Mark Tornetta
Antibodies to Challenging Receptor Targets through NGS and Cell-Based Antibody Phage Panning
Points to convey regarding receptor targets
▪ Antigen diversity- cells and engineered ECDs
▪ Be creative with your selections
▪ NGS can ‘screen’ virtually the whole selected population
▪ NGS along with appropriate analysis tools does help withcandidate selection
2
NGS and phage display: proof of concept
▪ Library QC
– Deep sequencing using 454 of natural and synthetic antibody libraries
▪ Matochko, W.L. et al. (2009), Glanville, J. et al. (2009)
▪ Phage panning
– Clone enrichment vs. ELISA screening
▪ Matochko, W.L. et al. (2012), Ravn, U. (2010), Ravn, U. (2013)
3
Where NGS may help most
4
Cell-based panning
Homologous proteins
Rare/novel epitopesConformation
specificity
5
Janssen Antibody Phage Display Libraries
Library V2.0 combines VH diversity with the germline VL scaffolds
Library V3.0 includes diversity within the VL scaffolds.
Diversified KappaGermline Kappa
169 323 551
Heavy Chain Libraries
X
V2.0 V3.0
Vκ3-20 Vκ4-1 Vκ3-11 Vκ1-39 Vκ3-20 Vκ4-1 Vκ3-11 Vκ1-39
6
NGS analysis process
• Upload fastq or fasta files
• ORF determination based on germ line V-region ‘templates’
• Perform sequence analysis (mostly based on HCDR3)• Number of sequences captured
• Germline distribution
• Candidate frequency
• Developability risks
• HCDR3 lineup
• Dynamic subtraction to determine candidate uniqueness between panning groups
In-house Sequence analysis software
7
Isolating antibodies to a GPCR
Target- GPCRc: Class C, 27aa N-terminal peptide, Extracellular loops – 9, 23 & 14 amino acids long
Peptide based panning followed by ELISA screen
▪ N-terminal peptide panning
– 12 hits, 9 cell binders – all with some non-specific cell binding
Cell-based panning followed by NGS
▪ Negative and competitive type selections
– Generated many target specific candidates
8
Challenges of cell-based phage panning
Dynamic, charged, complex mixture
GPCR
Small exposed peptide loops in membrane milieu
How does Fab-phage get access?
How are non-specific Fabs removed?
Standard cell panning: negative selection
10
Phage library
Parental Cells
Target cells
Parental Cells
Target cells
NGS NGSTarget selection
Parental cell subtraction
Amplify bound phage
Amplify unbound phage
2X
Final roundCollect both
FACS-based panning: competitive selection
11
Phage library
Target CellsDye-
labelled
Parental Cells
Target cells
Target cell phage
Target CellsDye-
labelled
Parental Cells
Sort target cells
Parental Cells
Target cells
NGS
NGS
Final Round
Collect both
Sort target cells
Panning against parental cells
Parental Cells
3X
NGS on round 3 phage
To create a list of Fab sequences generated to the “background” of parental cells
VH NGS Sequence processing
13
GPCRc cells HEK cells
Panning technique Standard FACS Standard
Total DNA sequences 553,858 497,205 2,104,535
HCDR3 sequences 13640 23351 72006
Dynamic subtraction: HCDR3 unique
600 848
Sequence comparison shows large overlap
between target and parental HEK cells
Comparison to parental cell panning
FACS(Total target cell sequences: 1,079,235)
V2.1 STD(Total target cell sequences: 252,158)
V3.0 STD (Total target cell sequences: 359,659)
0
200
400
600
v2.1 Std
vs
v2.1HEK
v3.0 Std
vs
v3.0HEK
FACS vs
v2.1HEK
FACS vs
v3.0HEK
Nu
mb
er o
f clo
nes
that
have >
50
co
pie
s
Dynamic Subtraction
Unique to
target cell
Fab-phage sequences from parental cell panning
Distribution of H-CDR3 lengths
15
0
20000
40000
60000
80000
100000
120000
140000
7 8 9 10 11 12 13 14
HCDR3 Length
Nu
mb
er o
f seq
uen
ces
STDv2.1
STDv3.0
FACSv2.1/3.00
20406080
100120140160180
7 8 9 10 11 12 13 14
HCDR3 Length
Nu
mb
er o
f seq
uen
ces
STDv2.1
STDv3.0
FACSv2.1/3.0
Total V-regions Unique CDR3 sequences
• Broad diversity of antibody sequences
• Shift in HCDR3 diversity from total to unique V-regions following dynamic subtraction
0
500000
VLk3-20
0
500000
VLk4-1
0
500000
VLk3-11
0
100000
200000
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
GC…
VLk1-39
63/87 bound specifically to
target cells
What do you get out of ELISA screening?
▪ Cell binding assay with Fabs from E. coli is sub-optimal
▪ Screen each panning experiment for Fab expression and sequence positive clones
– 56 Fab sequences identified from 300 picked colonies
– 31/56 were found in the NGS analysis
– 7 of the confirmed NGS binders identified in this ELISA screening format
17
mAb functionality: T-cell re-direction GPCRc/CD3 DuoBody Abs
18
T c e ll m e d ia te d c y to to x ic ity a s s a yM M IR (4 8 h rs )
D o n o r ID : M 6 4 5 7
0 .00 0 1
0 .01 1
1 0 0
1 0 0 0 0
0
2 0
4 0
6 0
8 0
1 0 0
G C D B 1 8
G C D B 1 9
G C D B 2 0
G C D B 2 1
G C D B 2 2
G C D B 2 3
G C D B 2 4
G C D B 2 5
C o n c e n tra t io n (n M )
% C
yto
tox
icit
y
T c e ll m e d ia te d c y to to x ic ity a s s a yM M IR (4 8 h rs )
D o n o r ID : M 6 4 5 7
0 .00 0 1
0 .01 1
1 0 0
1 0 0 0 0
0
2 0
4 0
6 0
8 0
1 0 0
G C D B 2 6
G C D B 2 7
G C D B 2 8
G C D B 2 9
G C D B 3 0
G C D B 3 1
G C D B 3 2
G C D B 3 3
C o n c e n tra t io n (n M )
% C
yto
tox
icit
y
T c e ll m e d ia te d c y to to x ic ity a s s a yM M IR (4 8 h rs )
D o n o r ID : M 6 4 5 7
0 .00 0 1
0 .01 1
1 0 0
1 0 0 0 0
0
2 0
4 0
6 0
8 0
1 0 0
G C D B 3 4
G C D B 3 5
G C D B 3 6
G C D B 3 7
G C D B 3 8
G C D B 3 9
G C D B 4 0
G C D B 4 1
C o n c e n tra t io n (n M )
% C
yto
tox
icit
y
T c e ll m e d ia te d c y to to x ic ity a s s a yM M IR (4 8 h rs )
D o n o r ID : M 6 4 5 7
0 .00 0 1
0 .01 1
1 0 0
1 0 0 0 0
0
2 0
4 0
6 0
8 0
1 0 0
G C D B 4 3
G C D B 4 4
G C D B 4 5
C N T O 7 0 0 8
C o n c e n tra t io n (n M )
% C
yto
tox
icit
y
Null arm (DB18-31)
CD3 arm (DB32-45)
CD3 arm
Cell panning advantages for challenging receptor targets
▪ Affinity maturation using cells
– Antibody to a single pass receptor bound to recombinant protein in uMrange, with cell-binding at ~2nM
▪ Cell panning with NGS isolated highly enriched set of mutations
▪ Cell binding improved into pM range
▪ Glycosylated TNFR-family single domain receptor
– Recombinant protein highly glycosylated
▪ Standard phage panning yielded no binders
– Cell-based panning: 50% of selected VH genes show positive cell binding
19
Cell panning summary
▪ Cell panning methods were effective in isolating GPCR antibodies
▪ In-house NGS analysis application effectively identified candidates to test in downstream assays
▪ NGS significantly expanded the number of viable candidates
▪ These methods have demonstrated our ability to generate multiple antibodies to a GPCR
▪ This provides confidence to include cell panning as another means for generating antibodies to membrane targets
20
Where NGS may help most
21
Cell-based panning
Homologous proteins
Rare/novel epitopesConformation
specificity
Protein selections utilizing NGS
22
Receptor with and without bound ligand
Domain specific panning
D1 D3D2
D3D2
D1 D2
R1ECD: Function-specific antibodies
23
NGS groups
3X
3X
Dynamic substraction
293 385101
779*
* 126 lost in analysis, probably in the “both”
category
Captured 3 possible functional groups
R1ECD Results▪ Selection method and NGS improved our efficiency regarding candidate selection,
functional testing, and manage the affinity maturation process.
▪ 26/66 clones bound specifically to target cells
▪ NGS allowed us to obtain candidates with agonist, antagonist, and enhancer activities
24
Ab 1 Ab 2
Ab 3
R2ECD: Domain specific antibodies
▪ Protein panning– 44 Vh sequence unique and domain specific hits– Vast majority confirmed NGS analysis with binding specificity assays– Vast majority bound specifically to target cells
25
D1 D3D2
D3D2
D1 D2
Protein Variant Selections
NGS and Dynamic
substraction
Domain specific antibody groups
D1
D2
D3
~50% of hits
~25% of hits
~25% of hits
*Additional candidates with different binding profiles were obtained from cell panning
Summary
▪ Next generation sequencing is a critical tool for screening cell-based phage selections
▪ Successfully applied to multiple receptors for discovery and affinity maturation
▪ Combining diverse panning strategies, NGS, and dynamic sequence analysis is an efficient way to choose candidates confidently for downstream characterization within leaddiscovery.
26
Acknowledgements
27
John WheelerRam PillarisettiSuzanne EdavettalMatt HusovskyJin LuRay BrittinghamJohn KehoeBrian WhitakerRama Reddy
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