Developing a Method of De Novo Drug Synthesis using Hydropathic Interactions

1. Developing a Method of De Novo Drug Synthesis using Hydropathic Interactions Meredith Wouters Andy Surface Dr. Glen Kelloggfrom the The Institute for Structural Biology and Drug Discovery In conjunction with The Bioinformatics and Bioengineering Summer Institute

2. Drug Synthesis • Must have a protein structure • PDBs • Normal Methods: use known a database of known substrates which “fit” and catalytically change/inhibit protein

3. A Novel Method • Instead of using whole substrates- • “Piece” substrates together using functional groups that are in energetically favorable locations • Work focused on establishing locations and quantifying favorability • How?

4. Replacing RelevantWaters with Functional Groups Quantifying with HINT and Rank algorithms

5. Identification of Relevant Waters • Located in or near active site • Using Rank and HINT algorithms to predict probability of conservation • Waters which remained in liganded complex We are using the same proteins from last year along with the identified relevant waters

6. Hydropathic Interaction water scoring and Rank Scores water-ligand and water-protein interactions • Non-newtonian forcefield based on • LogPoctanol/water • scoring non-covalent bonding • Proportional to entropy • Rank: • Detects waters with highest amount of bonds or those closest to the protein • Tells HINT to score higher order waters first • Scale 0 to 6

7. Rank Rank = Σn {(2.80 Å / rn) + [ Σm cos (θTd – θnm)] / 6} Scoring based on -amount of bonds -angle (can’t be less than 60 deg) rn is the distance between the water’s oxygen atom and the target heavy atom n. n and m are targets- have a maximum of 4 bonds scaled relative to 2.8 A˚ θTd =109.58 θnm = the angle between targets n and m M and n are equal in their number of valid targets

8. HINT score = Σj Σi bij = Σ jΣi (aiSi ajSj Tij Rij + rij) • Ai is partial logPoctanol/water • Si is solvent accessable surface areas • rij is exponential function • Rij is Lennard-Jones function • Tij assume +1 or-1

9. HINT and Rank increase from lowest to highest order: b. first external shell HOH c.2 Active site HOH d. Cavity HOH c.1 Buried HOH

10. Protocols • Had to be established • Addition of Hydrogens • Paritioning the protein and waters • Find LogP • Optimizing the waters

11. NH2, OH, CH3, COOH, SH, COO, NH, NCH, CONH, COH Insertion of functional groups

12. -Extract water and build onto it a functional group -Insertion of the dummy carbon

13. Insertion of functional groups:Segregating force fields • Minimize entire group • Tripos • Parition except for dummy carbon • Non-existent to HINT • Ligand optimization • HINT • Dummy carbon minimization • Tripos- reorient Dummy Tripos- SYBYL force field HINT has its own force field Difference of LogP

14. Automating the process: • Faster • All waters’ of a protein in minutes • All waters of a protein in 6+ hours • More accurate • Less human error • Constant checks and balances • Database of functional groups • Not reassembling them each time

15. Comparing the Automated Program and the Manual Protocol A Pair-wise t test showed no significant difference for HINT scores of the waters P<.05=.0985 -We cannot perform a pair wise t test for HINT scores of the waters. Too much variation.

17. Spearman Rank Correlation Coefficient • Like a pair wise t test- • Raw scores converted to ranks, and the differences di btw the ranks are summed and squared into: Much more positive correlations when comparing ranks of groups than individual waters • Smaller groups have better correlation btw the program and the protocol

20. Problems of note • The more highly conserved a protein, less likely a group will fit favorably • Unfavorable group has a large negative score • Very close to the protein • Atoms “bounce” off one another • Larger the functional group the more likely its conformation will vary

21. Do differences of correlations negate our method? • Probably not • Remember manual reassembly vs database • Smaller groups are more accurate • Consistency

22. References • Amadasi A, Surface JA, Spyrakis F, Cozzini P, Mozzarelli A, Kellogg GE (2008) Robust Classification of "Relecant" Water Molecules in Putative Protein Binding Sites. J. Med. Chem. (51) pp1063-1067.Spyrakis F, AmadasiA, Fornabaio M, Abraham DJ, Mozzarelli A, Kellogg GE, Cozzini P (2007) The Consequences of Scoring Docked Ligand Conformations Using Free Energy Correlations.  Eur. J. Med. Chem. (42) pp: 921-33.Verdonk ML, Chessari G, Cole JC, Hartshorn MJ, Murray CW, Nissink JWM, Taylor RD, Taylor R (2005) Modeling Water Molecules in Protein-Ligand Docking Using GOLD. J. Med. Chem. (48) pp: 6504-15.Fornabaio M, Spyrakis F, Mozzarelli A, Cozzini P, Abraham DJ, Kellogg GE (2004) Simple, Intuitive Calculations of Free Energy of Binding for Protein-Ligand Complexes. 3. The Free Energy Contribution of Structural Water Molecules in HIV-1 Protease Complexes.  J. Med. Chem. (47) pp: 4507-16.

23. Acknowledgements • Thanks to my advisor Dr. Kellogg, Ash, my partner Andy Surface and everyone in lab for their help and patience and for teaching me a great deal this summer. • Thanks to BBSI: Jen, Sherryl and especially Jeff for everything

24. The End • Andy’s presentation will continue with more description of the program and our results