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Target Selection Relevant to Health
Workshop on Target SelectionNIGMS 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 DesignSynthetic Medicines
Small – numerous examples
Large – a few under way
Proteins
Improved insulins
Humanized antibodies
Structure-Based VaccinesStructure-based Vaccine Stabilizers
HIV-protein:antibody complexes
Structure-Based Diagnostics
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?
• 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
Toxicity: How many of these 2,000,000??? potential binding sites in humans are distinctly disadvantageous if drug bound to them?
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)
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 proteinsNeurological 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 - CancerWhich 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 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?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
Enzymes have:Often good pockets
With hydrophobic groovesAre usually quite stable
Are often stand-alone entities
Liz Worthey, Peter Myler
David Kim, David Baker
Drug Target Selection for Structural Genomics of Pathogens
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)
Search for Enzyme-Relatives
P. falciparum proteins identified in PlasmoDB that contained an Enzyme Commission number in their annotation.
P. falciparum proteins with a significant BlastP/
Psiblast match to a protein occurring in the BRENDA enzyme
DB (Institute of Biochem, U of Cologne).
Selection of enzymes and enzyme-like proteins for P. falciparum
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).
103
5316 0
2 450152
P falciparum pairs:- Often stabilize each other- Sometimes have hydrophobic interacting grooves- Pair partners may suggest function-“Interaction Celebrities” likely very important functionP falciparum:human pairs:- Interesting from drug and vaccine point of view
Marissa Vignali, Doug LaCount, Lori Schoenfeld, Stan FieldsProlexys Pharmaceuticals, Inc.
Pradip Rathod group
Drug Target Selection for Structural Genomics of Pathogens
Search for Protein Pairs
• 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
530 783 BD fusion AD fusion
234 296 487
Current P falciparum Y2H* Dataset
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 effectNMRFrontal 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
Relative Intensity
1
0.5
Time (Min)0 2 4 6 8 10 12
Screening of Ligand MixturesFrontal Affinity Chromatography
Low Affinity
~20 M
5 M
< 1 M
10 l Beads, 2 M each compound
Tight Binders often increase crystal growth success rate
Jizhen Li, Erkang FanYuko Ogata
(Turecek Group, UW Chemistry)Christophe Verlinde
X
Numerous Protein:Ligand Complexes
Proteome Chemical Universe
Medicinal SG
Essential
And
Sufficiently
Different
From
Human
Essential
And
No
Human
Counterpart
Essential But Too
Human-Like
Non-essential
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
Medicinal Structural Genomics of Pathogens and Humans
leads toStructures of:
An accelerated translation of the genome sequence wealth into therapies
Thank You