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Literature Report
Computational Design of Ligand-binding
Proteins with High Affinity and Selectivity
Liping Xu
2013-11-22
We can make…
Small medicine molecule Penicillin
Complex natural product
Anti-cancer drug Taxol
Can we make?
Cyclic peptide Cyclosporine
Folded, functionalized unnatural protein
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Contents
• Introduction of protein design and David Baker
• Computational design of DIG-binding protein
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Problem being raised
Computational methodology
Experimental binding validation
Affinity Maturation
Crystal Structure
Binding Selectivity
Rosetta
• Summary
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Introduction
Protein design is the rational design of new protein molecules to fold to a target
protein structure.
Protein design has many applications in medicine, enzyme catalysis, and
bioengineering.
De novo design
Protein redesign
Challenges
Known protein
structure
Making calculated
variations
New protein structure
Structural flexibility
“side chain and backbone
flexibility”
Energy (scoring) function
“both accurate and
simple for computational
calculations”
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David Baker
Computer game to design new proteins
Running the Rosetta program on your
computer while you don't need it
David Baker
Computational biologist
University of Washington
Full-chain protein structure prediction
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David Baker
Recent publications on protein design:
Computational design of ligand-binding proteins with high affinity and selectivity
Nature, 2013, 501, 212
Computational design of an α-gliadin peptidase
JACS, 2012, 134, 20513
Principles for designing ideal protein structures
Nature, 2012, 491, 222
Computational design of self-assembling protein nanomaterials with atomic level
accuracy
Science, 2012, 336, 1171
Atomic model of the type III secretion system needle
Nature, 2012, 486, 276
Computational redesign of a mononuclear zinc metalloenzyme for
organophosphate hydrolysis
Nature chemical biology, 2012, 8, 294
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Problem Being Raised
Rational design of ligand-binding proteins have met with little success.
Schreier, B. et al. Computational design of ligand binding is not a solved problem. Proc.
Natl. Acad. Sci. USA 2009, 106, 18491
Baker’s group has developed a computational method for designing ligandbinding proteins with three properties characteristic of naturally occurring
binding sites:
1. Specific energetically favorable hydrogen-bonding and van der Waals interactions
with the ligand;
2. High overall shape complementarity to the ligand;
3. Structural pre-organization in the unbound protein state
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Problem Being Raised
However…
1. “narrow therapeutic window”
(margin between effectiveness and toxicity)
Digoxigenin (DIG)
2. Easily being overdosed
Nausea, dizziness, depression
1. Heart disease drug
2. Non-radioactive biomolecular
labelling reagent
So, anti-digoxigenin antibodies are needed to treat overdoses of digoxin.
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Computational Methodology
Linker-modified DIG
Pre-chosen hydrogen bonding interaction side
chains; Rotamers for each interaction side chain
Place ligand and interacting residues in scaffolds
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and design binding site sequence
Experimental Binding Validation
ZZ(-): negtive control
ZZ(+): positive control
DIG10 + DIG: with
unlabelled DIG
Experimental characterization of
the selected 17 designs.
Two of them perform better:
DIG10 and DIG5.
1Z1S: original scaffold
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Experimental Binding Validation
Yeast-surface expression of DIG10
Interface residues are shown.
Substitutions of DIG10-designed interface
residues reduce binding signals.
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Affinity Maturation
1. Optimization of DIG10 by site-saturation
mutagenesis increases binding affinity 75-fold,
yielding DIG10.1;
2. Mutations of Ala37Pro and His41Tyr
generates DIG10.2;
3. Further consideration of more residues in
combination leads to DIG10.3.
4. Tyr knockouts suggest that the designed hydrogen
bonds each contribute ~2kcal/mol to binding energy.
Table 1. Kd values of designs
ND, not determined
Computational model of DIG10.1 (blue),
DIG10.2 (orange) and DIG10.3 (green).
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Binding thermodynamics determined by ITC
Crystal Structure
DIG10.2
2.05 Å resolution
DIG10.3
3.2 Å resolution
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Crystal Structure and Computational Design
crystal structure (magenta)
computational model (grey)
Backbone superposition
1. r.m.s.d = 0.54 Å
2. high shape-complementarity on
ligand-protein interface
3. no water molecules in the binding pocket
DIG10.2-DIG
Binding site superposition
1. r.m.s.d = 0.99 Å
2. three hydrogen bonds as designed
Atomic-level agreement
The structure and binding mode is nearly identical in the X-ray structure and the design model;
The structure of DIG10.3-DIG also agree closely with the design model (r.m.s.d = 0.68 Å)
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Binding Selectivity
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3
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Losing O2 HBond;
2 fits better.
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Binding Selectivity
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3
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1. The selectivity is
conferred through the
designed HBond
Interactions;
2. This feature can be
programmed using
positive design alone
through the explicit
placement of designed
polar and hydrophobic
interactions.
Losing All HBonds;
More hydrophobic
compounds fit good.
Losing O3 HBond;
3 fits good.
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Methods
Design calculations were performed using RosettaMatch to incorporate five predefined interactions to DIG into a set of 401 scaffolds.
Rosettadesign was then used to optimize each binding sequence for maximal
ligand-binding affinity.
Designs having low interface energy, high shape complementarity, and high
binding site pre-organization were selected for experimental characterization.
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Rosetta
Rosetta – The premier software suite for macromolecular modeling
As a flexible, multi-purpose application, it
includes tools for structure prediction, design,
and remodeling of proteins and nucleic acids.
It has consistently been a strong performer in
Critical Assessment of Structure Prediction
(CASP) competitions.
It has grown to offer a wide variety of effective
sampling algorithms to explore backbone,
side-chain and sequence space.
Rosetta is freely available to academic and
government laboratories, with over 10,000
free licenses already in use.
University of Washington
Stanford University
Rosetta Design Group
Johns Hopkins University
New York Universiy
China Three Gorges University
…
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Summary
1. The binding affinity of designed protein DIG10.3 is similar to those of anti-digoxin
antibodies.
2. It is stable for extended periods and can be expressed at high levels in bacteria,
so it could provide a more cost-effective alternative for biotechnological and for
therapeutic purposes.
3. Computational protein design should provide an increasingly powerful approach
to creating small molecule receptors for synthetic biology.
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Thanks for your attention!
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