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Introduction to Microcalorimetry Muneera Beach, Ph.D. Malvern Instruments Northampton, Massachusetts USA [email protected] Why Microcalorimetry? Broad dynamic range 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) Information rich Rapid results for KD n, H H and H S from ITC experiments Determine Tm, H H and H Cp from DSC experiments 0 N D H , k c a l/m o le o f in je c ta n t 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? The DP is a measured power differential betwee Sample Reference the reference and sample cells to maintain a ze temperature between the cells DT

DP DT~0 Reference Calibration Heater Sample Calibration Heater Cell Main Heater DP = Differential power T = Temperature difference All binding parameters in a single experiment X + M XM Syringe Raw data Reported data (H)H)) Mechanism X (KB) Binding (n) stoichiometry In a single ITC experiment you get M Reference cell Sample cell Affinity (KD) strength of binding Heat of binding (H) and entropy (S) mechanism H) and entropy (H) and entropy (S) mechanism S) mechanism

and driving force of interaction Stoichiometry (n) - Number of binding sites Enzyme kinetics The energetics Elucidation of binding mechanisms: KD Macromolecule/Nanoparticle Primary Enthalpic Contributions: Hydrogen bonding Electrostatic interactions Primary Entropic Contributions

Waters, ions, protons Ligand DG = RT lnKD DG = DH -TDS van der Waals interactions 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 10 with unfavorable conformational change hydrophobic interaction C. Favorable hydrogen bonds and hydrophobic interaction 0 kcal/mole B. Binding dominated by 5 G H -TS -5 -10 -15 -20

A B C Favorable With ITC you can Measure target activity Confirm drug binding to target MicroCal VP-ITC 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) 0.20

0 10 20 30 40 50 60 0.00 -0.20 Problem ! Time (min) 0 10 20 30 40 50

60 0.00 -0.60 -0.20 Native acrylamide gel revealed 2 conformations of the RNA ! -1.00 -1.20 -1.40 -1.60 0.00 -5.00 -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 cal/sec -0.80 -0.40 -0.60 -0.80 KCal/Mole of Injectant cal/sec -0.40 KCal/Mole of Injectant Clear Drops

-1.00 2.00 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 nanoparticleprotein corona using methods to quantify exchange rates and affinities of proteins for nanoparticles T. Cedervall, I. Lynch, et. al, PNAS, 104, 20502055, 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, 20502055, 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 iTC 200 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

Sample preparation Experimental optimization The experiment Data analysis Sample preparation 1. Dialyze or buffer exchange proteins 2. Accurately measure protein concentration using A 3. Ensure that protein and small molecule solutions are 280 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 cal/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 l 950 l 250 M ligand in 5% DMSO Dialysate buffer Match DMSO in the protein solution Sample preparation

DMSO 50 l 950 l 1 ml of 23.75 M protein in 5% DMSO 25 M 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)

-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 cal/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 M protein and 100-200 M ligand Do you know the KD? Yes follow the column for estimated KDs Estimated KD M [Protein] M [Ligand] M [Protein]/ K D= 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 H)igh c 00 [Protein]/KD < 1 N fixed Fitted: KD, DH -0.4-4

0 0.5 4 1.0 8 1.5 12 Molar Ratio -2-2 kcal/m ole of injectant kcal/m ole of injectant -0.2-2 0.0 00 00

kcal/m ole of injectant kcal mol-1 of injectant Low c 10< [Protein]/KD <500 Fitted: N, KD, DH 2.0 0.0 0 [Protein]/KD >> 1000 Fitted: N, DH -4 -4 -4-4 16 -2 -2 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 M, C = 10 10 M, C = 20 10.00 cal/sec KD ~ 500 nM [BCA II], C 20 M, C = 40 9.50 0.0

kcal mol-1 of injectant [Furosemide] = 10 *[BCA II] 50 M, C = 100 9.00 -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 l (range 0.1-38 l) * An initial injection of 0.2 l is made followed by 18 * 2 l 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 cals/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 Dont 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 Use at least 10 M protein in the cell at the beginning Buffers must be matched in sample and syringe (protein, nucleic acid, lipid, etc.) Determine concentration as accurately as possible (A280, A260) 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 8.7 cal/sec 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 DH)obs (kcal/mol) -5 Hepes Mops -10 Imidazole -15 -20 0 Different pH can have different plot Tris 5

10 DH)ion (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 20 -1 kcal.mol TDS DH

Ts 10 0 -10 -20 260 TH DG 280 300 320 340 360 380 400 Temperature (K)

G(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 [M] = Mt [MX] [MX] Q K [M] 1 Q X Q = [MX]/Mt X X nQM t Q K 1 Q X t total X = free X + X bound to M Combining equations and elimination of [X] yields the quadratic equation:

X 1 X Q Q 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 (MtV0), the fraction of complex formed (Q), the number of sites (n), and the enthalpy of complex formation (DH): Q nQM DHV t 0

Inserting Q 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 Molar Ratio [L] tot/ [M]tot 2.0 2.5 3.0 DQi versus Qi 0 kcal/mole injectant -4 -8

[M]tot = 60 M c = 40 -12 -16 0.0 0.5 1.0 0 1.5 60 2.0 2.5 120 3.0 180 M 1

0,8 Q 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 KD - equalibrium dissociation constant = 1/ KB DG = -RT lnKB 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 cals for the second (first full) peak is ideal ~1 cals 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 cals If KD is unknown ? Experimental optimization No start with 20 M protein and 200 M ligand Do you know the KD? Yes Estimated KD M [Protein] M [Ligand] M [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 10.50 10.40 1* 0.5 l then 18 * 2 l injections 200MACZAinto 20MBCAII 10.50 10.30 10.20 10.40 10.10 10.30 cal/sec 10.00 9.90 9.80 10.20 10.10 9.70 10.00

9.60 9.90 9.50 200MEthoxylamideinto 20MBCAII 9.40 0.00 10.00 20.00 30.00 Time (min) 200MFurosemideinto 20 MBCAII 9.80 40.00 0.00 10.00 10.10

10.00 9.90 200MAMBSAinto 20MBCAII 9.80 0.00 10.00 20.00 30.00 Time(min) 40.00 20.00 30.00 Time(min) 200Msulfanilimideinto 20MBCAII 10.20 cal/sec

cal/sec 200MCBSinto 20MBCAII 10.60 50.00 40.00 50.00 Ethoxylamide and ACZA data Experimental optimization 0.0 -2.0 ~ 5 to 6 cals -1 kcal mol of injectant -4.0 10.50 200MACZAinto 20MBCAII 10.40 10.30

ACZA C ~ 250 -6.0 -8.0 -10.0 -12.0 10.20 -14.0 -16.0 10.00 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 200MEthoxylamideinto 20MBCAII 9.40 0.00 10.00 20.00 30.00 Time(min) 40.00 kcal mol-1 of injectant cal/sec 10.10 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 cal/sec 0.00 Heat of first full injection was 0.7 cals. 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. -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 l injections of 50 M Ethoxylamide into 5 M protein Reduced concentrations and injection volume CBS and furosemide data Experimental optimization 0.0 kcal mol-1 of injectant -2.0

200MCBSinto 20MBCAII 10.60 10.50 10.40 ~ 5 cals 10.20 CBS C ~ 22 -6.0 -8.0 -10.0 -12.0 0.0 10.10 0.5 1.0 1.5

2.0 Molar Ratio 10.00 ~ 3 cals 9.90 0.0 200MFurosemideinto 20MBCAII 9.80 0.00 10.00 20.00 30.00 40.00 -2.0 -1 Time (min)

50.00 kcal mol of injectant cal/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 200Msulfanilimideinto 20MBCAII 10.20 Sulfanilimide C~2 -4.0 -5.0 -6.0 -7.0 -8.0 ~ 2.5 cals

0.0 1.0 1.5 2.0 10.00 -1.0 ~ 1 cals 9.90 9.80 0.00 10.00 20.00 30.00 Time(min) 40.00 50.00 -1

200MAMBSAinto 20MBCAII kcal mol of injectant cal/sec 0.5 Molar Ratio 10.10 AMBSA C~2 -2.0 -3.0 0.0 0.5 1.0 1.5 Molar Ratio 2.0 Sulfanilimide optimization

Experimental optimization 18 * 2 l injections of 500 M Sulfanilimide into 50 M protein Sulfanilimide Heat is 7.4 cals - good KD is 8 M 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 l injections of 500 M AMBSA into 50 M protein kcal mol-1 of injectant 0.0 AMBSA Heat is 4.8 cals - good KD is 10 M 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 KD = 0.64 M DH = -18.2 kcal/mole DS = -34 cal/mole/K With MgAMPCPP 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 mV8.2 Adapted from Table 2 in Cho et al, Biochemistry 49, 92569268 (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 nanoparticleprotein corona using methods to quantify exchange rates and affinities of proteins for nanoparticles

T. Cedervall, I. Lynch, et. al PNAS, 104, 20502055, 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, 20502055, 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 -TS 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 -TS 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 M lead to 79nMM 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: Universal technique every reaction generates or absorbs heat Label-free In solution Can be used with suspensions Non-optical No molecular weight limitations

Knowledge of affinities, thermodynamics and stoichiometries of nanoparticle construction Knowledge of binding/absorption properties of biological molecules (e.g. proteins), lipids, ions, and nanoparticles 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 g 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

cal/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 g of protein. Thank you!! Additional slides Measuring bioactivity with ITC: affinity and stoichiometry 0 Anti-quinidine antibodies batches compared 50% Fully Active Protein quality -2 Kcal/mol injectant Partially Active

-4 -6 Fully Active -8 0.0.0 0 0. 1.0 5 1. 0 2.0 Molar Ratio 1. 5 3.0 2. 0 Measure active concentrations

Activity of antibodies immobilized on metal beads quantitatively measured 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

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    "Javascript by Example" (various publishers, if you can find it) "How to Set Up and Maintain a WWW Site", Addison Wesley "Essential Javascript for Web Professionals", Prentice Hall
  • WHY DO WE SELL POPCORN? - Trail&#x27;s End

    WHY DO WE SELL POPCORN? - Trail's End

    Check out the prize flyer for ideas and then build your Amazon.com Wish List. Learn more at Rewards.Trails-End.com ... Scouts work five 2-hour solo storefront shifts. Scouts average $125/hr. ... Q&A - Be ready for the questions the parents may...
  • Modify HOPE Act Variance to Include Other Organs

    Modify HOPE Act Variance to Include Other Organs

    Additional reporting and protocol development many be required to expand programs to additional organ types. OPOs may need to modify to accommodate expansion of program and volume of offers which should still be a small percentage in comparison to all...