Force Fields G Vriend 2-11-2004. It is all about time versus accuracy Quantum chemistry Approximations Force Fields Hybrid methods Self consistent fields

Force FieldsG Vriend 2-11-2004

It is all about time versus accuracy

• Quantum chemistry• Approximations• Force Fields• Hybrid methods• Self consistent fields• Molecular dynamics and energy calculations• Minimizers• Yasara-Nova

Force FieldsG Vriend master series 2-11-2004

Quantum chemistry is accurate, but slow




The largest ‘thing’ that can realistically be worked-out using the Schödinger equation is hydrogen. Other applications are the particle in a box that is mainly of theoretical importance, the postulates of quantum chemistry, etc.



Actually, pure quantum chemistry cannot be applied in our (protein) world. Which is good, because quantum chemistry is much too difficult (for me). But many of the results are very useful. For example, all atoms in proteins display sp2 - sp3 hybridization.

Pictures obtained from Clifford J Creswell

Approximations, faster, less accurate


Approximations can make quantum chemistry software faster, but at the cost of accuracy. A major part of all efforts in quantum chemistry is to think about short-cuts that have an optimal price/performance ratio.

So, we will use Newtonian mechanics


If we want to calculate with molecules that contain thousands of atoms, we have to totally abandon quantum chemistry, and use Newton’s laws of motion, treating atoms as macroscopical particles instead of quantum chemical entities. The following (YASARA) movie will explain how this is done.

ΔH wants to go down ΔS wants to go up and ΔCp cannot be calculated

H(T) = H(Tref) + Tref

T

Cp .dT

S(T) = S(Tref) + Tref

T

(Cp /T).dT

What can we do with EM and MD?


Despite all its shortcomings, MD can be used to calculate binding constants of ligands in active site pockets with reasonable accuracy.

This is done with so-called thermodynamic integration which works because binding a ligand is a state-function (the path is not important, only the end-points; so non-realistic paths are allowed):

Just take any closed cycle you want. Calculate the easy difference, and since the cycle is closed, you obtain also the value of the difficult cycle.

We can turn the thing inside-out


Other approaches are also possible. Rather than calculating the energy lost or gained to actually move an atom somewhere, we can calculate the potential energy for atoms at a certain position. This, of course, is again an approximation relative to the thermodynamic integration method. Examples: LUDI or GRID.

And one more approximation step....


Lets go yet one step further along the drug binding path. Assume we have a series of docked molecules. We superpose them, and determine what they have in common. The next drug should have those same characteristics. So, one more approximation step....

Other force fields


So far we discussed molecular dynamics force fields and ‘approximated’ them into ‘experience based’ drug design.Many other force fields exist. For example, many force fields exist for the purpose of validating protein structures or models. Example ProSa:

1) Measure Cα distance distributions2) Score good proteins per residue3) Normalize the scores4) Score protein of interest

Other force fields


Force fields do not need to be based on relative positions of atoms. A very different concept would be a secondary structure evaluation force field. Recipe:Take 4000 different proteins and determine their secondary structure.Determine how many residues are H, S, or RDetermine for each residue type how often it is Helix, Strand, Rest (HSR)Determine the preference parameters for the 20 aa in the 3 states

P(aa,HSR)=P(aa)*P(HSR)Pref(aa,HSR)=Ln (observed/predicted)observed is simply counting (aa,HSR) in the 4000 proteinspredicted is P(aa,HSR) * (total number of aa in the 4000 proteins)

Callibrate the method with a Jack-Knife procedureLoop over the aa in the protein to be tested and add up all Pref(aa,HSR)Express the outcome in energy or standard deviations (Z-score)

Electrostatic calculations


Electrostatic calculations are based on self-consistent field principles. This field is not a force field like we have seen so far, but a distribution of charges over a grid that covers the space in and around the molecule.



Often physics looks like Chinese typed backwards by a drunken sailer, but when you spend a bit of time, you will that things actually are easy. Take the Poisson Bolzmann equation that is used for electrostatic calculations:

which can be converted into:

This looks clearly impossible, but after a few days of struggling, it becomes rather trivial (next slide):



The Poisson Boltzman equation normally is worked out digitally, i.e., make a grid, and give every voxel (grid-box) homogeneous values for charge and dielectricum. Now make sure all neighbouring grid points have the correct pairwise relations. If one voxel has ‘too much charge’ it should give some charge to the neighbours..... This is done iteratively till self-consistent.

The same technology is used to design nuclear bombs, predict the weather (including the future path of tornados),

design the hood of luxury cars, predict how water will flow under a bridge in the Waal, optimize catalysts in mufflers, optimize the horse powers of a car given a certain amount of gasoline (turbo chargers), etc.

And the function is very simple!

Force Fields


So, what is a force field? There are so many different ones for totally different things (car design, electrostatics, nuclear bombs, tornados, etc)...

A force field is a set of rules that can predict the ‘optimal constellation’ of a system in the absence of external forces.

So, in case of electrostatic calculations, the field can be calculated in the absence of molecular motion, and new ‘things’ entering the system. But for a weather forecast one can only take small steps in a dynamic system as the sun adds energy to the system.

Most force fields can be used to optimize/minimize the system, and here we run into the multiple minimum problem.

Multiple minimum problem


But this is a very simple, one-dimensional case. How many dimensions and how minima do you think can be found in crambin (326 heavy atoms)?

Back to proteins and MD/EM


During an MD simulation atoms don’t move very far. A) Because molecules normally aren’t very mobileB) Because we cannot run the simulations long enoughC) Because the forcefields are far from precise enough

We can use this to do MD differently....

Back to proteins and MD/EM


We have seen that the few forces that we (think that we) understand mainly are of the form Q=k*(x-x0)In this equation x0 is known with great precision, while k can easily be wrong by a factor of two or more. Can we use the precision of x0?

2

MD with CONCOORD


In the CONCOORD software, all distances between atoms are forced at x0 plus or minus ‘a little bit’. This little bit is determined by the nature of the force between the atoms. In a way, concoord works a bit like NMR structure determination.

MD with CONCOORD


All x-es are close to their x0 in each CONCOORD structure. So a movie based on the CONCOORD structures shows a path of low energy, or a path along the X0 in Q=k*(x-x0)

CONCOORD

Molecular dynamics

x0

k

2

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Force Fields G Vriend 2-11-2004. It is all about time versus accuracy Quantum chemistry Approximations Force Fields Hybrid methods Self consistent fields