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6 • From Deep Generative Models to Structure-Based Simulations 121
FIGURE6.3 B-cell repertoires, feature representations, and computational tasks: Amino
acid sequences are initially converted into representations or features that computational
models can process. These representations are then used to address a range of protein science challenges, including the classication of existing sequences, the generation of new
sequences, the optimization of existing sequences, and the prediction of structure from
sequences.
space, guiding the design of proteins with intended functionalities. B‑cell repertoires,
with their rich sequence‑function relationships, have been invaluable sources for
ML‑based computational antibody design. To explore the potential effects of the choice
of representations on model performance, Wang etal. evaluated multiple embedding
models for predicting sequence properties and receptor specicity of B‑cell recep‑
tors or antibodies.63 These models included the antibody‑specic embedding model
(Immune2vec64), transformer‑based protein language models (ESM2,65 ProtT5,66 and
Ant iBE RTy67), and traditional amino acid encoding methods (physicochemical encod‑
ing and amino acid frequency). Despite the varying architectures of these models, most
embeddings effectively captured the sequence properties and specicity. Notably, the
antibody‑specic embeddings slightly outperformed general protein language models
in predicting specicity. Moreover, the inclusion of full‑length heavy chains and paired
light chain sequences enhanced the predictive performance across all embedding types,
indicating that functional signatures of antibodies are encoded within both the heavy
and light chains.
However, despite these technological strides, challenges persist, including the
need for standardized repertoire analysis methods, management of data complexity,
and interpretation of the functional implications of sequence diversity. Future efforts
may focus on enhancing single‑cell sequencing technologies for more comprehensive
repertoire capture, integrating functional assays for biological activity correlation, and
developing advanced computational models for antigen specicity and antibody ef‑
cacy prediction. The accumulated B‑cell repertoire data offers a valuable foundation
for generative models and LLMs engaged in antibody sequence generation and optimi‑
zation, signaling a promising frontier at the conuence of immunology and articial
intelligence.
122 Biopharmaceutical Informatics
6.2.2 Deep Generative Models and Large
Language Models in Protein Science
More specically, generative models and LLMs emerge as foundational elements within
the domains of ML and articial intelligence. Generative models, algorithms crafted to
generate new data samples that mirror the distribution of a specic dataset, play a cru‑
cial role in protein design. These models enable the creation of novel protein sequences
or structures with desired attributes such as improved stability, binding afnity, or enzy‑
matic activity. Meanwhile, LLMs, particularly those built on the transformer architec‑
ture, have advanced to decode and comprehend the “language” of proteins, a complex
sequence of amino acids. The transformer architecture employs attention mechanisms
that allow simultaneous analysis of various sequence parts, enabling the model to assess
the importance of each segment within the context of the entire sequence. This capacity
is crucial for understanding protein structures and functions, as it considers both local
and global interactions within the protein.
By harnessing extensive datasets of known protein sequences, including those from
B‑cell repertoires, transformer‑based LLMs can predict the three‑dimensional (3D)
structures,
sequences. This comprehensive understanding and predictive capability underline the
transformative impact of transformer architecture in decoding the complex language of
proteins, offering a profound tool for advancements in protein engineering and enhanc‑
ing our understanding of biological processes. However, while LLMs excel in capturing
patterns within sequences, proteins exhibit intricate 3D interactions that extend beyond
linear sequence relationships. The exploration of LLMs in protein science signies a
convergence with computational biology, particularly within natural language process‑
ing. There is a notable parallel between the linear structure of human language and the
sequential nature of proteins, yet the 3D structures of proteins introduce complexities
beyond those linear relationships. While LLMs can predict protein property and struc‑
ture from sequences, the dynamic and complex nature of proteins requires consideration
of their full 3D interactions, which can signicantly inuence their function and behavior.
new frontier in understanding protein sequences, although the complexity of protein
interactions necessitates ongoing renement of these computational approaches.
68–70
functions,
Ultimately, the integration of LLMs into protein modeling and design promises a
71,72
and mutational effects
73,74
of proteins from amino acid
6.2.3 Antibody Sequence Generation through
Deep Generative Models
Generative models and LLMs have been employed in antibody generation (Table6.1).
An early example is the humanoid antibody library developed by Amimeur et al.,
which used GAN architecture to process 400,000 sequences from human antibody
repertoires.
structural alignment. This alignment allowed for the direct comparison of residues at
75
All the antibody repertoire sequences were computationally modeled for
6 • From Deep Generative Models to Structure-Based Simulations 123
TABLE6.1 Machine learning applications for antibody sequence generation
EXPERIMENTAL
ANTIBODY
SEQUENCE
GENERATION
Antibody-GAN
(Amimeur
etal., 2020)
75
SeqDesign
(Shin etal.,
77
2021)
AB-Gen (Xu
etal., 2023)
78
AbLang (Olsen
etal., 2022)
80
ProGen2-OAS
(Nijkamp
etal., 2023)
83
IgLM (Shuai
etal., 2023)
82
Ig-VAE (Eguchi
etal., 2022)
86
DEEP
LEARNING
ARCHITECTURE
TRAINING SETS
FOR SEQUENCE
GENERATIONS
GAN OAS;79 400,000
FV sequences
CNN Naïve llama
repertoire
(~1.2million)
Transformer OAS; 75,204,905
sequences of
CDR-H3
Transformer OAS; 14,126,724
VH, 187,068 V
Transformer OAS; ~554million
FV sequences
Transformer OAS;79
~558million FV
sequences
VAE PDB;
87,88
10,768
Ig domains
REGIONS
GENERATED
IN THE
LITERATURE
F
V
CDR-H3 Yeast display library
76
CDR-H3 N/A
Missing/
ambiguous
L
residues
F
V
F
V
3D
coordinates
of VH domain
VALIDATIONS OF
COMPUTATIONALLY
GENERATED
ANTIBODIES
DSF (thermal stability),
SINS (self-interaction),
PEG solubility, SEC
(purity)
(antigen binding)
N/A
N/A
N/A
N/A
identical structural positions across the dataset, simplifying the relationships the GAN
needed to capture for generating new sequences. Experimental validation of the com‑
putationally generated antibodies involved expressing two examples in Chinese hamster
ovary (CHO) cells, which exhibited the expected biophysical characteristics. This vali‑
dation demonstrated the ability of the computer‑generated library to explore meaningful
antibody sequence space effectively.
Utilizing B‑cell repertoires from llamas,76 Shin etal. applied autoregressive mod‑
els with CNN architecture to create a computational nanobody library.77 Despite its
smaller size compared to existing synthetic libraries, it contained functional sequences
with micromolar‑binding afnities, offering the potential for further maturation through
additional experiments or computations.
AB‑Gen employs a generative pre‑trained transformer as the policy network of a
reinforcement learning agent for antibody library design.78 This transformer explores
the vast space of CDR‑H3 sequences, training on over 75million CDR‑H3 sequences
79
from the Observed Antibody Space (OAS) database.
Through this training, the
124 Biopharmaceutical Informatics
generative transformer of AB‑Gen learns the sequence landscape, enabling the gen‑
eration of novel sequences with property distributions akin to those in the training
dataset. This method allows AB‑Gen to create antibody libraries meeting multi‑prop‑
erty constraints, such as binding afnity to major histocompatibility complex (MHC)
class II, clearance, and viscosity, offering a robust resource for antibody discovery and
development.
The challenge of missing or ambiguous residues due to sequencing errors or tech‑
nological limitations is also addressed by generative models such as AbLang, fea‑
turing a transformer‑based architecture.80 Trained on B‑cell repertoires,79 this model
predicts amino acid likelihoods, aiding in the restoration and generation of sequences
with enhanced properties. Comparing AbLang with the general protein language
model ESM‑1b81 demonstrated its superior capacity to capture antibody‑specic
information.
IgLM, another antibody‑specic language model with a transformer‑based archi‑
tecture, generates variable‑length CDR libraries, intending for easier lead sequence
identication.82 Trained on a massive dataset of 558million antibody sequences,79 IgLM
enables tailored sequence creation based on species and chain type (e.g., heavy, kappa,
and lambda). The model validation, which involved evaluating the sequence diversity
and computational developability proles of generated libraries, suggested its efcacy
in antibody design.
ProGen2,83 a more general protein language model, was also employed in generat‑
ing antibody sequences. The models under ProGen2 were enhanced through the train‑
ing of a suite ranging from 151million to 6.4 billion parameters on diverse datasets,
cumulatively encompassing 1 billion protein sequences from genomic, metagenomic,
and B‑cell repertoire databases. ProGen2 demonstrated its efcacy in the generation of
antibody sequences by utilizing a 764million parameter model, ProGen2‑OAS, tailored
specically for natural antibodies from the B‑cell repertoire database.79 This particu‑
lar model was instrumental in producing antibody sequences, highlighting its capac‑
ity to generate diverse and potentially therapeutically relevant sequences suitable for
antibody library construction. This capability was evaluated through the distribution
of CDR lengths and computational analysis of predicted aggregation propensity84 and
solubility.
85
The antibody‑specic generative models mentioned above do not generate 3D
coordinates of antibodies. Consequently, while these models might create antibody
libraries with potential utility, they lack a rational basis, leaving us unable to compre‑
hend the efcacy of the generated antibodies. This dilemma mirrors the challenges
encountered with traditional synthetic antibody libraries. Predicting structures could
shed light on the physics underlying sequence generation, enhancing our understand‑
ing. For the exploration of antibody libraries targeting specic antigens, 3D structures
prove even more indispensable. To address this gap, Eguchi etal. employed VAEs to
generate 3D coordinates of antibodies, focusing on direct structure generation rather
than sequences.86 The specialized loss functions used in Ig‑VAE for distance matrix and
torsion angle inference, alongside Rosetta‑based sequence design calculations, ensure
structural accuracy. This approach facilitates the development of detailed epitope‑tai‑
lored antibody libraries.
6 • From Deep Generative Models to Structure-Based Simulations 125
6.3 ANTIBODY OPTIMIZATION THROUGH
STRUCTURE‑BASED SIMULATIONS
AND MACHINE LEARNING
In advancing antibody design, structure‑based simulations and ML techniques have each
emerged as pivotal strategies, though often employed independently. Structure‑based
simulations examine the complex molecular architecture of antibodies, utilizing molec‑
ular dynamics (MD) and Monte Carlo (MC) simulations to explore the vast confor‑
mational landscape of these proteins. Such simulations are crucial for understanding
the dynamic behavior of antibodies, their interactions with antigens, and their stability
under various physiological conditions.
Alongside these computational efforts, classical ML has transformed the eld in
its own right. By analyzing extensive datasets of protein structures and sequences, ML
algorithms uncover patterns and relationships that might not be immediately observ‑
able. Techniques ranging from regression models to clustering and advanced ML algo‑
rithms like deep learning are utilized to predict antibody behavior, enhance binding
afnities, and improve stability. For ML‑based approaches, the training sets from
which predictive models learn are key factors. While several data resources are pub‑
89–91
lic,
some standardization and normalization are often necessary to integrate data
from various sources, and caution should be exercised when comparing experimental
observables from different studies. To address this, simulations can generate training
datasets for building ML‑based predictive models, as seen in the Absolut! framework,18
which enables parameter‑based unconstrained generation of synthetic data relating to
antibody‑antigen binding. Although simulations themselves still require improvement,
high‑throughput simulated functional data, otherwise inaccessible, may be acceptable.
Thus, although simulations and ML have each propelled antibody design forward, inte‑
grating these approaches represents a promising frontier yet to be fully explored.
This section rst outlines two critical concepts in computational protein modeling
and design—sampling and scoring—as they relate to structure‑based simulations, and
discusses their implications for ML‑based protein design. Subsequently, it explores how
these structure‑based simulations and ML‑based approaches have been independently
applied in successful examples of computer‑aided antibody design. It highlights the
individual strengths of structure‑based simulations and ML‑based methods, as well as
the potential synergies that could emerge from their integration.
6.3.1 Sampling and Scoring
In computational protein modeling, “sampling” refers to exploring the vast confor‑
mational space of proteins to identify potential structures they may adopt. Given the
immense number of possible protein folds, sampling is crucial for nding the most rele‑
vant conformations for subsequent analysis. Two primary methods used in sampling are
MD and MC simulations. On the one hand, MD simulations explore the atomic motions
126 Biopharmaceutical Informatics
within a protein over time, leveraging principles of classical mechanics. This approach
provides insights into the dynamic nature of proteins, including how they move and
change shape. On the other hand, MC simulations engage in the random exploration
of protein conformations, often utilizing the Metropolis algorithm to predict protein
folding and interactions. The stochastic nature of MC simulations facilitates the exami‑
nation of a wide array of conformations, including those that might not be captured
by more deterministic methodologies. In both types of simulations, protein dynamics
often depend on initial structures, and they can still become trapped in local minima of
protein energy landscapes. Therefore, to sample a wider array of conformations through
MD or MC simulations, generalized ensemble algorithms,92 such as multi‑canonical or
replica exchange techniques, are often necessary to escape from these local minima.
Once a variety of protein conformations has been sampled, the challenge shifts to
evaluating and ranking these conformations through “scoring”. Scoring utilizes scoring
functions or potentials to assess the physical plausibility and stability of each sampled
conformation. There are two main types of scoring functions: (1) physics‑based force
elds, which are derived from fundamental physical principles to calculate protein con‑
formation energies based on atomic interactions, playing a crucial role in MD simu‑
lations; and (2) statistical potentials, which, in contrast to physics‑based approaches,
are derived from the statistical analysis of known protein structures to estimate the
likelihood of specic atomic interactions and conformations in proteins. Protein design
calculations often rely on predicting the change in free energy upon mutations (i.e.,
ΔΔG = ΔG
–ΔGWT), with each ΔG value calculated using scoring functions. Although
Mut
statistical potentials heavily depend on experimental data, physics‑based force elds
are also partly parametrized using experimental observations, including structures of
model compounds and results from spectroscopy experiments.
93,94
The interplay between sampling and scoring is pivotal in computational protein
modeling and design. While sampling produces a broad spectrum of potential protein
conformations, identifying the most naturally occurring or functionally relevant ones
without an efcient scoring system is challenging. Scoring functions assess and rank
each conformation based on calculated stability or occurrence likelihood, enabling the
renement of the vast conformational space to the most plausible and signicant struc‑
tures for further exploration or application.
As discussed in the previous section, ML‑based generative models possess the
unique ability to directly generate protein sequences, thereby seamlessly integrating
the processes of sampling and scoring. A prime example of such a model tailored for
computational protein optimization is ProteinMPNN.95 This model utilizes geometric
features of proteins, trained on a message‑passing neural network (MPNN), to innovate
in the realm of sequence generation. When provided with a specic pair—an amino
acid sequence alongside its structural counterpart—ProteinMPNN is procient at gen‑
erating modied sequences better suited to the existing structure. While Dauparas etal.
demonstrated the success of ProteinMPNN in various computational protein design
tasks,95 the method relies on certain assumptions about the rigidity or prior knowledge
of protein backbone conformations for sequence generation. Therefore, while it may
be possible to improve the stability of antibodies due to the conserved nature of their
framework regions through ProteinMPNN, enhancing their function poses a greater
6 • From Deep Generative Models to Structure-Based Simulations 127
challenge. This difculty arises from the structural diversity of CDR‑H3, which plays a
critical role in antigen recognition and is the most variable and functionally signicant
of the CDRs.
6.3.2 Machine Learning in the Context of
Protein Sequence Design
In recent decades, ML algorithms have emerged as pivotal tools in analyzing extensive
datasets of antibody sequences and structures. These algorithms identify patterns and
relationships among sequences, structures, and experimental data from physicochemi‑
cal measurements.
cochemical properties such as stability, viscosity, immunogenicity, and antigen‑binding
afn it y.33 A wide array of ML‑based methods has been developed, pushing the bound‑
aries of predictive protein engineering. Typically, these models use sequences or struc‑
tures as inputs and provide outputs that predict whether a sequence or structure is stable,
has low or high viscosity, and is immunogenic or non‑immunogenic, either qualitatively
or quantitatively.
However, while ML‑based methods excel in predicting the properties of given
amino acid sequences, they do not inherently design these sequences. In essence, tra‑
ditional ML approaches have been more aligned with the scoring aspects of protein
modeling and design rather than with direct sequence generation. Protein engineering
aims to enhance wild‑type sequences to improve protein characteristics. Generating
a pool of random sequences and ltering them through prediction algorithms to nd
enhanced sequences is both laborious and inefcient. Considering the vastness of
sequence space—for instance, the FV region of antibodies, which consists of about 200
amino acids, resulting in an impractical number of possible sequences (approximately
260
10
)—this method proves to be unfeasible.
To address these challenges, more efcient methodologies that combine property
prediction with sequence sampling and scoring have been developed. Known as compu‑
tational design calculations, these innovative methods seamlessly merge the prediction
of protein properties with the creative process of designing amino acid sequences. This
synergy signicantly enhances the efciency of antibody engineering and the develop‑
ment of protein therapeutics, representing a notable advancement in the eld.
32,89,91
As a result, they enable accurate predictions of crucial physi‑
6.3.3 Computational Strategy to Improve
Stability of Antibodies
In the initial stages of biotherapeutic development, “stability” emerges as one of the pri‑
mary concerns. An antibody that is challenging to isolate and express or is structurally
fragile will not be suitable for long‑term storage, rendering it impractical, regardless of
its superior functionality. Protein expression level is sometimes referred to as an indica‑
tor of protein stability and could serve as an initial metric to assess antibody stability.
128 Biopharmaceutical Informatics
However, the terms “expression level” and “stability” are not always interchangeable.
The relationship between protein expression level and stability remains an area of ongo‑
ing research.
Within the rigorous framework of protein engineering, protein stability can be
categorized into three main types: thermodynamic stability, colloidal stability, and
chemical stability. Signicant computational efforts have been dedicated to improving
thermal and colloidal stability and, more recently, the chemical stability of proteins.
This initially involved structure‑based simulations and later incorporated ML‑based
methods. Since the functional sites of antibodies, or CDRs, are exposed and localized
on the surface, design efforts often target the framework regions across the FV region to
enhance stability without compromising functionality.
Advancements in computational power and prediction algorithms have now made
it feasible to explore a point mutation‑based approach. This method involves assessing
stability changes (ΔΔG) through exhaustive replacement with 19 amino acids (i.e., all
natural amino acids other than the wild type) at each position, from the N‑terminal
region to the C‑terminal region, along an antibody FV sequence. Despite the rise of the
big data era and ML‑based approaches, structure‑based simulations continue to play a
pivotal role in antibody design.
101
FoldX,
it is possible to sample all potential single mutations along an FV region in
16,9 6–99
Using protein design software like Rosetta
100
or
several hours or less, even with a standard laptop computer. Therefore, in the realm of
computational stability engineering, the scoring step emerges as a crucial challenge.
Thermodynamic stability of proteins is often experimentally assessed using dif‑
ferential scanning calorimetry (DSC) and differential scanning uorimetry (DSF),
which measure the melting temperature (Tm). A single mutation can increase the Tm
of an antibody by up to ~8°C, particularly when the target site is a part of a cavity and
deeply buried within the structure.
102
However, cavity‑lling stabilizing mutations could
alter the structural dynamics of antibodies, potentially leading to diminished function‑
ality. Thus, to signicantly enhance antibody thermal stability without compromising
function, multi‑point mutations are often necessary. However, the number of potential
mutations grows exponentially when sampling two or more amino acids simultaneously,
and addressing long‑range interactions—where a mutation at one site may inuence
another at a distant site—remains a challenge. Protein design simulations typically treat
the protein backbone as xed or permit only minor adjustments, failing to account for
allosteric‑like effects that might emerge from distant mutation sites. MD simulations,
supplemented by mathematical graph theory analysis, are frequently used to investigate
the allosteric effects on protein structures.
103
Nevertheless, these approaches are still in
the exploratory phase, and there is no universally accepted method for computation‑
ally analyzing the allosteric effects of mutations. Therefore, combinations of mutations
are often applied by integrating the outcomes of computationally identied single‑point
mutations.
Recent studies exemplify the efcacy of computational point mutation scanning in
stability engineering. Tomimoto etal. leveraged the dStability score from MOE molecu‑
lar modeling software to assess the impact of single‑point mutations across an entire
domain on the thermal stability of a V
H antibody, yielding a mutation that enhanced
H
6 • From Deep Generative Models to Structure-Based Simulations 129
stability by over 5°C, as veried by DSF.
104
In an approach that uses a more rational
strategy for selecting design positions, Okazaki etal. improved the stability of a scFv
antibody by targeting its exible regions.
105
Identied through triplicate 50‑ns MD sim‑
ulations, four mutations were strategically chosen to promote hydrogen bond forma‑
tion between these exible sites and surrounding residues. The feasibility of forming
such hydrogen bonds was then reassessed through additional MD simulations, wherein
the sidechain conformations of these mutations underwent evaluation via a continuous
rotamer search, guided by the CHARMM36 force eld. Consequently, one mutant dis‑
played a thermal stability (Tm) increase of 5°C over the wild type.
Stability often poses a challenge in the process of humanizing antibodies, where
there is a concerted effort to generate sequences that are more human‑like than those
derived from other animals.
106
To address this challenge, Tennenhouse etal. introduced
CUMAb, a simulation‑based methodology facilitating the systematic grafting of animal
CDRs onto human frameworks.
107
Utilizing Rosetta for selecting frameworks based on
energy and structural integrity, CUMAb‑designed humanized antibodies matched or
surpassed the afnities of their animal counterparts, with notable stability enhance‑
ments observed.
Furthermore, by leveraging immune repertoire sequencing data, ML‑based meth‑
ods have emerged as viable alternatives for stabilizing antibodies in the context of
antibody humanization. Prihoda et al. developed a deep learning‑based framework
called BioPhi to be used for the antibody humanization process.
108
BioPhi optimizes
the humanization process for therapeutic antibodies by offering an automated human‑
ization workow that incorporates novel methods based on deep learning and natu‑
ral antibody repertoires. Specically, BioPhi introduces a humanization method called
Sapiens, which is trained on the OAS79 using masked language modeling.
109
This deep
learning approach allows BioPhi to generate humanized antibody sequences at scale
while achieving results comparable to those of human experts.
The integration of structure‑based simulations and ML‑based methods represents
an innovative and growing trend in the eld of protein engineering. Lai etal. tackled
the prediction of antibody aggregation and viscosity by integrating molecular dynamics
simulations with ML models, yielding insights critical for therapeutic protein develop‑
110
ment.
Their study demonstrated that features derived from MD simulations could
effectively inform ML models to predict aggregation rates and viscosity classications.
Notably, the k‑nearest neighbors (KNN) regression model showed remarkable accuracy
in predicting antibody aggregation rates, utilizing spatial positive charge maps and sol‑
vent‑accessible surface areas of hydrophobic residues. This model achieved a correlation
coefcient (r) of 0.89 with experimental aggregation rate measurements. Meanwhile, a
logistic regression model excelled in classifying antibodies by viscosity, drawing on
spatial charge distribution features. In a related effort, Harmalkar et al. developed a
CNN‑based classier, trained on Rosetta’s energetic features, illustrating the potential
of integrating structure‑based simulations with ML to identify thermostable residues
with a 90% success rate.
111
These examples, from Lai etal.
110
and Harmalkar etal.,
111
highlight the synergistic potential of structure‑based simulations and ML in rening
antibody sequences, offering new pathways for antibody engineering.
130 Biopharmaceutical Informatics
6.3.4 Computational Strategy to Improve
Functionality of Antibodies through
Structure‑Based Simulations
Afnity maturation, a critical process in the adaptive immune response, refers to the
evolutionary mechanism by which B cells produce antibodies with increasingly higher
afnity for their specic antigens. This process occurs through somatic hypermuta‑
tion (SHM), a targeted mutation mechanism affecting the variable regions of antibody
genes within the germinal centers of lymph nodes. During an immune response, B cells
that recognize an antigen undergo rapid proliferation and mutation. This results in an
antibody repertoire that is increasingly specic and effective against the target antigen,
enhancing our ability to ght infections and respond to vaccines. Afnity maturation is
fundamental to the development of long‑lasting immunity and a key consideration in the
design of therapeutic antibodies. Similarly, but more rapidly, antibodies can be evolved
in computers. Several such approaches have been proposed to improve binding afni‑
ties and/or other biophysical properties of antibodies. Many of such successful examples
were based on molecular simulations (Table6.2).
In cases where structures of antibody‑antigen complexes are unavailable, mod‑
eling such complex structures becomes a crucial starting point for functional design.
Despite steady improvements in protein‑protein docking algorithms,
dicting the structures of antibody‑antigen complexes remains a formidable challenge.
There are a few instances where homology modeling followed by docking simulations
has been employed, leading to computational afnity maturation based on these mod‑
eled complex structures.
113,114
However, these approaches often necessitate iterative and
labor‑intensive processes, such as experimental mutagenesis studies and advanced MD
techniques, such as replica exchange MD and umbrella sampling, to rene predictions.
While the exploration of conformational space in docking simulations is somewhat con‑
strained due to the nature of antibodies (which recognize antigens primarily via CDRs),
probing the conformational space on the antigen side of the antibody‑antigen interface
or identifying epitopes continues to be highly complex.
The recent emergence of AlphaFold2 (AF2) has signicantly impacted the elds of
protein science in various ways.
115
Originally developed for predicting the structures of
monomers from their amino acid sequences, it is now becoming feasible to utilize AF2
for modeling protein‑protein complexes.
116
Although the initial version of AF2 was not
specically designed for predicting structures of antibody‑antigen complexes, given the
independent evolutionary paths of antigens and antibodies, a recent update to AF2 has
reportedly achieved success in this area, although details are yet to be disclosed at the
time of writing this chapter.
antibody‑antigen complexes
tional insights,
119
integrating machine learning tools like AF2 with simulation‑based
117
Given the growing repository of structural information on
118
and the availability of B‑cell repertoire data with func‑
approaches appears to be a promising direction for accurately modeling antibody‑anti‑
gen complexes.
26,28,120
Due to the difculty of predicting antibody‑antigen complex structures, early efforts
in computational antibody design focused on enhancing species cross‑reactivity
112
accurately pre‑
121
and