Week 5 MD simulations of protein-ligand interactions
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compound libraries using experimental methods. Using computational methods and peptide ligands from Nature (e.g. toxins) offer alternative methods and means for drug discovery
their accuracy needs to be confirmed for larger, charged peptide ligands
The first issue is addressed with many experimental (e.g. HTS) and computational methods (e.g. docking), and there is a huge data base about high affinity ligands.
The second issue is harder to address with traditional methods and would especially benefit from a rational drug design approach.
Example: Kv1.3 is one of the the main targets for autoimmune diseases
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 accurately from umbrella sampling MD simulations. For selectivity, one could use the free energy perturbation (FEP) method (computationally cheaper). The FEP method is especially useful if one is trying to improve selectivity via minor modifications/mutations of a ligand.Challenges in computational design of drugs from peptides
Find the initial configuration for the bound complex using a docking algorithm (e.g., HADDOCK).
Refine the initial complex(es) via MD simulations.
a) Determine the key contact residues involved in the binding and compare with mutagenesis data to validate the complex model.
b) Calculate the potential of mean force for the ligand, determine the binding constant and free energy, and compare with experiments.
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/selectivity are candidates for new drugs.
Binding of charybdotoxin (ChTx) to KcsA* (shaker mimic)
K27 - Y78 (ABCD)
R34 - D80 (D)
R25 - D64, D80 (C)
K11 - D64 (B)
K27 is the pore
inserting lysine –
a common thread in
scorpion and other
K+ channel toxins.
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
Can be obtained from the crystal structure of Kv1.2 (over 90% homology and 1-1 correspondence between residues).
Note: care must be exercised for the V H404 mutation because H404-D402 side chains cross link (several publications have the wrong Kv1.3 structure because of this).
Monomers A and C Monomers B and D
Monomers A and C Monomers B and D
Kv1.3 ShK Dock. MD av. 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-Ce1 9.7 3.6 *
V406–C1(B) M21–Ce9.4 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)
HADDOCK is not
very good for hydrophobic int’s
** denotes strong coupling and * intermediate coupling
The RMSD of ShK relative to the NMR structure remains flat throughout
For k=30 kcal/mol/A2, the overlap is about 10% in bulk, which is an optimal value for umbrella simulations (only one extra window needed)
Comparison of the binding free energies of ShK and its analogues to Kv1.x channels
Complex Gb(PMF) Gb(exp) (kcal/mol)
Kv1.1–ShK -14.3 ± 0.6 -14.7 ± 0.1
Kv1.2–ShK -10.1 ± 0.6 -11.0 ± 0.1
Kv1.3–ShK -14.2 ± 0.7 -14.9 ± 0.1
Kv1.1-ShK-K-amide -11.8 ± 1.0 -12.3 ± 0.1
Kv1.3-ShK-K-amide -14.0 ± 0.4 -14.4 ± 0.1
Kv1.1-ShK[K18A] -11.7 ± 0.7 -11.3 ± 0.1
Kv1.3-ShK[K18A] -13.9 ± 0.6 -14.2 ± 0.1
Excellent agreement with experimental values for all channels, which provides an independent test for the accuracy of the complex models.
Binding free energy differences for Kv1.1 and Kv1.3, and the selectivity free energy for Kv1.3/Kv1.1. (in units of kcal/mol)