SOME EXAMPLES

•SOME EXAMPLES

CHRISTOPH PFISTERER

DAN MIHAILESCU

JENNIFER REED

11 Sequences in 9 clades

• A1 LEU PRO CYS ARG ILE LYS GLN PHE ILE ASN MET TRP GLN GLU VAL +2• B1 LEU PRO CYS ARG ILE LYS GLN ILE VAL ASN MET TRP GLN GLU VAL +2• C1 ILE PRO CYS ARG ILE LYS GLN ILE ILE ASN MET TRP GLN GLU VAL +2• D2 LEU PRO CYS ARG ILE LYS PRO ILE ILE ASN MET TRP GLN GLU VAL +2• E2 LEU PRO CYS LYS ILE LYS GLN ILE ILE ASN MET TRP GLN GLY VAL +3• E3 LEU PRO CYS LYS ILE LYS GLN ILE ILE LYS MET TRP GLN GLY VAL +4• F1 LEU LEU CYS LYS ILE LYS GLN ILE VAL ASN LEU TRP GLN GLY VAL +2• G2 LEU PRO CYS LYS ILE LYS GLN ILE VAL ARG MET TRP GLN ARG VAL +5• 1A0 LEU PRO CYS LYS ILE LYS GLN ILE VAL ASN MET TRP GLN ARG VAL +4• 2A3 LEU GLN CYS ARG ILE LYS GLN ILE VAL ASN MET TRP GLN LYS VAL +4• OC4 ILE PRO CYS LYS ILE LYS GLN VAL VAL ARG SER TRP ILE ARG GLY +5

Does the gp120 recognition peptide have a similar structure in all clades of HIV-1 ?

Questions

Are the gp120 recognition sequence peptides structured in aqueous solution?

Threading of sequences on the peptide backbone

A1 oc4

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

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

MT D

MD simulations

Convergence of gp120 peptide structurefrom 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?

Detection of Individual p53-Autoantibodies in Human Sera

Cancer Biotechnology.

Rhodamine 6G

O H

N

O

O

N

N

MR121

Fluorescence Quenching of Dyes by Trytophan

Dye

Quencher

Fluorescently labeled Peptide

?

Rhodamine- Tryptophan complex MD simulations in water

• NPT 300K, 1 atm, explicit water (~1000 TI P3P waters)– TO periodic boundary conditions

• 7 ns production runs f or each starting structure (28 ns total)

• PME electrostatics

0 5 1 0 1 5 2 0

0

2 0

4 0

6 0

8 0

1 0 0

1 2 0

1 4 0

1 6 0

1 8 0

P M F

[ k c a l / m o l ]

r [ Å ]

[deg]

0

1 . 0

2 . 0

3 . 0

4 . 0

5 . 0

PMF Landscape R6G/ W

Quenching efficiency at 50 mM ~ 75%

Analysis

r

Analysis 2

0 5 1 0 1 5 2 0

0

2 0

4 0

6 0

8 0

1 0 0

1 2 0

1 4 0

1 6 0

1 8 0

P M F

[ k c a l / m o l ]

r [ Å ]

[deg]

0

1 . 0

2 . 0

3 . 0

4 . 0

5 . 0

Strategy:

Quenched Fluorescent

Results:

HealthyPersonSerum

CancerPatientSerum

Drug Design

Finding the Right Key for the Lock

Ligand Binding.

vibrational changes?

physicochemical understanding

Protein

Ligand

Complex

Bovine Pancreatic

Trypsin Inhibitor

Normal Mode Analysis

Vibrational Change onBurial of a Water Molecule

Frequency Shifts

STEFAN FISCHER

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

Change in Entropy due to Frequency Shifts

Frequency Shifts

Vibrational Change on Methotrexate Binding to Dihydrofolate Reductase

ERIKA BALOGTORSTEN BECKER

0 2 4 6 8 10 12 14 16 18 20

0.0

0.5

1.0

1.5

2.0

g(

) (m

ode

/cm

-1)

(cm-1)

0 40 80 1200

4

8

12

jVIBRATIONALFREQUENCYDISTRIBUTION

complexed uncomplexed

Vibrational Thermodynamics

Gvib = -4.0 1.0 kcal/mol-TSvib= -6.0 1.5 kcal/molHvib = +2.0 0.5 kcal/mol

High Throughput Screening

104 ligands per day

Drug Design

But: Hit Rate 10-6 per ligand

What is the binding free energy?

]][[

][

1

1

LP

C

k

kKbind

bindbind KRTΔG ln

ligand

protein

complex

water

polar and

non-polar

interactions with the solvent

polar and

non-polar

protein-ligand interactions

entropic effects

k1 k-1

FRAUKE MEYER

Electrostatics: Thermodynamic Cycle

+

+

)(PGsolv )(LGsolv )(CGsolv

)80( elG

)4( elG

80

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

Fig: Calculated versus experimental binding free energies

-16.0

-14.0

-12.0

-10.0

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

-12.0 -10.0 -8.0 -6.0 -4.0 -2.0 0.0

dG(exp) [kcal/mol]

dG

(ca

lc)

[kca

l/mo

l]

flexible system,CHARMM-AM1

fixed system,MAB

RMSD(flex)

= 1.3 kcal/mol

RMSD(fix)

= 3.2 kcal/mol

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

Fig: Range of calculated binding free energies and the contributing terms for the ligands of set n0 and n1

-40.0

-30.0

-20.0

-10.0

0.0

10.0

20.0

30.0

dGbind dGtr dGvib dGval dGqm dGsolv,p dGcoul dGvdw dGsolv,np

dG

[k

ca

l/mo

l]

<dG>

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

Theiterative 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. Protein expression

II. Purification

III. crystallization

X-ray crystallography

• High resolution

• Wide range of proteins

• Ordered H2O molecules are visible

• 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

• „The process of structure-based drug Design“-Amy C. Anderson, Chemistry & Biology, Vol. 10, 787-797, September 2003

• „Structure-based drug Design“-Celia M. Henry, Science & Technology, June 4, 2001, Vol. 79, Number 23

• “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

• „A virtual space odyssey“-Cath O´Driscoll, Charting chemical space, May 2004

• 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

• “Disarming Flu Viruses” By W. Graeme Laver , Norbert Bischofberger and Robert G. Webster, January 06, 1999 http://www.sciam.com/print_version.cfm?articleID=00023064-8039-1CD6-B4A8809EC588EEDF