Introduction to Microcalorimetry
Muneera Beach, Ph.D.
Malvern Instruments
Northampton, Massachusetts USA
Muneera.Beach@malvern.com
Why Microcalorimetry?

Broad dynamic
range
Information rich
Direct
measurement of
heat change
Direct
measurement of
melting
temperature as
an indicator of
thermal stability
 No molecular
weight
limitations
(ITC)
 Native
molecules in
solution
(biological
relevance)
 Rapid results
for KD n, ΔH
and ΔS from
ITC
experiments
 Determine Tm,
ΔH and ΔCp
from DSC
experiments
0
NDH, kcal/mole of injectant

Label-free
-3
-6
-9
-12
0
1
Xt/Mt
2
Ease-of-use




No immobilization
necessary
No/minimal assay
development
Free choice of
solvent
How do they work?
Sample
Reference
The DP is a measured power differential between
the reference and sample cells to maintain a zero
temperature between the cells
DT
DP
Reference Calibration Heater
Sample Calibration Heater
DT~0
Cell Main Heater
DP = Differential power
∆T = Temperature difference
All binding parameters in a single experiment
X + M  XM
Reported data
Raw data
Syringe
(ΔH)
Mechanism
(KB) Binding
(n) stoichiometry
X
In a single ITC experiment you get…
M
Reference cell Sample cell
 Affinity (KD) – strength of binding
 Heat of binding (ΔH) and entropy (ΔS) – mechanism
and driving force of interaction
 Stoichiometry (n) - Number of binding sites
 Enzyme kinetics
The energetics
Elucidation of binding
mechanisms:
›
Primary Enthalpic
Contributions:


KD

Macromolecule/Nanoparticle
Waters, ions, protons
Ligand
DG = RT lnKD
DG = DH -TDS
›
Hydrogen bonding
van der Waals
interactions
Electrostatic
interactions
Primary Entropic
Contributions


Hydrophobic effect-water
release (favorable)
Conformational changes
and reduction in degrees
of freedom (unfavorable)
ITC provides more than binding
affinity – characterize binding forces
Similar KD
Different binding
mechanisms
Same affinity, different energetics
ITC results are used to gain insights into the
mechanism of binding
Unfavorable
A. Good hydrogen bonding with
10
unfavorable conformational
change
5
B. Binding dominated by
hydrophobic interaction
C. Favorable hydrogen bonds
and hydrophobic interaction
kcal/mole
0
∆G
∆H
-T∆S
-5
-10
-15
-20
A
B
C
Favorable
With ITC you can…
 Measure target activity
MicroCal VP-ITC
 Confirm drug binding to target
MicroCal iTC200
 Get quick KDs for secondary screening/hit
validation
 Use thermodynamics to guide lead optimization
 Validate IC50 and EC50 values
MicroCal PEAQ
 Characterize mechanism of action
 Measure enzyme kinetics
MicroCal PEAQ
ITC characterizes a broad range of interactions
• Proteins
• Receptor
• Antibodies
• Membrane
• Small Molecules
• Metal ions
• Drug/ligand
• Carbohydrates
• Nucleic Acids
• Nanoparticles
•
•
•
•
Polymers
Metals
Quantum dots
Beads
• Vaccines/Adjuvants
• Lipids/Micelles
• Detergents/Surfactants
Applications Examples
Crystallization of a RNA/ligand complex
E. Coli TPP riboswitch bound to thiamine
pyrophosphate
Courtesy of Dr Eric Ennifar
CNRS University of Strasbourg, France
Background Riboswitches-ligand interactions
Riboswitches
> non coding RNA (ncRNAs)
> Specific binding of metabolites
> Regulate expression of protein involved in biosynthesis of
riboswitch substrates
> Very attractive targets for the design of a new class of
antibiotics
E.coli TPP riboswitch
> Responds to the coenzyme thiamine pyrophosphate (TPP)
Goal of the study -solve the crystal structure
of the TPP riboswitch bound to TPP
Initial trails were unsuccessful
> TPP riboswitch was produced in large-scale by T7 transcription
> RNA is refolded after heat denaturation
> Crystallization using the classical sparse matrix approach failed
Use ITC to identify potential problems and
to aid crystallization
Optimization of RNA preparation guided by ITC
KD = 33 nM
DH = -32.0 kcal/mol
DS = -69.0 cal/mol/K
N = 0.58
Time (min)
0
10
20
30
40
50
60
0.20
0.00
Problem !
-0.40
20
30
40
50
60
0.00
-0.60
-0.20
-1.00
Native acrylamide
gel revealed 2
conformations of
the RNA !
-1.20
-1.40
-1.60
0.00
-5.00
µcal/sec
-0.80
-10.00
-15.00
New RNA folding
protocol
-20.00
-25.00
-30.00
-35.00
0.0
0.5
1.0
1.5
Molar Ratio
2.0
2.5
-0.40
-0.60
-0.80
KCal/Mole of Injectant
µcal/sec
Time (min)
10
0
-0.20
KCal/Mole of Injectant
Clear
Drops …
2.00
-1.00
0.00
-2.00
-4.00
-6.00
-8.00
-10.00
-12.00
-14.00
-16.00
-18.00
-20.00
-22.00
-24.00
0.0
0.5
1.0
1.5
2.0
Molar Ratio
Kd = 24 nM
DH = -22.3 kcal/mol
DS = -38.6 cal/mol/K
N = 1.01
Assessment of protein quality by
MicroCal™ iTC200 system
Peptide binding to proteinBatch #1
›100% of Batch 1 protein active
Peptide binding to protein Batch #2
›23% of Batch 2 protein active
based on stoichiometry
based on stoichiometry
Presented by L.Gao (Hoffmann-La Roche), poster at SBS 2009
Understanding the nanoparticle–protein
corona using methods to quantify
exchange rates and affinities of proteins
for nanoparticles
T. Cedervall, I. Lynch, et. al, PNAS, 104, 2050–2055, 2007
Background
›
›
›
In living systems’ biological fluids, proteins
associate with nanoparticles, and proteins on the
surface of the particles leads to an in vivo
response.
Proteins compete for nanoparticle ‘‘surface,’’
leading to a protein ‘‘corona’’ that defines the
biological identity of the particle.
Used ITC to study the affinity and stoichiometry
of protein binding to nanoparticles.
ITC of HSA and nanoparticles
ITC titration of HSA into solutions of
70 nm nanoparticles with 50:50
(Left) and 85:15 (Right) NIPAM/BAM
in 10 mM Hepes/NaOH, 0.15 M
NaCl, 1 mM EDTA, pH 7.5, is shown.
Experiments at 5 °C
(Upper) Raw data.
(Lower) Integrated heats in each
injection versus molar ratio of protein
to nanoparticle together with a fit
using a one site binding model
(Inset) Size comparison of
albumin and particles of 70 or 200
nm diameter.
From Cedervall, et al, PNAS, 104, 2050–2055, 2007
Conclusions
› Degree of nanoparticle surface coverage by
albumin calculated from ITC results.
›
›
›
 620 protein molecules per 70-nm particle
 4,650 protein molecules per 200-nm particle.
Suggests that a single layer of albumin is
adsorbed to the surface of hydrophobic particle,
less absorption to more hydrophilic particles
Stoichiometries depend on particle
hydrophobicity and size.
Demonstrated that ITC can be used to
characterize protein-nanoparticle interactions
MicroCal™ ITC systems
training course
Achieving high quality data using MicroCal™ iTC200 system
Objectives
 Outline the practical steps you should take to
achieve high quality data
 Demonstrate the rewards for following a few
simple rules in sample preparation
 Use a case study to demostrate how to
optimize your experiment
Four crucial steps to great isothermal titration
calorimetry (ITC) data
Sample
preparation
Experimental
optimization
Data analysis
The
experiment
Sample preparation
Sample preparation
Experimental
optimization
The experiment
Data analysis
Sample preparation
1. Dialyze or buffer exchange proteins
2. Accurately measure protein concentration using A280
3. Ensure that protein and small molecule solutions are
well matched
Step 1: Dialyze or Buffer Exchange
Sample preparation
The cell and syringe buffers must be carefully
matched. This is best accomplished by dialyzing
both the macromolecule and the ligand in the
same buffer.
If the ligand is too small for dialysis then dialyze
the macromolecule and then dissolve the ligand in
the dialyze buffer
Poor sample preparation leads to poor data
Sample preparation
›The data shown here shows
2.0
1.5
µcal/sec
before and after dialysis
›The large peaks were due
to differences in the NaCl
concentration between
buffers
2.5
1.0
0.5
Without
dialysis
without
dialysis
0.0
With
with dialysis
dialysis
-0.5
0
20
40
60
80
100
Time (min)
120
140
160
180
Step 2: Accurately measure protein and ligand
concentrations
Sample preparation
Protein concentration should be determined using A280
Be as accurate as you can weighing the ligand. UV
absorption is better if ligand has a chromophore.
Step 3: Match buffers
Sample preparation
The ligand
Dilute an aliquot of the ligand stock solution
containing dimethylsulfoxide (DMSO) with the
dialysate and then…
The protein
Add a corresponding amount of DMSO to the
protein solution
Ligand preparation from DMSO stock
Sample preparation
5 mM ligand
in 100% DMSO
50 µl
950 µl
250 µM ligand
in 5% DMSO
Dialysate
buffer
Match DMSO in the protein solution
Sample preparation
DMSO
50 µl
950 µl
1 ml of 23.75 µM
protein in 5% DMSO
25 µM dialyzed
protein
DMSO mismatch
Sample preparation
Large heats from DMSO dilution, if buffers are not
matched
Buffer into buffer
5% DMSO into 5% DMSO
5% DMSO into 4.5% DMSO
5% DMSO into 4 % DMSO
0.5 cal/sec
0.00
5.00
10.00
15.00
20.00
Time (min)
25.00
30.00
35.00
40.00
pH mismatches
Sample preparation
pH mismatches can arise when using high
concentrations of ligand i.e. mM concentrations
and above
To prevent this; back titrate with acid or base to
the required pH and/or increase the buffer
concentration until the ligand charge does not
change the pH
Choice of buffer
Sample preparation
ITC is robust, almost all buffers can be
used e.g. HEPES, PBS, glycine, acetate
If reducing agent is required, it is best to
use
• Tris (2-carboxyethylphosphine) hydrochloride (TCEP)
• β-mercaptoethanol (BME)
• Avoid DTT - Unstable and undergoes oxidation, High
background heat
Limit glycerol to 10% V/V, and detergents to
below CMC
Use conditions in which your protein is
‘happy’
The experiment
Sample preparation
Experimental
optimization
The experiment
Data analysis
Clean the cell
The experiment
11.00
10.50
µcal/sec
10.00
Rinse with 20%
Contrad™ (14% Decon™)
and water
9.50
9.00
8.50
8.00
0.00
10.00
20.00
Time (min)
30.00
40.00
50.00
Experimental set up and key questions
The experiment
How much sample do I need?
What are the ideal run parameters?
What controls should I perform?
How much sample is required?
The experiment
No  start with 10-20 µM
protein and 100-200 µM ligand
Do you know the KD?
Yes  follow the
column for estimated KDs
Estimated
KD µM
[Protein]
µM
[Ligand]
µM
[Protein]/
KD= C
<0.5
10
100
>20
0.5-2
20
200
10-40
2-10
50
500
5-25
10-100
30
40*KD
0.3-3
>100
30
20*KD
<0.3
C value
The experiment
BAD GOOD
0
1
OPTIMAL
10
GOOD
500
BAD
∞
1000
Low c
High c
00
-0.2-2
[Protein]/KD < 1
N fixed
Fitted: KD, DH
-0.4-4
0.0
0
0.5
4
1.0
8
1.5
12
Molar Ratio
-2-2
kcal/mole of injectant
kcal/mole of injectant
00
kcal/mole of injectant
kcal mol-1 of injectant
00
10< [Protein]/KD
<500
Fitted: N, KD, DH
2.0
[Protein]/KD >> 1000
Fitted: N, DH
-4-4
-4-4
16
-2-2
0.0
0
0.5
0.5
1.0
1.0
1.5
1.5
Molar Ratio
Molar ratio
2.0
2.0
0.0
0
0.5
0.5
1.0
1.0
1.5
1.5
Molar Ratio
2.0
2.0
The effect of C value
The experiment
10.50
5 µM, C = 10
10 µM, C = 20
10.00
20 µM, C = 40
µcal/sec
KD ~ 500 nM
[BCA II], C
9.50
50 µM, C = 100
9.00
0.0
kcal mol-1 of injectant
[Furosemide] = 10 *[BCA II]
-2.0
-4.0
-6.0
8.50
-8.0
0.00
5.00
10.00
15.00
20.00
25.00
Time (min)
30.00
35.00
40.00
0.0
0.5
1.0
1.5
Molar Ratio
2.0
Typical run parameters for
MicroCal™ iTC200 system - Injection parameters
Volume
typical 2 - 3 µl (range 0.1-38 µl)
* An initial injection of 0.2 µl is made
followed by 18 * 2 µl injections
Duration
4 - 6 seconds (double the
injection volume in sec.)
Spacing
150 seconds between injections
Filter period
5 seconds, data acquisition for
data averaging
Typical run parameters for
MicroCal™ iTC200 system
Reference power: 3 to 10 µcals/sec
Stir speed: 750 rpm
Feedback: High
The control experiments
Ligand solution should be injected into the buffer
under the same experimental conditions as the
titration experiment
• Don’t forget the DMSO if that is used in the buffer or stock
solution!
0
-2
-4
200 M RNaseA
into 10 M 2'CMP
50mM KAc pH 5.5
Proper Controls:
• Buffer into Buffer
• Ligand into buffer
kcal/mole of injectant
0
at 25 C
-6
-8
-10
-12
RNaseA into 2'CMP
Buffer into buffer
Buffer into 2'CMP
RNaseA into buffer
-14
0.0
0.5
1.0
Molar Ratio
1.5
2.0
Sample Preparation Summary
•
•
•
Start with fresh macromolecule
(protein, nucleic acid, lipid, etc.)
 Determine concentration as accurately as possible
(A280, A260)
Use at least 10 µM protein in the cell at the beginning
Buffers must be matched in sample and syringe
•
•
•
•
Size Exclusion Chromatography
Desalting Column
Dialysis (overnight with at least one buffer change, use as buffer
control)
Additives must be matched as well for example - DMSO, salt,
EtOH, etc.
Additional Considerations
Single Injection Method iTC200
›High Speed Mode
›High Quality Data in 7
minutes
0
8.9
-2
8.8
-4
kcal/mole of injectant
µcal/sec
8.7
8.6
8.5
-6
-8
-10
-12
8.4
-14
8.3
-16
8.2
0.00
1.67
3.33
5.00
Time (min)
6.67
8.33
-18
-0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0
Molar Ratio
DHobs versus buffer heat of ionization
All reactions at same pH
Slope: # protons
released (negative value)
Y intercept: DHint of binding,
buffer-independent
Phosphate
DHobs (kcal/mol)
-5
Hepes
Mops
-10
Imidazole
Tris
-15
-20
0
Different pH can have
different plot
5
10
DHion (kcal/mol)
15
If slope = 0, then no buffer
effect at that pH
DHobs = DHint + n DHion
Evaluation of Linked Protonation Effects in Protein Binding Reactions Using
Isothermal Titration Calorimetry, Biophysical J., 1996, Brian Baker et al.
The Energetics
Temperature dependence of the free energy,
enthalpy and entropy for the binding of TBP to DNA
40
Slope=DH/T= DCp
30
-1
kcal.mol
TDS
DH
20
Ts
10
0
-10
-20
260
TH
DG
280
300
320
340
360
380
400
Temperature (K)
DG(T0)=DH(T0)-T0[[DH(T)-DG(T)]/T+DCPln(T0/T)]
Temperature Dependence
10
9.4 oC
14.8 oC
6
20.5 oC
4
25.3 oC
kcal mol
-1
of Injectant
8
2
30.2 oC
0
0.0
0.5
1.0
1.5
2.0
Molar Ratio
2.5
3.0
3.5
Experimental conditions
›
›
For full characterization of binding interaction,
need to do experiment at different conditions




Temperature
pH
Buffer
Ionic strength
For comparison studies (e.g. mutant protein
studies, drug binding screening) need to do
experiments at identical conditions
Thank you for your
attention!
Questions?
The Expressions
[MX]

K
1[M]
 · X 
[M] = Mt – [MX]
 = [MX]/Mt
X   X   nM
t

K
1   X 
t
total X = free X + X bound to M
Combining equations and elimination of [X] yields the quadratic equation:

X
1  X
   1 


0

 nM nKM  nM
2
t
t
t
t
t
The heat released or consumed due to complex formation is proportional
to the amount of compound (Mt·V0), the fraction of complex formed (),
the number of sites (n), and the enthalpy of complex formation (DH):
Q  nM DHV
0
t
Inserting  from equation above yields
nM DHV

Q
1
2
t
o
X
nM
t
t

1
nKM
t
 1 
X
nM
t
t

1
nKM

2
t
4X
nM
t
t

13
Q(i) = sum of all peak areas up to
12
cal/sec
11
ith injection
10
9
8
7
0
20
40
60
80
Time (minutes)
100
120
140
nM DHV

Q
1
2
t
X
nM
o
t
For each individual injection:
dV
DQ(i )  Q(i ) 
V
i
o
t

1
nKM
t
 1 
X
nM
t
t

1
nKM

2
t
4X
nM
t
t

Q(i )  Q(i  1)   Q(i  1)


2
Small correction factor due to small volume dVi expelled from cell
0
kcal/mole injectant
-4
-8
-12
-16
DQ(i)
0.0
0.5
1.0
1.5
2.0
Molar Ratio [L] tot/ [M]tot
2.5
3.0
DQi versus Qi
0
kcal/mole
injectant
-4
-8
[M]tot = 60 µM
c = 40
-12
-16
0.0
0.5
1.0
0
1.5
60
2.0
2.5
120
3.0
180 µM
1
0,8

0,6
final Q scaled to 1
Q(i)
0,4
Q(i-1)
0,2
0
0
20
40
60
80
100
120
140
Micromolar concentration [L]tot
160
Thermodynamics
›
DG = DH - T DS
›
KB (or KA) – binding constant – relative strength of
interaction
›
›
DG = -RT lnKB
KD - equalibrium dissociation constant = 1/ KB
Data analysis
Sample preparation
Experimental
optimization
The experiment
Data analysis
Experimental optimization
Sample preparation
Experimental
optimization
The experiment
Data analysis
Guidelines for high quality data
Experimental optimization
›
›
Heat of injection
 >2.5 µcals for the second (first full) peak is
ideal
 ~1 µcals for second peak is minimum heat
C value
 >1 and <1000
• Best between 5 and 500
 If C < 5 then heat should be >2.5 µcals
If KD is unknown ?
Experimental optimization
No  start with 20 µM
protein and 200 µM ligand
Do you know the KD?
Yes
Estimated
KD µM
[Protein]
µM
[Ligand]
µM
[Protein]/
KD= ‘c’
<0.5
10
100
>20
0.5-2
20
200
10-40
2-10
50
500
5-25
10-100
30
40*KD
0.3-3
>100
30
20*KD
<0.3
Raw data using standard protocol
Experimental optimization
1* 0.5 µl then 18 * 2 µl injections
10.50
200 M ACZA into 20 M BCAII
10.40
10.50
10.30
10.20
10.40
10.10
10.30
µcal/sec
10.00
9.90
9.80
10.20
10.10
9.70
10.00
9.60
9.90
9.50
200 M Ethoxylamide into 20 M BCAII
200 M Furosemide into 20 M BCAII
9.80
9.40
0.00
10.00
20.00
30.00
40.00
Time (min)
0.00
10.00
10.10
10.00
9.90
200 M AMBSA into 20 M BCAII
9.80
0.00
10.00
20.00
30.00
Time (min)
40.00
20.00
30.00
Time (min)
200 M sulfanilimide into 20 M BCAII
10.20
µcal/sec
µcal/sec
200 M CBSinto 20 M BCAII
10.60
50.00
40.00
50.00
Ethoxylamide and ACZA data
Experimental optimization
0.0
-2.0
~ 5 to 6 µcals
-1
kcal mol of injectant
-4.0
10.50
200 M ACZA into 20 M BCAII
10.40
10.30
ACZA
C ~ 250
-6.0
-8.0
-10.0
-12.0
10.20
-14.0
10.10
0.0
0.5
1.0
1.5
2.0
Molar Ratio
9.90
9.80
0.0
9.70
-2.0
9.60
9.50
200 M Ethoxylamide into 20 M BCAII
9.40
0.00
10.00
20.00
30.00
Time (min)
40.00
kcal mol-1 of injectant
µcal/sec
-16.0
10.00
Ethoxylamide
C ~ 1150
-4.0
-6.0
-8.0
-10.0
-12.0
-14.0
0.0
0.5
1.0
1.5
Molar Ratio
2.0
Ethoxylamide optimization
Experimental optimization
Time (min)
0
10
20
30
40
50
60
70
0.00
 Heat of first full injection was
0.7 µcals. This is low,
underestimate the DH by ~10 %
but rewarded by a good C value.
 KD is 6 nM, C = 880.
Great, at least 2 data points in the
transition region.
µcal/sec
-0.02
-0.04
-0.06
-0.08
kcal mol-1 of injectant
›Ethoxylamide
0.0
-2.0
-4.0
-6.0
-8.0
-10.0
-12.0
0.0
0.5
1.0
1.5
2.0
Molar Ratio
37 * 1 µl injections of
50 µM Ethoxylamide
into 5 µM protein
Reduced concentrations and
injection volume
CBS and furosemide data
Experimental optimization
0.0
kcal mol-1 of injectant
-2.0
200 M CBSinto 20 M BCAII
10.60
10.50
10.40
~ 5 µcals
CBS
C ~ 22
-6.0
-8.0
-10.0
-12.0
10.20
0.0
10.10
0.5
1.0
1.5
2.0
Molar Ratio
10.00
~ 3 µcals
9.90
0.0
200 M Furosemide into 20 M BCAII
9.80
0.00
10.00
20.00
30.00
40.00
-2.0
-1
Time (min)
50.00
kcal mol of injectant
µcal/sec
10.30
-4.0
No need for optimization
Furosemide
C ~ 36
-4.0
-6.0
-8.0
0.0
0.5
1.0
1.5
Molar Ratio
2.0
Sulfanilimide and AMBSA data
Experimental optimization
-2.0
-1
kcal mol of injectant
-3.0
200 M sulfanilimide into 20 M BCAII
10.20
Sulfanilimide
C~2
-4.0
-5.0
-6.0
-7.0
-8.0
~ 2.5 µcals
0.0
0.5
1.0
1.5
2.0
Molar Ratio
10.00
-1.0
~ 1 µcals
9.90
9.80
0.00
10.00
20.00
30.00
Time (min)
40.00
50.00
AMBSA
C~2
-1
200 M AMBSA into 20 M BCAII
kcal mol of injectant
µcal/sec
10.10
-2.0
-3.0
0.0
0.5
1.0
1.5
Molar Ratio
2.0
Sulfanilimide optimization
Experimental optimization
›
18 * 2 µl injections of
500 µM Sulfanilimide
into 50 µM protein
Sulfanilimide
Heat is 7.4 µcals - good
KD is 8 µM
C = 6
kcal mol-1 of injectant
-2.0
-4.0
Increased concentrations
-6.0
0.0
0.5
1.0
Molar Ratio
1.5
2.0
AMBSA optimization
Experimental optimization
18 * 2 µl injections of
500 µM AMBSA into
50 µM protein
kcal mol-1 of injectant
0.0
›AMBSA
Heat is 4.8 µcals - good
KD is 10 µM
C = 5
-2.0
Increased concentrations
-4.0
0.0
0.5
1.0
1.5
Molar Ratio
2.0
ITC for mechanism of action – direct
measure of cofactor effects to binding
interaction
Without
MgAMPCPP
With
MgAMPCPP
KD = 0.64 M
DH = -18.2 kcal/mole
DS = -34 cal/mole/K
KD = 0.21 M
DH = -12.6 kcal/mole
DS = -12.3 cal/mole/K
Adapted in part with permission from Biochemistry 2005, 44,
11581-11591. Copyright 2005 American Chemical Society
ITC and structure-activity
relationships – compare wild type
and mutant proteins
›Thermodynamic
signatures of wildtypeSEC3 and three
evolved variants of
SEC3 interacting with
mVß8.2
Adapted from Table 2 in Cho et al,
Biochemistry 49, 9256–9268 (2010)
Nanoparticles in biomedical research
›
›
›
Drug delivery platforms




Specific targeting and delivery
Minimize risk to normal cells
Reduce toxicity and maintain therapeutic properties
Greater safety
Nucleic acid delivery/gene therapy
Quantum dots - semiconducting nanocrystals
 When illuminated with ultraviolet light, they emit a wide
spectrum of bright colors
 Diagnostics - Can be used to locate and identify
specific kinds of cells and biological activities
Design of nanoparticles for drug delivery
From Bouchemal, Drug Discov. Today, 13, 960-972, 2008
How ITC is used in nanoparticle characterization
›
›
›
Characterize binding/absorption of
protein/DNA/lipid/small molecule to functional
nanoparticle
Study energetics of nanoparticle assembly
Study of drug delivery systems
Isothermal Titration Calorimetry Studies on the
Binding of Amino Acids to Gold
Nanoparticles
›
›
›
H. Joshi, P. S. Shirude, et al
J. Phys. Chem. B, 108, 11535-11540, 2004
Authors used ITC to follow the binding of amino
acids to the surface of gold nanoparticles
Binding of aspartic acid to gold nanoparticles
ITC titration data for interaction of
aspartic acid with gold nanoparticles
at physiological pH.
Panels A and B show the raw
calorimetric data obtained during
injection of 10-3 and 2 x 10-3 M
aqueous aspartic acid solutions into
the ITC cell containing 1.47 mL of
10-4 M gold nanoparticles.
Panels C and D show the integrated
data of the curves in panels A and B
respectively plotted as a function of
total volume of the amino acid
solution added to the reaction cell.
From Joshi, et al, J. Phys. Chem. B, 108, 11535-11540, 2004
Binding of lysine to gold nanoparticles
ITC titration data for the interaction
of lysine with colloidal gold
nanoparticles at various pH.
Experiments at 4 °C
Panels A and B show the
raw calorimetric data obtained
during injection of 10-3 M and 10-2 M
lysine solution into the ITC cell
containing 1.47 mL of 10-4 M
aqueous gold nanoparticles at pH 7
and 11, respectively.
Panels C and D show the integrated
data of the curves in panels A and B
respectively plotted as a function of
total volume of the amino acid
solution added to the reaction cell.
From Joshi, et al, J. Phys. Chem. B, 108, 11535-11540, 2004
Conclusions
›
›
Showed that ITC can be used to monitor ligandnanoparticle interactions.
Binding of lysine and aspartic acid as a function
of solution pH indicates that the amino acids bind
to the gold particles extremely strongly provided
the amine groups are unprotonated.
 Lack of ITC signatures of binding of the ligand to the
gold surface should not be construed to indicate a lack
of binding of the ligand to the surfaces – weak
electrostatic interactions between lysine and the gold
nanoparticles at pH 7 not detected by ITC resulted in
significant coverage of the nanoparticle surface by the
amino acid.
Understanding the nanoparticle–protein corona
using methods to quantify exchange rates and
affinities of proteins for nanoparticles
›
›
›
›
›
T. Cedervall, I. Lynch, et. al
PNAS, 104, 2050–2055, 2007
In living systems’ biological fluids, proteins
associate with nanoparticles, and proteins on the
surface of the particles leads to an in vivo
response.
Proteins compete for nanoparticle ‘‘surface,’’
leading to a protein ‘‘corona’’ that defines the
biological identity of the particle.
Used ITC to study the affinity and stoichiometry
of protein binding to nanoparticles.
ITC of HSA and nanoparticles
ITC titration of HSA into solutions of
70 nm nanoparticles with 50:50
(Left) and 85:15 (Right) NIPAM/BAM
in 10 mM Hepes/NaOH, 0.15 M
NaCl, 1 mM EDTA, pH 7.5, is shown.
Experiments at 5 °C
(Upper) Raw data.
(Lower) Integrated heats in each
injection versus molar ratio of protein
to nanoparticle together with a fit
using a one site binding model
(Inset) Size comparison of
albumin and particles of 70 or 200
nm diameter.
From Cedervall, et al, PNAS, 104, 2050–2055, 2007
Conclusions
› Degree of nanoparticle surface coverage by
albumin calculated from ITC results.
›
›
›
 620 protein molecules per 70-nm particle
 4,650 protein molecules per 200-nm particle.
Suggests that a single layer of albumin is
adsorbed to the surface of the largest/most
hydrophobic particle, less absorption to more
hydrophilic particles
Stoichiometries depend on particle
hydrophobicity and size.
Demonstrated that ITC can be used to
characterize protein-nanoparticle interactions
Optimizing diaminopyrimidine renin
inhibitors aided by ITC and structural data
Abstracted from Ron Sarver,
Current Trends in Microcalorimetry
The binding orientation of lead compound from high
throughput screening
Favorable ∆H is
consistent with the
strong network of
hydrogen bonds.
The unoccupied S2
and S3 pockets are
opportunities to
increase affinity
Proceeding of the 2007 Current Trends in Microcalorimetry Conference Book
Data suggests substituting aryl-benzamide with
aryl-sulfonamide to improve H-bonds
10
Decrease in -T∆S due to
conversion of
hydrophobic binding in
S2 pocket to H-bonds
kcal/mol
5
0
1
2
3
4
-5
-10
Dramatic Increase in
∆H is consistent with
increase in S2 pocket
H-bonds
-15
-20
∆G
∆H
-T∆S
Another 3.4X improvement in affinity
Proceeding of the 2007 Current Trends in Microcalorimetry Conference Book
Renin inhibitor affinity improved 45X from
initial 3.6 μM lead to 79nM
S3 Pocket - Improved enthalpy due to van der Waals bonds
S2 Pocket – Improved binding enthalpy while retaining
hydrophobic advantage
S2
Aryl-Sulfonamide
S3
S2
Ether
Proceeding of the 2007 Current Trends in Microcalorimetry Conference Book
S3
The best drugs have more enthalpic binding
HIV-protease inhibitors
12 years
Data from Freire, Drug Discov Today, 2008 October; 13 (19-20) 869-874
Summary - ITC in lead optimization

Accurate KDs

Enthalpy and entropy data support structure based lead
optimization programs

Enthalpy data can be used to find strong polar interactions

Drugs that bind more enthalpically may be more selective and
specific than ‘entropic analogues
Isothermal titration calorimetry
›
›
›
›
›
›
›
ITC provides:
 Knowledge of affinities, thermodynamics and
stoichiometries of nanoparticle construction
 Knowledge of binding/absorption properties of
biological molecules (e.g. proteins), lipids, ions, and
nanoparticles
Universal technique – every reaction generates
or absorbs heat
Label-free
In solution
Can be used with suspensions
Non-optical
No molecular weight limitations
Introducing the New PEAQ ITC
MicroCal PEAQ-ITC
›
The latest and 5th generation ITC from MicroCal
 Guided workflows, experimental design software and
fully integrated wash module for consistently high
quality data
 Robust and rapid data analysis
 Improved signal to noise
MICROCAL™ PEAQ ITC
Direct measurement of binding constants
• Sensitivity to investigate any biomolecular
interaction
as little as 10 µg of protein is required
• No labeling required and easily handles turbid
solutions
• Easy to use with user friendly experimental
design wizards
easy filling and cleaning procedures.
• Fast time to first result
up to two runs per hour are easily accomplished.
• Scalable to a fully automated system
Simulation software can aid in experimental
design for new users.
Single Injection Method – results in 7 minutes
caedta_NDH
caedta_Fit
caedtaRAW_cp
0.5
0
0.0
-0.5
-1
Data: caedta_NDH
Model: OneSites
Chi^2/DoF = 914.5
N
0.915 ±7.38E-4 Sites
-1
K
1.14E5 ±1.13E3 M
DH
-4293 ±5.463 cal/mol
DS
8.97 cal/mol/deg
kcal/mole of injectant
µcal/sec
-1.0
-1.5
-2.0
-2.5
-2
-3
-3.0
-3.5
-1.67
0.00
1.67
3.33
5.00
6.67
8.33
10.00
11.67
13.33
-4
Time (min)
0.0
0.5
1.0
1.5
Molar Ratio
2.0
2.5
3.0
MICROCAL™ AUTO-PEAQ ITC
Automated label-free in solution assay for detailed
thermodynamic binding characteristics
• Throughput of up to 75 samples per day
a capacity to run 384 samples unattended.
• Unattended operation
all filling, data collection and cell cleaning
functions are fully automated.
• Direct measurement of binding constants
from sub-millimolar to picomolar binding
constants (102 to 1012 M-1)
• Sensitivity to investigate any molecular
interaction
using as little as 10 µg of protein.
Thank you!!
Additional slides
Measuring bioactivity with ITC: affinity and
stoichiometry
Kcal/mol injectant
0
Anti-quinidine antibodies
batches compared
50%
“Fully Active”
-2
Protein quality
Partially
Active
-4
Measure active
concentrations
-6
Activity of antibodies
immobilized on metal beads
quantitatively measured
Fully Active
-8
0.0
0.0
0.5
1.0
1.0
2.0
Molar Ratio
1.5
3.0
2.0
4.0
ITC and biotherapeutics and vaccines




Confirm binding to target
Determine binding affinity
Determine if binding is specific
Determine active concentration by
stoichiometry
 Characterize mechanism of action
 Characterize excipient binding