Jätst der Lätstertaal von Edenar und Nucleotid Stutturäne.
Imagine you can predict two-dimensional structures or secondary structure with no errors.
Would you be a happy person?
Maybe not.
You might be terribly disappointed.
Here are two possibilities.
These have got names, kinetic trapping and slow formation.
And these come from the protein world.
Let us start with a friendly energy surface.
As I often draw, here are all of the possible configurations of our molecule.
This is some kind of energy measure.
And as always in the universe, most things tend to go downhill
and the more populated states will be down here.
So this looks like a very happy, friendly energy landscape.
Consider an alternative.
Here is my friendly energy surface from the previous slide.
But there are some other possibilities.
What would the energy configuration, the energy landscape look like for a ribo switch?
So a ribo switch in biochemistry lectures is a molecule which they say can exist in two conformations.
That would mean there is an energetic minimum here and an energetic minimum over there.
So my molecule is sometimes over here and then sometimes it jumps over to this minimum here.
So that is one possibility.
And I am not talking about ribo switches today.
Another possibility, which is a bit nastier, is my molecule is synthesized and unfolded.
So it starts life amongst these configurations here.
It folds down and finds this minimum over here.
But this is not where you want to be.
This minimum here has an even better energy.
So the molecule goes down and then it has to overcome an energy barrier to get to reach this minimum over here.
Now, if this barrier is high enough, the molecule will never have enough energy to go over it.
And high enough only means it is higher than the typical kinetic energy of a molecule in solution.
So it is not crazy high energies.
But that means that if you have a high barrier, your molecule simply will not find the best energy configuration.
Even though you have the perfect secondary structure prediction program,
and your program has predicted that this here is the optimal energy configuration.
So bad luck.
This is a serious, potentially a serious problem.
But I'll tell you, nobody really knows how bad it is.
What I can tell you is, again, imagine you're looking at a guanosine, for example.
There are many cytosines that this guanosine could base pair with.
Of all of those possibilities, each one corresponds to a local minimum.
But if we're talking about optimal structures, only one of them is correct.
So I have an energy landscape. I do not know how bumpy it is.
But I know that there are lots of local minimum on this energy landscape.
So potentially, it is a problem.
Let's make it even more interesting.
And let's get to the issue of kinetics.
So what can you predict so far?
We cannot predict anything to do with bumps on the energy landscape.
We cannot account for any of these possible little mountains or hills on the energy landscape.
You could try to approach the problem with simulation methods, things like Monte Carlo
or a time-based method like molecular dynamics.
You would start with an unfolded molecule,
use classic methods to get to low energy structures and try to simulate the folding steps
and measure where your confirmation is at any point in time.
It turns out people have tried to do this.
So now I'm going to talk about a real model system.
So it's pretty simple, but it shows what the problems could be.
So what they did is they have an unfolded molecule.
Then they used classic methods to look at the best energy structure
and some of the other high probability alternatives, a finite number.
And then they tried to do a Monte Carlo simulation, letting the molecule jump around between energy minima.
And then they say, as I go through time, which minima have I reached?
Let me explain in more detail.
So what they've done here is here we have the axis of time or steps, and it's a logarithmic scale.
And on this axis, I have what percentage of the molecules have reached certain confirmations.
So at the start, I've got on this scale, it's about 0.9.
Ninety percent of the molecules are in a certain conformational state, which is the unfolded state.
At the start, my system is not folded.
Now, from a classic calculation, they have identified a low energy configuration, which is state one.
It's just a certain folded state.
And they say, as time goes on, what fraction of the molecules are in state one?
So at the start, it's near zero.
But as time goes on, molecules are finding this low energy configuration.
And on this landscape picture, that corresponds to this minimum here.
At the same time, I have another better energy minimum over here, which might be the global optimum.
And at the start, nobody has found that optimum, that minimum.
And it very slowly starts to go up.
Some molecules reach that configuration.
And if I wait long enough and long enough, eventually molecules can jump over the energy barriers,
and they find their way to the optimum here.
So by this stage here, you would say the system has reached equilibrium.
And the better energy minimum here is more populated than the energy minimum here.
So state two has a higher population than state one.
But remember, this is a logarithmic timescale.
So the system has taken very quickly, it reaches state one.
And only after a very long time, after years, has it reached is state two the winner.
Although state two is the optimum that you would predict for the structure.
But there is a problem here. So the calculations are fine.
They are a model simulation in a simple energy model and doing simple moves, which correspond to the passage of time.
But what happens if RNA is degraded, which it is in biochemistry?
So the molecules start here, they reach structure one pretty fast.
But of course, molecules are also disappearing from the system because RNA is broken down up about.
But that means that molecules find structure one.
But very few molecules find structure two because an RNase comes along and eats them first.
So what this means is that structure one, this group over here, although it is not the energy optimum,
it is kinetically preferred because the molecules find it faster.
So the kinetically preferred conformations may well be more relevant to biology than the optimal energy configurations.
And don't forget, RNA has a short life in the body in real biochemistry.
Short means minutes or hours.
So let's summarize all of this. Really everything.
So tertiary structure is important for RNA and you need tertiary structure if we're going to talk about binding ligands.
Much of what I said is based on secondary structure and the idea of secondary structure calculations and predictions.
Everything to do with secondary structure is fast on a computer.
But there are limits on what you can predict.
You cannot predict anything that has a pseudo not in it.
Even if you could, the predictions are not so reliable.
However, you will see them everywhere in the literature and even in biochemistry seminars in IMSBIDL if we were physically in IMSBIDL.
And even if you could account for tertiary structure and do secondary structure predictions perfectly, you may lose anyway because of kinetics.
Pekka harped.