Transcript Lecture 14 - HIV vaccine_cancer diag-drug design.ppt

•SOME EXAMPLES
CHRISTOPH PFISTERER
DAN MIHAILESCU
JENNIFER REED
Does the gp120 recognition peptide have a
similar structure in all clades of HIV-1 ?
11 Sequences
in 9 clades
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A1
B1
C1
D2
E2
E3
F1
G2
1A0
2A3
OC4
LEU PRO CYS ARG ILE LYS GLN PHE ILE ASN MET TRP GLN GLU VAL
LEU PRO CYS ARG ILE LYS GLN ILE VAL ASN MET TRP GLN GLU VAL
ILE PRO CYS ARG ILE LYS GLN ILE ILE ASN MET TRP GLN GLU VAL
LEU PRO CYS ARG ILE LYS PRO ILE ILE ASN MET TRP GLN GLU VAL
LEU PRO CYS LYS ILE LYS GLN ILE ILE ASN MET TRP GLN GLY VAL
LEU PRO CYS LYS ILE LYS GLN ILE ILE LYS MET TRP GLN GLY VAL
LEU LEU CYS LYS ILE LYS GLN ILE VAL ASN LEU TRP GLN GLY VAL
LEU PRO CYS LYS ILE LYS GLN ILE VAL ARG MET TRP GLN ARG VAL
LEU PRO CYS LYS ILE LYS GLN ILE VAL ASN MET TRP GLN ARG VAL
LEU GLN CYS ARG ILE LYS GLN ILE VAL ASN MET TRP GLN LYS VAL
ILE PRO CYS LYS ILE LYS GLN VAL VAL ARG SER TRP ILE ARG GLY
+2
+2
+2
+2
+3
+4
+2
+5
+4
+4
+5
Questions
Are the gp120 recognition sequence peptides
structured in aqueous solution?
Threading of sequences
on the peptide backbone
LEU PRO CYS ARG ILE LYS GLN PHE ILE ASN MET TRP
GLN GLU VAL
 ILE PRO CYS LYS ILE LYS GLN VAL VAL ARG SER
TRP ILE ARG GLY
A1
oc4
Molecular Dynamics
Simulation Setup
• Box dimensions: 53x40x40 Ǻ
• Explicit water molecules (TIP3P)
(~8600 atoms)
• Explicit ions
(Sodium and Chloride, 26 ions in total);
physiological salt: 0.23M
• ~240 peptide atoms
=> approx. 8900 atoms in total
• Uncharged system
• NPT ensemble: 300K, 1atm
• 5ns simulation time for each strain
=> 55ns total simulation time
Root Mean Square Coordinate Deviation
MD simulations
T
M
D
Convergence of gp120 peptide structure
from three different experimental starting geometries
Questions
Are the gp120 recognition sequence peptides
structured in aqueous solution?
Do the structures of peptides from different
clades resemble each other?
Dihedral angles


Questions
Are the gp120 recognition sequence peptides
structured in aqueous solution?
Do the structures of peptides from different
clades resemble each other?
Does the consensus structure present
a common shape/electrostatic surface?
Shape and electrostatic properties conserved.
Questions
Are the gp120 recognition sequence peptides
structured in aqueous solution?
Do the structures of peptides from different
clades resemble each other?
Does the consensus structure present
a common shape/electrostatic surface?
Can the consensus shape/electrostatic surface
be mimicked with a synthetic molecule?
Can this molecule be used as a lead for vaccine design?
Cancer Biotechnology.
Detection of Individual p53Autoantibodies in Human Sera
Rhodamine 6G
Fluorescence Quenching of Dyes
by Trytophan
Quencher
N
N
O
OH
O
MR121
Dye
N
Fluorescently labeled
Peptide
?
Rhodamine-Tryptophan complex
MD simulations in water
• NPT 300K, 1 atm, explicit water (~1000
TIP3P waters)
– TO periodic boundary conditions
• 7 ns production runs for each starting
structure (28 ns total)
• PME electrostatics
PMF Landscape R6G/W
1 80
1 60
Quenching efficiency
at 50 mM ~ 75%
1 40
]
g
e
d
[

P M F
[ k c a l/ m o l ]
1 20
5. 0
4. 0
1 00
3. 0
8 0
2. 0
6 0
1. 0
0
4 0
2 0
0
0
5
1 0
r [Å ]
1 5
2 0
Analysis
r
1 80
1 60
P M F
1 40
[ k c a l/ m o l ]
]
g
e
d
[

1 20
5. 0
4. 0
1 00
3. 0
8 0
2. 0
6 0
1. 0
0
4 0
2 0
0
0
5
1 0
r [Å ]
1 5
2 0
Strategy:
Quenched
Results:
Healthy
Person
Serum
Cancer
Patient
Serum
Fluorescent
Drug Design
Finding the Right Key for the Lock
Ligand Binding.
Ligand
Protein
Complex
physicochemical understanding
vibrational changes?
STEFAN FISCHER
Vibrational Change on
Burial of a Water Molecule
Bovine Pancreatic
Trypsin Inhibitor
Frequency Shifts
Normal Mode Analysis
Dissecting the Vibrational Entropy Change on Protein/Ligand Binding:
Burial of a Water Molecule in BPTI
Librational modes = 9.4 cal mol-1 K-1
Softening of protein = 4.0 cal mol-1 K-1
Frequency Shifts
Change in Entropy due
to Frequency Shifts
Vibrational Change on Methotrexate Binding to
Dihydrofolate Reductase
ERIKA BALOG
TORSTEN BECKER
complexed
uncomplexed
2.0
12
VIBRATIONAL
FREQUENCY
DISTRIBUTION
j
8
g() (mode/cm -1)
1.5
Vibrational Thermodynamics
4
0
1.0
0
40
80
Gvib = -4.0  1.0 kcal/mol
-TSvib= -6.0  1.5 kcal/mol
Hvib = +2.0  0.5 kcal/mol
120
0.5
0.0
0
2
4
6
8
10
12
 (cm )
-1
14
16
18
20
Drug Design
High Throughput
Screening
104 ligands per day
But: Hit Rate 10-6 per ligand

FRAUKE MEYER
What is the binding free energy?
entropic
effects
protein
polar and
K bind
k1
[C ]


k 1 [ P ][ L]
non-polar
ligand
k1
k-1
ΔGbind   RT ln Kbind
interactions
with the solvent
polar and
non-polar
water
complex
protein-ligand
interactions
Electrostatics:
Thermodynamic Cycle
Gel (  80)
  80
+
 Gsolv (P)  Gsolv (L)
 4
Gsolv(C )
Gel (  4)
+
Methods
• flexibility (Jon Essex)
• MD (Daan van Aalten)
• scoring functions, virtual
screening (Martin Stahl,
Qi Chen)
• prediction of active sites
(Gerhard Klebe)
• active site homologies
Ligands: set n0
Fast Calculation of Absolute Binding Free Energies:
Interaction of Benzamidine Analogs with Trypsin
Benzamidine-like Trypsin Inhibitors
Energy Terms and Results
- van der Waals protein:ligand
- hydrophobic effect (surface area dependent)
- electrostatic interactions (continuum approach)
- translational, rotational, vibrational degrees of freedom
End ss 2004
Results: Binding free energies
4.0
2.0
0.0
-10.0
-8.0
-6.0
-4.0
-2.0
0.0
-2.0
dG(calc) [kcal/mol]
-12.0
-4.0
flexible system,
CHARMM-AM1
fixed system,
MAB
-6.0
-8.0
-10.0
-12.0
-14.0
RMSD(flex)
= 1.3 kcal/mol
RMSD(fix)
= 3.2 kcal/mol
-16.0
dG(exp) [kcal/mol]
Fig: Calculated versus experimental binding free energies
CONCLUSION:
Including flexibility improves the prediction of
binding free energies
OUTLOOK:
Introduction of polarisation energy
Automation of the free energy calculation protocol
THANKS! Jeremy, Stefan, Sonja, Bogdan, girls’ room
Results: Energy contributions
30.0
<dG>
20.0
dG [kcal/mol]
10.0
0.0
dGbind
dGtr
dGvib
dGval
dGqm
dGsolv,p
dGcoul
dGvdw
dGsolv,np
-10.0
-20.0
-30.0
-40.0
Fig: Range of calculated binding free energies and the
contributing terms for the ligands of set n0 and n1
Successes and failures in
structure-based drug design
Mercedes L. Dragovits
The drug discovery and development
pipeline
Choice of a target
• Link to a human disease
• Binds a small molecule to carry out a function
• The drug competes with the natural molecule
The
iterative
process
of
SBDD
Overview of the process:
first cycle
• Cloning, purification, structure determination of the target
– X-ray-crystallography
– NMR
• Compounds or fragments of compounds are placed in selected
regions of the target using computer algorithms
• Test the best compounds with biochemical assays
Overview of the process:
second cycle
• Structure of the target in complex with a lead from the first
cycle
Several additional cycles:
• Synthesis of optimized lead
• Structure determination of the new complex
• Further optimization of the lead compound
X-ray crystallography
• About 80 percent of the protein structures that are known have been
determined using X-ray crystallography
X-Ray Beam
Crystal
Scattered X-Rays
Detector
Computed Image of atoms in crystal
X-ray crystallography
I.
II.
III.
Protein expression
Purification
crystallization
X-ray crystallography
<
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High resolution
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Wide range of proteins
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Ordered H2O molecules are visible
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Information might be ambiguous
<
<
=
NMR
• Conc. nucleic acid or
protein in solution (½
ml)
NMR
• No limitation to molecules that crystallize well
<
• Flexibility of molecules and their interaction with other
compounds
<
• Size limitation
=
• NMR is 20 years younger than X-ray crystallography
NMR vs. X-ray crystallography
• Both methods have their advantages and
disadvantages
• Ideally, these two techniques complement one
another
Drug Design methods
1)
Inspection
2)
Virtual screening
3)
De novo generation
Drug lead evaluation
• Computer graphics
• Is the drug lead orally bioavailable?
• Chemical and metabolic stability
• Ease of synthesis
• Biochemical evaluation in the lab
Pharmaceutical companies
• „Syrrx“
Robotics in several parts of the
process
Pharmaceutical companies
• „Vertex Pharmaceuticals“
„chemogenomic approach“-> gene families
• „Ariad Pharmaceuticals“
Successful in finding inhibitors for Src
Pharmaceutical companies
• „Triad Therapeutics“
Biligand enzymes
Druglike mimic for the
cofactor
Enzyme inhibitors
Nelfinavir
Amprenavir
Enzyme inhibitors
Zanamivir (Relenza)
Outlook
• Presently only a few drugs on market
• Availability of X-ray derived information is increasing
• Advances in structural genomics and bioinformatics
Sources of information
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„The process of structure-based drug Design“-Amy C. Anderson, Chemistry & Biology,
Vol. 10, 787-797, September 2003
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„Structure-based drug Design“-Celia M. Henry, Science & Technology, June 4, 2001,
Vol. 79, Number 23
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“Application and Limitations of X-ray Crystallographic Data in Structure-Based Ligand
and Drug Design”-Andrew M. Davis,* Simon J. Teague, and Gerard J. Kleywegt,
Angewandte Chemie, Ed. 2003, 42, 2718-2736
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„A virtual space odyssey“-Cath O´Driscoll, Charting chemical space, May 2004
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The structures of life, CHAPTER 4: Structure-Based Drug Design: From the Computer
to the Clinic http://www.nigms.nih.gov/news/science_ed/structlife/chapter4.html
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“Disarming Flu Viruses” By W. Graeme Laver , Norbert Bischofberger and Robert G.
Webster, January 06, 1999
http://www.sciam.com/print_version.cfm?articleID=00023064-8039-1CD6B4A8809EC588EEDF