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

1. Adam Tenderholt, Stanford University CONCERTS: Dynamic Connection of Fragments as an Approach to de Novo Ligand Design CreationOfNovelCompounds by Evaluation of Residues at TargetSites David A. Pearlman and Mark A. Murkco Vertex Pharmaceuticals Incorperated Cambridge, MA

2. Adam Tenderholt, Stanford University Outline • Background • Implementation • HIV-1 aspartyl protease • FK506 binding protein • Conclusions

3. Adam Tenderholt, Stanford University 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

4. Adam Tenderholt, Stanford University 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

5. Adam Tenderholt, Stanford University CONCERTS: Implementation

6. Adam Tenderholt, Stanford University 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

7. Adam Tenderholt, Stanford University CONCERTS: Testing 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

8. Adam Tenderholt, Stanford University 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

9. Adam Tenderholt, Stanford University 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

10. Adam Tenderholt, Stanford University 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!

11. Adam Tenderholt, Stanford University 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!

12. Adam Tenderholt, Stanford University 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

13. Adam Tenderholt, Stanford University 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

14. Adam Tenderholt, Stanford University 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

15. How well does CONCERTS sample the conformational space available? Sampling Issues: Thoroughness 20 hexamer or larger macrofragments during peptide run (A set) against HIV1-AP Adam Tenderholt, Stanford University

16. Does the energy function used in CONCERTS have predictive qualities? Sampling Issues: Energy Function • HIV-1 AP • Hydrogen bonds with protein residues • Enb for Set A inhibitors Adam Tenderholt, Stanford University

17. Adam Tenderholt, Stanford University 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