Muneera Beach PhD Malvern Instruments Northampton Massachusetts USA MuneeraBeachmalverncom Why M icrocalorimetry No molecular weight limitations ITC Native molecules in solution biological relevance ID: 929287
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Slide1
Introduction to Microcalorimetry
Muneera Beach, Ph.D.
Malvern Instruments
Northampton, Massachusetts USA
Muneera.Beach@malvern.com
Slide2Why Microcalorimetry?
No molecular weight limitations (ITC)
Native molecules in solution (biological relevance
)
Direct measurement of heat change
Direct measurement of melting temperature as an indicator of thermal stability
No immobilization
necessary
No/minimal assay development
Free choice of solvent
Rapid results for K
D
n, ΔH and ΔS from ITC experimentsDetermine Tm, ΔH and ΔCp from DSC experiments
Label-free
Broad d
ynamic
range
Ease-of-use
Information rich
Slide3How do they work?
Reference Calibration Heater
Cell Main Heater
Sample Calibration Heater
DP
D
T
Sample
The DP is a measured power differential between
the reference and sample cells to maintain a zero
temperature between the cells
D
T~0
DP = Differential power∆T = Temperature difference
Reference
Slide4Raw data
Reported data
All binding parameters in a single experiment
X + M
XM
Reference
cell
Sample cell
Syringe
X
In a single ITC experiment you get…
Affinity (K
D) – strength of bindingHeat of binding (ΔH) and entropy (ΔS) – mechanism and driving force of interactionStoichiometry (n) - Number of binding sites Enzyme kinetics
M
(
Δ
H)
Mechanism
(K
B
) Binding
(n) stoichiometry
Slide5The energetics
K
D
Macromolecule/Nanoparticle
Waters, ions, protons
Ligand
Elucidation of binding mechanisms:
Primary Enthalpic
Contributions:
Hydrogen
bonding
van
der Waals
interactionsElectrostatic interactionsPrimary Entropic ContributionsHydrophobic effect-water release (favorable)Conformational changes and reduction in degrees of freedom (unfavorable)G = RT lnKDG = H -TS
Slide6ITC provides more than binding affinity – characterize binding forces
Similar K
D
Different binding
mechanisms
Slide7Good hydrogen bonding with unfavorable conformational change
Binding dominated by hydrophobic interaction
Favorable hydrogen bonds and hydrophobic interaction
Same affinity, different energetics
Favorable
Unfavorable
A
B
C
ITC results are used to gain insights into the mechanism of binding
Slide8MicroCal
iTC
200
With ITC you can… Measure target activityConfirm drug binding to targetGet quick KDs for secondary screening/hit validationUse thermodynamics to guide lead optimizationValidate IC50 and EC50 values
Characterize mechanism of action Measure enzyme kineticsMicroCal VP-ITC
MicroCal PEAQMicroCal PEAQ
Slide9ITC characterizes a broad range of interactions
Proteins
Receptor
AntibodiesMembraneSmall MoleculesMetal ionsDrug/ligandCarbohydratesNucleic Acids
NanoparticlesPolymersMetalsQuantum dotsBeadsVaccines/AdjuvantsLipids/MicellesDetergents/Surfactants
Slide10Applications Examples
Slide11Crystallization
of a RNA/ligand
complex
E. Coli TPP riboswitch bound to thiamine pyrophosphateCourtesy of Dr Eric EnnifarCNRS University of Strasbourg, France
Slide12Riboswitches
non
coding
RNA (
ncRNAs
)Specific binding of
metabolitesRegulate expression of protein involved in biosynthesis of riboswitch substratesVery
attractive targets for the design of a new class of antibiotics
E.coli TPP riboswitch
Responds to the coenzyme thiamine pyrophosphate (TPP)
Background Riboswitches-ligand interactions
Slide13Initial
trails
were unsuccessfulTPP riboswitch was produced in large-scale by T7 transcription
RNA is refolded after heat denaturationCrystallization using the classical sparse
matrix approach failed
Goal of the
study
-
solve
the crystal structure of the TPP riboswitch bound to TPP
Use ITC to identify potential problems and to aid crystallization
Slide14K
D
= 33
nM
D
H = -32.0 kcal/molD
S = -69.0 cal/mol/KN = 0.58
Problem
!
Native acrylamide gel
revealed 2 conformations of the RNA !
New RNA
folding protocol
Clear Drops …
Kd = 24 nM
D
H = -22.3 kcal/mol
D
S = -38.6 cal/mol/K
N = 1.01
Optimization of RNA preparation guided by
ITC
Slide15Crystallization
of a
protein
/peptide complexPseudomonas aeruginosa sliding clamp bound to a short peptide inhibitor
Slide16Sample
concentration
crystallization
b
sliding clamp
Confers high processivity to DNA polymerasesInteraction between the clamp and a short conserved peptide of the DNA polymeraseTarget for the rationale design of antibiotics
Wolff et al, J Med Chem 2014
Slide17Conclusions
ITC
technique can be implemented
in crystallization workflowof biological macromolecules complexes and can be used as a guide to improve the success rate of crystallization:Sample requirement for structural studies are well-suited for ITC analysisAssessment of the real active protein and/or ligand concentration (provided by the observed stoichiometry)Real-time determination of an optimized protein/ligand ratio to be used for crystallization of the complex by monitoring the stoichiometryHelp in optimization of the complex formation (sample homogeneity, affinity, specificity)Complete thermodynamic profile of the interaction is provided “for free”. One just needs to concentrate
thesample after ITC analysis.
Slide18Assessment of protein quality byMicroCal™ iTC
200
system100% of Batch 1 protein active
based on stoichiometry23% of Batch 2 protein active based on
stoichiometry
Presented by L.Gao (Hoffmann-La Roche), poster at SBS 2009
Peptide
binding to proteinBatch #1Peptide
binding to protein Batch #2
Slide19Understanding 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
Slide20Background
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.
Slide21ITC 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 fitusing a one site binding model
(Inset) Size comparison ofalbumin and particles of 70 or 200 nm diameter.From Cedervall, et al, PNAS, 104, 2050–2055, 2007
Slide22ConclusionsDegree 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 particlesStoichiometries depend on particle hydrophobicity and size.Demonstrated that ITC can be used to characterize protein-nanoparticle interactions
Slide23Protein-metal ion
ITC shows differential
binding of
Mn(II) ions toWT T5 5’ nuclease
-2
0
2
4
-10
0
10
20
30
40
50
60
70
80
90
100
Time (min)
µcal/sec
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
0
2
Molar Ratio
kcal/mole
K
a
= 3.0 x 10
5
M
-1
D
H
= -0.59 kcal mol
-1
K
a
= 1.0 x 10
4
M
-1
D
H
= +1.6 kcal mol
-1
Feng
, et al, Nat.
Struct
. Mol. Biol. 11, 450-456 (2004)
Slide24Molar Ratio
Kcal/
mol
injectant
1.0
1.5
2.0
0.5
0.0
0
-2
-4
-6
-8
Assess protein quality
Clearly distinguish between genuine SAR and batch to batch variations in protein quality
‘Fully active’
50%
‘Fully active’
Different binding mechanism
Measuring bioactivity with ITC:
affinity and stoichiometry
Slide25MicroCal™ ITC systemstraining course
Achieving high quality data using MicroCal™ iTC
200
system
Slide26ObjectivesOutline the practical steps you should take to achieve high quality data
Demonstrate
the rewards for following
a few simple rules in sample preparationUse a case study to demostrate how to optimize your experiment
Slide27Four crucial steps to great
isothermal
titration
calorimetry (ITC) data
Slide28Sample preparation
Sample preparation
The experiment
Data analysis
Experimental optimization
Slide29Sample preparation
Dialyze or buffer exchange
proteins
Accurately measure protein concentration using A280Ensure that protein and small molecule solutions are well matched
Slide30Step 1: Dialyze or Buffer ExchangeSample 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
Slide31Poor sample preparation leads to poor dataSample preparation
The data shown here shows before and after dialysis
The large peaks were due
to differences in the NaCl concentration betweenbuffersWith dialysis
Without dialysis
Slide32Step 2: Accurately measure protein and ligand concentrationsSample preparation
Protein concentration should be determined using A
280
Be as accurate as you can weighing the ligand. UV absorption is better if ligand has a chromophore.
Slide33Step 3: Match buffersSample preparation
The ligand
Dilute an aliquot of the ligand stock solution containing
dimethylsulfoxide (DMSO) with the dialysate and then…The proteinAdd a corresponding amount of DMSO to the protein solution
Slide34Ligand preparation from DMSO stockSample preparation
5
mM
ligand in 100% DMSO50 µl Dialysate buffer
950 µl 250 µM ligand in 5% DMSO
Slide35Match DMSO in the protein solutionSample preparation
DMSO
50
µl
25 µM dialyzedprotein950 µl 1
ml of 23.75 µM protein in 5% DMSO
Slide36DMSO mismatchSample preparation
Large heats from DMSO dilution, if buffers are not matched
B
uffer into buffer5% DMSO into 5% DMSO5% DMSO into 4.5% DMSO5% DMSO into 4 % DMSO
Slide37pH mismatchesSample preparation
pH mismatches can arise when using high concentrations of ligand i.e.
mM
concentrations and aboveTo 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
Slide38Choice of bufferSample preparation
ITC
is
robust, almost all buffers can be used e.g. HEPES, PBS, glycine, acetateIf reducing agent is required, it is best to useTris (2-carboxyethylphosphine) hydrochloride (TCEP)β-mercaptoethanol (BME) Avoid DTT - Unstable and undergoes oxidation, High background heatLimit glycerol to 10% V/V, and detergents to below CMCUse conditions in which your protein is ‘happy’
Slide39The experiment
Sample preparation
The experiment
Data analysis
Experimental optimization
Slide40Clean the cell The experiment
Rinse with 20%
Contrad
™ (14% Decon™) and water
Slide41Experimental set up and key questionsThe experiment
How much sample do I need?
What are the ideal run parameters?
What controls should I perform?
Slide42How much sample is required?The experiment
Do you know the K
D
?Estimated KD µM
[Protein] µM[Ligand] µM
[Protein]/ KD= C<0.5
10100
>20
0.5-2
20
200
10-402-1050
5005-25
10-10030 40*K
D
0.3-3
>100
30
20*K
D
<0.3
Yes
follow the
column for estimated K
D
s
No
start
with
10-20
µM protein
and 100-200 µM ligand
Slide43C value
The
experiment
C = 10-100 GreatC = 5-500 GoodC = 1-5 and 500-1000 OK C = < 1 and > 1000 competition ITC
C = 0.05
C = 0.5C = 5C = 50C = 500
C = [Protein]/KD
Slide44C valueThe experiment
-0.4
-0.2
0
0
4
8
12
16
[Protein]/K
D
< 1
N fixed
Fitted: K
D
,
H
-4
-2
0
0
0.5
1.0
1.5
2.0
10< [Protein]/K
D
<500
Fitted: N, K
D
,
H
kcal mol
-1
of injectant
-4
-2
0
0
0.5
1.0
1.5
2.0
[Protein]/K
D
>> 1000
Fitted: N,
H
Molar ratio
Low
c
High
c
0
1
10
5
00
1000
∞
BAD
GOOD
OPTIMAL
GOOD
BAD
Slide45The effect of C valueThe experiment
[BCA II], C
5
µM, C = 1010
µM, C = 2050 µM, C = 10020 µM, C = 40KD ~ 500 nM[Furosemide] = 10 *[BCA II]
Slide46Typical run parameters for
MicroCal™ iTC
200
system - Injection parametersVolume 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 injectionsFilter period 5 seconds, data acquisition for data averaging
Slide47Typical run parameters for MicroCal™
iTC
200
systemReference power: 3 to 10 µcals/sec Stir speed: 750 rpmFeedback: High
Slide48The control experimentsL
igand 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!Proper Controls:Buffer into BufferLigand into buffer
Slide49Sample 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 ChromatographyDesalting ColumnDialysis (overnight with at least one buffer change, use as buffer control)Additives must be matched as well for example - DMSO, salt, EtOH, etc.
Slide50Additional Considerations
Slide51Single Injection Method iTC200
High Speed Mode
High Quality Data in 7 minutes
Slide52DHobs versus buffer heat of ionization
0
5
10
15-20-15-10
-5
D
H
obs
(kcal/mol)
D
H
ion
(kcal/mol)
Hepes
Mops
Phosphate
Imidazole
Tris
All reactions at same pH
Slope: # protons
released (negative value)
Y intercept:
D
H
int
of binding,
buffer-independent
Different pH can have
different plot
If slope = 0, then no buffer
effect at that pH
Evaluation of Linked Protonation Effects in Protein Binding Reactions Using Isothermal Titration Calorimetry, Biophysical J., 1996, Brian Baker et al.
D
H
obs
=
D
H
int
+ n
D
H
ion
Slide53The Energetics
Slope=
D
H/T=
DCp
Ts
TH
D
G(T
0)=DH(T0)-T0[[D
H(T)-DG(T)]/T+DCPln(T0/T)]
Slide54Temperature Dependence
9.4
o
C
14.8
oC
20.5 oC
25.3 oC
30.2
o
C
Slide55Experimental conditionsFor full characterization of binding interaction, need to do experiment at different conditions
Temperature
pH
BufferIonic strengthFor comparison studies (e.g. mutant protein studies, drug binding screening) need to do experiments at identical conditions
Slide56Thank you for your attention!
Questions?
Slide57total
X
= free
X
+
X bound to M
[M] = Mt – [MX] Q = [MX]/Mt
[
MX
]
[
M] ·Combining equations and elimination of [X] yields the quadratic equation:
The Expressions
Slide58The heat released or consumed due to complex formation is proportional
to the amount of compound (
M
t
·V0), the fraction of complex formed (Q), the number of sites (n), and the enthalpy of complex formation (DH):
0
Inserting Q from equation above yields
Time (minutes)
m
cal/sec
Q
(i) = sum of all peak areas up to
ith injection
Slide59kcal/mole injectant
For each individual injection:
Small correction factor due to small volume
dV
i
expelled from cell
D
Q
(i)
Molar Ratio [L] tot/ [M]tot
Slide60kcal/mole injectant
Micromolar concentration [L]
tot
0 60 120 180 µM
[M]
tot
= 60 µM
c = 40
Q
(i)
final Q scaled to 1
Q
(i-1)DQi versus Qi
Slide61Thermodynamics
D
G =
DH - T DS DG = -RT lnKBKB (or KA) – binding constant – relative strength of interactionKD - equalibrium dissociation constant = 1/ KB
Slide62Data analysis
Sample preparation
The experiment
Data analysis
Experimental optimization
Slide63Experimental optimization
Sample preparation
The experiment
Data analysis
Experimental optimization
Slide64Guidelines for high quality dataExperimental 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 <1000Best between 5 and 500If C < 5 then heat should be >2.5 µcals
Slide65If KD is unknown ?
Experimental optimization
Do you know the K
D?Estimated KD µM
[Protein] µM[Ligand] µM
[Protein]/ KD= ‘c’
<0.510100
>20
0.5-2
20
200
10-402-10
505005-25
10-10030
40*KD
0.3-3
>100
30
20*K
D
<0.3
No
start
with
20
µM protein
and
200
µM ligand
Yes
Slide66Raw data using standard protocolExperimental optimization
1* 0.5
µ
l then 18 * 2 µ
l injections
Slide67Ethoxylamide
and ACZA data
Experimental
optimizationEthoxyl-amideC ~ 1150ACZA
C ~ 250 ~ 5 to 6 µcals
Slide68Ethoxylamide optimizationExperimental optimization
Ethoxylamide
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.
37 * 1 µl injections of 50 µM Ethoxylamideinto 5 µM protein Reduced concentrations andinjection volume
Slide69CBS and furosemide data
Experimental optimization
~ 3
µ
calsCBSC ~ 22FurosemideC ~ 36No need for optimization~ 5 µcals
Slide70Sulfanilimide
and AMBSA data
Experimental optimization
SulfanilimideC ~ 2AMBSAC ~ 2
~ 1 µcals~ 2.5 µcals
Slide71Sulfanilimide optimizationExperimental optimization
Sulfanilimide
Heat is 7.4 µ
cals - goodKD is 8 µMC = 618 * 2 µl injections of500 µM Sulfanilimideinto 50 µM protein
Increased concentrations
Slide72AMBSA optimizationExperimental optimization
AMBSA
Heat is 4.8 µ
cals - goodKD is 10 µMC = 518 * 2 µl injections of 500 µM AMBSA into50 µ
M proteinIncreased concentrations
Slide73ITC for mechanism of action – direct measure of cofactor effects to binding interaction
Without
MgAMPCPP
With
MgAMPCPP
K
D = 0.64 mMDH = -18.2 kcal/moleDS = -34 cal/mole/K
K
D
= 0.21 mMDH = -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
Slide74ITC and structure-activity relationships – compare wild type and mutant proteins
Thermodynamic signatures of wild-typeSEC3 and three evolved variants of SEC3 interacting with mVß8.2
Adapted from Table 2 in Cho
et al
, Biochemistry 49, 9256–9268 (2010)
Slide75Nanoparticles in biomedical researchDrug delivery platforms
Specific targeting and delivery
Minimize risk to normal cells
Reduce toxicity and maintain therapeutic propertiesGreater safetyNucleic acid delivery/gene therapyQuantum dots - semiconducting nanocrystalsWhen illuminated with ultraviolet light, they emit a wide spectrum of bright colorsDiagnostics - Can be used to locate and identify specific kinds of cells and biological activities
Slide76Design of nanoparticles for drug delivery
From Bouchemal, Drug Discov. Today, 13, 960-972, 2008
Slide77How 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
Slide78Isothermal Titration Calorimetry Studies on the Binding of Amino Acids to GoldNanoparticles
H. Joshi, P.
S.
Shirude, et al J. Phys. Chem. B, 108, 11535-11540, 2004Authors used ITC to follow the binding of amino acids to the surface of gold nanoparticles
Slide79Binding 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
Slide80Binding of lysine to gold nanoparticles
ITC titration data
for
the interaction of lysine with colloidal gold nanoparticles at various pH. Experiments at 4 °CPanels A and B show theraw calorimetric data obtained during injection of 10-3 M and 10-2 Mlysine 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 Brespectively plotted as a function of total volume of the amino acidsolution added to the reaction cell.From Joshi, et al, J. Phys. Chem. B, 108, 11535-11540, 2004
Slide81ConclusionsShowed that ITC can be used to
monitor ligand-nanoparticle
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.
Slide82Understanding 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, 2007In 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.
Slide83ITC 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 fitusing a one site binding model
(Inset) Size comparison ofalbumin and particles of 70 or 200 nm diameter.From Cedervall, et al, PNAS, 104, 2050–2055, 2007
Slide84ConclusionsDegree 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 particlesStoichiometries depend on particle hydrophobicity and size.Demonstrated that ITC can be used to characterize protein-nanoparticle interactions
Slide85Optimizing diaminopyrimidine renin inhibitors aided by ITC and structural data Abstracted from
Ron Sarver
,
Current Trends in Microcalorimetry
Slide86The binding orientation
of lead compound from high throughput screening
The unoccupied
S2
and S3 pockets are opportunities to increase affinity Favorable ∆H is consistent with the strong network of hydrogen bonds.
Proceeding of the 2007 Current Trends in Microcalorimetry Conference Book
Slide87Dramatic Increase in ∆H is consistent with increase in S2 pocket H-bonds
Decrease in -T∆S due to conversion of hydrophobic binding in S2 pocket to H-bonds
Data suggests substituting aryl-
benzamide
with aryl-sulfonamide to improve H-bonds
Another 3.4X improvement in affinity
Proceeding of the 2007 Current Trends in
Microcalorimetry
Conference Book
Slide88Renin inhibitor affinity improved 45X from initial 3.6 μM lead to 79nM
S2
S2
S3
S3
Aryl-Sulfonamide
Ether
S3 Pocket - Improved enthalpy due to van der Waals bonds
S2 Pocket – Improved binding enthalpy while retaining hydrophobic advantage
Proceeding of the 2007 Current Trends in
Microcalorimetry
Conference Book
Slide89HIV-protease
inhibitors
The best drugs have more enthalpic binding
Data from
Freire, Drug Discov Today, 2008 October; 13 (19-20) 869-87412 years
Slide90Summary - ITC in lead optimization
Accurate K
D
sEnthalpy 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
Slide91Isothermal titration calorimetry
ITC provides:
Knowledge
of affinities, thermodynamics and stoichiometries of nanoparticle constructionKnowledge of binding/absorption properties of biological molecules (e.g. proteins), lipids, ions, and nanoparticlesUniversal technique – every reaction generates or absorbs heatLabel-freeIn solutionCan be used with suspensions Non-opticalNo molecular weight limitations
Slide92Introducing the New PEAQ ITC
Slide93MicroCal PEAQ-ITCThe latest and 5
th
generation ITC from
MicroCalGuided workflows, experimental design software and fully integrated wash module for consistently high quality dataRobust and rapid data analysisImproved signal to noise
Slide94MICROCAL
™
PEAQ ITC
Direct measurement of binding constants
Sensitivity to investigate any biomolecular interactionas little as 10 µg of protein is requiredNo labeling required and easily handles turbid solutionsEasy 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
Slide95Simulation software can aid in experimental design for new users.
Slide96Single Injection Method – results in 7 minutes
Slide97Throughput 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.
MICROCAL™ AUTO-PEAQ ITCAutomated label-free in solution assay for detailed thermodynamic binding characteristics
Slide98Thank you!!
Slide99Additional slides
Slide100Measuring bioactivity with ITC: affinity and stoichiometry
Molar Ratio
Kcal/mol injectant
1.0
1.5
2.0
0.5
0.0
0
-2
-4
-6
-8
Fully Active
50%
“Fully Active”
Partially Active
Anti-quinidine antibodies batches compared
Protein quality
Measure active concentrations
Activity of antibodies immobilized on metal beads quantitatively measured
0.0
1.0 2.0 3.0 4.0
Slide101ITC and biotherapeutics and vaccines
Confirm binding to target
Determine binding affinity
Determine if binding is specificDetermine active concentration by stoichiometryCharacterize mechanism of actionCharacterize excipient binding
Slide102Peptide-Mediated Constructs of Quantum Dot Nanocomposites for Enzymatic Control of Nonradiative Energy Transfer
U. O. S.
Seker
, T. Ozel, and H. V.DemirNano Lett. 11, 1530–1539, 2011Describes an approach for constructing colloidal semiconductor quantum dot (QDot) nanocomposites that using polyelectrolyte peptidesUsed ITC to characterize thermodynamics of polypeptide interactions with altering chain lengths, as evaluation of Qdot formation and assembly
Slide103ITC of polyelectrolyte peptides
ITC titrations
for the peptide pairs:
PLKC25-PLE25 (A) PLKC100-PLE25 (B) PLKC25-PLE100 (C) PLKC100-PLE100 (D) PLKC – poly-L-lysine (25 or 100mer chain length)PLE – poly-L-glutamic acid (25 or 100mer chain length)Peptide concentration in ITC syringe 163 μM and
peptide concentration in ITC cell peptide concentration was 18.7 μM. Experiments at 25°C in PBS buffer.From Seker, et al, Nano Lett. 11, 1530-1539, 2011
Slide104ITC data of peptide pairs
From Seker, et al,
Nano Lett.
11, 1530-1539, 2011
Slide105ConclusionsITC for each peptide
pair has
a positive enthalpy change, which indicates
that interaction is an endothermic process. Peptide chain length controls binding energy and affinity through functional side chains.Large positive entropy change may arise due to the loss of the structured water shell during peptide-peptide interaction.Affinity and thermodynamics of peptide pair interactions is important to help guide QDot film formation