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- •Contents
- •Foreword
- •Preface
- •About the Editors
- •Contributors
- •References
- •2.3.4 Barriers to Automation Adoption
- •2.4 Core Ingredients for Successful Digital Transformation
- •2.1 Introduction
- •2.3.1 Operational Challenges
- •2.3.2 Cultural Challenges
- •2.4.2 Cloud Computing
- •2.5 Case Studies of Successful Digital Transformation
- •2.6 Conclusion
- •References
- •3. Computational Protein Design Strategies for Optimization of Antigen Generation to Drive Antibody Discovery
- •3.1 Introduction
- •3.3 Antigen Generation Strategies
- •3.4 Computational Methods
- •3.4.2 Computational Protein Structure Prediction
- •References
- •4. Bioinformatic Analyses of Antibody Repertoires and Their Roles in Modern Antibody Drug Discovery
- •4.1 Introduction
- •4.6 Summary and Future Directions
- •Acknowledgments
- •References
- •5.1 Introduction
- •5.2 Databases
- •5.2.1 Databases in Machine Learning Approaches
- •5.2.2 Database Types
- •5.3 Applications of Machine Learning in Antibody Discovery and Development
- •5.3.1 Structure Prediction with Deep Learning
- •5.3.3 Developability
- •5.4 Antibody Generation and Design by Language Models
- •5.4.1 Antibody Representations
- •5.4.2 Representation Learning
- •5.4.3 Language Models
- •References
- •6.1 Introduction
- •6.2 Antibody Generation through Deep Generative Models
- •6.3.1 Sampling and Scoring
- •6.5 Conclusions and Perspectives
- •Acknowledgments
- •References
- •7.1 Introduction
- •7.2.3 Computational Approaches to Predict Antibody–Antigen Interaction
- •7.3 Conclusion
- •Competing Interests
- •Acknowledgments
- •References
- •8.2 Common Types of Molecular Simulations for Biomolecules
- •8.2.1 Molecular Dynamics (MD) Simulations
- •8.2.2 Monte Carlo (MC) Simulations
- •8.2.3 Challenges of Molecular Simulations
- •8.3.1 Periodic Boundary Conditions
- •8.4 Uses of Molecular Simulation in Antibody Drug Development
- •8.5 Conclusion
- •References
- •9. Considerations of Developability During the Early Stages of Antibody Drug Discovery and Design
- •9.1 Introduction
- •9.2 Historical Perspective
- •9.3 Clinical Antibody Data Set
- •9.5 Control Antibodies
- •9.7 Assessment of Chemical Liabilities
- •9.8 Conclusions and Future Perspectives
- •Acknowledgments
- •References
- •Abbreviations
- •10.1 Introduction
- •10.4.1 Conclusions and Outlook
- •Acknowledgments
- •References
- •11.8 Conclusions and Future Directions
- •References
- •12.1 Introduction to PK/PD and QSP Modeling
- •12.1.1 PK/PD Modeling
- •12.1.2 QSP Modeling
- •12.2.1 Monoclonal Antibodies (mAbs)
- •12.2.3 Cell Therapies
- •12.2.4 Gene Therapies
- •12.2.5 Vaccines
- •12.2.6 mRNA/siRNA/Oligonucleotide Therapeutics
- •12.4 Case Studies
- •12.5 Conclusions and Future Perspectives
- •References
- •13.1 Introduction
- •13.2 AI/ML: A Game Changer for Antibody Design
- •13.3 Multispecific Antibody Design
- •13.4 Adapting AI to the Design of Multispecific Antibodies
- •13.4.1 Structure Prediction and Modeling
- •13.4.2 Developability Prediction and Optimization
- •13.4.4 In Silico Modeling and Simulation
- •13.5 The Future: Beyond Optimization
- •13.5.1 Market Trends and Commercialization
- •13.5.2 Logic Gates, Biosensors, and De Novo Design
- •13.5.3 Challenges and Opportunities
- •13.6 Conclusion
- •Acknowledgments
- •References
- •Index

3 • Computational Protein Design Strategies 51
effect (e.g. blocking antibody, antibody–drug conjugate) is key, and computational meth‑
ods can enhance outcomes. This is illustrated in the following selected case studies.
In the rst example, a strategy was designed to isolate antibodies that acted to
‘staple’ a molecular heterodimer by binding a composite epitope of two‑receptor extra‑
cellular domains (Figure3.4b). Interleukin‑4 (IL‑4) signals through either a type I het‑
erodimeric receptor comprising IL‑4Rα and the common γ‑chain or a type II receptor
composed of IL‑4Rα and IL ‑13Rα1. A crystal structure of IL‑4 in a ternary complex
with the cytokine receptor extracellular domains showed that the two‑receptor mem‑
brane proximal domains were in contact forming a neoepitope. An engineered variant
of IL‑4 was available (Super‑4) with 3,700‑fold higher afnity than IL‑4, which sta‑
bilized the IL‑4 receptor complex. With this information, an antigen preparation was
made in which a receptor was tagged with a C‑terminal biotin‑acceptor peptide allow‑
ing biotinylation and expressed receptor subunits were complexed with Super‑4. This
biotinylated complex was used with a yeast scFv library to isolate a scFv, which bound
specically to the membrane–proximal receptor interface. This binding mode was con‑
rmed by solving a structure of the antibody (reformatted as a Fab) with the ternary
complex, which showed the antibody binding to the receptor interface [98].
In a second case study, the concept of de novo design of antibody Complementarity
determining regions (CDR) loops was explored based on targeting epitopes for proteins
in which a structure is available either from an experimentally solved structure or a com‑
putational model [95]. This approach required the exploitation of large structural data‑
bases to use a fragment‑based procedure to design CDRs complementary to a selected
target epitope. This approach was experimentally tested using single‑domain antibodies
based on their monomer structures, ease of production, and small size. To develop this
approach, a database of CDR‑like fragments and corresponding antigen‑like regions
was compiled from the non‑redundant PDB to build the so‑called AbAg database.
Given a known structural target epitope, the database is searched to locate antigen‑like
regions similar to the epitope. To perform the search, the input epitope is fragmented
into either linear or surface‑patch fragments. The search method identies CDR‑like
fragments that may interact with the target epitope. These fragments are evaluated for
favourable interactions and ranked. Top‑ranking designed CDR motifs are then grafted
to an antibody scaffold that is tolerant to loop replacement. This method does require
that the structure of the target antigen is known but is capable of working with both
experimentally derived structures and models created by tools such as AlphaFold2 [95].
This technique was applied to three target antigens– human serum albumin, bovine
trypsin, and SARS‑CoV‑2 spike protein receptor‑binding domain (RBD). The six sin‑
gle‑domain antibodies designed bound to the target proteins with afnities ranging
from 120 to 1,800 nM, with two antibodies targeting the RBD exhibiting afnities of
130 and 210 nM.
A nal case study example is drawn from the eld of vaccine research targeting
RSV, which is the leading cause of serious respiratory disease in infants. Although
a prophylactic, humanized monoclonal antibody, palivizumab, which targets RSV F
protein, is approved, vaccine design has lagged behind. RSV F protein has been the
primary antigen target for vaccine development. This is a class I fusion glycoprotein
and requires proteolytic cleavage for activation and formation of the mature trimer.

52 Biopharmaceutical Informatics
The RSV F protein is responsible for fusing the viral and host cell membranes during
virus cell entry and exists in two antigenically unique forms–a pre‑fusion form and a
post‑fusion form, which undergoes a dramatic conformational change. The pre‑fusion
form is the target of most neutralizing antibodies [99]. A key issue in targeting the
pre‑fusion form of RSV F is that the protein is metastable and can spontaneously change
conformation when extracted from membranes with detergent or if subjected to vari‑
ous stresses (e.g. physical, chemical, or temperature stresses). To tackle this issue, sta‑
bilization of the pre‑fusion protein is an attractive strategy. To achieve stabilization,
pre‑fusion stabilizing mutations were computationally designed using a combination of
methods–introduction of cysteine residue pairs to form stabilizing disulphide bonds;
introduction of non‑polar amino acids to ll cavities in the pre‑fusion protein that
might otherwise allow for movement; and introduction of charged residue mutations to
decrease ionic repulsion or increase ionic interaction between residues that are close in
the pre‑fusion state [90]. These residue mutations were computationally assessed using
a combination of Schrodinger BioLuminate, Molecular Operating Environment [100],
and Rosetta‑based stability prediction protocols [101]. Using computational design, 398
hypothetical F domain constructs with combinations of disulphide, cavity lling, and
charge mutations were expressed as ectodomain constructs, including a britin foldon
trimerization domain at the C‑terminus. In general, the disulphide mutations had the
greater stabilizing effect on the pre‑fusion conformation. Pre‑fusion F constructs that
exhibited greater stabilization were tested and elicited a 10‑fold higher serum‑neutral‑
izing titre than a prototype vaccine F protein candidate. Introduction of the stabilizing
mutations onto F‑proteins of two major RSV subgroups, followed by immunization led
to complete protection against RSV challenge.
These selected case studies illustrate how a combination of protein struc‑
ture‑informed design and computational techniques can enhance antibody generation
in both a therapeutic antibody generation context and in vaccine immunogen design.
3.6 CONCLUSIONS: COMPUTATIONAL
ANTIGEN DESIGN AND FUTURE
DEVELOPMENTS
In this chapter, the topic of protein antigen and immunogen design has been explored, and
the potential impact of computational tools in facilitating aspects of antigen generation from
protein expression to protein structure has been examined. Advances in computing power,
increasing capture of data from existing experimental approaches, and enhancements to
the speed of determining protein structures, coupled with MI approaches, are leading to
rapid breakthroughs and opportunities in biologics drug discovery. The clear, long‑term
ambition of structure‑based and computational antigen and antibody design is the ability to
design, entirely in silico, antibodies, which bind to a selected target. This breakthrough is
imminent, although it seems certain to bring further challenges. Designing antibodies de
novo presents several scientic and technical challenges due to the complexity of antibody

3 • Computational Protein Design Strategies 53
structures and their interactions with antigens. Assuming we can computationally design
an antibody which binds to a target antigen with high afnity there are several other chal‑
lenges to consider. This includes, but is not limited to, issues such as the antibodies devel‑
opability prole and its pharmacokinetic, pharmacodynamic and toxicity prole. We are
also experiencing an era where novel formats are being explored such as bi‑ and multi‑spe‑
cics, antibody–drug conjugates, and alternative scaffolds, each of which brings its own
challenges. At the core of this challenge is the requirement to generate target antigen to
drive biologics drug discovery and validate the performance of designed biologics. Given
the diversity and complexity of potential protein targets, there remains much to be learned,
and computational techniques must be central to advances in this area.
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Bioinformatic Analyses of Antibody Repertoires and Their Roles in Modern Antibody Drug Discovery
Melody Shahsavarian, Thomas Watkins,
Ponraj Prabakaran*, Adrian Carr,
Maria Wendt, and Yu Qiu
4
4.1 INTRODUCTION
The eld of antibody discovery has evolved dramatically with the integration of next‑gen‑
eration sequencing (NGS), high‑throughput screening, and computational methods,
reshaping the traditional paradigms of immunization and in vitro antibody discovery.
This has allowed for an expanded understanding of antibody diversity as well as the selec‑
tion of highly specic antibodies with desirable therapeutic traits. Historically, antibody
discovery relied heavily on animal immunization, a method with its own set of advan‑
tages, including the in vivo maturation of antibodies, which often results in high afnity
*
author for correspondence
1–3
59

60 Biopharmaceutical Informatics
and specicity.4 The process of antibody discovery has been signicantly enhanced by the
precision and scale afforded by NGS technologies, which facilitate a deeper exploration
of the antibody repertoire.5 Technological limitations and logistical complexities associ‑
ated with animal use have driven the development of alternative methods. In vitro display
technologies, such as phage, yeast, or mammalian display, represent a crucial shift toward
non‑animal‑derived antibody discovery.6 These methods not only minimize animal use
but also allow for precise control over the selection process, targeting specicities that are
challenging to achieve in vivo. Thus, in vitro display technologies have become founda‑
tional in the transition from animal models to more controlled experimental setups, where
libraries of antibodies can be screened against a myriad of antigens with high throughput
and specicity. Integration of NGS with these display platforms enables the rapid screen‑
ing of vast combinatorial libraries that are directly cloned from a pool of B‑cells or syn‑
thetically designed, thus capturing a broader diversity of antibody sequences.
7
Deep sequencing of antibody repertoires with NGS in recent years has allowed an
unprecedented quantitative understanding of immune repertoire dynamics applied to anti‑
body discovery,8 both from natural B‑cells isolated either from human donors9 or immu‑
nized animal models10 and from synthetic antibody display libraries.11 Paired sequencing
strategies, more recently, can even provide information at the single‑cell level on the natu‑
ral pairing of antibody heavy and light chains, thus revealing essential information about
the antigen specicity and function of the antibodies.
12–14
These technologies have had a
signicant impact on time, efciency, and throughput of antibody discovery platforms.
Recently, machine learning (ML) algorithms have become instrumental in creating
innovative approaches that impact antibody discovery and development.
15–26
By lever‑
aging large datasets of antibody sequences generated from NGS, ML can help develop
models that could predict antibody behavior, optimize binding afnities, and enhance
the developability of therapeutic antibodies. This synergy between NGS and ML is set‑
ting new standards in the rapid identication and optimization of antibodies, pushing
the boundaries of what can be achieved in therapeutic development.
27,28
In this chapter, we will briey overview the NGS technologies for antibody discovery.
We will then explore NGS‑enabled in vivo, in vitro, and in silico methods, representing the
integration of NGS technology with traditional immunization, display libraries, and articial
intelligence/machine learning (AI/ML) for advanced antibody discovery. This integrated
approach exemplies how cutting‑edge technologies are reshaping the eld of antibody dis‑
covery, leading to the development of more rapid and effective antibody therapeutics.
4.2 NGS TECHNOLOGIES, TOOLS,
AND DATA ANALYSIS FOR MODERN
ANTIBODY DISCOVERY
NGS technologies have greatly transformed the eld of antibody discovery, providing
an unprecedented ability to explore antibody repertoires derived from immunizations
and display libraries at the individual molecule level.
methods, such as Sanger sequencing, which was useful for identifying dominant clones
3,29,30
The shift from traditional
Соседние файлы в папке Библиотека им академика М.И. Перельмана
