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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5387_Библиотеки_им_академика_М_И_Перельмана.pdf
Computational
Biophysical Analyses
7
of Antibody
Structure-Function
Relationships with
Emphasis on Therapeutic
Antibody-Based Biologics
Puneet Rawat, Eva Smorodina, Divya Sharma,
R. Prabakaran, Jack Wade, Rahmad Akbar,
Amrinder Singh, Sandeep Kumar,
Victor Greiff, and M. Michael Gromiha
7.1 INTRODUCTION
Antibodies or immunoglobulins (IGs) are a key component of the adaptive immune
response. They play a pivotal role in recognizing and binding to a foreign molecule,
followed by triggering an immune response against the antigen by recruiting other
cells and molecules. Antibodies are heavy proteins with an approximate size of 10 nm
and a weight of 150 kDa (Reth, 2013). These molecules are roughly Y shape, composed
161
162 Biopharmaceutical Informatics
of two identical heavy and light chains connected by disulde bonds (Glockshuber
etal., 1992). The recognition of any foreign molecule called “antigen” mainly involves
the variable region of the antibody (upper tips of the Y shape including both heavy and
light chains), and the activation of the immune response is regulated by the constant
region of the antibody (Segal etal., 1974; Sela‑Culang etal., 2013; Sinclair etal., 1968).
Antibodies possess a high level of diversity, allowing them to target a wide range of
antigens, particularly in the antibody variable regions. The diversity in the antibody
chains is generated through V(D)J recombination, insertion/deletion during the recom‑
bination process, and somatic hypermutation. The variable region of the antibody fur‑
ther consists of four framework regions (FRs) and three complementarity‑determining
regions (CDRs) on both light and heavy chains. The CDRs on the antibody predom‑
inantly interact with the antigen (Padlan et al., 1995). However, the highly diverse
CDR3 on the heavy chain (CDRH3) contributes signicantly to the antibody–antigen
interactions, in most cases (Akbar etal., 2021; Chothia & Lesk, 1987; Xu & Davis,
2000). The interacting residues in the antibody–antigen complex are called “para‑
topes” on the antibody side and “epitopes” on the antigen side (Akbar et al., 2021;
Sela‑Culang etal., 2013). Therefore the main features of antibodies can be classied
as (i) specicity toward the antigens due to high diversity on the CDRs, (ii) antibody
diversity through V(D)J recombination and somatic hypermutations to recognize wide
range of antigens, (iii) tolerance toward host proteins/cells, (iv) optimized biophysical
properties to work effectively at the biological conditions, (v) immunological memory
to build immunity upon reinfection, (vi) trigger Fc mediated effector functions by neu‑
tralization (direct binding to the pathogen to prevent infection), opsonization (activa‑
tion of phagocytic cells), complement activation (activation of downstream cascade to
neutralize the pathogens), and antibody‑dependent cellular cytotoxicity (ADCC; lysis
of infected cells activated by antibodies) (Lu etal., 2018).
There are two major aspects related to antibody–antigen interaction, namely,
“afnity and avidity” (Rudnick & Adams, 2009; Yin etal., 2021). Afnity measures
the strength of the epitope binding to an antibody and is often represented by the dis‑
sociation constant KD. The paratope and epitope residues interact through various types
of non‑covalent interactions, such as hydrogen bonds, ionic bonds, Van der Waals, and
hydrophobic interactions. Avidity measures the overall strength of the antibody–antigen
complex. Avidity includes the valency of the protein, the binding afnity of the anti‑
body–antigen complex, as well as the structural arrangement of the antibody(s) (Evans
& Thurber, 2022).
Antibodies are also attractive therapeutic candidates due to their biological role
in the immune response and high specicity toward the antigen (Lu et al., 2020).
Monoclonal antibodies or mAbs are widely used as therapeutic candidates because
they are highly specic to particular antigens. Currently, there are approximately
100 Food and Drug Administration (FDA)‑approved mAbs with an estimated market
size of ~185.50 billion USD, which is expected to reach more than 500 billion USD
by 2030 (Mullard, 2021). Antibody‑based therapeutics development also faces several
challenges. For example, antibody repertoire sizes for an individual range from 108 to
10
in humans (Elhanati et al., 2014; Glanville etal., 2009). Although antibody rep‑
10
ertoires may show convergence based on post‑exposure to similar antigens, there is
still vast diversity in the repertoires to be analyzed experimentally (Greiff etal., 2017).
7 • Antibody Structure-Function 163
For example, Wardemann et al. used single‑cell cloning strategies and antibody
expression to exhibit self‑expression of newly generated B‑cells in the bone marrow
(Wardemann etal., 2003). This opened an arena to several immunological insights and
furthered the isolation of antibodies to neutralize clinical pathogens like SARS‑CoV,
inuenza, Human immunodeciency viruses (HIV), and many others using these B‑cell
cloning strategies. However, the main limitation posed by this technique is that it pro‑
vides only a sliver of information on the full antibody repertoire and is usually limited
to a subset of antigen binding activity. Further, Ig‑sequencing limitations include iden‑
tication of a suitable source of DNA sequence and quantication errors; ability to dis‑
tinguish which V
genes pair with VL genes in each B‑cell; use of appropriate data and
H
visualization tools to detangle the large amounts of information furnished post‑analysis;
cross‑reactivity of antibodies with host protein; and lack of structural information about
the antibodies (Brown etal., 2019; Georgiou et al., 2014; Greiff et al., 2015). Once
the binding with the target antigen is established, there are several other developabil‑
ity challenges for naturally occurring antibodies to be used as therapeutic antibodies,
which include folding stability, aggregation, viscosity, etc. (Ahmed etal., 2021; Akbar
etal., 2022; Jain etal., 2017; Młokosiewicz etal., 2022; Pérez etal., 2022; Raybould
etal., 2019; Xu etal., 2019). Therefore, experimental validation of potential therapeutic
antibody leads is challenging due to high production costs and a lack of large‑scale and
high‑throughput methods for developability prediction (Schlander etal., 2021).
With the advent of computational sciences, researchers have probed machine‑learn‑
ing (ML) methods and other informatics technologies to study plausible relationships
between an antibody’s structure and function. For example, there are several com‑
putational resources available for antibody structure modeling, in silico screening of
epitope/paratope regions, docking of antibodies with antigen, estimation of binding
energies, and calculation of biophysical properties of antibodies such as aggregation
propensity, solubility, and melting temperature (Ahmed etal., 2021; Chiu etal., 2019a;
Harmalkar etal., 2022; Narayanan etal., 2021a; Sankar etal., 2022). However, even in
these methods, there are certain limitations wherein predicting models of disordered
or post‑translationally modied (e.g., glycosylated) proteins is not possible. The dock‑
ing methods require prior information on epitope regions for better prediction of the
antibody–antigen complex, and binding energy prediction methods include inaccura‑
cies in the calculation of absolute binding free energies. Moreover, the sequence‑based
approach to study binding prediction is unreliable because the structural conformation
of the CDRs can signicantly affect the binding (Akbar etal., 2022).
Conventionally, antibody repurposing and optimization of the therapeutic antibodies
have been widely used by in silico researchers due to limited resources and long down‑
stream validation processes (Mason etal., 2021; Rawat etal., 2021; Rodriguez‑Quijada
etal., 2020; Wang, Gallolu Kankanamalage, etal., 2021). However, there have been
signicant advances in the computational approaches for the design and development of
antibodies in recent years (Hummer etal., 2022; Norman etal., 2020; Tiller & Tessier,
2015). In this chapter, we will be focusing on the computational resource developed to
curate antibody‑related information, antibody–antigen binding (docking and binding
afnity prediction), and biophysical parameters affecting antigen design (Figure7.1).
We have also highlighted the role of language models and molecular dynamics (MD)
simulations in antibody structure‑function relationship prediction.
164 Biopharmaceutical Informatics
FIGURE 7.1 Overview of the topics related to antibody structure-function considered
in the book chapter. There are several antibody-related databases that contain sequence,
structure, and other relevant information related to antibodies (e.g., interacting pathogen,
residue-level interaction, binding afnity, epitope, developability-related information, and
so on). There are several possible approaches for the antibody–antigen structure prediction. Docking (faster computation time) and MD simulations (very high computation time)
are classical approaches widely used to predict antibody–antigen structures, whereas
ML/DL methods and language models are more recently developed approaches showing
much better speed and accuracy compared to the classical approaches. Several binding
afnity prediction methods are also developed, which can use the sequence/structure information of the antibody–antigen complex to predict (i) the binding afnity of the complex or
(ii) the change in binding afnity upon point mutation at the interaction interface.
7 • Antibody Structure-Function 165
7.2 COMPUTATIONAL RESOURCES FOR
ANTIBODY STRUCTURE AND FUNCTION
7.2.1 Antibody‑Related Online
Resources and Databases
As antibodies become an increasingly interesting topic in the eld of biotherapeutics,
the demand for antibody‑specic data in public repositories is growing (Norman etal.,
2020). The online resource and databases on antibodies can be divided into two major
categories: (i) primary sequence/structure databases and (ii) derived databases, which
contain the secondary information obtained from the antibody sequence/structure or
biological activity (such as antibody–antigen interaction, binding afnity/neutralization
activity, and epitope/antigen information) (Table7.1).
7.2.1.1 Primary databases
Primary databases contain the sequence/structure of antibodies or immune reper‑
toire sequences from B‑cells. The international ImMunoGeneTics information system
(IMGT) is one of the comprehensive resources on IGs or antibodies, T‑cell recep‑
tors (TCRs), and major histocompatibility (MH) of human and other vertebrate spe‑
cies. It consists of sequence databases, genome databases, structure databases, and
mAbs’ databases, which are embedded into several web resources and interactive tools
(Ehrenmann etal., 2010). Antibody sequences and structures are also curated in the
abYsis database (Swindells etal., 2017), providing an inbuilt analysis platform. The
AntiBodies Chemically Dened (ABCD) database is a manually curated repository of
sequenced antibodies (Lima etal., 2020). The Protein Data Bank (PDB) is a general
resource for experimentally determined protein structures, which also include struc‑
tures of antibodies/antibody complexes (Rose etal., 2021). Several antibody‑ specic
structure databases were also developed using PDB, which contains PDB struc‑
tures as well as related annotated information. For example, SAbDab (Dunbar etal.,
2014) contains the antibody structures from PDB, which are annotated with several
details, including experimental details, antibody nomenclature (e.g., heavy‑light pair‑
ings), curated afnity data, and sequence annotations; abYbank contains sequences
(EMBLIG, Kabat, and AbPDBSeq databases) and renumbered experimental structures
(Kabat, Chothia, and Martin antibody numbering scheme in the AbDb database) of
antibodies from PDB (Ferdous & Martin, 2018). The B‑cell repertoire‑specic data‑
bases include observed antibody space (OAS) (Olsen etal., 2022a), VBASE2 (Retter
etal., 2005), cAb‑Rep (Guo etal., 2019), Pan Immune Repertoire Database (PIRD)
(Zhang etal., 2020), and VDJbase (Omer etal., 2020). These immune repertoire data‑
bases can be used as benchmarking datasets for humanness and developability param‑
eters of therapeutic antibodies.
TABLE7.1 List of antibody-related sequence, structure, and other specialized databases
SR.NO. DATABASE LINK DESCRIPTION REFERENCE
Sequence/Structure Database
1 IMGT https://www.imgt.org/ Comprehensive sequence/structure/genome/
monoclonal antibody database
2 SAbDab https://opig.stats.ox.ac.uk/
webapps/newsabdab/sabdab/
3 PDB https://www.rcsb.org/ A generalized structure database which also contains
4 abYbank http://www.abybank.org/ Antibody sequence and renumbered structures data Ferdous and
5 cAb-Rep https://cab-rep.c2b2.columbia.edu/ Database of curated antibody repertoires (Guo etal., 2019)
6 OAS http://opig.stats.ox.ac.uk/webapps/
oas/
7 VDJbase https://vdjbase.org/ Database of adaptive immune receptor genes,
8 PIRD https://db.cngb.org/pird/ Database of raw and processed sequences of IGs and
9 VBASE2 http://www.vbase2.org/ Database of human germline variable region
10 ABCD https://web.expasy.org/abcd/ Database is a manually curated depository of
11 abYsis http://www.abysis.org/abysis/ Integrated database of antibody sequence and
Structure database for antibodies Dunbar etal.
antibodies
Annotated immune repertoire database Olsen etal.
genotypes, and haplotypes
T-cell receptors (TCRs) of human and other vertebrate
species
sequences
sequenced antibodies
structure data
Ehrenmann etal.
(2010)
(2014)
Rose etal. (2021)
Martin (2018)
(2022a)
Omer etal. (2020)
Zhang etal. (2020)
Retter etal. (2005)
Lima etal. (2020)
Swindells etal.
(2017)
(Continued)
166 Biopharmaceutical Informatics
TABLE7.1 (Continued ) List of antibody-related sequence, structure, and other specialized databases
SR.NO. DATABASE LINK DESCRIPTION REFERENCE
12 iReceptor http://ireceptor.irmacs.sfu.ca/ NGS sequence data on B-cell receptors Corrie etal. (2018)
Specialized Sequence/Structure Database
1 Thera-SAbDab http://opig.stats.ox.ac.uk/webapps/
newsabdab/therasabdab/
2 CoV-Ab-Dab http://opig.stats.ox.ac.uk/webapps/
covabdab/
3 Ab-CoV https://web.iitm.ac.in/bioinfo2/
ab-cov/home
4 IEDB https://www.iedb.org/ Antibody epitope database Vita etal. (2019)
5 bNAber http://bnaber.org/* Database of broadly neutralizing HIV antibodies Eroshkin etal.
6 AgAbDb http://bioinfo.net.in/AgAbDb.htm* Antibody–antigen interaction database Kulkarni-Kale etal.
7 CPAD2.0 https://web.iitm.ac.in/bioinfo2/
cpad2/
8 AL-Base https://wwwapp.bumc.bu.edu/
BEDAC_ALBase/
9 AB-Bind https://github.com/sarahsirin/
AB-Bind-Database
10 SKEMPI 2.0 https://life.bsc.es/pid/skempi2/ Database of kinetics and energetics information upon
11 PROXiMATE https://www.iitm.ac.in/bioinfo/
PROXiMATE/
The links that are not active (as checked on Dec 2022) are denoted with “*” sign.
Sequence database for approved or clinical-stage
therapeutic antibodies
Sequence database for coronavirus-related antibodies Raybould etal.
Experimental neutralization prole of
coronavirus-related antibodies
Experimental protein aggregation information which
also includes antibodies
Experimental amyloidogenic antibody light chain
database
Database of experimentally determined changes in
binding free energies
mutation and includes antibody–antigen complexes
A mutant protein–protein interaction kinetics and
thermodynamics database
Raybould etal.
(2020)
(2021)
Rawat etal. (2022)
(2014)
(2014)
Rawat etal. (2020)
Bodi etal. (2009)
Sirin etal. (2016)
Jankauskaitė etal.
(2018)
Jemimah etal.
(2017)
7 • Antibody Structure-Function 167
168 Biopharmaceutical Informatics
7.2.1.2 Specialized sequence/structure databases
Specialized databases contain a variety of secondary information derived from
the primary databases. There are several specialized sequence databases such as
Thera‑SAbDab (Raybould et al., 2020) for approved and clinical‑stage antibodies,
CoV‑AbDab (Raybould etal., 2021) for coronavirus‑related antibodies, broadly neu‑
tralizing antibodies electronic resource (bNAber) for broadly neutralizing HIV anti‑
bodies (Eroshkin etal., 2014), Amyloid Light Chain Database (AL‑Base) for antibody
light chains with aggregation capability (Bodi etal., 2009), and so on. AgAbDb is a
unique database, which contains the antibody–antigen interaction details at the resi‑
due level along with other parameters such as interaction type and accessible surface
area (Kulkarni‑Kale etal., 2014). The AB‑Bind database contains the experimentally
determined change in binding free energy values (Sirin etal., 2016). SKEMPI 2.0 and
PROXiMATE databases contain changes in thermodynamic parameters and kinetic
rate constants upon point mutations for protein–protein interaction, which also includes
antibody–antigen interactions (Jankauskaitė et al., 2018; Jemimah et al., 2017). The
curated protein aggregation database (CPAD) 2.0 database provides comprehensive
experimentally determined information on aggregation‑prone regions and aggregation
kinetics for all proteins, including antibodies (Rawat etal., 2020). The Ab‑CoV database
is a coronavirus‑specic antibody database that contains the antibodies’ neutralization
prole (IC50 and EC50) and binding afnity (KD), as well as computationally predicted
changes in stability and binding afnity upon epitope/paratope residue mutation (Rawat
etal., 2022). The Immune Epitope Database (IEDB) considers the antigen‑side informa‑
tion and collects epitope information (Vita etal., 2019).
7.2.2 Computational Methods for Investigating
Structure‑Function Relationship
7.2.2.1 Docking tools for antibody–antigen
complex structure prediction
Docking is a molecular modeling technique, which predicts the conformation of one
molecule (ligand) on the surface of another static and larger molecule (receptor). In the
case of antibody–antigen docking, antibodies are usually considered receptors, while
antigen is considered a ligand. Molecular docking allows us to see residue‑level interac‑
tions between the receptor and ligand, which is crucial for drug discovery (Pagadala
etal., 2017; Pinzi & Rastelli, 2019). Most docking methods generate several docked
structures and provide docking scores to rank each pose. These scores can be unrelated
to real estimation of the binding strengths of complex structures, but they allow the
comparison of different conformations of one binder or different binders between each
other within one docking tool. Lower scores usually represent better binder conforma‑
tions. Recent docking methods prefer ensemble of protein structures (usually from MD
simulations) to identify the correct pose (Amaro etal., 2018). Docking poses can also
be evaluated through an afnity scoring function representing electrostatic and Van
der Waals interactions (Pagadala etal., 2017; Rawat etal., 2021). Docking programs
7 • Antibody Structure‑Function 169
typically rely on an estimated binding site to accurately predict the binding interfaces,
and they often lack precision in predicting binding energies (Wang etal., 2003). MD
simulations provide a more precise estimation of binding energies (Fernández‑Quintero
etal., 2022; Kralj etal., 2021; Salmaso & Moro, 2018). Some docking models allow
the ligand to be treated as a rigid body object (all atoms and residues are static and
immovable) or exible (when some amino acids are allowed to move during the docking
procedure). The docking procedure that requires many ligands to bind to one receptor
is called virtual screening. It’s a very common approach in the early stages of drug
discovery (Schneider etal., 2022). Docking is the widely used approach to investi‑
gate the function or binding of the antibody against an antigen using protein struc‑
tural information (Brooks etal., 2020; Chaves etal., 2020; Guest etal., 2021). ZDOCK
(Pierce etal., 2014), Haddock (Dominguez etal., 2003), ClusPro 2.0 (Comeau etal.,
2004), LightDock (Jiménez‑García et al., 2018), and Rosetta (Schoeder etal., 2021)
are highly used for protein–protein docking tasks, including antibody–antigen docking
(Ambrosetti etal., 2020). Antibody–antigen‑specic docking models include Antibody
i‑Patch and Absolut!. Antibody i‑Patch denes antibody residues which are most likely
in contact with antigen (Krawczyk etal., 2013) and Absolut! generates coarse‑grained
synthetic antibody–antigen complexes with information about paratope and epitope
conformations and afnity (Robert etal., 2022). A list of the most used docking tools
for antibody–antigen docking is provided in Table7.2.
ML techniques can outperform classical molecular docking results (Ganea etal.,
2021). One of the models is based on graph neural networks (EquiDock) and predicts
rotations and translations of molecules in rigid docking (Ganea etal., 2021). Another
model is a diffusion generative model (DiffDock), which renes random docking
poses to reach the best complex conformation via translations, rotations, and torsion
angles (Corso etal., 2022). The recently introduced architecture called Hierarchical
Equivariant Renement Network (HERN) in “abdockgen” allows not only rigid dock‑
ing but also renes side chains after pose generation (Jin etal., 2022).
7.2.3 Computational Approaches to Predict Antibody–Antigen Interaction
In this section, we have summarized different methods for antibody–antigen binding
and afnity prediction tools that do not involve docking approaches. The details regard‑
ing the tools predicting only paratope or epitope regions can be found elsewhere and
not discussed here (Akbar etal., 2022; Chinery etal., 2023; Lo etal., 2021). The ini‑
tial methods for antibody–antigen complex prediction were based on amino acid usage.
For example, Bepar uses a sliding window of amino acids between the antigen and
CDR regions of antibodies to predict the epitope residues using sequence informa‑
tion (Zhao & Li, 2010). EpiPred is another method that predicts the epitope regions
for antibodies using conformational matching and specic antibody–antigen scores
(Krawczyk etal., 2014). The methods developed later utilized machine learning (ML)
or deep learning (DL) approaches for the prediction of epitope–paratope residues. The
random forest‑based ML model “PEASE” (Sela‑Culang etal., 2014, 2015) calculates the
“residue‑score” for both antibody and antigen to identify interacting epitope–paratope
TABLE7.2 List of molecular docking tools widely used for antibody–antigen complex prediction
DOCKING
SR.NO.
1 ZDOCK https://zdock.umassmed.edu/ Fast Fourier Transform-based protein docking Pierce etal. (2014)
2 Haddock https://wenmr.science.uu.nl/
3 ClusPro 2.0 https://cluspro.org/login.php Fast Fourier Transform-based rigid docking Comeau etal. (2004)
4 LightDock https://lightdock.org/ Docking protocol based on the Glowworm
5 Rosetta
6 PatchDock http://bioinfo3d.cs.tau.ac.il/
7 AbAdapt https://sysimm.org/abadapt/ From sequence to docked structure
8 EquiDock https://github.com/octavian-ganea/
9 DiffDock https://github.com/gcorso/DiffDock A diffusion generative model over the
10 abdockgen https://github.com/wengong-jin/
11 Antibody
12 Absolut! https://github.com/csi-greifab/
TOOL LINK DESCRIPTION REFERENCE
Information-driven exible docking approach Dominguez etal.
(SnugDock)
i-Patch
haddock2.4/
https://www.rosettacommons.org/
software
PatchDock/
equidock_public
abdockgen
http://opig.stats.ox.ac.uk/webapps/
newsabdab/sabpred/antibodyipatch
Absolut
Swarm Optimization (GSO) algorithm
Simulates the induced-t mechanism Schoeder etal.
Geometry-based molecular docking
algorithm
generation pipeline
Pairwise-independent SE(3)-equivariant graph
matching network-based rigid docking
non-Euclidean manifold of ligand poses
Antibody–antigen docking and design via
hierarchical equivariant renement
Contact likelihood score to each residue Krawczyk etal.
Unconstrained lattice-based antibody–
antigen bindings generator
(2003)
Jiménez-García etal.
(2018)
(2021)
Schneidman-Duhovny
etal. (2005)
Davila etal. (2022)
Ganea etal. (2021)
Corso etal. (2022)
Jin etal. (2022)
(2013)
Robert etal. (2022)
170 Biopharmaceutical Informatics