1. Electrostatic properties of human beta defensin-2 Nic Novak, Chris Kieslich, Dimitrios Morikis Biomolecular Modeling and Design Laboratory, Department of Bioengineering University of California, Riverside Summer 2008

2. Overview Characteristics of Defensins – Sequence comparison – Structure comparison – Mechanism of antimicrobial action Main Objective and Defensin Analysis – UCRESI Protocol – Cluster analyses of electrostatic potentials – Alanine scan of all ionizable residues Results Conclusions Future Work Acknowledgements References

3. Characteristics of Defensins Endogenous in all mammals Antimicrobial Short peptides (41-78 AAs) Multiple varieties – Alpha defensins (4) - Stomach – Beta defensins (6) – Skin, saliva Features – Conserved cysteines (6) – Cationic (Net positive charge of 4-11) HβD-2 (1FD3)

4. Sequence comparison HβD 1-6: HβD 1-3: HβD 4-6: Analysis performed with ClustalW (www.ebi.ac.uk/clustalw) HβD 1-6 (5 dropped): Disulfide bonds

5. Structure comparison HβD-1HβD-2 HβD-3 Human beta defensin models showing secondary structure in addition to point-representation of basic, acidic, polar, and nonpolar residues. +4 +6+11 PDB: 1KJ5 PDB: 1FD3PDB: 1KJ6

6. Structure comparison (Above) The locations of the 3 disulfide bonds that connect the 6 conserved cysteines in HβD-1, HβD-2, and HβD-3. (Left) Superimposed models of HβD-1, HβD-2, and HβD-3. HβD-1 HβD-3 HβD-2 HβD-3

7. Antimicrobial Action Antimicrobial peptides (AmPs) – Gram-negative and gram-positive bacteria – Fungi – Some viruses Shai-Matsuzaki-Huang Mechanism 1.Accumulation of defensin on microbial membrane due to electrostatic interactions 2.Insertion into outer leaflet causing stress 3.Destabilization of microbial membrane Internal space of bacteria HβD-2 (+6 net charge) Outside of bacterial cell Bilayer image from Shental-Bechor et al. Negatively charged bacterial membrane

8. Mutant Analysis Objective: predict molecules based on the defensin structure that will have improved antimicrobial action Analysis of 9 pre-selected sets of HβD-2 mutations (Princeton collaborators) UCRESI Protocol – Computational, theoretical mutagenesis – Comparison of electrostatic similarity indices (ESIs) – Visualized by dendrograms (cluster analysis)

9. UCRESI Protocol Unpublished protocol developed at BioMoDeL A series of Python and Perl scripts Created EZ-UCRESI, a GUI wrapper for the protocol, to automate the following tasks: Parent PDB Generate mutant PDB files Generate PQR files Generate DX files Calculate ESIs Calculate distances Output Protein Data Bank WHATIF PDB2PQR APBS Perl script MATLAB & PyMOL

10. Cluster Analysis (Sets 09-17) Analysis of all 90 Princeton mutants together

11. Cluster Analysis (Sets 09-17) Analyses of individual sets

12. Mutant set 14 Flexible template from MD simulations with explicit solvation 0° 90° 180° 270° Charge Isopotential contours +6 +7 +6 +5 Sequence selection: weighted average model Max mutations: 10

13. Mutant set 14 Par.1G1G 2I2I 3G3G 4D4D 5P5P 6V6V 7T7T 8C8C 9L9L 10 K 11 S 12 G 13 A 14 I 15 C 16 H 17 H 18 V 19 F 20 C ID2RRI ID3RI ID4NM ID5NI ID6NI ID7RF ID8QRF ID9NI ID10NI ID11NI Par.21 P 22 R 23 R 24 Y 25 K 26 Q 27 I 28 G 29 T 30 C 31 G 32 L 33 P 34 G 35 T 36 K 37 C 38 C 39 K 40 P ID2IILRWWL ID3IILQRWWL ID4IFLNRWWL ID5VILNRWWL ID6IILQRWWY ID7IFLNRWWY ID8IFLRWWL ID9YILNRWWL ID10IFLQRWWL ID11YFLNRWWL

14. Alanine Scans High-throughput computational protocol Mutate each ionizable residue into alanine, one at a time, to determine the residue’s effect the peptide’s electrostatic potential Performed on HβD1-3 Acidic (-) Aspartic acid Glutamic acid Basic (+) Arginine Lysine Histidine

15. Alanine Scans of HβD1-3 +6 +4 +11 * * * *

16. Alanine Scan of HβD1 0° 90° 180° 270° Charge Isopotential contours +4 +5 *

17. Alanine Scan of HβD2 0° 90° 180° 270° Charge Isopotential contours +6 +7 *

18. Alanine Scan of HβD3 0° 90° 180° 270° Charge Isopotential contours +11 +12 * *

19. Conclusions In most cases, the mutations suggested by our collaborators at Princeton and those generated by the alanine scans were predicted to have an equal or lower net charge than their parent protein. However, a small number of mutants (7/121 = 5.8%) were predicted to have a higher net charge and larger isopotential contours than the parent. According to the Shai-Matsuzaki-Huang mechanism, these mutants should theoretically exhibit improved attraction to microbial membranes. Provided that no major structural changes were introduced by the mutations, these mutants should have improved antimicrobial properties.

20. Future Work Analyze top 20 mutants (instead of top 10) Expand mutant sets Perform additional literature analyses to see what efforts are already in progress for creating synthetic defensins Synthesize the mutants predicted by these calculations to be the best binders Perform experimental studies based on these predictions

21. Acknowledgements The BioMoDeL lab members Our Princeton collaborators Jun Wang and the BRITE program Bioengineering

22. References Baker N.A., Sept D, Joseph S, Holst M.J., McCammon J.A. Electrostatics of nanosystems: application to microtubules and the ribosome. Proc. Natl. Acad. Sci. A 98, 10037-10041 2001. (APBS) ClustalW web service. Available online: http://www.ebi.ac.uk/Tools/clustalw2/index.html Dolinsky T.J., Nielsen J.E., McCammon J.A., Baker N.A. PDB2PQR: an automated pipeline for the setup, execution, and analysis of Poisson-Boltzmann electrostatics calculations. Nucleic Acids Research 32 W665-W667 (2004). Fung, H., Floudas, C., Taylor, M., and Morikis, D. (2007). Toward full-sequence de novo protein design with flexible templates for human beta-defensin-2. Biophysical Journal. 94:584-599. Kisich K.O., Carspecken C.W., Fieve S., Boguniewicz M., Leung D.Y. (2008). Defective killing of Staphylococcus aureus in atopic dermatitis is associated with reduced mobilization of human beta-defensin-3. J Allergy Clin Immunol. 122(1): 62-68. Krishnakumari V., Nagarj R. (2008). Interaction of antibacterial peptides spanning the carboxy-terminal region of human beta-defensins 1-3 with phospholipids at the air-water interface and inner membrane of E. coli. Peptides. 29(1):7-14. Krishnakumari V., Singh S., Nagaraj R. (2006). Antibacterial activities of synthetic peptides corresponding to the carboxy- terminal region of human beta-defensins 1-3. Peptides. 27(11):2607-2613. Shental-Bechor, D., Haliloglu, T., Ben-Tal, N. (2007). Interactions of cationic-hydrophobic peptides with lipid bilayers: A Monte Carlo simulation method. Biophysical Journal. 93:1858-1871. Yang, J., Kieslich, C., Gunopulos, D., and Morikis, D. (2008). Insights into protein-protein interactions using a high-throughput computational protocol for alanine scans and clustering analyses of the spatial distributions of electrostatic potentials, In Preparation.

23. Questions?