1. 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

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
Modified AMBER/SANDER 4.0 minimization/MD program
Active site is filled with user-defined fragments
“Connection vectors” are chosen for each fragment
Define a volume for a known protein of interest
Randomly orient fragments in defined volume
Fragment minimization and MD (two steps)
Start CONCERTS
Adam Tenderholt, Stanford University

5. CONCERTS: Implementation
Adam Tenderholt, Stanford University

6. CONCERTS: Improvements
CONCERTS has several improvements over CONCEPTS:
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
Adam Tenderholt, Stanford University

7. CONCERTS: Testing 1000 copies of peptide fragment
Begin testing CONCERTS on two targets using 3 types of “basis sets”:
1000 copies of peptide fragment
700 copies of benzene, 1000 copies each of methane, ammonia, formaldehyde, and water
300 copies each of ammonia, benzene, cyclohexane, formic acid, ethane, ethylene, formaldehyde, formamide, methane, methanol, sulfinic acid, thiophene, and water
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
Adam Tenderholt, Stanford University

10. 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!
Adam Tenderholt, Stanford University

11. 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
Adam Tenderholt, Stanford University

15. 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
Adam Tenderholt, Stanford University

17. 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
Adam Tenderholt, Stanford University