Adam Tenderholt, Stanford University 1

CONCERTS: Dynamic Connection of Fragments as an Approach to de Novo

Ligand Design

David A. Pearlman and Mark A. MurkcoVertex Pharmaceuticals Incorperated

Cambridge, MA

Creation Of Novel Compounds by Evaluation of Residues at Target Sites

Adam Tenderholt, Stanford University 2

Outline

● Background● Implementation● HIV-1 aspartyl protease● FK506 binding protein● Conclusions

Adam Tenderholt, Stanford University 3

Previous Work: CONCEPTS

● Active site is filled with atoms● Run MD simulations, and form/break bonds● Generates useful de Novo leads● Limitations

– Difficult to incorporate charge models– Slow convergence, especially for “spacer”

regions– Only 1 suggestion per cpu-intensive run

Adam Tenderholt, Stanford University 4

CONCERTS: Implementation

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

Modified AMBER/SANDER 4.0 minimization/MD program

Adam Tenderholt, Stanford University 5

CONCERTS: Implementation

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CONCERTS: Improvements

● 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

CONCERTS has several improvements over CONCEPTS:

Adam Tenderholt, Stanford University 7

CONCERTS: Testing

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

Begin testing CONCERTS on two targets using 3 types of “basis sets”:

Adam Tenderholt, Stanford University 8

HIV-1 AP, Results A

● 82 macrofragments were found– 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

Adam Tenderholt, Stanford University 9

HIV-1 AP, Results A2

● 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

● 138 macrofragments were generated– Combination of 4+ fragments

● Reproduces backbone of JG-365, despite not being made from amino acids

● Bonus: only one chiral center!

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HIV-1 AP, Results C

● 151 macrofragments were generated– Combinations of 4+ fragments

● Not good agreement with backbone of JG-365● However, places atoms in regions of space for

all but one of the side chains of the drug!

Adam Tenderholt, Stanford University 12

FKBP-12, Results A

● “A number” of macrofragments were identified● Mimics the “binding core” of nM inhibitor FK506● Interesting that peptide fragments modeled a

non-peptide inhibitor reasonably well

Adam Tenderholt, Stanford University 13

FKBP-12, Results B

● 122 macrofragments were generated● Places atoms in regions occupied by FK506● Unfortunately, a significant number of fragments falls at

the edge or outside of the active site● Contains zero chiral centers

Adam Tenderholt, Stanford University 14

FKBP-12, Results C

● 130 macrofragments were generated– A majority were outside or on the edge of the active

site● Less concise than B set● 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

Adam Tenderholt, Stanford University 16

Sampling Issues: Energy Function

Does the energy function used in CONCERTS have predictive qualities?

●HIV-1 AP

●Hydrogen bonds with protein residues

●Enb

for Set A inhibitors

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Conclusion● CONCERTS works: it generates inhibitors

– For two targets: HIV-1 protease and FKBP-12● 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

– However, could produce new lead structures● Less diverse fragment sets results in greater

“convergence”● For targets with unknown inhibitors, multiple structures

can be generated

– Identify trends or new leads for better modeling