Week 5 MD simulations of protein-ligand interactions •Lecture 9: Fundamental problems in description of ligand binding to proteins: i) determination of the complex structure, ii) calculation of binding free energies. Examples from toxin binding to potassium channel Kv1.3. Target selectivity problem in drug design and structure-based methods to solve the selectivity problems.
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Week 5 MD simulations of protein-ligand interactions
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Week 5
MD simulations of protein-ligand interactions
•Lecture 9: Fundamental problems in description of ligand binding to
proteins: i) determination of the complex structure, ii) calculation of
binding free energies. Examples from toxin binding to potassium
channel Kv1.3. Target selectivity problem in drug design and structure-
based methods to solve the selectivity problems.
Why study proteinligand interactions?
• Quantitative description of protein–ligand interactions is a fundamental
problem in molecular biology
• Pharmacological motivation: drug discovery is getting harder searching
compound libraries using experimental methods. Using computational
methods and peptide ligands from Nature (e.g. toxins) offer alternative
methods and means for drug discovery
• Computational methods would be very helpful in drug design but
their accuracy needs to be confirmed for larger, charged peptide ligands
• Proof of concept study: Binding of charybdotoxin to KcsA* (Shaker)
Realistic case study: Binding of ShK toxin and analogues to Kv1.1,
Kv1.2, and Kv1.3 channels
Two essential criteria for development of drug leads
1. Should bind to a given target protein with high affinity
2. Be selective for the target protein
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
• ShK toxin binds to Kv1.3 with pM affinity
• But it also binds to Kv1.1 with pM affinity
• Need to improve selectivity of ShK for Kv1.3 over Kv1.1
Challenges in computational design of drugs from peptides
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 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.
1. Complex structure determination:
Find the initial configuration for the bound complex using a docking algorithm (e.g., HADDOCK).
Refine the initial complex(es) via MD simulations.
2. Validation:
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.
3. Design:
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.
Computational program for rational drug design from peptides
• Complex structure is determined from NMR, so it 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 of ChTx calculated from the potential of mean
force (PMF): -7.6 kcal/mol
• experimental value: -8.3 kcal/mol
Proof of concept study:
Binding of charybdotoxin (ChTx) to KcsA* (shaker mimic)
Structure of the KcsA*- ChTx complex
Important pairs:
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.
K11R34
• Motivation:
– Kv1.3 is the main target for autoimmune diseases
– 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 have been developed for this purpose
1. Find the complex structures of ShK with Kv1.1, Kv1.2 and Kv1.3, and
validate them using mutagenesis data. Determine the PMFs and the
binding free energy and compare with experiment.
2. Repeat the above study for ShK-K-amide (an analogue with improved
selectivity) to rationalize the experimental results.
3. WT complex structures indicate that K18A mutation should improve
selectivity. Perform PMF and FEP calculations to quantify this
prediction.
Realistic case study: ShK toxin binding to Kv1 channels