Toward high-throughput
antibody-antigen modeling
Daron Standley, Osaka University

Brief Bio• 1998: PhD in computational chemistry, Columbia Univ.
• 1998-2003: Software developer, Schrodinger Inc.
• 2003-2008 : Researcher, Osaka Univ., Inst. for Protein research• 2008-2014: Associate Prof., Immunology Frontier Research Center• 2014-2016 : Professor, Kyoto Univ., Inst. for Virus Research• 2016: Co-founder, KOTAI Biotechnologies• 2016-present: Professor, Osaka Univ., Research Inst. for Microbial Diseases

Part 1: A personal story
How our lab became interested in antibodies

The lab

About helf of the lab is working on antibodDaronKazutaka

Diego

Shunsuke

SonglingFloris

John

Mara

ArthurJanAnaXu

AybarsMartinXieHendra

Nita

MasakoIn 2013 my lab was invited to join a contest

The second antibody modelingassessment:“To assess the state of the art inantibody 3D modeling”

Organization of the Contest
Organizers send
sequences ofantibodies toparticipants

Organizers compare the3D models to x-ray
crystal structures andassess errors

Participants prepare 3Dmodels and send backto organizers

The participants• Accerlys Inc• Chemical Computer Group (CCG)• Schrodinger, Inc.
• Jeff Gray's lab (John Hopkins University)• Macromoltek• Astellas Pharma/Osaka University• Prediction of ImmunoGlobulin Structure (PIGS).

The organizers
Big Pharma

Academics

Scripps ResearchOur team

Haruki Nakamura
Inst. Protein Research
Osaka Univ.

Hiroki Shirai
Astellas Pharma
Experienced in antibody modeling

D. Standley
IFReC
Osaka Univ.

Our strategy
Nakamura Lab: Molecular dynamics(Slow, but reliable)
Hiroki Shirai: Initial
analysisof BCR sequence

Choose bestmodel
D. Standley: Fragment assembly(Fast, but risky)
We wrote the software in 1 month!

Results: Our models had the lowest error!
4

Average Error (Angstroms)

Lower is Better!
3210Astellas/Osaka

Rosetta

Schrodinger

Macromoltek

AccelrysCCGIt turned out the fast/risky approach was just as accurate as the slow approach

Our fast method (minutes of CPU)
Refined fast method (1 hour of CPU)
Submitted to the contest (weeks of CPU)AMA-II BCR
sequences

Average Error (A)43210AllFW

CDR (not H3) CDRH3

“we?”

Kazuo Yamashita
CEO KOTAI Bio

We should make acompany!

Summary of part 1• The AMA-II contest provided an important lesson• Our lab had no experience working on antibody modeling• Nevertheless, we were able to make an important breakthrough• Why did we do so well on antibody modeling?
• Possibly, because were not antibody ‘experts’ we tried something verynaïve• It just happened to work.
• It’s important to try different projects in your career• Don’t become too attached to one project for your whole life
• Some projects “just happen to work” most projects don’t!

Part 2: Background on antibodies

How does nature make antibodies?
Antigen
B cells with unique B cell receptors (BCRs)
1 weekOne or more of these BCRs will bind to the antigen of interes
Antibodies are the soluble form of BCRs

Each antibody variable region (Fab) is composed oftwo chains (heavy and light)

LAb

HFab

A Fab contains 6 complementarity-determining regions (CDRs)
L1L2L3 H3 H2 H1L

H

These six CDR loops give eachantibody its unque antigen-binding
characteristics

Antigen binding surfaces (paratopes) are defined by CDRs

L1 L2 L3 H3 H2 H1

CDR sequences are generated randomlyThis is a continuous process ocurring
throughout our lives

The number of unique antibodies in humans is greater than the number
grains of sand on earth: ~1019

Let’s take a closer look at B cell receptors (Antibodies)Antibody binding sites (epitopes) can beanywhere on an antigen surface

Different antibodies targeting
influenza hemagglutinin

Epitopes can be anywhere (e.g. Hemagglutinin)

Paratopes correspond to CDRs
L1

H1

L2

L3

H2

H3

B cells that bind self-molecules are killed

95% of B cells are killed
because of selfreactivity

Surviving B cells that bind non-self molecules
undergo further optimization
Mutation

Non-self
AntigenSelection

AffinitySome cells become long-lived memory B cells
Mutation

Non-self
AntigenSelection

Affinity

MemoryB cell sequencing is an emerging technology

This technology has created new era
3000

No. of sequences

2250

Era of too little data

1500

Era of too much data

750
0
1998

2004

2009

Year

2015

2020
VDJdb

Traditional therapeutic antibody discovery takes time
• Identify target antigen• Develop antibodies in animals• Select several monoclonal antibodies• Humanize (i.e. graft CDRs from animal framework onto human framework)• Optimize antigen binding etc.
• Evaluate safety• Evaluate efficacy

Two important examples from Japanese academia

Tasuku Honjo, Kyoto University
Discovered PD-1 cancer checkpoint therapy

Tadamitsu Kishimoto, Osaka University
Discovered IL6-based autoimmune therapy

Invention:
PD-1 discovery (1992)

Commercialization:
Global sales of checkpoint-therapies(2014-2018)

Invention:
IL-6 discovery (1988)

Commercialization:
Global sales of anti-IL6 therapy(2005-2017)

> 20,000,000 antibodies associated with 4 viruses

Antibody sequences from Literature

3000000

2500000

2000000
Covid19
Influenza

1500000

HIV
HBV

1000000

500000

0

2011

2012

2013

2014

2015

2016

2018

2019

2020

Summary of Part 2• Collectively, antibodies exhibit a kind of “Immune intelligence”• They distinguish between “self” and “non-self”• They “learn” to bind to non-self molecules with high affinity and specificity• They remember non-self antigens for the future• High-throughput antibody sequencing marks the beginning of anew era• There is room for new bioinformatics methods in this exciting field• Hopefully, we can reduce the time to discover new therapeutic
antibodies

Part3: High-throughput Antibody ModelingRepertoire Builder: Our latest software
Input:Up to 10,000 Antibody
or sequences

30min

Output:Up to 10,000
receptormodelsD Schritt, et al. Molecular Systems Design & Engineering 4 (4), (2019)

Repertoire Builder utiluzes MAFFT-ASH-derived featurevectors

Repertoire Builder Accuracy

Summary of Part 3• Antibody modeling can be very fast (seconds per model)• Other very fast modeling methods include Lyra (Marcatili et al.) andAbodyBuilder (Deane et al.)• Repertoire Builder appears to be both fast and acurate• Sometimes huge computational cost (e.g. MD-based methods) are
not better than fast methods (i.e. using structural templates)• However, making lots of antibody structural models is not enough• We need to find a way to use this information to understandantibody function• This means: predicting antigen specificity

Part 4: Antibody Clustering

BCR/TCR

InterClone: Pairwise classifier with hierarchical clustering

Input(Receptor model pair)

Output(Probability of targetingsame epitope),,0.00001

0.94

Xu et al Molecular Systems Design & Engineering 4 (4), (2019)

InterClone AI similar to facial recognition

CATNAP：HIV-1 antibody libraryV3 siteCD4bindingsite

EpitopeV1-V2 site
V2 siteCD4i site

Fusion peptide
site

gp120
gp41 fusion domain
site
MPER

gp41

V1-V2
V2
V3
V2-CD4bs
CD4bs
CD4i
gp41 fusion domain
Fusion peptide
MPER
variable loops (V1,V2,V3)
CD4-induced (CD4i)
CD4 binding site (CD4BS)
membrane proximal external region (MPER)

Cocluster CANTAP dataset by InterClone
H chain

H+L chains
EpitopeV1-V2
V2V3
V2-CD4bs
CD4bs

CD4i
gp41 fusion domain
Fusion peptide
MPER

InterClone performance on CATNAP antibodies
AI has seen both Abs

AI has seen one Ab

AI hasn’t seen either Ab

Application: Clustering Ab sequences from different donors
Donors of interest

Receptor models

Receptor Clusters

AB

Docking etc.
CInterClone

Healthy Control

Z. Xu, et al. MSDE 4 (4), (2019); Xu et al (in prep)

InterClone: Can identify multi-donor antibody clusters for various viruses
HBVStudyDonors

Virus

All sequences>95% unique

Galson-2015

9HBV260644692820326844287

Galson-2016

9

HBV2752578103253127312086

Ellebedy-2016

8

Influenza

91994

88734

27525369

Galson-2016

38

Influenza

342940278685027849037

Gupta-2019

3

Influenza

107395744755528179494

Jian-2020

5

Influenza

981706596052

lead contact

Ellebedy-2020

3

Influenza

26747

25321

32661157

Wu-20112

HIV

18467210334221835983

Zhu-2012

1

HIV

9442

3661

23024643

Liao-2013

1

HIV

34722

26937

23552890

Zhu-2013

1HIV31677

18828

24106303

Schanz-2014

1

HIV

32555

11711

25364977

Wu-20151HIV35495221205925865483

DoriaRose-2015

1

HIV

16815164601

24590074

Zhou-2015

4

HIV

27201

20800

26004070

Huang-2016

1

HIV

58156

8663

27851912

Setliff-2018

6

HIV

37266630720829861170

Waltari-2018

4HIV

20012570742

29632541

Armita-2019

7

HIV99283181908031209469

Jia-2020

2

HIV

91026

70716

32315598

Roskin-2020

87

HIV

74789154606931959979

Jian-202011

Covid19

485034279924

collaboration
32941787

PMID

Nielsen-2020

5

Covid19155297

143813

Galson-2020

19

Covid19

745666673499Montague-2020

19

Covid19

72148

49050

Christoph-2020

37

Covid19264906248281

32668194

Cervantes-2020

10

Covid1912172810033532669287

Wen-2020

10

Covid19

6207

6092

32377375

Zhang-2020

13

Covid19

8229

7788

32788748

Xu et al (in prep)

Influenza

Large circles mean large
clusters
Dark circles mean virus-specificHIV

COVID-19

Antibodies in HIV-specific clusters are indeed HIV antigen specific100150250300400600743502010610090807060504030201003Enrichment of
HIV positive antibodies (%)

2 HIV+ donors

Cluster rank by p-value

Antibdies in HBV-specific clusters are also HBsAg antigen specific706050403036102050
10020030040050070090
25 0041 091

Enrichment ofHBsAg positive antibodies (%)

18 HBV donors

Cluster rank by p-value

Antibodies in influenza-specific clusters are HA antigen specific10090807060
50403020100HA positive seq

310 20 50 100 150 300 400 600 988

Cluster rank by p-valueEnrichment of
HA negative antibodies (%)

Enrichment of
HA positive antibodies (%)

4 Flu donors
1009080706050403020100

HA negative seq

310 20 50 100 150 300 400 600 988

Cluster rank by p-value

Antibodies in COVID19-specific clusters are Spike-protein antigen specific

RBD

BSA

S1

BSA

S1+S2

BSA

Xu et al (in prep)

Summary of Part 4• Antibody models can be clustered using sequence and structural features• InterClone was trained on existing 3D structural data
• Antibody clusters enriched in sequences in donors of interest (i.e.
virus or vaccine exposed) compared to healthy controls can beidentified• These virus-specific clusters are indeed enriched in antigenspecific antibodies• InterClone thus appears to perform well on new sequence data
• To our knowledge, InterCone is the only method avilable for
antibody clustering• Web server coming soon!

Part 5: Antibody-antigen docking

Adapt: Docking antibody-antigen pairs from sequenc
Repertoirebuilder

Antibody
sequence

Repertoirebuilder

Antibody
model

Paratope
prediction

Predicted
paratope Piper docking
Piper scoring
Clustering
Cluster scoringHex dockingHex scoring

Forte, Spanner,Antigen
SCWRL4

sequence

Antigenmodel

Epitopeprediction

Predictedepitope

Top poses

Key features of Adapt• Adapt allows input of sequences and builds structures for docking internally• Adapt uses epitope and paratope prediction to help docking• Adapt uses two docking engines (Piper and Hex)
• Adapt uses several customized machine-learning models:
1. Epitope prediction
2. Paratope prediction
3. Piper Scoring
4. Hex scoring
5. Scoring clusters of Piper and hex models (poses)

Validating Adapt

Holdout test (100 different Ab-Ag pairs)

567-fold Leave-one-out cross validation

For each Ab-Ag pair:Remove one pair (test pair)
Train the machine learning models on all other (566) pairs

Train the machine learning models on all other (567

Test the complete Adapt pipeline on 100 different p
Report the average ROC AUC

Test the complete Adapt pipeline on the one test pair (ROC AUC)
Report the average ROC AUC

Paratope and Epitope Prediction validation

ROC AUCROC AUC

567-fold Leave-one-out cross validation
Holdout test (100 different Ab-Ag pairs)

Paratope Epitope

Paratope EpitopeReperesentative Examples
6T3J

1P2C

6W4S

5VIC

5I6Y

6II8

6B0N

0.602

0.614

0.664

0.732

0.885

0.945

0.960

NativeEpipred 1

Epipred 2

Epipred 3

Adapt

ROC AUC

Docking validation: Leave-one-Out Cross Validation
Piper Docking

Piper Scoring

True Ranks

Top Poses
Hex Docking

Hex ScoringDocking validation: Holdout Set
True Ranks
Piper Docking

Piper Scoring

Top Poses

Hex Docking

Hex ScoringSummary of part 5• Performance of leave-one-out cross validation was very similar toperformance on holdout set (means that we did not over-fit themodels)• The main point of Adapt is to throw away bad models; in this regard,
we were successful• The “success rate” (defined as the ability to produce at least one“True” pose) of using Adapt was 8-9% higher than that of Piper or Hex
alone• Limitations of Adapt: right now it is slow (hours)• The quality of the final models is “acceptable” but not “high”• Web server coming soon!