Download Target Selection Relevant to Health Workshop on Target Selection

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
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
* And promising lead compounds
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?
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
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)
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
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
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
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 homologue
turning 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?
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
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
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
Examples of P. falciparum enzymes where a
homologous enzyme has small molecule inhibitors
adenosine deaminase, putative
33 Patents
adenylosuccinate synthetase
10 Patents
DNA topoisomerase II, putative
5 Patents
farnesyl pyrophosphate synthase, putative
4 Patents
glyoxalase I, putative & glyoxalase II family protein, putative
6 Patents
GMP synthetase
5 Patents
DEAD-box RNA helicase, putative
22 Patents
Histone deacetylase, putative
77 Patents
N-myristoyltransferase
14 Patents
ornithine aminotransferase
7 Patents
protoporphyrinogen oxidase, putative
26 Patents
pyruvate kinase, putative
5 Patents
Drug Target Selection for Structural
Genomics of Pathogens
Search 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
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)
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.
103
316
0
5
152
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).
2
450
P. falciparum proteins
with a significant BlastP/
Psiblast match to a protein
occurring in the BRENDA enzyme
DB (Institute of Biochem, U of Cologne).
Drug Target Selection for Structural
Genomics of Pathogens
Search 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
Non-classical Experimental Y2H
Strategy
• 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
Current P falciparum Y2H* Dataset
234
296
530
BD fusion
487
783
AD 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
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
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
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
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
Screening of Ligand Mixtures
Frontal Affinity Chromatography
Relative Intensity
10 ml Beads, 2 mM each compound
1
Low Affinity
~20 mM
5 mM
0.5
< 1 mM
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
Time (Min)
Essential
And
Essential
Sufficiently
But
Too
Different
HumanLike
From
Human
Essential
And
No
Human
Nonessential
X
Counterpart
Proteome
Chemical Universe
Medicinal SG
Numerous Protein:Ligand Complexes
Medicinal Structural Genomics
of Pathogens and Humans
leads to
Structures 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