Transcript Adam1

CONCERTS: Dynamic Connection of
Fragments as an Approach to de Novo
Ligand Design
Creation Of Novel Compounds by Evaluation of Residues at Target Sites
David A. Pearlman and Mark A. Murkco
Vertex Pharmaceuticals Incorperated
Cambridge, MA
Adam Tenderholt, Stanford University
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Outline
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Background
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Implementation
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HIV-1 aspartyl protease
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FK506 binding protein
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Conclusions
Adam Tenderholt, Stanford University
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Previous Work: CONCEPTS
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Active site is filled with atoms
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Run MD simulations, and form/break bonds
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Generates useful de Novo leads
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Limitations
Difficult to incorporate charge models
– Slow convergence, especially for “spacer”
regions
– Only 1 suggestion per cpu-intensive run
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Adam Tenderholt, Stanford University
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CONCERTS: Implementation
Modified AMBER/SANDER 4.0 minimization/MD program
1) Active site is filled with user-defined fragments
2) “Connection vectors” are chosen for each
fragment
3) Define a volume for a known protein of interest
4) Randomly orient fragments in defined volume
5) Fragment minimization and MD (two steps)
6) Start CONCERTS
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CONCERTS: Implementation
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CONCERTS: Improvements
CONCERTS has several improvements
over CONCEPTS:
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Fragments can inherently have charge
Fragments span larger region of space; don't
have to worry about “spacer” regions
Many suggested molecules can be built during
a run
Greater control over types of molecules
generated
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CONCERTS: Testing
Begin testing CONCERTS on two targets using 3 types
of “basis sets”:
A) 1000 copies of peptide fragment
B) 700 copies of benzene, 1000 copies each of
methane, ammonia, formaldehyde, and water
C) 300 copies each of ammonia, benzene,
cyclohexane, formic acid, ethane, ethylene,
formaldehyde, formamide, methane, methanol,
sulfinic acid, thiophene, and water
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HIV-1 AP, Results A
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82 macrofragments were found
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35 tetra-, 27 penta-, 17 hexa-, and 3 hepta-peptides
Reproduces backbone of JG-365, a sub-nM
peptide-based inhibitor
Good fit suggested start with this structure, and
add amino-acid side chains
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HIV-1 AP, Results A2
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Start with 10 copies of previous fragment and 150
copies of each standard amino acid side-chain
A side-chain was added to each of the six α carbons in
every peptide seed
Lowest energy result mimics known inhibitor quite well
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HIV-1 AP, Results B
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138 macrofragments were generated
Combination of 4+ fragments
Reproduces backbone of JG-365, despite not
being made from amino acids
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Bonus: only one chiral center!
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HIV-1 AP, Results C
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151 macrofragments were generated
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Combinations of 4+ fragments
Not good agreement with backbone of JG-365
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However, places atoms in regions of space for
all but one of the side chains of the drug!
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FKBP-12, Results A
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“A number” of macrofragments were identified
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Mimics the “binding core” of nM inhibitor FK506
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Interesting that peptide fragments modeled a
non-peptide inhibitor reasonably well
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FKBP-12, Results B
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122 macrofragments were generated
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Places atoms in regions occupied by FK506
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Unfortunately, a significant number of fragments falls at
the edge or outside of the active site
Contains zero chiral centers
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FKBP-12, Results C
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130 macrofragments were generated
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A majority were outside or on the edge of the active
site
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Less concise than B set
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Contains several chiral centers
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Sampling Issues: Thoroughness
How well does CONCERTS sample the
conformational space available?
20 hexamer or larger macrofragments during peptide
run (A set) against HIV1-AP
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Sampling Issues: Energy Function
Does the energy function used in CONCERTS
have predictive qualities?
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HIV-1 AP
Hydrogen bonds
with protein residues
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Enb for Set A
inhibitors
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Conclusion
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CONCERTS works: it generates inhibitors
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Peptide fragments produce more structures that are
similar to known inhibitors
More fragment types lead to increased diversity, but
often have less similarity to inhibitors
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For two targets: HIV-1 protease and FKBP-12
However, could produce new lead structures
Less diverse fragment sets results in greater
“convergence”
For targets with unknown inhibitors, multiple structures
can be generated
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Identify trends or new leads for better modeling
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