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Structural proteomics lecture 4: Biophysical dissection of protein complexes Protein complexes and their interactions are the basis of all biology True.

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Presentation on theme: "Structural proteomics lecture 4: Biophysical dissection of protein complexes Protein complexes and their interactions are the basis of all biology True."— Presentation transcript:

1 Structural proteomics lecture 4: Biophysical dissection of protein complexes Protein complexes and their interactions are the basis of all biology True understanding of cellular processes requires understanding of the underlying molecular mechanisms BUT… molecules should not be seen in isolation!

2 T cell Surface Composition T cell Surface Organisation

3 e.g. a key complex: the T cell receptor or One per complex Dimer of heterodimers Whats in the complex – how is it assembled? Need to understand this to know how it can signal. How does it bind its ligand – how is it so specific? What is the effective range of affinities? etc etc

4 Biophysical in vitro techniques to dissect protein complexes & their interactions 1.AUC 2.SPR 3.ITC 4.FRET / BRET 5.Single molecule microscopy (?)

5 1. AUC (Analytical Ultracentrifugation)

6 What is Analytical Ultracentrifugation for? The measurement of properties of molecular species such as mass and shape constants and their alteration with concentration (e.g. during self-association or multi- component assembly) Why use Analytical Ultracentrifugation ? The possible mechanisms of protein complex function will often be limited by its organisation: AUC can assess complex size and degree of self-association as well as giving a measure of monodispersity of in vitro reagents.

7 Ultracentrifugation Preparative –separate complex mixtures –fractionate cellular components –density gradients to separate by molecular mass Analytical –sedimentation equilibration Thermodynamic Absolute MW Shape independent Aggregation Protein-protein interactions –sedimentation velocity Hydrodynamic Relative MW Molecular shape Aggregation behavior

8 Theory: The Svedberg equation Consider a particle m in a centrifuge tube filled with a liquid. The particle (m) is acted on by three forces: –F C : the centrifugal force –F B : the buoyant force (Archimedes principle) –F f : the frictional force between the particle and the liquid Will reach constant velocity where forces balance:

9 Define s, the sedimentation coefficient: s = s is a constant for a given particle/solvent, has units of seconds, but use Svedberg (S) units (10 –13 s). Cytochrome s=1S, ribosome s=70S, composed of 50S and 30S subunits (s does not vary linearly with M r ) Values for most biomolecules betwwen 1 and S Theory: The Svedberg equation

10 S = D = diffusion coefficient, N = Avogadros number or Therefore can directly determine M r in solution by measuring physical properties of the particle (s and v) under known experimental conditions (D, T and ), c.f. PAGE, chromatography – comparative & non-native (Because M r = Nm 0 )

11 Equipment E.g. Beckman XL-A (or XL-I) analytical ultracentrifuge Samples loaded into special centrifuge cells with transparent windows for optical measurements. The distribution of solute molecules during the experiment is monitored by an optical system. The cells are scanned across their entire radius and data automatically collected for subsequent analysis

12 1A. Equilibrium sedimentation Moderate centrifuge speed After sufficient time, an equilibrium is reached between sedimentation & diffusion, resulting in a montonic solute distribution across the cell Cell bottomMeniscus Non-linear curve fitting can rigorously determine: – the solution molecular weight –association state – equilibrium constant for complex formation

13 Equilibrium sedimentation – monomer-dimer equilibrium Data for transferrin at three loading concentrations. All three datasets were fit simultaneously to a monomer-dimer equilibrium model. The fit returned a K d of about 100 mM for the dimerization. The relatively small and randomly distributed residuals indicate that the model provided a good fit to the data.

14 Experimental considerations Wavelength for detection (280nm – 230nm) Choice of buffer - typically PBS, pH 7.0, 200 mM NaCl –Tris buffer can be used at 280 nm. (interferes at 230 nm) – If a reducing agent is needed, -mercaptoethanol is better than dithiothreitol as it doesnt absorb at 280 nm. Protein concentration –Ensure detection at wavelength chosen –Sufficient range to detect association Temperature Equilibrium time – typically hours –depends on length of cell, viscoity etc. Rotor Speed

15 Rotor speed selection chart

16 Data collection Sample preparation: Dialyze against buffer, scan from 350 to 200 nm to test for contaminants & test for aggregation by micro-centrifugation. Sample loading: Reference cell side by side with sample, with slightly more volume (105ul c.f. 100ul). If using multiple chambers, place most concentrated sample closest to the center of rotation. Calibration: radial & wavelength calibration on first use or rotor change Experiment: set vacuum, temperature & speed and read every 2-3 hours, two identical readings = equilibrium, repeat at further speeds. Stability test: check equilibrium again after an additional hour spin Baseline: Repeat reading at very high speeds when all solute at base. 12 mm3 mm

17 Data analysis Editing the raw data Baseline correction Test mass recovery Data modeling

18 Editing raw data Remove the meniscus Remove the bottom of the cell Remove the bumps and spikes

19 Baseline correction Deplete all macromolecular components using v. high speeds Record baseline absorbance Subtract this from observed absorbances Test mass recovery Initial absorbance x volume of the sample = total mass Integration of experimental plot of absorbance vs squared radial position is used to monitor recovery of total mass Large loss of mass after an increase in the speed suggests occurrence of aggregation/precipitation An increase in recovery at higher speed is suggestive of breakdown of the molecules

20 Data modeling A plot of ln(c) vs r 2 should be a straight line with a slope proportional to molecular weight Single ideal homogeneous species M p (1- ) = d ln(c) 2RT d r 2 2

21 Curvature in log plots Indicates heterogeneity of the system Arises from self-association of protein Slope at any radial position is proportional to the weight average molecular weight Residuals Start of with a single component model and execute a fit Ideally you should have a residuals like in panel (a). If residuals systematically vary, try another model!

22 Lymphotactin little or no curvature 10 ºC, 200 mM NaCl 40 ºC, 100 mM NaCl 26K 19K 31K 40K obvious curvature – mass also lost after spin

23 Direct fitting: Self association at 10 ºC & 200 mM NaCl

24 10 ºC, 200 mM NaCl 40 ºC, 100 mM NaCl Effect of salt & temperature on aggregation

25 Affinity/avidity and function in costimulation Bivalency: stabilizes complexes ~100-fold But are B7-1 and B7-2 really different (proposed from crystals) ?

26 Protein concentration (mg/ml) M w,app (Da/10 4 ) sB7-1 Importance of valency: Dimerization of sB7-1

27 Importance of valency: sB7-2 & LICOS are monomers sB7-2 sLICOS Concentration (mg/ml) M w (kDa)

28 1B. Velocity sedimentation High centrifuge speed Forms a sharp boundary between solute depleted region (at top) and a region of uniform solute conc n (at bottom) The concentration gradient (dc/dr) defines the boundary position Non-linear curve fitting can rigorously determine: –number of mass species –molecular weight –shape information for a molecule of known mass

29 Velocity sedimentation - data analysis sedimentation coefficient (s) is the rate at which the sedimentation boundary moves –depends on the molecular weight & shape –globular (more spherical) protein has the largest sedimentation coefficient for a given molecular weight –unfolded or elongated proteins experience more friction -- smaller sedimentation coefficients diffusion coefficient is related to minimum width of the sedimentation boundary, multiple species broaden the boundary beyond effects of diffusion alone

30 Velocity sedimentation - data analysis g(s*) distribution

31 This antibody gives only one distinct peak, centered at s ~ 6.5 S, which corresponds to the native antibody 'monomer. This is low for a 150 kDa species due to its highly asymmetric 'Y' shape. However, a more detailed analysis quickly reveals that this sample is not homogeneous. The red curve is a fit of these data as a single species. It does not match the data in the region from 8-12 S, indicating the presence of some multimer. From the width of the main peak we can calculate the apparent diffusion coefficient (D) of the monomer. From the ratio of s to D we can calculate a mass of 151 kDa for this species, which matches the known value well within 3-5% error expected for masses determined in this fashion. Velocity sedimentation - data analysis

32 The example of SLAM (CD150) Claimed to self-associate with nM K d raising serious problems for all known models of cell surface protein interactions. Equilibrium data couldnt be fitted – concentrations too high! Velocity data confirmed shape of complex and approximate strength of association

33 2. SPR / BIAcore (Surface Plasmon Resonance)

34 What is Surface Plasmon Resonance for? The accurate measurement of the properties of inter- molecular interactions without a wash step. (Contrast with interaction screens and crude measurements of bond strength e.g. AUC or washed systems like ELISA) Why use Surface Plasmon Resonance? A full understanding of the function of proteins requires accurate knowledge of the nature of their interactions.

35 3D K d ( M) hCD2 TCR CD28 CTLA-4KIR CD8 SLAM CD4rCD2 The range of affinities seen for transient interactions at the cell surface Ab:Ag Inactive LFA-1fully active LFA-1 fully active Mac-1 Selectins

36 BIAcore Why use Surface Plasmon Resonance? A full understanding of the function of proteins requires accurate knowledge of the nature of their interactions. Example: Costimulation vs. Inhibition (again!) B7.1 and B7.2 both bind to CD28 and CTLA-4. BUT B7.2 & CD28 are constitutively expressed, others on activation B7.1 is dimeric, B7.2 is not CD28, although dimeric, is monovalent CTLA-4 binds its ligands much more strongly than CD28 B7.1 binds its ligands more strongly than B7.2 RESULT: The inhibitory B7.1:CTLA-4 complex is ~1000 times more stable than the costimulatory B7.2:CD28 complex.

37 Principle of Surface Plasmon Resonance Angle of dip affected by: 1) Wavelength of light 2) Temperature 3) Refractive index n 2 Dip in light intensity

38 Surface Plasmon Resonance in the BIAcore

39 Basic Idea… NB 4 channels (flow cells) per chip 2 steps: Immobilisation: Stick something (or up to 3 things) to the chip (NB also stick a control down) Inject analyte: Inject something else and see if it binds, how much binds and how fast it binds

40 Immobilisation 2 Main options: Direct: Covalently bind your molecule to the chip Indirect: First immobilise something that binds your molecule with high affinity e.g. streptavidin / antibodies Direct:Indirect:

41 Immobilisation: Carboxymethyl binding CM5 Sensor Chip N.B. Carboxymethyl groups are on a dextran matrix: This is negatively charged => Need to do a preconcentration test to determine optimum pH for binding (molecule needs to be +ve)

42 Immobilisation: Other sensor chips SA NTA HPA

43 Sensorgrams (raw data) pH: Pre-concentration: An antibody was diluted in buffers of different pH and injected over an non-activated chip. Maximum electrostatic attraction occurs at pH 5 15,400 RU ABCD Immobilisation: A.Inject 70 l 1:1 EDC:NHS B.Inject 7 l mAb in pH5 buffer (in this g/ml) C.Inject 70 l Ethanolamine D.Inject 30 l 10mM Glycine pH2.5

44 Sensorgrams – ligand binding

45 Specific Binding Each chip has four flow-cells Immobilise different molecules in each flow-cell Must have a control flowcell Specific binding is the response in flow-cell of interest minus response in the control flowcell Response in control / empty flowcell due to viscosity of protein solution injected – therefore control response DOES increase with concentration (this is NOT binding!!) Specific response in red flowcell Measured response Is it specific?

46 Equilibrium Binding Analysis N.B. Measurement of affinities etc. should usually be done at physiological temperature (i.e. 37°C), although this is more difficult. Sometimes 25°C data can be used to compare fold differences in binding or to test for any binding at all (i.e. specificity studies).

47 Equilibrium Binding Analysis - continued Binding curve can be fitted with a Langmuir binding isotherm (assuming a 1:1 binding with a single affinity) Scatchard plot: rearrangement of binding isotherm to give a linear plot. Not so good for calculating Kd, as gives undue weight to least reliable points (low concentration) Plot Bound/Free against Bound Gradient = 1/K d

48 Kinetics Harder Case: 2B4 binding CD48

49 Potential pitfalls Protein Problems:Aggregates (common) Concentration errors Artefacts of construct Importance of controls:Bulk refractive index issues Control analyte Different levels of immobilisation Use both orientations (if pos.) Mass Transport:Rate of binding limited by rate of injection: k on will be underestimated Rebinding:Analyte rebinds before leaving chip k off will be underestimated Last two can be spotted if measured k on and k off vary with immobilisation level (hence importance of controls)

50 Less common applications 1. Temperature dependence of binding vant Hoff analysis: Gradient Intercept

51 Less common applications 1. Temperature dependence of binding Non-linear vant Hoff analysis:

52 Less common applications 2. Combination with mutagenesis Q30RQ40KR87A Binding of CD2 by CD48 mutants at 25°C (WT K d = 40 M) Reduce / abolish binding Do not affect binding Not yet tested

53 Less common applications 3. Estimation of valency

54 Less common applications 3. Screening Newer BIAcore machines are capable of high throughput injection. With target immobilised, many potential partners / drugs can be tested for binding. 4. Identification of unknown ligands Mixtures e.g. cell lysates, tcs, food samples etc. can be injected over a target and bound molecules can then be eluted into tandem mass spectroscopy for identification.

55 One last warning: take care CD48 binding to immobilised CD2 (van der Merwe et al.) What a lot of people would have used (straight out of the freezer) Correct result

56 3. ITC (Isothermal Titration Calorimetry)

57 What is Isothermal Titration Calorimetry for? The direct measurement of heat released from a reaction (e.g. a binding event) allowing the calculation of thermodynamic parameters (enthalpy & entropy) Why use Isothermal Titration Calorimetry ? The thermodynamics of an interaction can give clues to the mechanisms involved. ITC also has the advantage of testing affinity in solution using unlabelled protein (but lots of it!!).

58 Isothermal Titration Microcalorimetry: Using the heat of complex formation to report on a binding interaction. The Basic Experiment: 1.Fill the upper syringe with ligand at high concentrations. 2.Fill the larger lower reservoir with protein at a lower concentration. 3.Titrate small aliquots of ligand into protein. 4.After each addition, the instrument returns the reservoir temperature to the temperature of the control cell and measures the heat required to cause this change. 5.Typically, subtract appropriate blank titrations (ligand into buffer & buffer into protein) to control for heats of dilution.

59 Data Analysis Get a plot of heat ( J or Cal) / s following each injection, integrate each peak for total heat released and plot against concentration of protein injected – binding isotherm. c = conc n / K d

60 Data Analysis – e.g. of B7-1 & CTLA-4 Curve fitting gives values for H (enthalpy) and G (Gibbs free energy, related to affinity) – from these one can also calculate S (entropy) kcal/mole of injectant molar ratio H = G = -8.9 T S = -2.7 kcal/mol -1

61 Calculating heat capacity H and S are not constant with temperature, hence direct measurement by ITC is better than deriving them from binding data across several temperatures (e.g. by SPR) Relationship of DH to temperature can be used to calculate heat capacity change on binding ( C p )

62 TCR recognition Willcox et al. (1999) Immunity 10:357

63 TCR recognition Willcox et al. (1999) Immunity 10:357

64 4. FRET / BRET (Forster / Bioluminescence resonance energy transfer)

65 What is FRET? Förster Resonance Energy Transfer –Fluorescence if both Donor and Acceptor are fluorescent Radiation-less energy transition between a Donor and Acceptor occurring finite probability based on proximity Energy is transferred through the resonant coupling of the dipole moments of the Donor & Acceptor

66 The beginning… Theodor Förster, 1940s –proposed a mathematical law for dependence of fluorescence decay of donor (D) on the concentration of acceptor (A), assuming a dipole- dipole interaction in solution (J.Phys.Chem. 1965, 69, ) Exponential decay for one Donor molecule

67 Impact of FRET After the initial fuss about the validity of Försters derivations, little else. Resurgence in past several years, especially in biology/biochemistry/biophysics thanks to: –FRET capable spectral GFP mutants –Engineering of peptides with novel fluorescent reagents –Ability to couple this phenomenon to different imaging techniques –AND the need to see finer details

68 Power of FRET Probe macromolecular interactions –Interaction assumed upon fluorescence decay Study kinetics of association/dissociation between macromolecules Estimation of distances (?) In vitro OR on live cells Single molecule studies

69 FRET

70 Effects of FRET Intensity of Donor decreases Sensitized fluorescence of Acceptor appears upon Donor excitation Lifetime of Donor excited stated decreases Polarization anisotropy increases Taking advantage of these points… –Curr. Opin. Immun. 2004, 16,

71 FRET Efficiency (E) Measuring FRET efficiency Measure acceptor emissions – can be measured in solution, no confoccal but background high due to overlap of frequencies. Measure increase in donor emissions after photobleaching acceptor - usually done with high power laser setting in confocal on cell surface molecules. FLIM (fluorescence lifetime imaging) – measure lifetime of donor excited state in presence & absence of acceptor.

72 Live cell FRET imaging Does CD4 specifically associate with the TCR/CD3 complex on triggering? Non-specific peptideSpecific peptide * marks contacts between cells. High FRET signal between CD4 and CD3 when correct antigen is present but not with non-specific antigen.

73 DeepBlueC hf 1 hf 2 Luciferase >10nm GFP 2 BRET: Bioluminescence Resonance Energy Transfer

74 BRET analysis of human B7-1 dimerization at the cell surface B7-1luc:B7-1YFP CTLA-4luc:CTLA-4YFP B7-1luc B7-1luc:CTLA-4YFP YFP luc B7-1YFPB7-1luc substrate h 2 (530 nm) h 1 (470 nm)

75 BRET Theory R0R0

76 BRET vs. FRET BRET analysis can be achieved at physiological levels of protein expression No problems with photobleaching or photoconversion seen in FRET techinques Both methods involve the same physical processes and so can be analysed in a similar manner BRET cannot be used in microscopy-based techniques such as FRAP or FLIP, or FACS-based analysis

77 Construction of Fusion Proteins The gene of interest is fused to both luciferase (donor) and GFP (acceptor) in two separate vectors A positive control is used to determine maximal BRET

78 B7-Family BRET Energy transfer can occur solely by random interactions

79 Quantitative (F)RET Analysis Random Interactions Dimer Interactions As more acceptors are present at the cell surface, BRET increases as more donors are paired with an acceptor, to the point where every donor is productively paired (saturation) Decreasing the numbers of donors does not affect average distance between donors and acceptor molecules

80 Quantitative BRET Analysis The relative ratio of the luciferase (donor) and GFP (acceptor) can be systemically varied at a constant total surface expression Energy transfer from oligomeric interactions are predicted to depend on this ratio in a hyperbolic manner Random interactions should be insensitive to these changes in ratio

81 BRET Method pGFP-N3 HEK-293T prLuc-N3 FuGene DeepBlueC

82 Comparison to T cell surface molecules with known oligomerisation status! Strong dimers Weak dimer Monomers

83 T Cell Interactions Goodness of fit to the two alternative models clearly demonstrates CD86 is monomeric

84 Ligand binding causes specific increase in dimerisation Specific ligand engagement can be observed when receptor is presented in solution or cell-surface bound

85 GPCRs Are Likely To Be Monomeric Two GPCRs exhibit no dependence on the acceptor:donor ratio Equivalent BRET is seen when GPCR is co-expressed with CD2, demonstrating random interaction

86 A BRET survey of the T cell surface The majority of T cell surface molecules are monomeric at the cell surface

87 5. Single molecule microscopy

88 Single Molecule Spectroscopy T cell activation occurs at the level of single molecules, i.e., TCR complex binding to pMHC There are almost no methods to probe cell surface to this level of detail in living cells, or to deal with complexes larger than the 10nm limit of FRET/BRET Need to understand the interactions that occur in order to build a realistic model of T cell activation New therapeutic agents may rely more and more on targetting our immune response by precisely altering these interactions

89 Single Molecule Spectroscopy 10 fl (10 -8 l) 0.25 m 2 of cell surface

90 Single Molecule Method Antibodies are fragmented to Fabs and labelled with bright fluorescent dyes, Alexa 488 and Alexa 647 T cell hybridomas are incubated with labelled Fabs to saturation Two lasers are focussed on the apical membrane of cell Movement of Fab-labelled molecules can be recorded in real time to single molecule precision

91 Single Molecule Confocal Microscopy 10 fl (10 -8 l) 0.25 m 2 of cell surface

92

93

94 Controls for Coincidence Detection No peaks are observed without Fab labels Fab binding was specific to Vb8 TCR Fluorescence was only detected at the proximal and apical membranes PFA-fixed cells displayed constant fluorescence Changes in laser power did not affect results TCR complex diffused at expected rate Blocking internalisation did not alter signal observed

95 Coincident events can be detected

96 Molecules in complex have higher coincidence

97 TCR stoichiometry at the cell surface After background subtraction… TCR:TCR coincidence is identical to CD2:CD2 CD3:TCR and CD3:CD3 are significantly higher

98 Total Internal Reflection Microscopy Extension of coincidence detection using TIR microscopy, allowing tracking of molecules in real time Models of T cell activation can be directly tested

99 Techniques you may need… Probing molecular interactions: SPR Probing homodimeric interactions: AUC Probing size & shape of complexes: AUC Probing detailed thermodynamics: ITC Probing oligomerisation state or associations in cell or on their surface: FRET / BRET Probing for longer range associations or assocaitions between things you cant make: single molecule microscopy


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