Molecular dynamics simulations of

toxin binding to ion channels

• Quantitative description protein –ligand interactions is a

fundamental problem in molecular biology with applications in

pharmacology, medicine, biotechnology, etc.

• Pharmacological motivation: drug discovery is getting harder

using

traditional compound libraries. Peptide-ligands from Nature

(e.g. toxins) offer an alternative source for drug discovery

• Computational methods would be very helpful in such studies

but

their accuracy needs to be improved to tackle large ligands

• Proof of concept study: Binding of charybdotoxin to KcsA*

(shaker) Realistic case study: Binding of ShK toxin to Kv1.1,

Kv1.2, and Kv1.3

Two main problems in computational studies of

protein-ligand interactions

1. Apart from a few cases, the complex structure is not known.

Assuming that structures (or homology models) of protein

and ligand are known, the complex structure can be

determined via docking followed by refinement with MD

simulations.

2. Affinity and selectivity of a set of ligands for target proteins

need to be determined with chemical accuracy (1 kcal/mol).

Binding free energies can be calculated

from umbrella sampling MD simulations (standard method).

For selectivity, one could use the computationally cheaper

free energy perturbation method. The FEP method is

especially useful if one is trying to improve selectivity via

minor modifications/mutations of a ligand.

2

Charybdotoxin binding to KcsA* (shaker mimic)

– Complex structure is determined from NMR, so provides a

unique test case for MD simulations of peptide binding.

– Using HADDOCK for docking followed by refinement via MD

simulations reproduces the experimental complex structure.

– Binding free energy calculated from the potential of mean

force agrees with experimental value within 1 kcal/mol

ShK toxin binding to Kv1.1, Kv1.2, and Kv1.3 channels

– Kv1.3 is the main target for autoimmune disases

– ShK binds to Kv1.3 with pM affinity (but also to Kv1.1)

– Need to improve selectivity of ShK for Kv1.3 over Kv1.1

– Some 400 ShK analogues has been developed for this purpose

Toxin binding studies to potassium channels

Find the initial configuration for the bound complex using a

docking algorithm (HADDOCK is recommended )

Refine the initial complex(es) via MD simulations

Calculate the potential of mean force for binding of the ligand

along a reaction coordinate → binding constants and free

energies

Determine the key residues involved in the binding

Consider mutations of the key residues on the ligand and

calculate their binding energies (relative to the wild type) from

free energy perturbation in MD simulations

Those with higher affinity are candidates for new drug leads

Computational program for rational drug design from toxins

Structure of the KcsA*- charybdotoxin complex

Important pairs:

Y78 (ABCD) – K27

D80 (D) – R34

D64, D80 (C) -

R25

D64 (B) - K11

K27 is the pore

inserting lysine –

a common thread

in

scorpion and

other

toxins.

K11R34

NMR structure ofShK toxin

ShK toxin has three

disulfide bonds and

three other bonds:

D5 – K30

K18 – R24

T6 – F27

These bonds confer

ShK toxin an

extraordinary

stability not seen in

other toxins

Homology model of

Kv1.3

Can be obtained from the

crystal structure of Kv1.2

(over 90% homology and 1-

1 correspondence between

residues). Initial model did

not work because V H

mutation was not handled

correctly. H404 side chains

make bonds with the

neighbouring D402 and

these were broken during

the relaxation.

Kv1.3-ShK complex

Monomers A and C Monomers B and D

Pair distances in the Kv1.3-ShK complex (in A)

Kv1.3 ShK HADDOCK MD aver. Exp.

D376–O1(C) R1–N1 5.0 4.5

S378–O(B) H19–N 3.2 3.0 **

Y400–O(ABD) K22–N1 2.9 2.7 **

G401–O(B) S20–OH 2.9 2.7 **

G401–O(A) Y23–OH 3.5 3.5 **

D402–O(A) R11–N2 3.2 3.5 *

H404-C(C) F27-C"1 9.7 3.6 *

V406–C1(B) M21–C" 9.4 4.7 *

D376–O1(C) R29–N1 12.2 10.2 *

** strong, * intermediate ints. (from alanine scanning Raucher,

1998)

R24 (**) and T13 and L25 (*) are not seen in the complex

(allosteric)

RMSD of ShK as a function of umbrella window

The RMSD of ShK relative to the NMR structure remains flat throughout

Convergence of the PMF for the Kv1.3-ShK complex

PMF of ShK for Kv1.1, Kv1.2, and Kv1.3

Comparison of binding free energies of ShK to Kv1.x

Binding free energies are obtained from the PMF by

integrating it along the z-axis.

Complex Gwell Gb(PMF) Gb(exp)

Kv1.1–ShK 18.0 14.3 ± 1.1 14.7 ± 0.1

Kv1.2–ShK 13.8 10.1 ± 1.1 11.0 ± 0.1

Kv1.3–ShK 17.8 14.2 ± 1.2 14.9 ± 0.1

Excellent agreement with experiment for all three

channels, which provides an independent test for the

accuracy of the complex models.

Average pair distance as a function of window position

** **

**

**

* **

** denotes strong coupling and * intermediate coupling

Conclusions

Docking methods are useful for providing the initial

configurations of the bound complex

But their predictions for binding energies are not adequate (it is

unlikely that one can optimize a single energy functional which

can predict the binding energies for all protein-ligand pairs.)

Thus we need to rely on MD simulations for refinement of a

protein-ligand complex and accurate calculations of binding free

energies.

Once a protein-ligand complex is characterized, one can study

the effects of mutations on the ligand by performing free energy

perturbation calculations. Those with higher affinity relative to

the wild-type would offer promising drug leads.