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DESIGN OF INORGANIC BINDING PROTEINS
RAM SAMUDRALA
ASSOCIATE PROFESSOR
UNIVERSITY OF WASHINGTON
How can we design peptides and proteins capable of binding to
inorganic substrates with specific selectivity and affinity?
MOTIVATION
The functions necessary for life are undertaken by proteins.
Protein function is mediated by protein three-dimensional structure.
A vast number of computational methodologies have been developed for the
analysis and modelling of the sequences and structures of naturally
occurring proteins.
We can harness these knowledge- and biophysics-based computational
methodologies to design peptides and proteins capable of binding to
inorganic substrates with specific affinity and selectivity.
Goal is to develop generalised computational techniques to construct
molecular building blocks based on peptides and proteins that can be easily
assembled to design higher order structures.
Applications in the area of medicine, nanotechnology, and biological
computing.
KNOWLEDGE-BASED DESIGN
Proteins that are evolutionarily related generally have similar sequences,
structures, and functions.
We hypothesised that this applies to experimentally discovered peptides
capable of binding to inorganic substrates.
We then examined similarity of sequences between experimentally
discovered peptides and random peptide sequences using standard
sequence comparison tools.
Random peptide sequences most similar to a particular group of
experimentally discovered peptides were considered to possess the same
functional property.
Some examples of experimentally discovered peptides (from Mehmet
Sarikaya): Platinum binders:
Hydroxyapatite binders:
Quartz binders:
DRTSTWR
RLNPPSQMDPPF
MLPHHGA
TSPGQKQ
QTWPPPLWFSTS
TTTPNRA
IGSSLKP
LTPHQTTMAHFL
PVAMPHW
SIMILARITY ANALYSIS
The similarity of two protein or peptide sequences is determined by their
optimal alignment score (PSS) according to a scoring matrix:
QSVTSTK
SVTQNKY
QSVTSTK-SVTQNKY
PSS=12
The scoring matrix determines the similarity of any two amino acids based
on their evolutionary and biophysical preferences.
BLOSUM and PAM are two popular scoring matrices derived from amino
acid preferences observed in representative sets of proteins:
Henikoff S, Henikoff JG. Proc. Natl. Acad. Sci. USA 89: 10915-10919, 1992.
The optimal alignment and score is determined using dynamic programming.
SIMILARITY ANALYSIS
Our goal is to determine to the total similarity score (TSS) of one set of
sequences (which may contain only one member) to another set of
sequences.
The total similarity score is effectively a normalised sum of pairwise similarity
scores between sequences in the two sets:
TSS A B

Set A
Set B
TSTYKHS
NTATKKV
EASTRHG
HSTDNKT
SVTQNKY
RATLNQQ
. . .
KNWHSLH
LGPSGPK
EPYNQNM
AYPTQLD
SEWLSAL
RGLPAPT
. . .

 Axa -  Bxb 
ya
yb
xa , xb
1
 PSSij 1   ij AB 
xa   xb   AB  i 1, j 1
Initial studies were done by Ersin Emre Oren using the BLOSUM and PAM
matrices on a set of 39 strong (10), moderate (14) and weak (15) quartz
binding sequences provided by Mehmet Sarikaya.
PRIMARY HYPOTHESIS VERIFICATION
Strong
Moderate
Weak
BACKTESTING PREDICTIVE POWER
The total similarity score of each quartz binder to the set of strong quartz
binders were calculated and used as an indicator of binding affinity.
Quartz
binders
TSTYKHS
NTATKKV
EASTRHG
HSTDNKT
SVTQNKY
RATLNQQ
KNWHSLH
LGPSGPK
EPYNQNM
AYPTQLD
. . .
Strong quartz
binders
KNWHSLH
LGPSGPK
EPYNQNM
AYPTQLD
SEWLSAL
RGLPAPT
OPTIMISATION OF SCORING MATRICES
We perturbed the PAM 250 scoring matrix systematically to produce a higher
strong-strong self-similarity and lower strong-weak cross-similarity score,
and backtested the predictive power of the new QUARTZ I matrix.
TSSs-s as high as possible
TSSs-w as low as possible
OPTIMISATION OF SCORING MATRICES
We perturbed the PAM 250 scoring matrix systematically to produce a higher
strong-strong self-similarity and lower strong-weak cross-similarity score,
and backtested the predictive power of the new QUARTZ I matrix.
KNOWLEDGE-BASED PEPTIDE DESIGN
We hypothesised that random sequences similar to a set of sequences with
a particular functional property must also possess that property.
We calculated the TSS of 1,000,000 random sequences (12,000,000 aa) to
the set of experimentally determined strong quartz binding sequences.
EXPERIMENTAL VERIFICATION
Three sets of experiments were performed by Mehmet Sarikaya’s group to
validate the computationally designed sequences.
DESIGN OF SECOND GENERATION MATRICES
DESIGN OF CROSS-SPECIFIC BINDERS
Quartz matrix scores
Hydroxapatite matrix scores
DESIGN OF CROSS-SPECIFIC BINDERS
This procedure can be generalized to any number of inorganic substrates as
long as there is enough initial data to calculate the TSS.
CHARACTERISTICS OF SCORING MATRICES
Quartz I – PAM 250
CS T PAGNDE QH R K M I L V F Y W
C
S
T
P
A
G
N
D
E
Q
H
R
K
M
I
L
V
F
Y
W
CHARACTERISTICS OF SEQUENCES
CHARACTERISTICS OF SEQUENCES
Strong quartz binding peptides likely have extended conformations since
the bulky hydrophobic side chains of Tryptophan (W) or Phenlyalanine (F) in
a small peptide require adequate spacing, and the Proline (P) residue
reduces conformational flexibility.
Weak quartz binding peptides have residues that may allow for collapse of
the peptides either directly (through the formation of disulphide and salt
bridges or collapse of the smaller hydrophobes) or indirectly (Glycine (G),
which increases conformational flexibility).
DS072: Y E S I R I G V A P S Q
DS202: R L N P P S Q M D P P F
BIOPHYSICS-BASED DESIGN
Characterise sequences and structures of naturally occurring proteins in
terms of their total similarity scores using different scoring matrices. This will
produce a database of sequences with predicted and known structures with
specific selectivity and affinity to different inorganics.
This database can be analysed for atom-atom preferences, torsion angle
preferences, and other characteristics to define energy functions and move
sets for performing protein structure simulations.
We will combine this with our all-atom energy function capable of handling
inorganics and our protein structure simulation software.
Design higher order protein-like scaffolds with specific functionalities:
Strong hydroxyapatite
binding region
Active site
Strong quartz binding region
ACKNOWLEDGEMENTS
People:
Ersin Emre Oren
Mehmet Sarikaya and his group
Candan Tamerler-Behar
Samudrala group
Primary support from:
Defense University Research Initiative on NanoTechnology
Genetically Engineered Materials Science and Engineering Center
Other support from:
National Institutes of Health
National Science Foundation
Kinship Foundation