Automated Disulfide Mapping - Waters · Oral presentation at ASMS 2011 – Denver - Wed p.m. WOF...
Transcript of Automated Disulfide Mapping - Waters · Oral presentation at ASMS 2011 – Denver - Wed p.m. WOF...
Automatic Disulfide Bond Mapping and Reporting
for Biotherapeutics by High Resolution LCMS
Oral presentation at ASMS 2011 – Denver - Wed p.m. WOF
©2011 Waters Corporation 1
St John Skilton, Hongwei Xie, Scott Berger and Weibin Chen
Why are Disulfide Bonds Important?y p
Organisations need to show that they have understood their process and/ or product mapping the disulfide bonds is process and/ or product - mapping the disulfide bonds is essential to demonstrate this knowledge to the regulator
Arrangement of bonds may affect the efficacy of a biotherapeutic product
May relate to stability of a biotherapeutic protein
P R O C E S SS T R U C T U R E
Primary Assembly
P R O C E S SS T R U C T U R E
Secondary
Tertiary
Folding
Packing
©2011 Waters Corporation 2
Quaternary
ac g
Interaction
Relevance to the Field of Protein Analytics – Regulatory Authoritiesy g y
“Our current ability to predict the potency of biologics would be
enhanced if we had improved ability to measure and quantify
the correct (major) three-dimensional structure, aberrant
three-dimensional structures (misfolding), and the distribution
of different three-dimensional structures”.
• [TESTIMONY BEFORE THE SUBCOMMITTEE ON TECHNOLOGY AND INNOVATION COMMITTEE ON SCIENCE AND TECHNOLOGY U.S.
]HOUSE OF REPRESENTATIVES. SEPTEMBER 24, 2009]
©2011 Waters Corporation 3
Steven Kozlowski, M.D.Director, Office of Biotechnology Products, Office of Pharmaceutical Science. CDER
Previous and Recent Work - ASMS 2010
Disulfide bonds by ‘differential’ MS (Yi and Hoang, Merck; ASMS 2010): reduced and non-reduced in multiple runs— ‘Software tools are highly in demand to predict theoretical
fragment ion masses to facilitate the disulfide containing peptide identification and characterization’
Manual interpretation precludes ‘routine’ application
©2011 Waters Corporation 4
Obtaining Disulfide Bond Informationg
‘Prerequisites’:— Use of chemical/ enzymatic knowledge – e.g. LysC Use of chemical/ enzymatic knowledge e.g. LysC
cleavage at Lysine useful to create large fragments of IgG molecules
— ‘Effective’ resolution: 20,000 FWHM on Xevo™ G2 Tof, f f l h lQTof or Synapt™ G2 facilitates this analysis
— Wide Mass Range: large and small (e.g. 15kDa peptide vs 400 Da) peptides are analysed identically on a G2 QTof/ SynaptQTof/ Synapt
Analysis tools:— Simultaneous MS/MS capability to confirm assignmentsSimultaneous MS/MS capability to confirm assignments
o UPLC/MSE is the leading tool on the market
— Logical workflow to effectively perform the automated assignment of species
©2011 Waters Corporation 5
o BiopharmaLynx to process the data automatically
Challenges in Computing Disulfide Bonded Peptides – Simple Casesp p
Type 1a: Intra-Peptide (‘Simplest’ Case): only one species to consider
d t ti t i i lXXCXXCXX
Disulfide Bond
and computation trivial
Type 1b: Inter-Peptide: Computationally more demanding, b t li it d b f
Disulfide Bonded Peptides
but a very limited number of possibilities
XXCXX XXCXXXX
[Segment removed by
©2011 Waters Corporation 6
Enzymatic cleavages, e.g. Trypsin, LysC, …]
Challenges in Computing Disulfide Bonded Peptides – Complex Casesp p
Type 2: One or more peptide has two Cys
— All options for S-S bonds have to be computed but possibilities are limited XXCXXCXX XXCXXXX
— Peptides have to be considered together and separately
Cases of different bond re arrangement
XXCXX
— Cases of different bond re-arrangement may become ‘Type 1’
XXCXXCXX XXCXX
©2011 Waters Corporation 7
XXCXX
Challenges in Computing Disulfide Bonded Peptides – Highly Complex CasesPeptides – Highly Complex Cases
Type 3: One or more peptide has XXCXXXXCXXCXXCXX
three or more Cys residues
These present a ‘Combinatorial’ Challenge as connection possibilities
XXCXX
XXCXXCXXCXX XXCXX
XXCXXCXXCXX
g pproliferate:
— Multiple possible arrangements for bondsbonds
o Internal to peptide
o External to other peptides
Multiple missing bonds possible
XXCXCXX
XXCXXCXXCXX
XXCXXCXXCXX
— Multiple missing bonds possible
— Potentially more than a hundred possibilities from just 4 bonds (E.g. G pta et al Anal Chem 2010 82
XXCXXCXXCXX
XXCXX
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Gupta et al, Anal. Chem. 2010, 82, 8313–8319) (etc.)
Classes of Compound that Exhibit Multiply-bonded Peptidesp y p
BSA, monoclonal Antibodies all have types 1 and 2
— BSA illustrates the variety of bondsS ust ates t e a ety o bo ds
— IgGs illustrate the applicability to large, therapeutic proteins
XXCXXCXX XXCXX
XXCXX XXCXXXXCXXCXX
XXCXXCXX XXCXX
XXCXX
IgGs also have Type 3 intramolecular bonds between Heavy and Light Chains
BSA has a type 3 variant where one bond is ‘nested’ within another BSA has a type 3 variant where one bond is nested within another peptide:
T28-T37 CASIQK
©2011 Waters Corporation 9
ECCHGDLLECADDTryptic Peptide 28 is linked to Tryptic Peptide 37
Expected Bonds in IgG1 after LysC digestion
Trastuzumab (IgG1) S-S bonds are predominantly type 1
g
14 Expected Bonds in ‘Canonical’ scheme
Symmetry of molecule means that some LysC peptides are identical irrespective of chain so that there are 8 unique peptidesidentical irrespective of chain so that there are 8 unique peptides
Light Chain (1)1:K01 1:K04 1:K07 1:K13 1:K14
Disulfide Bond
Light Chain (1)
Heavy Chain (2)
2:K052:K01 2:K212:K16 2:K312:K272:K132:K082:K07 2:K14
1:K01 1:K04 1:K07 1:K13 1:K14
Peptides from LysC digestion (K)
4:K084:K07 4:K214:K16 4:K314:K274:K13
Heavy Chain (4)
4:K054:K01 4:K14
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3:K01 3:K04 3:K07 3:K13 3:K14 Light Chain (3)
Heavy Chain (4)
BSA – Schematic Representation of Theoretical S-S Tryptic Peptidesyp p
TCVADESHAGCEKT07
T08-T10-T12 NECFLSHKSLHTLFGDELCK ETYGDMADCCEK
T14-T21-T22 LKPDPNTLCDEFK YNGVFQECCQAEDK GACLLPK
CASIQK ECCHGDLLECADDT28-T37
T39-T41-T42 YICDNQDTISSK SHCIAEVEK ECCDKPLLEK
T44-T50-T51 DVCK EYEATLEECCAK DDPHACYSTVFDK
T54-T61-T62 QNCDQFEK MPCTEDYLSLILNRVGTRCCTKPESER
©2011 Waters Corporation 11
RPCFSALTPDETYVPKLCVLHEK CCTESLVNRT63-T66-T67
T69-T76-T77 LFTFHADICTLPDTEK CCAADDK EACFAVEGPK
Complexity of Raw Data: Timep y
Typical LC/MS peptide map data with alkylated, non-reduced protein digestreduced protein digest— Method needs to cope with many disulfide peptides in BSA of
different types
W d i ll d i ll id — We need to automatically process and assign all peptide types … All in a single run
©2011 Waters Corporation 12
Complexity of Raw Data: Spectral Interpretation
Raw Spectral Data is very complexM l i t t ti i hibiti l ti i
p
— Manual interpretation is prohibitively time-consuming
Each Peak would have to be deconvoluted Separately in a manual analysis
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Complexity of Raw Data: Sequence Confirmation by MS/MS
Each Peptide Generated with Sulfide Bonds creates multiple termini this creates a high computational searching load
y
termini - this creates a high computational searching load for MS/MS data [manually prohibitive]
NECFLSHKSLHTLFGDELCK ETYGDMADCCEKXX XX
Each Peptide Created leads to multiple new peptide termini to
T08-T10-T12 NECFLSHKSLHTLFGDELCK ETYGDMADCCEKXX XX
compute and match from Complex, Overlapping MS/MS spectra
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A d P i d A i b Bi h L ™Automated Processing and Annotation by BiopharmaLynx™
©2011 Waters Corporation 15
Automated Workflow to Assign Peptides in a Peptide Map – for ALL typesp p yp
Same rules need to be applied whether Peptides are S-S bonded or not
In-silico Digestion of Peptides are S-S bonded or not
Workflow must be transparent and not introduce bias— No database is used: calculations
Sequence
— No database is used: calculations performed on the fly to create a list of potential matches
— Matches are made to the ‘list’ using
Assign Peptides by Accurate Mass
accurate mass and then multiple other criteria
Validate Assignments Allow Alternatives within Criteria (mass using MS/MS
(error, modifications, RT window,
fragment ions)
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Tabulate and Display
C4/ C18 BEH Peptide columns - UPLC MSE
– Xevo or Synapt Qtof – BiopharmaLynx
Basics of BiopharmaLynx™ Displayp y p y
Entire Data Set Tabulated Here
Each Processed Peak labelled Each Processed Peak labelled with Peptide Number‘Control’ Sample Display
for Chromatogram (processed)
Hoverbox for
‘Analyte’ Sample Display
‘=‘ Symbol represents
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Peptide Information S-S bond
BSA Results - Automated assignment of Expected Peptides from Tryptic Digest p p yp g
With BiopharmaLynx™ All 9 peptides with expected S-S bonds are assigned automaticallyare assigned automatically
‘hoverboxes’ show sequences at processed peak apex
T08 T10 T12
T14-T21-T22T39-T41-T42
T07
T08-T10-T12
T63-T66-T67
T28-T37 T44-T50-T51 T54-T61-T62
©2011 Waters Corporation 18T69-T76-T77
Results of Automated Search for low intensity S-S peptide in BSAy p p
T69-T76-T77 is at lowest intensity of expected S-S peptides
— 0.47% of highest intensity peptide (T14-T21-T22)
o NB: Ionisation efficiencies vary for different S-S peptides
Unique Match (2ppm from theoretical)— Unique Match (2ppm from theoretical)
T69-T76-T77
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‘Non-canonical’ proposals – example from BSA
Some additional proposals are plausible and supported
T62
are plausible and supported by fragment ion information— Can now be investigated
further
T67
— Manual investigation may not have identified this
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Automated Mapping of an IgG1
(Alkylated to protect exposed Cysteine residues and inhibit alternative S-S bond formation)alternative S S bond formation)
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IgG1 - Seven of Eight Expected Peptides assigned automaticallyg y
Only one S-S peptide not assigned – not detected in the raw data [hydrophilic peptide elutes in void volume]data [hydrophilic peptide elutes in void volume]
2:K14
4:K142:K212:K16
1:K07 1:K13
2:K082:K07
1:K01 1:K04
2:K052:K01
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2:K312:K27
Automated Validation of Assignments –Expected S-S peptide in IgG1p p p g
BiopharmaLynx provides additional evidence with Fragment Ion data even with large peptides (8kDa)data even with large peptides (8kDa)
2:K082:K07
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Automated Mapping of Scrambled S-S Bonds
(Non-alkylated IgG1 – alternative S-S bonds may form)(Non alkylated IgG1 alternative S S bonds may form)
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Method editor allows a choice of ‘Strict’ or ‘Relaxed’ Search Criteria
Strict Criteria generally usable for IgGs where bonds should be well characterizedwell-characterizedRelaxed Criteria for unknown conditions where criteria allow multiple possibilities for further investigationTi k b t ll f bl d di lfidTick-box to allow for scrambled disulfides— IgG1 Sample not alkylated prior to digestion to allow disulfide bond
scrambling
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Scrambled Disulfide Bond Sample –IgG1g
7 New Species identified with high confidence (more than 5 fragment ions in MS/MS spectrum)fragment ions in MS/MS spectrum)
1 Canonical S-S bonded peptide no longer detected (2:K01-2:K05)
1:K01 1:K04 1:K07 1:K13 1:K14 Light Chain (1)
Heavy Chain (2)
2:K052:K01 2:K14 2:K212:K16 2:K312:K272:K132:K082:K07
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4:K14
Heavy Chain (4)
Scrambled Bonds in Non-alkylated IgG1 – New Species in processed datap p
dd l bl d b d
2:K162:K13
Additional Scrambled S-S bond Assignments Made Automatically in non-alkylated Sample
2:K132:K08
2 K011 K13
1:K07 2:K16
1:K07 2:K07
2:K07 2:K16
2:K011:K13
2:K161:K13
1:K07 2:K16
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Appearance of New Assignment in Non-alkylated Sample within Heavy Chainy p y
E d B d
2:K212:K16
1 K07 1:K13
Expected Bond Expected Bond
1:K07 1:K13
2:K162:K13
Additional Scrambled S-S bond in non-alkylated sample
©2011 Waters Corporation 28
Automated Validation of Assignments –Scrambled S-S peptide in IgG1p p g
BiopharmaLynx provides additional evidence also for scrambled disulfides with fragment ion information obtained disulfides with fragment ion information obtained simultaneously
©2011 Waters Corporation 29
Conclusions
A bioinformatics approach for confirmation of known disulfide bonds, and search for scrambled disulfides has been developed.
The approach automatically assigned disulfides of several The approach automatically assigned disulfides of several structural classes within BSA and IgG digests.
Such analyses can be executed from a single analytical run.Such analyses can be executed from a single analytical run.
We believe this tool will allow biopharmaceutical organisations and researchers to more quickly satisfy increasing regulatory requirements for higher order structural analysis of new biotherapeutics and biosimilars.
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Acknowledgmentsg
Richard DennyWeibin Chen
Scott BergerHongwei Xie
y
g
Barry Dyson
Keith Richardson
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Keith Richardson