tools and workflows to simplify method development for targeted … · 2016-11-23 · ©2016 waters...
TRANSCRIPT
©2016 Waters Corporation 1
Tools and workflows to simplify method development for targeted
MRM methods
Nikunj Tanna Senior Scientist, Scientific Operations
Waters Corporation, Milford, MA
©2016 Waters Corporation 2
Journey of a large molecule Protein in
Plasma/Serum
LC-MS Data
Protein-level clean-up (optional)
Peptide-level clean-up
(optional)
Peptides Digestion
Purified Protein
Purified Peptides
Pick unique peptides & transitions
Optimize/Fine-tune MRM transitions
©2016 Waters Corporation 3
•BLASTp •SIM
•Protein Prospector •Skyline
•ProteinWorks •SPE
•MassLynx
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ANALYTE
Biomarker
ADC
Modified Peptides/ Proteins
Therapeutic mAb
• Whole Protein? • Sub-unit?
• Human/pre-clinical? • HC Isotype? • CDR sequence?
• Sequence change? • AA modifications?
• Total Ab? • Conjugated Ab? • Payload/Linker?
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©2016 Waters Corporation 6
Tools used
• NCBI BLASTp – Uniqueness against proteome of interest
• SIM – Protein sequence alignment tool
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Find the most similar sequences of LC/HC • Protein BLAST against the NR NCBI database
Align the therapeutic LC/HC sequence with the common mAb sequence • SIM (alignment tool from ExPASy Proteomics server)
Generate tryptic peptides while maintaining the sequence alignment of LC/HC •Visually identify possible unique peptides
Find how unique are the un-matched tryptic peptides from therapeutic mAb • Protein BLAST against the NCBI database
Workflow for finding unique tryptic peptides from therapeutic mAbs
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Trastuzumab BLAST Results
Trastuzumab LC sequence
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Trastuzumab LC vs IgG1kappa 1
mouse IgG1 LC peptide
proteotypic peptide deamidation site
OK
too long (36 AA)
monkey peptide mouse peptide human/mouse peptide human/lama peptide bacterial
peptide OK OK
human/lama peptide
OK OK monkey/human peptide OK
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Peptide Level Blast: IYPTNGYTR HC
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FINAL LIST of unique peptides
Light Chain – ASQDVNTAVAWYQQK (mouse
peptide)
– LLIYSASFLYSGVPSR – FSGSR
Heavy Chain – EVQLVESGGGLVQPGGSLR (monkey
peptide) – LSCAASGFNIK (mouse peptide, has
Cys) – DTYIHWVAR – QAPGK (human/Llama peptide) – GLEWVAR (human/mouse peptide) – IYPTNGYTR (bacterial peptide) – YADSVK (human/Llama peptide) – FTISADSK – NTAYLQMNSLR (has Met) – AEDTAVYYCSR (human/Llama
peptide, has Cys) – WGGDGFYAMDYWGQGTLVTVSSASTK
(has Met and is too long: 26 AA) – EEMTK (too short: 5 AA, missed
cleavage)
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Tools used
• Protein Prospector – Precursor/product mass prediction • MassLynx – PIC, Cone Voltage and Collision Energy optimization • Skyline
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Found and optimized Precursor
MRM Fragments
Protein Prospector prediction - FTISADTSK
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100 nM trastuzumab digest – Product scan
y5 y6 y7 y8 0
10000
20000
30000
40000
50000
60000
70000
80000
90000
100000
10 20 30 40 50 60
CV (V)
Peak area
485.2->721.4
485.2->608.3
485.2->822.5
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
15 20 25 30 35 40 45 50
CE (eV)
Peak area
485.2->721.4 485.2->608.3
485.2->822.5
CE formula = 0.034 m/z +3.1
Fixed CV = 35 V
MassLynx Optimization - FTISADTSK
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Skyline Workflow
Target/Analyte sequence and matrix information
In-silico digestion, peptide selection, transition prediction
Pick best 5 MRM transitions from DDA/MSe data
CE Optimization
Final MRM Method
PrecursorPrecursor scan. Pick best precursor
Generate base MRM method with calculated CE
Pick best 5 MRM transitions from MRM data
CE Optimization
Final MRM Method
HRMS Data No HRMS Data
Acquisition & Data Review 1
Acquisition & Data Review 2
Acquisition & Data Review 1
Acquisition & Data Review 2
Acquisition & Data Review 3
Acquisition & Data Review 4
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SkyLine
In silco generation of MRM transitions for peptides
Output scouting MRM method
MassLynx/Xevo TQ-XS
Acquisition of MRM traces for all proposed transitions
SkyLine
Comparison of overlaid MRM traces, to pick optimal transitions &
collision energies
Output final MRM method
MassLynx/Xevo TQ-XS
Sample analysis
SkyLine is developed by the MacCoss laboratory in the University of Washington
Skyline Workflow
©2016 Waters Corporation 18
MRM Method Creation
With Skyline, importing a protein sequence elicits an in silico digestion algorithm and MRM transition prediction
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MRM Method Creation
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Sample Analysis with MassLynx
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MRM Method Optimization Xevo TQ-XS
Transitions are optimized by collision energy and the results are visualized
The optimized method can be exported automatically
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• ProteinWorks Kits
• Protein level clean up (depletion, generic affinity, specific affinity)?
• Peptide level clean up (SPE)?
Sample Preparation Optimization
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ProteinWorks Kits in the Workflow Protein in
Plasma/Serum
LC-MS Data
Protein-level clean-up (optional)
Peptide-level clean-up
(optional)
Peptides Digestion
Purified Protein
Purified Peptides
eXpress Digest Kit •Denaturation •Reduction •Alkylation •Enzymatic Digestion •Quench
µElution SPE Kit Target Peptide
Clean-up
Pick unique peptides & transitions
Optimize/Fine-tune MRM transitions
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Kit Flexibility: A Diversity of Proteins
Cytochrome C Small Protein MW 12,327 35 µL sample 10 min digestion
Apolipoprotein a1 Biomarker MW 28,300 15 µL sample 15-30 min digestion
Infliximab Biotherapeutic MW 149,100 35 µL sample 2 hour digestion
ProteinWorks Achieves Fast Digestion Times
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Flexibility: Across different mAbs & multiple plasma volumes
Protein Peptide Linear fit (r2) with 1/x weighting Mean % Accuracy
Std. Curve Range Standard Curve Range 5.0-50.0 (µg/mL)
15 µL plasma
35 µL plasma
70 µL plasma
15 µL plasma
35 µL plasma
70 plas
Infliximab SINSATHYAESVK 0.999 0.999 0.997 100.00 99.99 99.
DILLTQSPAILSVSPGER 0.999 0.998 0.994 99.99 100.00 100
Trastuzumab
FTISADTSK 0.997 0.993 0.998 100.00 100.01 99.
DTYIHWVR 0.995 0.995 0.996 100.00 100.02 100
IYPTNGYTR 0.998 0.996 0.991 99.98 99.98 98.
Bevacizumab STAYLQMNSLR 0.999 0.998 0.995 100.00 100.01 99.
FTFSLDTSK 0.999 0.999 0.993 100.02 100.00 100
Adalimumab APYTFGQGTK 0.994 0.997 0.995 99.99 99.99 99.
NYLAWYQQKPGK 0.997 0.998 0.999 99.99 100.02 100
Linear, precise and accurate with multiple plasma volumes for multiple mAbs
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Calibration curves and QC’s
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0.5-500 µg/mL
Calibration curves and QC’s
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Thank you!
Acknowledgements
• Mary Lame • Paula Orens • Erin Chambers • Mark Wrona • Kerri Smith • Yun Alelyunas • Kelly Doering Useful insights from • Kevin Bateman (Merck) • Dan Spellman (Merck) Skyline Development Team (University of Washington)