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
Toxin binding studies to potassium channels

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
Computational program for rational drug design from toxins

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
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.
K11
R34
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
DGwell
DGb(PMF)
DGb(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.