Electrostatic properties of human beta defensin-2Nic Novak, Chris Kieslich, Dimitrios Morikis

Biomolecular Modeling and Design Laboratory, Department of BioengineeringUniversity of California, Riverside

Summer 2008

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

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)

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

Structure comparisonHβD-1 HβD-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: 1FD3 PDB: 1KJ6

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

Antimicrobial Action• Antimicrobial peptides (AmPs)

– Gram-negative and gram-positive bacteria– Fungi– Some viruses

• Shai-Matsuzaki-Huang Mechanism1. Accumulation of defensin on microbial membrane due

to electrostatic interactions2. Insertion into outer leaflet causing stress3. Destabilization of microbial membrane

Internal space of

bacteria

HβD-2 (+6 net charge)

Outside of bacterial cellBilayer image from Shental-

Bechor et al.

Negatively charged bacterial membrane

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)

UCRESI Protocol• Unpublished protocol developed at BioMoDeL• A series of Python and Perl scripts• Created EZ-UCRESI, a GUI wrapper for the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

OutputProtein Data Bank

WHATIF

PDB2PQR

APBS

Perl script

Perl script

MATLAB & PyMOL

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

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

Mutant set 14Flexible template from MD simulations with explicit solvation

0° 90° 180° 270° Charge

Isopotential contours

+6

+6

+6

+7

+6

+5

+5

+5

+5

+5

+5

Sequence selection: weighted average model

Max mutations: 10

Mutant set 14Par. 1

G2I

3G

4D

5P

6V

7T

8C

9L

10K

11S

12G

13A

14I

15C

16H

17H

18V

19F

20C

ID2 R R I

ID3 R I

ID4 N M

ID5 N I

ID6 N I

ID7 R F

ID8 Q R F

ID9 N I

ID10 N I

ID11 N I

Par. 21P

22R

23R

24Y

25K

26Q

27I

28G

29T

30C

31G

32L

33P

34G

35T

36K

37C

38C

39K

40P

ID2 I I L R W W L

ID3 I I L Q R W W L

ID4 I F L N R W W L

ID5 V I L N R W W L

ID6 I I L Q R W W Y

ID7 I F L N R W W Y

ID8 I F L R W W L

ID9 Y I L N R W W L

ID10 I F L Q R W W L

ID11 Y F L N R W W L

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

Alanine Scans of HβD1-3

+6

+4

+11

*

*

**

Alanine Scan of HβD1

0° 90° 180° 270° Charge

Isopotential contours

+4

+5*

Alanine Scan of HβD2

0° 90° 180° 270° Charge

Isopotential contours

+6

+7

*

Alanine Scan of HβD3

0° 90° 180° 270° ChargeIsopotential contours

+11

+12+12

**

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.

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

AcknowledgementsThe BioMoDeL lab membersOur Princeton collaborators

Jun Wang and the BRITE program

Bioengineering

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.

Questions?