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Target Selection Relevant to Health. Workshop on Target Selection NIGMS Protein Structure Initiative NIH 13 –14 November 2003. Wim G.J Hol Structural Genomics of Pathogenic Protozoa (SGPP) University of Washington and HHMI Seattle.

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Target selection relevant to health

Target Selection Relevant to Health

Workshop on Target Selection

NIGMS Protein Structure Initiative

NIH

13 –14 November 2003

Wim G.J Hol

Structural Genomics of Pathogenic Protozoa (SGPP)

University of Washington and HHMI

Seattle


Target selection to optimize medical benefits of structural genomics for health

Target Selection to Optimize Medical Benefits ofStructural Genomics for Health

Structure-Based Drug Design

Synthetic Medicines

Small – numerous examples

Large – a few under way

Proteins

Improved insulins

Humanized antibodies

Structure-Based Vaccines

Structure-based Vaccine Stabilizers

HIV-protein:antibody complexes

Structure-Based Diagnostics


The mode of action of drugs varies tremendously

Drugs acting on Proteins

Active Site Blockers

Cofactor Site Blockers

Receptor Binding Site Binders

Conformational Change Preventers

Conformational Change Accelerators

Protein Assembly Inhibitors

Multi-protein Disassembly Inhibitors

Protein- Protein Glues

The mode of action of drugs* varies tremendously.

* And promising lead compounds


What do safe drugs not do

What do safe drugs not do?

They do not bind to too many essential, human proteins, nucleic acids, bilayers, and their complexes

They do not covalently modify too many essential human proteins, nucleic acids, bilayers

They do not bind to or react with too many human metabolites

GOOD DRUGS ARE GREAT AVOIDERS


Toxicity how many potential binding sites in humans for small molecules

Toxicity:How many potential binding sites in humans for small molecules?

Guestimate upon Guestimate:

~ 35,000 human genes?

~ 100,000 variant proteins? - splicing

~ 200,000 mature proteins? - splicing plus post-trans modifications

~ 400,000 different single proteins plus protein-protein complexes?

including splicing and post trans modifications

~ 800,000 different conformations for the above?

assuming two distinct conformations per above

~ 1,600,000 binding pockets?

  assuming about 2 binding pockets per above.

How many binding sites for the RNAs, DNA, bilayers? 400,000?

So about 2,000,000 binding pockets per human proteome plus transcriptome??


Beneficial versus harmful effects

Beneficial versus Harmful Effects

Cancer: How many of these 2,000,000??? potential binding sites are fatal for a cancer cell if a drug bound to them?

Infectious diseases: How many of the ~200,000?? Potential binding sites are fatal for a pathogen if a drug bound to them?

(Pathogen genomes are typically 10 times smaller than the human genome – except for viruses, which are ~1000 times smaller)

Toxicity: How many of these 2,000,000??? potential binding sites in humans are distinctly disadvantageous if drug bound to them?

Human and Pathogen Structural Genomics

superb way to evaluate

binding sites and binding modes.


Drug target selection human diseases a wealth of functional information available

Drug Target Selection Human Diseases(A wealth of functional information available)

1. Modulating wt human proteins

Neurological disorders

Blood pressure irregularities

Heart disease

Inflammation

Immune modulators

Diabetes

Asthma

Trauma’s

Surgery needs

Painkillers

Etc, etc

2. Human genetic diseases

3.Cancer

Each of these categories have quite different target selection characteristics


Drug target selection cancer

Drug Target Selection - Cancer

Which Biomacromolecule to target?:

Modified protein?

or

Regular wt protein, or DNA, RNA?

Selectivity:

Usually difficult to achieve since there is often a close homologue of human protein in healthy cells.

Are there opportunities for drugs to compensate problem at all?

Loss of function mutations very tricky to restore with drug.

Loss of stability mutations perhaps to restore with drug

 Attempts with p53.

One drug might stabilize several different p53 mutants.

Selectivity might be less of a problem

Note: Drug Resistance a major problem


Drug target selection genetic diseases

Drug Target Selection - Genetic Diseases

Which Biomacromolecule to target?:

Modified protein – usually

Or pathway of affected protein

But in CF – bacterial proteins…

Selectivity:

Maybe not such a major problem, except perhaps in cases of a member of a protein family with numerous close homologs

Are there opportunities for drugs to compensate problem at all?

Loss of function mutations very tricky to restore with drug.

Loss of stability mutations perhaps to restore with drug

Specific case: preventing aggregation very challenging

Very well-known case : sickle cell Hemoglobin.

Note: Number of patients per specific mutation often very small.


Drug target selection infectious diseases

Drug Target Selection - Infectious Diseases

Which Biomacromolecule to target?:

Essential proteins & nucleic acids

Sufficiently different from, or absent, in humans

Selectivity:

Often great opportunities

Sometimes selective uptake by pathogen is helpful (CQ)

Sometimes no selectivity is required since human homologueturning over very fast (DFMO)

Are there opportunities for drugs to compensate problem at all?

Yes

Note: For certain diseases billions of patients at risk are very poor.

Note: Drug resistance a major problem.


Drug target selection infectious diseases how

Drug Target Selection - Infectious DiseasesHow?

Functional Information – often not available

- Classical biochemistry

- Functional Genomics

  - Target from a HT screen

Essentiality Information – even more often not available

- Genome-wide RNAi

- Genome-wide Gene disruption

Sufficient Dissimilarity with Human Proteins – information available

Potential Approaches:

- Relative of Drug Target in any species ("Piggy backing")

- Relative of Any Enzyme in Any Species

- Interaction information

Interaction celebrity

Interacting with interaction celebrity


Drug target selection for structural genomics of pathogens piggy backing

Drug Target Selection for Structural Genomics of PathogensPiggy-backing

Searching Patent Databases To Identify Proteins that have Inhibitors as Leads for Drug Development

Wes Van Voorhis

Michael Gelb

Gene Quinn

Fred Buckner


Piggybacking bypass the bottleneck of identification of drug like lead inhibitors

Use the aggregate findings of decades of pharmaceutical pursuit for drug-like leads

Identify enzymes where inhibitors have already been generated

Use these inhibitors as leads for further development

Piggybacking:Bypass the Bottleneck of Identification of Drug-Like Lead Inhibitors


Cross reference databases

637 Plasmodium falciparum enzymes from PlasmoDB

Search World’s Patent Databases for Enzyme + inhibit* = 163 enzymes

50 enzymes with 3 or more small molecule inhibitor patents

These enzymes are placed in the SGPP pipeline, also examining currently L. major, T. cruzi, and T. brucei

Cross Reference Databases


Examples of p falciparum enzymes where a homologous enzyme has small molecule inhibitors

Examples of P. falciparum enzymes where a homologous enzyme has small molecule inhibitors


Target selection relevant to health

Drug Target Selection for Structural Genomics of PathogensSearch for Enzyme-relatives

Enzymes have:

Often good pockets

With hydrophobic grooves

Are usually quite stable

Are often stand-alone entities

Liz Worthey, Peter Myler

David Kim, David Baker


Target selection relevant to health

Search for Enzyme-Relatives

Redundant dataset comprised:

424 proteins annotated with EC number in PlasmoDB

475 proteins belonging to COGs containing a protein with an EC number

457 proteins from Blastp against BRENDA enzyme database

~470 proteins from Psiblast against BRENDA enzyme database

After removal of proteins due to redundancy between datasets, standard filtering (e.g. M and C content), and exclusion of proteins that showed more than 60% identity over 100 aa to human proteins we have:

720 proteins selected for expression (plus the number from the psiblasting)


Target selection relevant to health

103

316

0

5

152

450

2

Selection of enzymes and enzyme-like proteins for P. falciparum

P. falciparum proteins identified

in PlasmoDB that contained an

Enzyme Commission number

in their annotation.

P. falciparum proteins

belonging to Clusters of

Orthologous Genes (David

Roos lab, U of Penn), where the

cluster contained proteins identified

as enzymes (Gene Ontology characterizations).

P. falciparum proteins

with a significant BlastP/

Psiblast match to a protein

occurring in the BRENDA enzyme

DB (Institute of Biochem, U of Cologne).


Target selection relevant to health

Drug Target Selection for Structural Genomics of PathogensSearch for Protein Pairs

P falciparum pairs:

- Often stabilize each other

- Sometimes have hydrophobic interacting grooves

- Pair partners may suggest function

“Interaction Celebrities” likely very important function

P falciparum:human pairs:

- Interesting from drug and vaccine point of view

Marissa Vignali, Doug LaCount, Lori Schoenfeld, Stan Fields

Prolexys Pharmaceuticals, Inc.

Pradip Rathod group


Target selection relevant to health

Pick, at random, 6,144 (64x96) yeast clones expressing Binding Domain (BD) fusions

Mate each BD clone with an Activation Domain (AD) fusion library

Plate under selective conditions

Pick positives

Sequence inserts in BD and AD plasmids to determine identity of interacting proteins

Analyze data

Non-classical Experimental Y2H Strategy


Target selection relevant to health

Current P falciparum Y2H* Dataset

234 296 487

530783

BD fusionAD fusion

  • Three types of interactions:

  • Both partners have annotation (21%)

  • One partner has annotation, one is hypothetical (49%)

  • Both partners are hypothetical (30%)


Match the biomacromolecular world with the chemical universe

Match the Biomacromolecular Worldwith the Chemical Universe

About 200,000 to 2,000,000?? Binding Sites in the Bioworld

to be matched with

the effectively infinite Chemical Universe

(10 60 small molecules below 800 Daltons…)

Good representation of the Chemical Universe a Challenge


The useful part of the chemical universe

The useful part of the chemical universe

For oral drugs:

The Lipinski's "rule of 5" states that poor absorption or permeation is more likely when:

- molecular weight (MW) is over 500 

- more than 5 H-bond donors (expressed as the sum of OHs and NHs). 

- more than 10 H-bond acceptors (expressed as the sum of Ns and Os). 

- the calculated ClogP is greater than 5 (or MlogP > 4.15) 

Citation: C. A. Lipinski, F. Lombardo, B. W. Dominy, and P. J. Feeney, "Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings," Advanced Drug Delivery Reviews 23, 3-25 (1997)


Matching the proteome and transcriptome with the chemical universe

Matching the Proteome and Transcriptome with the Chemical Universe

Find small molecules which interact with one or more important binding sites.

Binder Discovery:

Each drug target protein vs. each compound

Pair Stabilizer Discovery:

Each Interacting Protein Pair vs. each compound

Pair- Forming-Preventer Discovery:

Each Known Protein pair vs. each compound

Glue discovery:

All proteins vs. all proteins vs. each compound


Binder discovery

In Solution:

- General Screens

ThermoFluor – thermal denaturation effect

NMR

Frontal Affinity Chromatography

- Specific Screens

In Crystals:

- Prior to Crystal Growth

Random co-crystallants with protein-loving properties

- After Crystal Growth

Soak with smart cocktails

Binder Discovery


Target selection relevant to health

Special Types of General Screens needed for:

Pair Stabilizer Discovery:

Each Interacting Protein Pair vs. each compound

Pair Forming Preventer Discovery:

Each Known Protein pair vs. each compound

Glue discovery:

All proteins vs. all proteins vs. each compound

Pair Stabilizers and “Glue”s likely to promote crystal formation


Target selection relevant to health

Screening of Ligand MixturesFrontal Affinity Chromatography

Relative Intensity

10 ml Beads, 2 mM each compound

1

Low Affinity

~20 mM

5 mM

< 1 mM

0.5

Time (Min)

0

2

4

6

8

10

12

Tight Binders often increase crystal growth success rate

Jizhen Li, Erkang Fan

Yuko Ogata

(Turecek Group, UW Chemistry)

Christophe Verlinde


Target selection relevant to health

Essential

And

Sufficiently

Different

From

Human

Essential

And

No

Human

Counterpart

Essential

But

Too

Human-

Like

Non-

essential

X

Proteome

Chemical Universe

Medicinal SG

Numerous Protein:Ligand Complexes


Medicinal structural genomics of pathogens and humans leads to structures of

Medicinal Structural Genomics of Pathogens and Humansleads toStructures of:

1. Human Drug targets,

If possible with compounds bound

2. Pathogenic Drug Targets

Preferably not present in humans

Preferably with compounds boun

3. All human Proteins revealing Potential Toxic Binding pockets

An accelerated translation of the genome sequence wealth into therapies


Thank you

Thank You


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