcription never happens at just any place on the DNA. There is a special make-a-model protein (RNA Polymerase) that makes the copies(mRNA) for proteins. This Make-A-Model protein starts to make its model when it comes across the specific code on the DNA which indicates that the model starts there. The Make-A-Model protein can attach itself to that spot. That place is shown in purple and is called the promotor.
green:		The Turn-Off-This-Gene protein also has a place where it can attach itself to the DNA. This is shown in Green, and is called the repressor.
brown,yellow,orange:		These are the codes for the three proteins necessary to break down lactose. They are indicated by the letters z,y and a.
Figure (b). inducer absent
If there is no lactose present in a cell, Turn-Off-This-Gene protein is produced continuously, which attaches itself to the green piece of code where the Turn-Off-This-Gene protein fits perfectly. As a result, the Make-A-Model protein is incapable of attaching itself to the pieces of code it needs in order to make a translation to a model. No copies are produced and no z-, y-, and a-proteins are produced.
Figure (c). Inducer present. Lactose is the inducer.
The Turn-Off-This-Gene protein is still being made continuously, but this protein has a keyhole, where the lactose key, and only the lactose key, fits exactly. This locks the Turn-Off-This-Gene protein, so that it can no longer attach itself to the (green) piece of code. The Make-A-Model protein is now able to find the Model-Starts-Here code, and copies are being made. From these copies, the z-, y-, and a-proteins are then made. The z-protein is the Break-Down-Lactose protein. When the lactose is gone, the Turn-Off-This-Gene protein is no longer locked, which means that the production of copies ceases, so that it returns to the situation in figure(b).

genes and their proteins co-operate closely

You see here a mechanism which only comes into play when it is necessary. There are different kinds and degrees of mechanisms. Here, too, you can see that a protein can have various functions. The Turn-Off-This-Gene protein can sit on a specific location on the DNA with a specific code in order to thwart the Make-A-Model protein, but it also has a place where lactose can attach itself, so that it can be locked.^[4] In order to fit itself to the DNA, this protein needs a three-dimensional structure which fits into the DNA spiral precisely, and then a few very specific amino acids at the right locations, which attach to very specific bases (A, C, G, or T). This is applicable to the Break-Down-Lactose protein, which has to be able to pick up lactose, and needs the right molecular tongs for that, to cut it in two at the right places. The y-protein is a Transport-Lactose protein. It can pass through the cell membrane to the outside, handcuff a lactose molecule, subsequently passing through the membrane again on the way in, because it has the right ID card, and there it releases the lactose again, so that it can be broken down by the Break-Down-Lactose protein. Now that’s working together!

gene teamwork works with ID’s and keys

Nothing in a cell happens by itself! Everything is arranged by proteins. All proteins are made by the cell itself. All proteins co-operate. One does this, the other does that. One arranges it that the other comes into play when it is necessary, the other arranges it so that yet another does something else. It is hopelessly complicated! It is also admirable that humans are capable of unravelling tiny pieces of the mystery time and again.

Figure 2. A molecular key: because the Signal protein (drawn as rods) fits exactly into the lock of the larger Receiver protein, (chemical) action is initiated (or not).

Figure 3. GLUT1 (green) is a Transport protein which recognises the ‘ID’ of glucose (red) and therefore allows it to pass through the cell wall. It is a Bouncer-for-a-Private-Club protein.

Figure 4. Biochemistry, pp. 880; Leapfrog protein

The point here is that a gene does not stand alone. One gene is nothing. One gene is only good for one protein. That protein has to do something very specific if it wants to be useful. That protein has to be active at the right place and at the right time. For that purpose, it needs other proteins and therefore other genes. I will call this gene teamwork. Gene teamwork says that one gene on its own is useless. Genes have to work together, be in tune with each other. If a Turn-Off-This-Gene protein suddenly (due to a mutation) no longer feels like binding lactose, then it goes very wrong, then all those other genes (z, y, and a) also do not act. Or if the Turn-Off-This-Gene protein suddenly attaches itself to a molecule other than lactose, the whole system is thrown into disarray. That is why such proteins have keys, so that they can only attach themselves to lactose. They have, as it were, gotten a certificate of uniqueness: there is absolutely no other molecule which fits.
So gene teamwork is full of unique keys, because in a cell, all sorts of molecules are jumbled together. If there were no keys, how would such a soup of proteins ever be able to work well? And a lot ofproteins have ID cards. Some are allowed to go outside, others are not. Yet others can bring others inside, or outside. Some have to be inside special buildings in the cell (with names like mitochondria or Golgi apparatus), or be brought out of those buildings. Intruders are unwelcome in these specialised factories, so you cannot enter without an ID or a guide. Teamwork. Rules of the game. Keys. Agreements. Locks. Pre-programmed or programmed.

6.3  The Leapfrog Protein
Now that we have gotten a hint of how genes work together, we are going to take a look at a concrete example of a protein-robot. That is practical because it is important to know what we are actually talking about when we discuss a ‘protein’ (and the gene behind it).

Tug-of-war
When a cell splits, resulting in two identical cells, the DNA is copied during that process. The two strands of DNA are pulled apart and the corresponding bases are added to both sides, resulting in two identical DNA molecules. This is a process that is intensively monitored by all sorts of proteins. One of those is the Leapfrog protein, which is actually called Topoisomerase.
Suppose you take a piece of rope a few meters long. You knot one side firmly to a chair, and you split the other side in two. Next you pull those two pieces apart. What happens then? The rest of the rope is rolled up very tightly and curls up on itself and gets all knotted up. That happens to DNA too, and that is not permissible. If the tension gets too great, it can break, or if it gets knotted, it can no longer be duplicated.^[5]
How does that work? Well, that is where the Leapfrog protein comes in. The Leapfrog protein has two large arms for gripping, one on top and one on the bottom, which together form the shape of a C. It takes hold of one half of a DNA strand with both paws right next to each other. It cuts through that half of the strand, but keeps hold of the ends. It then rotates the two cut ends in different directions toward the other side, and in the meantime lets the second half, as it were, inside, so that it embraces the strand. Then it glues the two cut pieces back together. When that has happened, it lets go and begins again there or somewhere else. In this way, the Leapfrog protein can take one entire DNA strand straight through another, so that unravelled DNA does not come to resemble a piece of yarn so entangled that is impossible to de-tangle it.

In figure 5 is a computer representation of the Leapfrog protein and how it cuts a strand of DNA and lets another through. As you can see, it is a highly specialised molecular robot.

Figure 6.the Leapfrog Protein at work
A.	Leapfrog protein at rest.
B.	The Leapfrog protein attaches itself to a single strand of DNA (green) at the red dot.
C.	The strand is severed and the Leapfrog protein opens up.
D.	Now a double (as in this case) or single strand of DNA can be allowed to pass through.
E.	The two sections of the severed DNA strand are glued together again.
F.	The Leapfrog protein opens again.
G.	The ‘imprisoned’ strand of DNA can get out. The process can start again, or the Leapfrog protein returns to its resting position.

6.4  Adoption
What does adoption mean? There are two forms of adoption to be distinguished from each other. When a protein, for instance already mutating, changes function and takes on a new, different function (which it did not already have), it adopts that new function, as it were. I will call this functional adoption. This is therefore no mere small alteration in which the same functionality remains, but is carried out differently, faster, or less, or better, etc. That is only a functional change or even damage. Functional adoption could perhaps be an accumulation of functional change, but if functional adoption is being discussed, the original function has to differ structurally or be essentially different from what it was before. It has to adopt a different, new function that it did not have at first, and it has to expand to the same refined degree of specialisation as the Leapfrog protein. Functional adoption is therefore specialisation, in which the whole structure of the protein is such that it is optimally equipped for its task, as is the case with all 100,000 genes in our cells!
Therefore, it is not even a question of functional adoption if a few amino acids change so that the existing protein suddenly has an effect on something that it previously did not have. That is throwing a wrench into the workings of another refined mechanism. Only if the protein became specialised in that function and all the parts were in tune with that function could you speak of functional adoption.
The second form of adoption is that a new gene (for instance a gene in which functional adoption has taken place) is adopted into the family of co-operating genes, in other words has to be regulated, turned on and off at the right time. For such an adoption, many other genes are necessary, such as, for instance, a regulator gene, a ‘binding site’ it can attach itself to, a promotor and/or a repressor, and such. This kind of adoption I call metabolic adoption. If the six genes of lactose metabolism as it is described above were to have developed from another metabolism that once did not have that functionality at all, then you could speak of metabolic adoption: A protein which has gone through an essential functional change has to be taken into some new chemical balance, process, mechanism (called metabolism).

6.4.1 Why is adoption absolutely necessary for evolution?
Although we have already looked at some arguments for why adoption is necessary, I will still give an extensive example here of why gene growth and the adoption it entails are absolute conditions of macro-evolution, the change from one species or type to another.

1.Darwin’s most primitive eye needs genes
As an example, take the classic argument of the eye, which has been used before by Darwin and many others after him. Is there something new to be said about it then? Is there anything to be added to the discussion which has not yet been said? Yes, there certainly is, because we can now look, on the lowest level of evolution, the level of DNA, at genes and proteins to see how an eye could have developed. Darwin did not have that option. We no longer have to speculate or even fantasise about what is and is not possible. On the DNA level, the possibilities and chances can even be calculated! So, let’s have a look.

Darwin says this:
If we must compare the eye to an optical instrument, we ought in imagination to take a thick layer of transparent tissue, with a nerve sensitive to light beneath, and then suppose every part of this layer to be continually changing slowly in density, so as to separate into layers of different densities and thicknesses, placed at different distances from each other, and with the surfaces of each layer slowly changing in form. Further we must suppose that there is a power always intently watching each slight accidental alteration in the transparent layers; and carefully selecting each alteration which, under varied circumstances, may in any way, or in any degree, tend to produce a distincter image. We must suppose each new state of the instrument to be multiplied by the million; and each to be preserved till a better be produced, and then the old ones to be destroyed. In living bodies, variation will cause the slight alterations, generation will multiply them almost infinitely, and natural selection will pick out with unerring skill each improvement. Let this process go on for millions on millions of years; and during each year on millions of individuals of many kinds; and may we not believe that a living optical instrument might thus be formed as superior to one of glass, as the works of the Creator are to those of man?^[6]

It is not hard to imagine. Close your eyes and try to see it before you. It can work. If that doesn’t work, the current computer programs might help. It is called morphing. Michael Jackson started it in a video clip, later advertisements followed, and these days you can buy simple programs which any amateur can put on their computers. You see an old man’s face transform fluidly into the face of a child, an African into a European, a woman into a man, a human into a panther. It is not hard to imagine that a layer of transparent skin with a light-sensitive nerve under it develops into an eye like a human’s. You can even make a computer animation of it. Except, Darwin knew nothing about genetics...

What does an organism need, an organism which does not yet have anything resembling an eye, to begin to develop one? Genes! Genes arrange the development of the eye during embryonic growth, genes maintain the eye, genes arrange the processes in the eye, in the nerves, in the brain which turns that light into a picture that our mind understands. Nothing in a cell happens by itself. That takes proteins and genes that code for those proteins. And all those genes have to come from somewhere. They don’t need to all end up in an organism at the same time, but, if evolution is going to happen, there does need to be an increase in genes which do this. In the beginning, it will be a few, the ones which make a nerve cell appear at the right place, and the ones which ensure that it is built in at the right moment during embryonic development and the ones which take care of communication with the brain and the co-ordination with the rest of the body. There is no point to this process if a stimulus from that primitive ‘eye’ does not become ‘conscious’ in a certain way, so that it can be reacted to. A photosensitive spot on my big toe is of no use to me if I cannot receive the signal in my brain, so that I can do something with it. Let us say that the most primitive form of the eye, as Darwin suggested it, needs ten genes. This is a bit less than is actually needed, but we have to start somewhere. I will propose this very simplistically the first time, in a way which does not do justice to the complex reality of genes. However, if I do it in a highly simplified way, it will in any case be clear to everyone how necessary gene growth is for evolution.

Here you see the ten genes. In actuality, the number of genes is a multiple of what is named above, even for a minimal primitive eye (see Box). However, it is clear that it is necessary that these genes are formed in some way, have to originate in the DNA of an organism, if a new organ, like an eye, is to be able to grow. Next, a steady increase in the number of genes for this eye is still necessary, because hundreds or even thousands of genes are necessary for the most complex eye there is.

gene growth is absolutely necessary

It is clear, gene growth is necessary, but a general increase in genes by itself is not enough. Functional adoption is also necessary. If an organism has no nerve cells and these nerve cells need to originate, then not only does the number of genes need to increase, but completely new functions must also be carried out by these genes, which had never been seen before. Proteins must be produced which have never been produced before. Robots need to appear which can do things that have never before been possible. A new gene must therefore also acquire a new useful function, or adopt one. Such a new function, for instance React-To-Light, or the function Make-A-Cell-Transparent, must also be regulated, that is: it must be adopted by the community of genes which is already in place, so that it will carry out its function at the right time and in the right place. Not only does a group of genes work together, groups of these groups work together in an organ, and those organs have to co-operate with all the groups of co-operating genes in other organs. Before an organ can function at all, and before the useful proteins in that organ are able to fulfil their functions, groups of co-operating genes have to have built that organ during the development of the embryo.
These three, therefore, gene growth, functional adoption, and metabolic adoption must be structurally possible if evolution on a large scale (macro-evolution) is going to be able to take place. Because evolution is, in the first place, arbitrary, such an infinite amount of variations on new (co-operative) genes needs to arise, as it were, that natural selection has more than enough to choose from to get the kind of specialisation and the level of co-operation that we observe in living organisms.

With this, the difference between micro-evolution and macro-evolution becomes clear almost immediately: macro-evolution could be (at least) defined as ‘the origin of new groups of co-operating genes which fulfil functions which have not previously been observed in that organism’. All other alterations in or combinations of existing genes are thus variation on a theme or micro-evolution, because through that by itself, nothing fundamentally new will be added.

6.4.2 Functional adoption and ‘function-acquiring
mutations’
What we are going to do now is look at how a protein, already in the process of mutating, can grow towards another function, in other words how functional adoption would arise. This accumulating, gradually mutating change towards another function I will cal gradual adoption, in contrast to the radical leaping transition to a new function, which is also called leaping adoption. The latter is covered in the next chapter.
In Genetic analysis on pp. 794 under the heading [Het ontstaan van nieuwe functies] is the only(!) example given in all of my sources: B. Hall has experimentally changed a gene to a new function in Escherichia coli^[7]. In addition to the lacZ genes specifying the usual lactose-fermenting ß-galactosidase activity in E. coli, another structural gene locus ebg specifies an other ß-galactosidae that does not ferment lactose, although it is induced by lactose. The natural function of this second enzyme is unknown. Hall was able to alter this gene into one specifying an enzyme that ferments another substrate, lactobionate. To do so, it was necessary to alter the regulatory element to a constitutive state and to produce three successive structural-gene mutations.

Does this not show clearly and convincingly enough that functional adoption is possible? Or does it? (The answer follows under point 11.)

1. Dead genes
Dead genes genes can lose their function by mutation
Genes can lose their function so that they no longer have any function, though they may still code for a protein. Recessive inheritance of a characteristic often indicates that:

A recessive allele often has lost part or all of its ability to perform the function of the normal allele. In a heterozygote, one copy of the dominant allele may provide enough of a given gene’s normal function to support the development of a normal phenotype. Thus in a pea plant heterozygous for round seed trait (Rr), the single R allele allows enough of a specific enzyme to be synthesized so that sufficient starch is manufactured to give a firm, round appearance to the seed. In a homozygous recessive plant (rr) , the protein controlled by the r allele is not enzymetically active, so that not enough starch is produced to make the seed plump, and it appears wrinkled.
Biology, pp. 246.

One single mutation is capable of paralysing a gene completely. Consider the Leapfrog protein. Suppose a mutation causes an amino acid in one of his claws to change, which makes him unable to take hold of the DNA strand (firmly). As a result, the entire protein can no longer work. That one mutation will cause the protein to become a dead protein or the gene a dead gene.^[8] Now the Leapfrog protein is so essential to the functioning of a cell that living creatures will never occur with a dead Leapfrog gene.^[9] In practice, that usually means that at a certain point the development of a fertilised egg cell cannot continue and the embryo dies.
On the other hand, it is also possible for a certain mutation not to bring all the functions of a protein to a stop immediately, because the change in base pairs does not make much difference in the amino acids, or because it is in a less essential part, or because the protein’s function only decreases partially. However, an accumulation of mutations will eventually result in the protein becoming totally defective. In other words, to the left, to the right, before and behind this ‘mutating protein’^[10] lie yawning chasms with sheer, deep walls. As soon as the protein goes one step (i.e. mutation) too far, it loses its function and the mutant falls into the precipice next to it and dies. The protein is, as it were, on  a mountain peak. Only on that peak can it do what it needs to do. If it descends too far, it loses its original function.
(The question of whether (hidden) paths can be found on that mountain will be covered in point 5.)

Figure  6, The Mount of Isolation: only on the uppermost surface does the protein retain enough of its function not to be a ‘dead’ protein.

2.The advantageous mutation
Now we get into an important matter which Master Crook Mutation conceals from us and where confusion of terms sets in, since he says that he can make new characteristics and is a source of variation. What happens internally on the DNA level?

the loss of a gene can benefit its carrier
When a protein has lost its function, it is quite possible that that the individual in which it lives benefits (!!) from it. Think for instance of the loss of skin colouring in an Arctic fox, which gives it a white pelt. That is a definite benefit to the fox in the snow. However, it is not the protein itself which has undergone a ‘change in function’. It is not the protein itself which causes a ‘new characteristic’ to appear. It is the loss of that protein that cause a ‘new characteristic’ to appear! This so-called ‘new characteristic’ can be advantageous, but the protein has not become ‘better’ for those circumstances. The protein has been lost, or damaged so that it can only do half of its work.

As long as the protein is fine and functioning, selection can take place for that functioning-of-the-protein, but as soon as a protein becomes a dead protein, the selection for the protein itself disappears. Selection can continue to exist for the absence of that protein, or its non-functioning.

in dead genes, selection no longer takes place on the protein sequence
It is important to make that distinction and to understand it thoroughly. As soon as a gene has lost its original function, even if that benefits the carrier, the pressure of selection on the functioning of that protein disappears, and related to that, the selection pressure on the structure of the protein, which is determined by the sequence of the amino acids. In other words, to put it succinctly, the selection pressure on the sequence of amino acids disappears.^[11] This is because it no longer matters if the protein ceases to function 100%, or ceases to function 50%. In both cases, it no longer functions. It then also no longer matters what kind of protein is being coded for, or even if a protein is being coded for. Each successive mutation can damage the protein further, it no longer matters. The protein has died. It has become a dead protein. The is no more selection which preserves the protein sequence, the order of the amino acids in the protein. The gene is delivered into the hands of ‘free mutation’, which means mutation-without-selection.

a distinction needs to be made between micro-evolution and molecular evolution
The reason that this is never fully understood is that the distinction is not made between the evolution of proteins, or the protein sequence, on the one hand, and the evolution of an individual or a species on the other hand. Because, again: selection can happen for the loss of a function in a protein, but the selection for one mutation instead of another in the protein itself has then disappeared.
For instance, it is said that: ‘a mutation causes a new characteristic’, or ‘mutations are the source of variation’. However, such statements do not take the difference between levels of evolution sufficiently into account. A mutation, which causes a ‘new’ characteristic and therefore gives rise to ‘new’ variation, can, speaking genetically, quite possibly have completely eliminated a gene. In that case, that mutation is totally not a source of variation, but a vessel which draws from the source, by removing something!

This is the confusing effect of a mutation if no distinction is made between the levels of evolution:

This is the clarifying effect of a mutation if the levels of evolution are clearly distinguished from each other:

Because of this, fruit flies in my compost are not seen. If I were to breed them and expose them to strong UV light so that some serious mutations happen, at some point in time, I could see some fruit flies with white eyes. A mutation has, as it were, pulled this new characteristic out of thin air. However, genetically speaking, a functioning protein has been put out of commission, namely the protein which would usually make red pigment in the eyes.

3.Free mutation.
As long as a protein continues to perform a useful function, selection for the protein sequence can take place. If a mutation changes the sequence of amino acids, thereby changing the function, thereby changing ‘something’ in the carrier of that gene, then one sequence can be chosen instead of another by natural selection.

free mutation means no evolution

What does that mean for a protein, that it is no longer selected for and that it can mutate freely? That that protein no longer evolves!

Because it went like this:

arbitrary mutation +  non-arbitrary selection = evolution.

If selection is no longer possible, because the protein is dead, there is no longer evolution in that protein!

arbitrary mutation + no selection = no evolution

It is an absolute condition for a ‘mutating protein’ that it never loses its function completely and becomes a dead gene. If it dies and can mutate ‘freely’, suddenly, all the rules of that absurd calculation of probabilities comes into effect which say that the chance for a coincidental new functional protein is 1 in 20³⁰⁰. Suddenly, the laws if King Entropy come into effect!

If a protein crosses the line of useful-functionality, because it moves downwards through the mist, then the Kingdom of King Entropy enters, the land of the dead, where another law applies than on the peak of the mountain. On top of the mountain, it is light. the Angel of Natural Selection rules, but below the peak, underneath the mist of functionality, it is dark, Fool Coincidence plays with amino acids and King Entropy calls the shots.

King Entropy’s strength Figure  7  is a typical example of the decreasing order in the protein of a freely mutating gene, made up of 100 amino acids. On the X-axis is the number of mutation, on the Y-axis the percentage of correspondence with the original protein (1 altered amino acid is 1%). The calculations were made by repeatedly allowing an arbitrary mutation to take place in one of the base pairs of the gene, and then to take a look at the degree of correspondence with the original protein. All plateaus (the longer or shorter horizontal lines) are caused by mutations which have no effect, they continue to code for the same amino acid, which is caused by the structure of the genetic code. An abrupt drop is caused by the appearance of a code for a stop signal. That results in a large part of the protein not being made any more. The chance that a stop signal will appear is 1 in 64, and therefore can occur several times in a graph of 250 mutations. The sudden increase in order is caused by a previous stop signal coding for an amino acid again, which results in the protein regaining its original length. However, it is clearly visible that despite the return of the tail, the order in general just continues to decrease. Every once in a while, a small increase can be seen. That is the work of Fool Coincidence who causes a wrong amino acid to become the right one again, which makes the graph rise 1%. The chance of that happening is bigger the more disordered it gets! The descent for the first 10 mutations is thus always steeply downwards. As  order decreases, the plates become longer and the chance for a single percent increase becomes larger.   Figure  7, example of the decrease in order in a ‘freely’ mutating protein All of this shows King Entropy’s power. If I let my computer make this sort of calculations for the next five billion years, there would still be no increase in order, not even if I would enter all the possible useful combinations of amino acids, simply because the number of useful combinations pales in comparison to the number of useless combinations (see below).[12]

It is of the UTMOST IMPORTANCE that this argument – no new devoted, specialised genes originate through free mutation, or by sheer coincidence -  is realised and understood. It is the essence of my argumentation, which by the way is completely in agreement with what the proponents of the evolution theory say themselves. (see 5.1.a).

Still, many people are not aware of this fact (or convinced of it!), as the conversation I had with a respected biologist makes clear:

H.R. :        There is a lot of DNA which does not code, which doe snot have any function at this point in time. That does not mean that it cannot be switched on at any given moment. A gene which does not have a function at first can get a function by mutation.

Peter:        But if it has no function, then it is also not selected for?

H.R.:         No, then it is just hitching a ride. A great deal of your genetic material has no function at this point in time, but can gain a function at any moment. It is a reservoir which can be accessed at a time when that serves a purpose.The parts of the DNA which do not code for anything right now have all the freedom possible to mutate, without it having damaging consequences for the organism. That is the incubator for new characteristics. At a certain point in time, part of the DNA which is in that incubator can add a characteristic to the characteristics which already exist.

King Entropy says: 'NO WAY!' to this biologist.
In the first place, it is still not certain that DNA which does not code for proteins has no function. (see ch. 12) Apart from that, nothing originates by coincidence alone anyway, not even in the dead genes which no doubt can be found in the DNA. A protein made up of 300 amino acids (‘a little one’ according to this biologist) has 20³⁰⁰, that is 10³⁹⁰ possible combinations, which in comparison to the 10⁸⁰ atoms in the universe is infinitely great. On the other hand, a human has at most 100,000 genes (or 10⁵) which have a useful function. If you then assume that there are 100 million species (which is on the high side), that these genes are all different (which is absurd, since many genes are the same), and that over the course of five billion years, each year there have been that number of species with that same number of functioning genes (which is obviously not true), then there would be 5 x 10⁸⁺⁵⁺⁹ is a maximum of 10²³ functional genes.
That this number is larger than is realistic is due to the face that, theoretically speaking, each new protein has to build on what already exists. Not just any combination of amino acids can do something useful in a living organism. On the contrary, it has to have a precise structure in order to fill a specific function and to be able to work with other genes. It has to ‘fit’ in the structures and co-operations which are already present. That means that only a very small number of combinations can be useful in a specific species and that 10²³ is somewhat exaggerated. Another indication of a very low number of useful protein sequences is the high number of damaging genes a mutation has had: only a very precise combination of amino acids has a useful role to play in living organisms, and the rest are discarded.

However, as yet we will suppose that there are still that many possibilities. The chance that a functional gene originates from a gene which was at first not functional by coincidence, is 10²³ in 10³⁹⁰, which is 1 in 10³⁶⁷. That last number is still infinitely  greater than the number of atoms in the universe and will therefore not have occurred even once in five billion years, or, if we give Fool Coincidence the insane benefit of the doubt: ... once.
Pure coincidence is in no way a useful mechanism for the origins of new genes. The only remaining option is that new genes evolve from existing genes (or copies of existing genes).

If the reader is still not convinced of this, there is no point in reading the rest of this chapter (or the next). For those readers, I will now describe a game which can occupy them until they are eventually convinced of the contrary:

Here is a description of a ‘protein’ with 142 amino letters (for which some 10¹⁸⁵, which is infinitely many, combinations are possible):

nonewusefulspecialisedproteinsorproteinsequenceswilleveroriginatebypurecoinci
dencenotfromthednascrapheapandnotfromexistingproteinsjustnotatall

At some arbitrary (so not chosen) point, change a letter into an arbitrary, coincidental other letter, for as long as it takes until, no matter what it takes, the sentence reads that something useful can originate by pure coincidence (we just imagine the spaces, and no letters can be added or removed, only replaced). This game is closely analogous to ‘mutating proteins’, because the number of letters (20-26) is comparable to the number of amino acids and the number of nonsense-combinations in relation to the number of useful combinations is even smaller than in proteins, so that the chances of success increase.If enough people (or a few computers!) try it for long enough, surely it will work at least once...

Now there will probably be people who want to steal my thunder. That’s right, they will say, nothing useful originates through pure coincidence. But a small word can originate through coincidence. By ‘selecting’ that word and not selecting for each subsequent change in that word, and to continue like that until another new word appears somewhere, etc., at some point in time a lovely new sentence will appear. And of course, they are absolutely right!

However, the mechanism which is applied there is that of coincidence-plus-selection. The point we are at in the discussion is that of coincidence-without-selection. What I am asking of the reader is that he admits that coincidence-without-selection does not result in new proteins, not that he admits that coincidence-PLUS-selection does not result in new proteins. The central question which follows is, from what point onwards can selection occur? Can it occur from the point at which coincidental ‘words’ appear in a protein? Or can selection only occur when a more-or-less legible sentence is formed? In terms of proteins, that question means: From what number of protein combinations onwards is there a case of some useful functionality? Or, to put it differently: From what point at the foot of the Mount of Isolation can the Angel of Natural Selection start his work?

Well, I am sorry to have to say this, since it means that the reader who does not wish to follow me on this point will have to be left hanging with the above sentence for the rest of his days. but the smallest known proteins are about 100 amino acids long. The largest proteins form, from a molecular point of view, gigantic mountains, complexes, cathedrals of thousands of amino acids, but we will leave those out of the thick of this argument. In the here and now of biochemical daily life, the smallest useful proteins are about one hundred amino acids long. Later we will see that, in many case, the border in the foothills of the Mount of Isolation, from which point on the Angel of Natural Selection is able to do his work, is much higher.
As far as our game is concerned, this means that selection can only take place from about 100 letters on, because that is the first point at which any kind of functionality can be discussed.You are thus only finished when there is a complete and legible sentence of at least one hundred letters. We will overlook a few grammatical errors...

As encouragement for the persistent folk: there are lots fewer proteins with a length of around a hundred than there are possibilities to say that functional proteins can originate through coincidence with a 142-letter sentence. Compared with that, the chance that the above game will result in a proper sentence is larger than the chance that, in reality. a functional protein of about 100 amino acids will originate.The choice is yours: keep playing or read further?

4.The Valley of Dead Genes
In Figure  8, three proteins are portrayed, two of which are closely related to each other. This is because it could be possible for the structure of a certain protein to be so close to that of another protein that it could change into the other protein through mutation, without ever, at any point in time, losing its functionality to such a degree that it becomes a dead gene. This is depicted by the second peak next to the first, which, albeit through a small dip, can be reached without descending beneath the highest border, the ‘line of death’.

In that case, it is conceivable that a protein could mutate from one peak to another, if the Angel of Natural Selection selects for that, at any rate. It is by the way a dangerous undertaking. One step too far across the border and the protein slips through the fingers of the Angel of Natural Selection, and immediately ends up in the valley of dead genes. Is this true? Is the Angel of Natural Selection so powerless on the other side of the border? Yes it is, take a look.

Figure 8, The Valley  of Dead Genes

proteins die a thousand deaths

The white-eye allele ( for fruit-flies, PMS) appears to cause a complete loss of normal gene function and thus an absence of eye pigment, while the other alleles cause a less drastic alternation of the normal gene function, and so the eyes contain different amounts of pigment. (…..) In fact, over 100 alleles of the white-eye gene have been discovered. Biology, pp. 247

As you can see, there are more than a hundred ways in which a protein can die, but there are only a few in which it can do what it has to do. If a protein, because of one or more mutations, falls below the functionality border and thus enters the kingdom of the dead, the organism loses the function which it performed. However, it makes no difference if the protein dies in one way or in another.In other words, a small peak with limited freedom of movement rises above the mist, and all around (the other hundred versions, the ones with different alleles) are gaping chasms.

resurrection is only possible in the beginning

Now the protein cannot simply be brought back to life. If a mutation happens in that gene again, it is a chance of 1 to 3 in 2700 (in a protein which has 300 amino acids^[13]) that the right amino acid returns. In other words, there are at least about a thousand ways to destroy it further and one to restore it to its previous state.^[14] This means that, after the first step across the border, the protein can only re-conquer its original function in very rare cases. After the second step across the border, the chance is 1 in 1,000,000 and only goes downhill from there. As soon as the border has been crossed, King Entropy’s dogs come after the protein. There is almost no escape. Once arrived at the bottom, King Entropy’s law applies: it is not permissible to take on any semblance of order.

In general terms, you can thus say that after one mutation across the border of functionality, there might be a way back through pure coincidence, but that after two or three mutations, the protein is so far gone that the way back is no longer a realistic option. In other words, the mist which hangs about the border of the kingdom of the dead is one or two mutations thick.

protein C is inaccessible

The third ‘mountain’ in the figure is a protein which differs structurally so much from the other two that it cannot be reached by mutation without losing its functionality and therefore becoming a dead gene which is not selected for.

Protein A can, as long as there is sufficient selection for it, mutate into protein B. Along the way, there will be many unfortunates who mutate in the wrong direction, but with sufficient selection pressure and sufficient time, it is possible. However, neither A nor B can mutate into protein C. Before they could get there, they would have to pass through the Valley of Dead Genes. And, as we have seen, King Entropy does not release his prisoners, so it is in no way possible for a protein, without the pressure of selection, to mutate ‘upwards’ along the steep cliffs to protein C.

Darwin’s driving force, called natural selection, has no jurisdiction in the valley of dead genes!

5. A hidden path?
If you read about a ‘function-acquiring mutation’ (see ch. 5.f), you do not get the impression that it is such a problem for a protein to take on another function at all. Why not? Because the landscape looks different, more like Figure  9.

Figure  9, the Mount of Connectedness

The suggestion created by the name ‘function-acquiring mutation’ as the engine for evolutionary change is that ALL those different peaks are joined together in some way or another, that an already-mutating protein never has to completely lose its function, step across the border of the kingdom of the dead, and in this way, without ever leaving the jurisdiction of the Angel of Natural Selection, can achieve another function, and from that function another and another.
The question is, therefore, which is closer to reality? Gently sloping hills with, it is true, a border with the kingdom of the dead, but nevertheless full of peaks and connections through which every protein can be reached in some way or another, or isolated peaks surrounded by steep crags?

For evolution to (have) work(ed),[15] each protein must be accessible from another protein (see Graph 1), through an accumulation of small changes in function and without ever losing its function to any significant degree, which would cause the selection pressure to disappear. Graph 1, Uninterrupted increase in functionality is an absolute condition In other words, there has to be a traceable path from every protein to another protein without it becoming a dead protein in between. In other words, every peak of the mountain has to be able to be uninterruptedly connected with another peak without ending up under the selection border (see Graph 2 and 3.) Graph >2, Interrupted increasing (or changing) functionality is an impossibility In reality, matters are really this clear-cut: if even one such protein can be found which stands on an isolated peak, without any connection to other peaks, it is already proven that structural evolution is impossible! We will call a protein which fills these specifications a Lone Ranger. Graph 3, Another example of impossible interrupted increasing (or changing) functionality