Interplay of Epitope Landscapes: Unraveling Molecular Recognition through Mapping, Docking, and Simulation

Abstract

Molecular docking is a crucial process in computational immunology that involves the precise binding of antibodies to antigens, illuminating the key sites responsible for immune recognition named epitopes. Understanding these molecular interactions at a detailed level is critical in unraveling immune responses and developing targeted interventions. Commencing with the intricacies of antibody design, the review will explore the connection among molecular features, including interaction environments, structural features, and the specificity of these interactions. The review will underscore how this approach not only expedites research processes but also offers cost-effective avenues for in-depth exploration of molecular landscapes. Finally, the practical applications of these insights in diagnostics and vaccine development will be discussed, showcasing the transformative potential of understanding and manipulating antibody-antigen interactions at the molecular level.

1. Introduction

The field of antibody production and engineering has advanced significantly, enabling the development of monoclonal antibodies tailored for specific therapeutic targets. High-throughput sequencing technologies have revolutionized our understanding of the B-cell receptor repertoire, providing insights into antibody diversity and facilitating the design of antibodies with enhanced binding affinity and specificity [1]. This progress has been instrumental in the production of antibodies for a variety of applications, from cancer immunotherapy to the treatment of autoimmune diseases. At the heart of antibody-antigen interactions lies the concept of epitope mapping, the process of identifying the precise binding sites on an antigen where an antibody attaches. This understanding is crucial for the design of vaccines and therapeutic antibodies as it allows for the precise targeting of pathogens or diseased cells. Epitope mapping not only aids in the development of targeted interventions but also enhances our understanding of immune recognition and response mechanisms.

Despite the advancements in sequencing and antibody engineering, accurately predicting the binding sites of antibodies on antigens remains a complex challenge. Traditional experimental methods, such as X-ray crystallography and mutagenesis, have provided valuable insights but are often time-consuming, expensive, and limited in their throughput. Computational methods offer a promising alternative, capable of high-throughput analyses and providing atomic-level details of antibody-antigen interactions [1, 2].

Molecular docking emerges as a powerful computational tool in this context, predicting how an antibody interacts with its antigen [3]. The development of docking algorithms, such as those implemented in ClusPro, has significantly advanced our ability to predict these interactions accurately. These tools use sophisticated models to simulate the physical forces between the antibody and antigen, providing valuable insights into the nature of their binding. The introduction of advanced computational platforms, such as ClusPro-AbEMap, represents a significant leap forward in epitope mapping [4]. By integrating template-based modeling with docking algorithms, these tools can predict epitope sites with unprecedented accuracy. This approach not only accelerates the discovery of potential therapeutic targets but also opens new avenues for vaccine development and the study of immune responses.

Molecular docking stands as a cornerstone in the interdisciplinary field of computational biology and immunology, offering a simulation-based approach that predicts the optimal interaction between two molecules—typically, a smaller molecule (ligand) and a larger molecule (receptor), such as an antibody or enzyme [3]. In computational immunology, this technique is pivotal for simulating the complex dance between antibodies (the defenders of the biological realm) and antigens (foreign invaders such as viruses and bacteria). This interaction is fundamental for identifying how the immune system recognizes, attaches to, and neutralizes pathogens. By simulating these interactions, molecular docking allows scientists to predict the binding affinity between molecules, providing insights into the structural configurations that underlie immune recognition and response mechanisms.

The origin of molecular docking can be traced back to the early 1980s, a period marked by increasing interest in leveraging computational power to solve biological problems [5]. Initially, the practice faced significant hurdles, including limited computational resources and a nascent understanding of molecular dynamics and interactions. However, as computational technology advanced, so too did molecular docking techniques, evolving from simplistic models to sophisticated algorithms capable of simulating the dynamic nature of molecular interactions with remarkable accuracy. This evolution was propelled by the advent of high-throughput computing and enhanced algorithms for predicting molecular behavior, marking a paradigm shift in how researchers approached the study of molecular interactions in variety of areas [6].

The principal aim of molecular docking in immunology is to elucidate the interactions between antibodies and antigens at a granular level [7]. This insight is critical for several reasons. First, it sheds light on the immune system’s recognition and neutralization pathways, revealing how antibodies can be engineered or elicited through vaccination to target specific pathogens effectively. By understanding these molecular dialogs, scientists can identify the most accessible and immunogenic parts of an antigen, guiding vaccine design and antibody therapy development.

Furthermore, molecular docking has revolutionized the field of drug discovery [6]. Its ability to screen vast compound libraries for potential interactions with biological targets offers a faster, more cost-effective route to identifying new drugs. This is particularly crucial in the rapid response to emerging infectious diseases, where time is of the essence. The predictive power of molecular docking has facilitated the development of targeted therapies, contributing significantly to personalized medicine by tailoring treatments to the individual’s specific molecular profile.

2. Understanding epitopes: types, features, and mapping

Epitopes represent the cornerstone of immune recognition, driving the development of targeted immunotherapies and diagnostic strategies. Understanding the diverse types and features of epitopes, coupled with sophisticated mapping techniques, empowers researchers in the quest for effective vaccines and immunotherapies against infectious diseases, autoimmune disorders, and cancer. Continued advancements in epitope research hold promise for personalized medicine and precision immunotherapy in the future.

2.1 Types of epitopes

2.1.1 Continuous epitopes

A linear peptide fragment that binds to the antibody is called a continuous epitope [1]. Since the classification criterion is based on binding activity, the continuous epitope is defined functionally, and there are no reports suggesting the role of every residue in the peptide making contact with the antibody. The binding of individual amino acids in the peptide epitope is determined by positional scanning with amino acid residue replacement [8]. Despite their rare occurrences, linear epitopes find potential use as reagents alternative to infectious agents in diagnostics and as immunogens in developing antibody reagents and synthetic vaccines overall [9].

2.1.2 Discontinuous epitopes

The second category of epitopes, called discontinuous or conformational epitopes, are formed by the amino acid residues brought together by protein folding [1]. The majority of the epitopes studied are discontinuous in nature, and their interaction with the antibody requires the folded form of the native protein. For this reason, discontinuous epitopes cannot be extracted as a separate segment from the original protein molecule nor is it possible to demonstrate their binding activity independently. Apart from practical use, this phenomenon also limits the functional assignment of the epitope as it can only be defined structurally, and mapping studies require sophistication, such as X-ray crystallography or NMR spectroscopy.

2.2 Features of epitopes

Epitopes play crucial roles in immune recognition and response. Firstly, they exhibit antigenic specificity, selectively binding to complementary antibodies or T-cell receptors, which forms the foundation for immune recognition and the generation of targeted immune responses against pathogens. Additionally, epitopes possess immunogenicity, the ability to induce an immune response. Several factors influence immunogenicity, including epitope accessibility, structural stability, and the host immune repertoire [7]. Moreover, epitopes demonstrate flexibility and plasticity, enabling them to accommodate structural variations without compromising antibody binding. This flexibility facilitates antigenic variation in pathogens, aiding immune evasion strategies. Finally, epitopes may display cross-reactivity, where antibodies raised against one epitope recognize structurally similar epitopes on unrelated antigens. This cross-reactivity has implications for vaccine design and diagnostic assays, highlighting the importance of careful epitope selection to minimize off-target effects.

2.3 Mapping techniques for epitopes

2.3.1 Experimental approaches

X-ray crystallography determines the three-dimensional structure of antigen-antibody complexes at atomic resolution, offering insights into conformational epitopes [10]. Nuclear magnetic resonance (NMR) spectroscopy studies protein structure and dynamics in solution, useful for mapping flexible epitopes [11]. Surface plasmon resonance (SPR) quantitatively analyzes epitope-paratope binding kinetics and affinity, providing real-time insights into molecular interactions [12].

2.3.2 Computational approaches

Homology modeling predicts epitope structures based on sequence similarity, aiding in peptide vaccine design. Molecular docking epitope binding modes and affinities, facilitating virtual screening of epitope libraries.

3. Antibody-antigen interaction and interfacial properties

3.1 Amino acid composition and propensity

In the complex interplay between antibodies and antigens, the amino acid composition of their interface plays a critical role in determining binding specificity and affinity. Analyzing the amino acid composition of both the epitope contact surface (ECS) of antigens and the paratope contact surface (PCS) of antibodies unveils key insights into their structural and functional characteristics [13]. The features of antibody-binding interfaces, such as size, shape, and chemical composition, have been analyzed extensively [13, 14]. Studies suggest that the interfaces of antigen-antibody complexes differ significantly from those of nonimmune protein complexes. These interfaces are particularly rich in aromatic residues, such as Tyr, Arg, His, Phe, and Trp. The role of enriched aromatic residues, especially Tyr and Trp, in antibody-antigen binding selectivity and specificity has garnered considerable interest. Mutagenesis studies have highlighted the unpredictable nature of affinity changes upon mutating these residues, with some identified as “hot spots” contributing significantly to high-affinity interactions [13]. Tyr and Trp are noted for their ability to form close interactions with antigen proteins due to their aromatic and hydroxyl groups. This contrasts with Phe, another aromatic residue, which is rarely found at antibody interfaces, possibly due to the absence of functional groups in its aromatic ring. The piling up of aromatic rings in these “aromatic islands (AI)” may provide extra stabilizing energy to protein complexes, creating a highly hydrophobic environment that favors hydrophobic interactions [14]. This dense gathering of aromatic side chains contributes significantly to antigen-antibody binding. It is also suggested that cooperative effects exist within AI, leading to an increase in binding affinity with more Tyr or Trp residues stacking together. Mutations of aromatic residues in aromatic islands can have significant effects on binding affinity, as suggested by studies [15].

The ECS of antigens exhibits a diverse range of amino acid residues, with peptide antigens displaying a smaller and more variable composition compared to larger protein antigens [13]. Interestingly, the PCS of antibodies contains almost twice the number of amino acid residues as the ECS, indicating a higher density of interaction. Furthermore, certain residues, such as TrpP and TyrR, are enriched in the interface, suggesting their pivotal role in the recognition process. The occurrence propensity of amino acids in the interface normalizes their tendencies to occur on protein surfaces and provides a measure of their significance in antibody-antigen interactions. Residues, such as TrpP and TyrR, exhibit higher occurrence propensity in the ECS, indicating their importance in driving specific interactions. Conversely, residues, such as CYS and Phe, show lower occurrence propensity, suggesting their lesser involvement in interface interactions.

3.2 Interaction specificity and substitutability

The specificity of interactions between amino acid residues in the antibody-antigen interface is crucial for understanding the molecular basis of binding affinity and selectivity. Analysis of interaction frequency and substitutability gives insights into the complex nature of these interactions and their functional implications.

Certain amino acid residues exhibit high substitutability, suggesting their roles in maintaining interface integrity and specificity [13]. By assessing the correlation between pairs of residues, it becomes evident which residues are more likely to interact with specific counterparts on the opposite surface. Residues with low correlation values indicate lower substitutability, while those with higher correlation values suggest higher substitutability and possibly more promiscuous interactions. Although the number of contacts in specific regions of antibodies can be mapped, it does not directly correlate with the energetic contributions to the binding interface [16]. However, regions such as CDRH3, known for mediating antibody contacts, show high populations of unique paratope residues (UPRs) suggesting a potential correlation between the number of UPRs and energetic contributions. Noncontact residues in antibodies may play a significant role in supporting the orientation and flexibility of paratope residues.

These findings provide valuable insights into the structural determinants of antibody-antigen recognition and have implications for engineering antibodies with enhanced binding properties. Understanding the specificity and substitutability of interface residues enables the design of antibodies with tailored binding affinities for therapeutic and diagnostic applications.

3.3 Spatial distribution and functional implications

The spatial distribution of amino acid residues within the antibody-antigen interface offers valuable insights into the structural organization and functional implications of different regions [13, 17]. Analysis of residue distribution unveils patterns that reflect the dynamic nature of interface interactions and their functional consequences. Residues on the PCS are distributed more evenly across the interface compared to those on the ECS, suggesting a dynamic role in mediating interactions with diverse epitopes. Certain residues, such as TyrR, exhibit preferential localization closer to the center of the interface, indicating their involvement in key interactions critical for stabilizing the complex.

Understanding the spatial distribution of residues is essential for deciphering the structural dynamics and functional implications of the antibody-antigen interface. By elucidating these aspects, researchers can gain deeper insights into the mechanisms driving antibody-antigen interactions and develop novel strategies for modulating immune responses and designing therapeutic interventions.

3.4 Salt bridges

Electrostatic interactions have major influence in protein interactions, significantly contributing to binding free energy [18]. Among these interactions, salt bridges, formed by oppositely charged residues pairing up (e.g., positively charged lysine with negatively charged aspartate), play a crucial role. Numerous investigations have underscored the pivotal role of salt bridges in binding affinity. For instance, in the NC10 Fab-NA complex, the presence of an AspH56-LysN432 salt bridge was identified [19]. Disruption of this salt bridge, along with two hydrogen bonds from SerN370 and SerN372, through the replacement of AspH56 with serine, alanine, or glycine, led to a significant loss in binding, underscoring the essential role of salt bridge formation. In a separate study aimed at enhancing the binding affinity of AspH56, replacement with glutamate was explored [20]. It was found that the terminal side chain carboxyl oxygen of GluH56 was brought into closer proximity to LysN432, thereby strengthening the salt bridge. However, this replacement resulted in a substantial 40-fold decrease in binding affinity, presumably due to steric clashes introduced by the bulkier side chain, hindering the formation of binding energies. Studies estimating the contribution of ΔG 5–7.0 kcal/mol energy by the charged Asp H52 further underscore its significance [21], suggesting its essential role in the functional epitope region, which encompasses several charged residues [22]. This functional significance of salt bridges is reiterated in various other studies [23, 24].

3.5 Hydrogen bonds in antibody binding

Despite assumptions regarding the heightened significance of hydrogen bonding in achieving specificity and affinity, discrepancies arise regarding their precise energy contribution values [25, 26, 27] primarily due to the presence of water molecules competing for hydrogen bonding sites. Interfacial water molecules emerge as key contributors to hydrogen bond formation in numerous protein-protein complexes [28]. In a recent investigation, hydrogen bonds within the HyHEL-10-HEL complex were found to be either buried or partially exposed [29]. Moreover, deletion of hydrogen bonds resulted in thermodynamic compensation for structural rearrangements caused by mutations.

Consistent with observations in various biomolecular complexes, unfavorable enthalpy changes are compensated by favorable entropy changes [30, 31], suggesting a degree of tolerance for the loss of hydrogen bonds within antigen-antibody complexes. Interfacial water molecules play a significant role in stabilizing antigen-antibody interactions by facilitating hydrogen bond formation. Additionally, hydrogen bonds are reported to reduce conformational flexibility, leading to the stiffening of antigen-antibody complexes [32, 33]. While interfacial water molecules are proposed to enhance the specificity of protein-protein interactions [34], their energy contributions have been studied in only a limited number of protein complexes [35, 36].

3.6 Shape complementarity

Molecular recognition relies heavily on the complementarity of shapes at protein interfaces (Figure 1). Previous studies have analyzed shape complementarity in protein interfaces, finding that antibody-antigen interfaces exhibit lower average shape complementarity compared to general protein-protein interfaces [37]. This difference is attributed to the independent evolution of antibodies and their antigens, whereas other protein interactions typically evolve together to optimize complementarity. However, later analyses suggested that the lower shape complementarity observed in antibody-antigen complexes may be due to limitations in the quality of crystal structures used [38].

Figure 1.

Shape complementarity and hydrogen bonds influencing antigen-antibody complex formation at the interfacial level.

As computational design aims to create novel interfaces by optimizing a single protein, similar to antibodies, understanding the level of shape complementarity achievable in such single-sided interface design is crucial [39]. Previous studies on protein-protein interfaces have shown that obligate interfaces, which are more hydrophobic, exhibit better shape complementarity, and evolve slower than transient interfaces. Protein-protein interfaces are also characterized by an enrichment of nonpolar and aromatic residues, with a depletion of charged residues, although arginine residues are a common exception and are often found in interface “hot spots” along with tryptophan and tyrosine residues. Further, analyses have identified residue-level local network patterns in protein-protein interfaces and have identified numerous atomic-resolution motifs in these interfaces [39].

4. Computational study of antigen-antibody interactions

The computational exploration of antigen-antibody interactions is a fundamental pursuit in immunological research, shedding light on the intricate dynamics associated. Research by Tsumoto and Caaveiro [40] underscores the fundamental role of antigenic nature in shaping the antigen combining site of antibodies. This phenomenon manifests distinctly across antibody classes: haptens elicit deep pocket formations, peptides evoke grooves, while protein binding elicits flat combining sites.

The interplay between the length and residue composition of complementarity determining regions (CDRs) and antigenic properties has garnered considerable attention. Al-Lazikani et al. [41] and Morea et al. [42] elucidated how the six hypervariable loops adopt characteristic conformations dictated by amino acid residues and length, termed canonical conformations. Furthermore, the compositional bias of CDR residues shapes the architecture of the antibody combining site. Residues such as methionine, isoleucine, and serine abound, while phenylalanine, cysteine, valine, and glycine are sparse. Notably, aromatic residues, such as tryptophan and tyrosine, confer a sticky nature to the combining site, crucial for molecular interactions [43, 44]. Distinct antigen-binding classes exhibit varying residue preferences, indicative of their binding specificities. For instance, alanine is underrepresented among hapten binders, while phenylalanine shows scarcity among peptide and hapten binders [45]. Interestingly, certain trends, such as the prevalence of arginine among nucleotide binders, are attributed to interactions with negatively charged phosphate backbones [43].

The significance of residues, such as tyrosine, lies in their multiple contact sites and broad interactive potential with epitopes, facilitating epitope-paratope interactions [13]. Moreover, hydrophobic aromatic residues, such as tyrosine and tryptophan, likely engage in interactive charged group interactions, enhancing specificity and affinity [13, 43]. Hydrogen bonds, van der Waals forces, salt bridges, and hydrophobic interactions collectively contribute to antigen-antibody binding affinity. While hydrogen bonds and van der Waals forces primarily enhance specificity, shape complementarity between binding surfaces and the exclusion of solvent molecules underscore the role of hydrophobic forces in driving interactions [40]. Understanding these molecular nuances not only advances our comprehension of immune responses but also paves the way for innovative therapeutic interventions targeting antigen-antibody interactions.

4.1 Computational antibody modeling

The antigenic interaction of antibodies hinges upon six critical CDRs: L1, L2, L3, H1, H2, and H3, with the hypervariable loops’ architecture shedding light on their interactions. Except for H3, all five CDRs have been assigned conformations known as “canonical structures” based on their amino acid sequences and lengths [41, 46]. Evolved through VDJ gene segment arrangements and somatic mutations, H3 loop structures exhibit indefinite conformations, adding complexity to structural determinations, with no established H3 loop structures to date [46, 47].

H3 loops vary in sequence, length, and structure, thereby acquiring diverse antigenic specificities. These regions are pivotal in antigenic recognition, adopting numerous conformations upon antigenic binding [47]. With the emergence of high-resolution antibody crystal structures, a new scheme proposed by [48] aimed to cluster CDR conformations based on conformational energies. This approach eliminated low-resolution CDR loops with high B-factors or high-energy backbone conformations suggested [46]. Utilizing sequence-to-structure relationships derived empirically, it is possible to model L1, L2, L3, H1, and H2 structures with high accuracy, with the root mean square deviation (RMSD) of the modeled structure’s backbone to the crystal structure falling below 1.0 Å.

Situated at the core of other loop structures, H3 loops play a significant role in antigen recognition, undergoing substantial mutations during the affinity maturation process compared to other loop structures [49]. They assume an infinite number of conformations, making structure prediction a daunting task, especially during antigenic binding, where they undergo significant structural rearrangements. Computational and experimental studies have built several models based on sequence-to-structure relationships, considering the behavior of H3 loops [50, 51, 52].

While H3 loops exhibit indefinite conformations, their C-terminal base regions form limited structures, often in “kinked” or extended forms. These sequence-to-structure rules simplify the challenge of H3 loop modeling by reducing the computational search space [53]. These strategies typically search for appropriate loop conformations from a set of existing loop conformations based on energy or scoring function methods derived from a crystal structural database search [54]. Another approach, adopted by the Rosetta antibody modeling server, combines fragment assembly with a cyclic coordinate descent approach and minimizes fragments using the Rosetta protocol [55]. While the antibody models generated are optimal for further docking analyses in the RosettaDock program [56, 57], the modeling protocols reportedly have limitations in H3 modeling.

In summary, the prediction becomes challenging with longer loop structures, exacerbated by larger flexibility in long surface loops and consequent conformational complexities during antigenic interactions or artifacts arising from crystal packing [58]. Several web servers are available for antibody modeling (Table 1) with varying features. Rosetta antibody modeling server, developed by Sircar et al. [59], employs an energy function and rigid body minimization-based VL-VH docking methodology to improve the orientations of H3 and other loops. The Web Antibody Modeling (WAM) server, as proposed by Whitelegg and Rees [60], follows a different approach.

Tool/platform	Methodologies	Features	Advantages and disadvantages
Rosetta antibody	Multistep protocol for modeling the variable domain, template selection, loop grafting, and de novo loop modeling.	Comprehensive modeling protocol for antibody variable domains; Integration of template-based modeling and de novo techniques.	Advantages: offers a comprehensive approach for modeling antibody structures; Enables accurate prediction of variable domain conformations.; Disadvantages: limited availability of templates, especially for nonhuman antibodies may require manual template selection for improved accuracy
AbPredict	Utilizes low-energy combinations of backbone fragments from experimentally determined structures.	Generates antibody models based on low-energy backbone fragment combinations.	Advantages: demonstrates promising performance in modeling antibody structures; Disadvantages: utilizes experimentally determined backbone fragments from related structures for modeling
RosettaCM	Multi-template homology modeling protocol suitable for antibodies with noncanonical features or unusual loop lengths	Offers an alternative approach for antibody structure prediction; Advantageous for modeling antibodies with nonstandard features.	Advantages: versatile modeling protocol for diverse antibody structures, effective for modeling antibodies with noncanonical features; Disadvantages: limited by the availability and quality of homology templates may require optimization for specific antibody structures
WAM Server	Web-based antibody modeling platform utilizes comparative modeling techniques.; Integrates experimental data and machine learning algorithms.	Provides a user-friendly interface for antibody modeling incorporates experimental data to enhance accuracy offers rapid and automated modeling of antibody structures.	Advantages: accessibility through a web interface-integration of experimental data improves modeling accuracy.; Disadvantages: limited customization compared to standalone software. Dependency on server availability and processing speed.

Table 1.

Overview of the methodologies, features, and advantages and disadvantages of antibody modeling tools and platforms.

The orientation of variable domains exhibits variability across different antibodies and antigenic interactions [47], impacting the surface involved in antigenic interaction when the structure is inferred from the sequence [39]. Studies examining residue properties and positions across various antibody sequences [61, 62] have revealed that variable domain orientation is sequentially encoded, although this reliance has proven elusive. Furthermore, the domain orientation can vary upon antigenic binding, emphasizing the importance of considering predicted domain orientation when selecting templates for antibody modeling [63]. Covariation analyses of residues representing the Ig-fold, derived from sequence repositories, have unveiled the presence of residue conservation networks in VH and VL domains [64]. These conserved residue network clusters were notably observed at the VL-VH and VH-CH domain interfaces, whereas the VL-CL domain interface lacked such conserved residue networks.

4.2 Computational docking of antibodies

Computational docking antibodies offer a timely and cost-effective alternative to experimentally deduced structures, which often require significant time, expertise, and expense for the crystal formation of complexes. This methodology involves modeling antibody structures and employing computational docking protocols to establish interaction patterns [65]. While computational docking is continuously improving, limitations arise when using modeled antigen and antibodies as starting structures [66]. However, combining computational predictions with experimental knowledge from epitope mapping or mutagenesis can enhance accuracy in understanding antigen-antibody interactions.

The Rosetta modeling program, as described by Sivasubramanian et al. [65], utilizes existing crystal structures as templates to assign framework regions and construct the beta barrel. It employs loop grafting and loop modeling to achieve non-H3 loop conformations, followed by refinement through Monte Carlo minimization and “all-atom energy function” algorithms. Model building error checking involves two tests: native loop recovery test and homology modeling test. These tests ensure fidelity to crystallographic CDR loop conformation and the ability to build homology models from sequence alone, respectively.

The effectiveness of protein-protein docking in studying antibody-antigen complexes was demonstrated using a modeled antibody and antigen crystal structure with the RosettaDock program [55, 65]. The protocol (Figure 2) involves rigid body Monte Carlo search and low-resolution interaction potential, followed by refinement using an all-atom scoring function. This approach was applied to study the epidermal growth factor receptor (EGFR) antibody-antigen complex, incorporating experimental mutagenesis data. Structural errors in homology-based models can lead to inaccuracies in subsequent protein-protein docking protocols, especially concerning backbone flexibility during interactions [67]. Ensemble docking, which incorporates conformational ensembles from sampling methodologies or molecular dynamics simulations, addresses this concern efficiently. Ensemble docking has demonstrated success in identifying near-native models and fixing backbone flexibility [68, 69].

Figure 2.

The RosettaDock scheme illustrates the computational docking process for antibodies, showcasing the workflows of the Rosetta modeling program and ensemble docking.

Among the various docking algorithms (Table 2), RosettaDock has emerged as a prominent tool for modeling antibody-antigen interactions. RosettaDock [70] employs a sophisticated protocol involving low-resolution rigid body perturbations and high-resolution sidechain and backbone minimization to generate models of the docked complex. The algorithm’s success lies in its ability to explore a vast conformational space and identify low-energy structures corresponding to native conformations. Benchmarking studies have validated the efficacy of RosettaDock in recovering native conformations, with encouraging results in both local and global searches. RosettaDock is a widely used docking algorithm that enables the prediction of antibody-antigen complexes with full backbone flexibility. The protocol involves low-resolution docking steps followed by high-resolution refinement, allowing for the exploration of conformational space and optimization of side-chain interactions. Despite its effectiveness, RosettaDock relies on the input structures of both docking partners, which may limit its applicability to cases with experimental structures available for both antibodies and antigens. Nevertheless, RosettaDock serves as a robust framework for investigating antibody-antigen interactions and guiding antibody engineering efforts.

Algorithm name	Key features	Accuracy	Computational requirements	Applications
ClusPro	Utilizes sophisticated models for accurate prediction of interactions.; Incorporates energy minimization and clustering algorithms.	Demonstrated high accuracy in predicting protein interactions.; Capable of identifying binding poses within a reasonable computational time.	Moderate computational resources are required.; Suitable for standard computing setups.	Predicting protein-protein interactions.; Drug discovery and design.
ClusPro-AbEMap	Integrates template-based modeling with docking algorithms.; Predicts epitope sites with unprecedented accuracy.	Provides accurate predictions of epitope structures and binding modes. Enhanced epitope mapping capabilities.	Requires moderate to high computational resources due to template-based modeling.; Parallel processing may improve efficiency.	Accelerating discovery of potential therapeutic targets vaccine development.
RosettaDock	Employs low-resolution rigid body perturbations and high-resolution refinement.; Able to explore a vast conformational space and identify low-energy structures.	Demonstrated efficacy in recovering native conformations suitable for both local and global searches.	Requires significant computational resources due to its extensive conformational sampling.; Parallel processing and high-performance computing are advantageous.	Modeling antibody-antigen complexes with full backbone flexibility guiding antibody engineering efforts.
SnugDock	Tailored perturbation moves specific to antibody-binding motifs.; Addresses conformational variability of antibody CDR loops.	Demonstrated superior performance in predicting antibody-antigen complexes.; Particularly effective in ensemble docking approaches.	Requires moderate computational resources compared to other algorithms parallel processing may enhance performance.	Improving accuracy in docking predictions; Enhancing antibody engineering and therapeutic development.

Table 2.

Comparison of the key docking algorithms, highlighting their features, accuracy, computational requirements, and applications.

The introduction of advanced computational platforms, such as ClusPro-AbEMap, represents a significant leap forward in epitope mapping. By integrating template-based modeling with docking algorithms, these tools can predict epitope sites with unprecedented accuracy. This approach not only accelerates the discovery of potential therapeutic targets but also opens new avenues for vaccine development and the study of immune responses.

Recent advancements include the development of SnugDock [71], a docking program tailored for antibody-antigen interactions. SnugDock optimizes paratope by CDR loop conformations and VL-VH domain orientations during docking, showcasing flexibility and success in general protein-protein docking protocols. Integration of experimental information, such as NMR data, enhances computational prediction accuracy, as demonstrated in studies like [72], where RosettaDock was applied to antibody ensemble models of PIGS and Rosetta antibody. These studies underscore the importance of considering backbone flexibility and experimental data in computational docking to improve predictive accuracy.

4.3 Advances in antibody docking

4.3.1 A stepwise docking molecular dynamics approach

The method [73] aimed to overcome the shortcomings of previous approaches by splitting the binding process into multiple steps, enabling a more realistic simulation of the docking process. Structural changes play significant roles in receptor-ligand recognition, but the underlying allosteric mechanisms are not fully addressed in the general molecular docking process. Molecular dynamics (MD) simulations have been widely used to trace the trajectory of atoms in predefined systems, aiding in the comprehension of specific biological processes.

The semi-biased stepwise docking MD was reported to yield better results, effectively recapping conformational rearrangements observed in experimental data. Traditional docking methods, on the other hand, failed to reproduce the conformational changes in the HCDR3 loop as observed in the test conditions. These findings suggest that MD-based approaches, such as the semi-biased stepwise docking MD, offer advantages over traditional docking methods in capturing complex allosteric mechanisms involved in antibody-antigen interactions.

The algorithm also highlighted important parameter considerations for the application of the stepwise docking MD method. These considerations include aspects such as binding orientation, separation distance, global influence, and endpoint criteria. Optimizing these parameters is crucial for ensuring the efficiency and accuracy of the simulation in capturing intricate allosteric mechanisms.

4.3.2 The SnugDock algorithm

SnugDock [71] represents an antibody-specific extension of the RosettaDock protocol, tailored to address the challenges associated with docking antibody structures derived from homology modeling. By incorporating antibody-specific moves and refinement steps for CDR loops, SnugDock improves docking accuracy and overcomes limitations observed in standard docking protocols. Benchmarking studies have demonstrated the superior performance of SnugDock in predicting antibody-antigen complexes, highlighting its utility in antibody engineering and therapeutic development.

Building upon the RosettaDock framework, the SnugDock algorithm represents a significant advancement in antibody-antigen docking accuracy. SnugDock introduces tailored perturbation moves specific to antibody-binding motifs, such as CDR loop remodeling and reorientation of the angle between the V domains. By addressing the conformational variability of antibody CDR loops, SnugDock enhances docking accuracy and improves the energy funnel landscape. Benchmarking studies have demonstrated the superior performance of SnugDock, particularly in ensemble docking approaches, which leverage multiple models to flatten the energy funnel and increase the proportion of high-quality structures.

4.3.3 The ClusPro program

ClusPro [74] underscores the efficacy of automated protein docking methods in accurately predicting molecular interactions, even in the absence of prior biological knowledge. A distinguishing feature of ClusPro is its utilization of structure-based potentials specifically tailored for antibody-antigen complexes. These potentials, derived from statistical mechanics principles, capture the frequency of observed interactions in protein structures, thereby enhancing the accuracy of docking predictions. By incorporating such potentials into its energy function, ClusPro achieves remarkable improvements in docking results for antibody-antigen pairs. Moreover, ClusPro’s integration of the Decoys as the Reference State (DARS) potential further enhances its performance in antibody-antigen docking. By generating a diverse set of docked conformations and leveraging interaction frequency data extracted from these decoys, ClusPro effectively discriminates near-native complex structures, facilitating the identification of optimal binding orientations. Unlike traditional docking approaches that rely on symmetric pairwise interaction potentials, ClusPro’s innovative methodologies, including the asymmetric DARS-type potential (ADARS), accommodate the unique characteristics of antibody-antigen interactions. By removing the constraint of interaction symmetry, ClusPro can accurately predict antibody-antigen binding orientations, offering valuable insights into molecular recognition processes.

4.3.4 Hybrid approaches for high-throughput epitope mapping

High-throughput epitope mapping remains a challenge due to the complexity of experimental techniques. Hybrid approaches that combine experimental data with computational strategies offer a promising solution to this challenge. These approaches leverage bioinformatics tools to identify interacting regions based on peptide binding assays, neutralization data, and structural analysis. By integrating diverse sources of experimental data, hybrid methods enable rapid and accurate epitope mapping, thereby facilitating the design of targeted antibody-based therapeutics.

While robust, obligate interactions are well-studied using established biochemical and structural methods and transient interactions pose challenges due to their low abundance and subtle nature. Consequently, sensitive techniques such as the yeast two-hybrid system and nuclear magnetic resonance spectroscopy are often required for their identification [75].

4.3.5 Integrating experimental restraints for enhanced docking performance

Experimental data, including alanine or site-directed mutagenesis, hydrogen-deuterium exchange mass spectrometry (HDX), NMR chemical shift perturbations, cryo-EM, and chemical crosslinking, provide valuable insights into the structural characteristics of antibody-antigen complexes [76]. By integrating these experimental restraints into docking simulations, researchers can constrain the conformational space and guide the selection of native-like docking poses. Techniques, such as RosettaDock and SnugDock, offer flexibility in incorporating experimental data, thereby enhancing the accuracy and reliability of docking predictions.

4.3.6 Artificial intelligence-driven methods to enhance predictive docking results of antibodies

A number of artificial intelligence-driven methods have emerged recently to enhance the docking accuracy of antibodies, and many such tools are currently under development (Table 3). PECAN, a novel AI-driven method for predicting antibody-antigen interfaces [77], employs a graph-based approach to represent antibody and antigen structures. The graphs are then fed into a neural network comprising graph convolution, attention, and fully connected layers. This network distinguishes between antibody and antigen residues within interface and non-interface regions. PECAN exhibits higher precision and recall rates compared to epitope prediction datasets [78] and paratope prediction methods [79]. Notably, these prediction methods also demonstrate high accuracies in identifying epitope and paratope regions.

Category	Description	Tool/software
Graph-based Neural Networks for Interface Prediction	Utilizes graph-based approach and neural network architecture to predict antibody-antigen interfaces.	PECAN shows high precision and recall rates compared to other prediction methods.
Deep Learning for Interaction Prediction	Identifies antibody-antigen interactions from amino acid sequences using convolutional neural networks.	AbAgIntPre offers generic and SARS-CoV-specific models trained on comprehensive antibody structures.
Epitope and Paratope Prediction with SVM (Support Vector Machine)	Predicts epitopes and paratopes through SVM classification of surface patches based on 3D descriptors.	EpiPred facilitates epitope prediction despite challenges in complex structure prediction.
Integrating Established Methods with Machine Learning	Combines modeling, docking, and machine learning to streamline antibody-antigen interaction characterization.	AbAdapt integrates epitope and paratope prediction with docking engines for efficient interaction analysis.

Table 3.

AI-driven methods contribute to enhancing the predictive docking results of antibodies.

AbAgIntPre-A deep learning-based prediction method [73] was created to rapidly identify antibody-antigen interactions using only amino acid sequences. The method utilizes a Siamese-like convolutional neural network architecture, which encodes the amino acid composition of both antigens and antibodies. The AbAgIntPre method revolutionizes antibody discovery by integrating the composition of k-spaced amino acid pairs (CKSAAP) encoding and a convolutional neural network (CNN) deep learning framework. This combination enables efficient prediction of antibody-antigen interactions, facilitating the identification of various antigen types. AbAgIntPre offers two prediction models: a generic model and a SARS-CoV-specific model, both utilizing CNN architecture with CKSAAP sequence encoding. To enhance the data availability, comprehensive antibody structures available in the SAbDab database, along with data from other immune-related databases, the generic model was trained. This model was developed using 918 high-quality antibody-antigen complexes, which were meticulously processed and clustered into 408 subgroups to ensure the representation of diverse interactions. By assigning high-quality positive and negative antibody-antigen pairs based on this clustering, AbAgIntPre is said to have demonstrated its efficacy in predicting antigen-antibody interactions with precision and reliability.

Epitope prediction, facilitated by EpiPred, relies on geometric fitting and knowledge-based asymmetric scoring of antibody-antigen interactions [78]. This paratope prediction method makes use of a support vector machine (SVM) classifier to distinguish interface surface patches from non-interface ones based on 3D descriptors capturing global and local protein surface shapes and physicochemical properties [79]. However, it is worth noting that even recent AI-based approaches, such as AF2-multimer still face challenges in accurately predicting antibody-antigen complex structures, as previously highlighted [80].

The AbAdapt [81] tool presents an innovative approach by combining established methods for antigen/antibody modeling and docking, supplemented with machine learning at critical decision points. This server streamlines the process through automated antibody and antigen structural modeling pipelines, accommodating direct sequence input. Methods for epitope and paratope prediction were developed and trained using sequence and structural features derived from these models. Docking engines, namely Piper and Hex, were employed for global and local docking, respectively, in alignment with epitope predictions. The AbAdapt workflow involves co-clustering and re-scoring the Piper and Hex docking poses to assess the quality of top-scoring poses. Additionally, antibody-specific epitope prediction of the program integrates epitope information from antibody-antigen docking. While AbAdapt demonstrates promising results in its ability to characterize antibody-antigen interactions, areas for further improvement include pose quality and computational speed.

4.3.7 Future directions and challenges

Despite significant advancements in antibody docking algorithms and epitope mapping techniques, several challenges remain to be addressed. Improving the accuracy and scalability of docking algorithms, integrating diverse experimental data sources, and enhancing computational modeling capabilities are key areas for future research. Additionally, the development of novel bioinformatics tools and machine learning algorithms holds promise for accelerating the discovery and design of antibody-based therapeutics.

In order to enhance the precision of docking and affinity predictions for antibody-specific epitopes, substantial improvements are required. To address this need, a comprehensive benchmark dataset encompassing camelid nanobodies, therapeutic monoclonal antibodies, and broadly neutralizing antibodies targeting viral glycoproteins was assembled [82]. Another approach to enhance prediction efficacy involves exploring protein regions that resemble known antibody-binding epitopes. This method capitalizes on the ability of certain antibodies to recognize multiple antigen proteins with similar surface regions and properties [80, 83].

Acknowledgments

The authors acknowledge the use of ChatGPT for language polishing of the manuscript.

Declaration

The authors declare that no conflict of interest exists.