Page 1
Advantages and Limitation of Hydrogen/Deuterium
Exchange with Mass Spec. Detection (H/DX-MS) in
Conducing Higher Order Structural Comparability
Studies on Protein Therapeutics
Steven Berkowitz, Principal Investigator
Biogen Idec
1st International Symposium on Higher Order Structure of
Protein Therapeutics
Sept. 26, 2011
Page 2
Uses of H/DX-MS in Developing Biopharmaceutical
Research
Find the Right Target Modifier
“Drug”
Make and modify via “Intelligent
Design”?
a. Structure-function
b. Epitope mapping
c. Mechanism of action
d. Stability
Process Development
Make a commercially viable drug
Process changes, consistency of
manufacturing, drug variants
a. Comparability/characterization
b. Formulation/stability
c. Impact of primary structure
changes (PTMs) on conformation
d. Silent (non-covalent) changes in
conformation
2
Page 3
Polypeptide chain
Amide hydrogen
(fraction of a sec
to days –108)
Side chain exchangeable
hydrogen (very rapid
exchange)
Non-exchangeable
hydrogen
Types of Hydrogens Present in Proteins (in Terms of Their Ability to Exchange)
3
Page 4
Engen & Smith (2001). Anal. Chem. 73, 256A-265A.
Wales & Engen (2006). Mass Spectrom. Rev. 25, 158-170.
(Amide) H/DX-MS: Continuous labeling Experiment
H’s at backbone amide
positions
D2O
(1:10 to 1:20)
H’s & D’s at backbone amide
positions
Structural Information is Inferred from Data Acquired on the
Amount and Rate of Hydrogen Exchange (HX) – By Simply Measuring the
Change in Mass Using MS
A) Take samples at
different times & stop
(slow) exchange by
lowering temp. & pH
B) Measure mass
change via MS
4
Because Conformation Effects HX
Page 5
0
1
2
3
4
5
6
7
0.01 0.1 1 10 100 1000
Time (min)
0
1
2
3
4
5
6
7
0.01 0.1 1 10 100 10000
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2
3
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5
6
7
0.01 0.1 1 10 100 1000
Deu
teri
um
co
nte
nt
0
1
2
3
4
5
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7
0.01 0.1 1 10 100 10000
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0.01 0.1 1 10 100 1000
Time (min)
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1
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3
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7
0.01 0.1 1 10 100 10000
1
2
3
4
5
6
7
0.01 0.1 1 10 100 1000
Deu
teri
um
co
nte
nt
Global Exchange Comparison of Two Intact
Biopharmaceutical Samples
Sample #1
Sample #2 (variant form of sample #1)
5
Page 6
Label, sample,
stop reaction by
lowering
temperature & pH
MS (mass change
in peptides) Digest (pepsin)
Local H/DX-MS (Bottom-Up Approach)
*Wales, T.E., et al.(2008). Anal Chem. 80, 6815-6820
Injector Pepsin-column RP-column MSLC
+
Sample Handling
Robotics
*Waters’ Synapt HDMS system
Nano-Acquity H/DX Synapt G1
(UPLC) Interface box (MS)
6
Page 7
Robot –sample
handling/injector
Nano – Acquity
UPLC
H/DX-MS
Box
Mass Spec
Synapt
Waters Commercial Prototype H/DX-MS System
7
Page 8
The Dynamic World of Proteins (Folding-Unfolding)
8
Page 9
Is This the Correct Picture of a Protein Structure?
Not Completely! 9
Page 10
H/DX-MS represents the first practical tool to assess this information on a routine basis
10
Very important role:
1) Folding
2) Stability
3) Interactions, e.g.
a. Catalysis
b. Signaling
c. Recognition
d. Assembly (Agg.)
Page 11
H/DX-MS Comparability Studies with Interferon -1a (IFN)
1 11 21 31 41 51
MSYNLLGFLQ RSSNFQCQKL LWQLNGRLEY CLKDRMNFDI PEEIKQLQQF QKEDAALTIY
61 71 81 91 101 111
EMLQNIFAIF RQDSSSTGWN ETIVENLLAN VYHQINHLKT VLEEKLEKED FTRGKLMSSL
121 131 141 151 161
HLKRYYGRIL HYLKAKEYSH CAWTIVRVEI LRNFYFINRL TGYLRN
3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0
Time (min)
Re
lati
ve
Ab
un
da
nc
e Undeuterated
10 seconds
1 minute
10 minutes
60 minutes
240 minutes
3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0
Time (min)
Re
lati
ve
Ab
un
da
nc
e Undeuterated
10 seconds
1 minute
10 minutes
60 minutes
240 minutes
996 998 1000 1002 1004 1006 1008
m/z
996 998 1000 1002 1004 1006 1008
m/z
• MW ~23kD
• 1 N-linked glycan
• 1 free -SH
• 167 residues
• Sequence coverage > 94% 11
Page 12
Data Analysis and Display Problem MSYNL
0
0.5
1
1.5
2
2.5
3
3.5
4
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R3 R2
R3
MSYNLLGF
0
1
2
3
4
5
6
7
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
SYNLL
0
0.5
1
1.5
2
2.5
3
3.5
4
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
NLLGF
0
0.5
1
1.5
2
2.5
3
3.5
4
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
LGFLQRSSN
0
1
2
3
4
5
6
7
8
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
GFL
0
0.5
1
1.5
2
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
FLQRSSN
0
1
2
3
4
5
6
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
FLQRSSNF
0
1
2
3
4
5
6
7
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
FQCQKL
0
1
2
3
4
5
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
QCQKL
0
0.5
1
1.5
2
2.5
3
3.5
4
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
LWQLNGRL
0
1
2
3
4
5
6
7
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
LWQLNGRLEY
0
1
2
3
4
5
6
7
8
9
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
LWQLN(deam)GRL
0
1
2
3
4
5
6
7
8
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
QLNGRL
0
1
2
3
4
5
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
LNGR
0
0.5
1
1.5
2
2.5
3
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
LNGR
0
0.5
1
1.5
2
2.5
3
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
KDRMNFDIPEE
0
1
2
3
4
5
6
7
8
9
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
KDRMNFDIPEEI
0
2
4
6
8
10
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
NFDIPEE
0
1
2
3
4
5
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
FDIPEE
0
0.5
1
1.5
2
2.5
3
3.5
4
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
DIPEE
0
0.5
1
1.5
2
2.5
3
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
DIPEEIKQLQQ
0
1
2
3
4
5
6
7
8
9
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
DIPEEIKQLQQFQKEDAAL
0
2
4
6
8
10
12
14
16
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
IKQLQQ
0
1
2
3
4
5
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
IKQLQQFQKE
0
1
2
3
4
5
6
7
8
9
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
IKQLQQF
0
1
2
3
4
5
6
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
IKQLQQFQKEDAAL
0
2
4
6
8
10
12
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
LQQFQKEDAAL
0
2
4
6
8
10
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
FQKEDAALT
0
1
2
3
4
5
6
7
8
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
FQKEDAAL
0
1
2
3
4
5
6
7
0 1 10 100 1000
Time (min)
Rela
tive D
eute
rium
Level
(Da)
R1 R2
R3
….
12
Page 13
Amino Acid Sequence
Each peptide is equally spaced along the x-axis in the order determined by each peptide’s
midpoint position in the Protein’s Linear Sequence relative to the N – terminus
1 2 4 5 6 7 8 9 3 10
Alternative Mode of Displaying & Comparing
H/DX-MS Data
…
…
Graphic x-axis
13
Graphic x-axis = quasi-sequence representation of protein
Page 14
Converting H/DX-MS Comparison Data to a Single Plot Using “Relative” Fractional Exchange, F(i,t) = (Mi,t change)/[#AA in Peptide “i” – (#Proline + 1)]
Peptide “i” exchange plot Ref & Exp, step 1 Top plot Ref Bottom plot (Exp) ( -1), step 2
Transpose data points to y-axis, step 3 Transfer peptide “i” data to correct x position, step 4
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0
Log (time, min)
Fra
cti
on
al
Ex
ch
an
ge
, F
(i,
t)
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0
Log (time, min)
Fra
cti
on
al
Ex
ch
an
ge
, F
(i,
t)
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0
Log (time, min)
Fra
cti
on
al
Ex
ch
an
ge
, F
(i,
t)
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
0 10 20 30 40 50 60 70
Peptide Positon, i
Fra
cti
on
al
Ex
ch
an
ge
, F
(i,
t)
14
Page 15
H/DX-MS: Comparison of 2 Different IFN Lots
Made Under Different Tissue Culture Growth Conditions
)(MS)(MS)MD( ti,expti,refti,
5
1t
ti,s )MD((i)D
Standard Culture Media
New Culture Media
T = 0.17 min
T = 1 min
T = 10 min
T = 60 min
T = 240 min
15 -1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0
Log (time, min)
Fra
cti
on
al
Ex
ch
an
ge
, F
(i,
t)
Mass Difference, Da, Data
Page 16
Types of Peptide HDX-MS Exchange Profiles (Mass
Difference vs Time) and Their Impact on Ds(i)
16 Morgan CR, Engen, JR, Current Protocol Protein (2009) Nov.; doi:10.1002/0471140864.ps1706s58
Page 17
H/DX-MS Mass Difference Display Format
17
)(MS)(MS)MD( ti,expti,refti,
5
1t
ti,s )MD((i)D
T = 0.17 min
T = 1 min
T = 10 min
T = 60 min
T = 240 min
Page 18
H/DX-MS Mass Difference, Da, Display Format
(continued)
18
Page 19
Uncertainty (SD) in H/DX-MS D(Mi,t) Data Points
19
Page 20
Uncertainty in (H/DX-MS) Mass Difference Measurements
20
Average Standard Deviation in D(Mi,t) 0.14 Da s
Uncertainty in D(Mi,t) at a given % confidence limit (%CL) = t%CL, df [s/(n)0.5]
Where:
t%CL, df = Student t factor for a give % confidence limit and number degrees of freedom (df = n-1)
s/(n)0.5 = Estimate of the standard error in the mean value for any D(Mi,t) based on “n”
measurements
5
1t
ti,s )MD((i)D
Uncertainty in Ds(i) (over all 5 time points) obtained from simple propagation of error analysis
98% confidence limit for D(Mi,t) determined from n = 3 measurements 0.5 Da
98% confidence limit for Ds(i) determined from n = 3 measurements 1.1 Da
Page 21
1. For peptide “i” one D(Mi,t) data point must exceed the 0.5 Da limit
and the Ds(i) value for the that peptide must also exceed 1.1 Da limit.
2. If one D(Mi,t) data point exceeds 0.5 Da limit, but the Ds(i) value for
that peptide does not exceed 1.1 Da limit, recalculate the value for
Ds(i) by summing the absolute (abs) differences
3. If Ds(i)abs exceeds the 1.1 Da limit, the two peptide “i” are not
non-comparable. However, if this new sum does not exceed the 1.1
Da limit, flag this peptide pair for manual review and assessment.
5
1t
ti,abss )]Mabs[D((i)D
Criteria for Assessing Difference in Conformation
21
Page 22
H/DX-MS Mass Difference Display
22
98% CL in D( Mi,t)
98% CL in Ds(i) Difference Indices
67
1i
s 1.1)(i)](abs[DDI(1)67
1i
5
1t
ti, )5.0)]M(abs[D(DI(2)
Quantitative “Difference Indices”
DI(1) = 0
DI(2) = 0
Page 23
DI(1) = 0
DI(2) = 0
H/DX-MS: Comparison of 2 Different IFN Lots Made Under Different
Tissue Culture Growth Conditions, Average of 4 Different Runs
IFN, standard growth conditions
IFN, experimental growth conditions
T = 0.17 min
T = 1 min
T = 10 min
T = 60 min
T = 240 min
23
Page 24
Separate Mass Difference Plots of Each of the Four H/DX-MS Runs
24
Page 25
Mass Difference Plot for the Same IFN Sample Run on
Different Days (1 Run of Each Sample)
25
Page 26
H/DX-MS: Comparison of IFN vs Alkylated (NEM) IFN (Average of 3 Exp.)
DI(1) = 92
DI(2) = 67
T = 0.17 min
T = 1 min
T = 10 min
T = 60 min
T = 240 min
26
Page 27
H/DX-MS: Comparison of IFN vs Oxidized (H2O2) IFN, (Average of 3 Exp.)
T = 0.17 min
T = 1 min
T = 10 min
T = 60 min
T = 240 min
DI(1) = 187
DI(2) = 143
27
Page 28
Alkylated IFN has very poor biological activity
while
Oxidized IFN is fully or nearly fully active!
However, both have very poor stability
(Aggregation)!
28
Page 29
-8
-6
-4
-2
0
2
4
6
8
0 5 10 15 20 25 30 35 40 45 50
Dif
fere
nc
e (
Da
)
FIX vs FIX+Ca2+
Gla EGF Catalytic
29
Page 30
FIX vs the FIX part of FIX-Fc
-8
-6
-4
-2
0
2
4
6
8
0 5 10 15 20 25 30 35 40 45 50
A
Dif
fere
nce
(D
a)
-8
-6
-4
-2
0
2
4
6
8
0 5 10 15 20 25 30 35 40 45 50
B
Dif
fere
nce
(D
a)
FIX with Ca2+ vs the FIX part of FIX-Fc with Ca2+
Peptide Number, i
30
DI(1) = 0
DI(2) = 0
DI(1) = 0
DI(2) = 0
Page 31
Heavy chain Light chain
Pepsin Mapping of an Intact Mab
> 95 % sequence coverage
1 11 21 31 41 51 61
EVQLVESGGG LAKPGGSLRL SCAASGFRFT FNNYYMDWVR QAPGQGLEWV SRISSSGDPT WYADSVKGRF
71 81 91 101 111 121 131
TISRENAKNT LFLQMNSLRA EDTAVYYCAS LTTGSDSWGQ GVLVTVSSAS TKGPSVFPLA PSSKSTSGGT
141 151 161 171 181 191 201
AALGCLVKDY FPEPVTVSWN SGALTSGVHT FPAVLQSSGL YSLSSVVTVP SSSLGTQTYI CNVNHKPSNT
211 221 231 241 251 261 271
KVDKKVEPKS CDKTHTCPPC PAPELLGGPS VFLFPPKPKD TLMISRTPEV TCVVVDVSHE DPEVKFNWYV
281 291 301 311 321 331 341
DGVEVHNAKT KPREEQYNST YRVVSVLTVL HQDWLNGKEY KCKVSNKALP APIEKTISKA KGQPREPQVY
351 361 371 381 391 401 411
TLPPSRDELT KNQVSLTCLV KGFYPSDIAV EWESNGQPEN NYKTTPPVLD SDGSFFLYSK LTVDKSRWQQ
421 431 441
GNVFSCSVMH EALHNHYTQK SLSLSPG
1 11 21 30 41 51
DIQMTQSPSS LSASVGDRVT ITCRASQDIR YYLNWYQQKP GKAPKLLIYV ASSLQSGVPS
61 71 81 91 101 111
RFSGSGSGTE FTLTVSSLQP EDFATYYCLQ VYSTPRTFGQ GTKVEIKRTV AAPSVFIFPP
121 131 141 151 161 171
SDEQLKSGTA SVVCLLNNFY PREAKVQWKV DNALQSGNSQ ESVTEQDSKD STYSLSSTLT
181 191 201 211
LSKADYEKHK VYACEVTHQG LSSPVTKSFN RGEC
31
Page 32
Important Attributes & Future Opportunities in Using
H/DX-MS in the Biopharmaceutical Industry
1. High spatial resolution – at present a few AAs, may reach 1AA with ETD!!
2. High sequence coverage
3. Can detect a very small change in a large molecule
4. Requires little material – sub-nanomoles/entire experiment! – variant analysis
5. Redundancy via overlapping sequences helps validate data
6. Reasonably good sample throughput – via robotic automation
7. Good reproducibility within a given day
8. Can provide routine kinetic (temporal) information about structural dynamics
9. Can conduct experiments under wide range of conditions
10. Can conduct experiments in the presence of other components
11. Ion mobility separation within MS – help deal with complex mixtures
12. Gas HX within MS (sub-millisecond dynamics) – assess side chain HX 32
Page 33
Challenges in Using H/DX-MS in the Biopharmaceutical
Industry
1. Further reduction in data analysis time is needed via computer automation! Days
to minutes!
2. Develop an effective & compact mode for presenting data – data display
3. Presently, the ability to detect small protein populations that have a structural
element change in its conformation is not that low (population needs to be ~20-
40% or greater to detect) – can we improve?
4. Improve long term reproducibility
5. Interference effects – sample matrix
6. Only one commercial instrument exists
7. New scientific capability & information – What do we do when we see low
level of difference(s)? What’s important, What isn’t?
33
Page 34
Speaker Would Like to Thank and Acknowledge the
Following People:
Dr. John Engen and members of his group (Northeastern Univ.)
Staff at Waters
Dr. Igor Kaltashov and members of his group (UMass, Amherst)
Special Thanks to Dr. Rohin Mhatre (Biogen Idec)
Damian Houde
34