Rational Antibody Design Research Articles

Prediction of Paratope-Epitope Pairs Using Convolutional Neural Networks.

Antibodies play a central role in the adaptive immune response of vertebrates through the specific recognition of exogenous or endogenous antigens. The rational design of antibodies has a wide range of biotechnological and medical applications, such as in disease diagnosis and treatment. However, there are currently no reliable methods for predicting the antibodies that recognize a specific antigen region (or epitope) and, conversely, epitopes that recognize the binding region of a given antibody (or paratope). To fill this gap, we developed ImaPEp, a machine learning-based tool for predicting the binding probability of paratope-epitope pairs, where the epitope and paratope patches were simplified into interacting two-dimensional patches, which were colored according to the values of selected features, and pixelated. The specific recognition of an epitope image by a paratope image was achieved by using a convolutional neural network-based model, which was trained on a set of two-dimensional paratope-epitope images derived from experimental structures of antibody-antigen complexes. Our method achieves good performances in terms of cross-validation with a balanced accuracy of 0.8. Finally, we showcase examples of application of ImaPep, including extensive screening of large libraries to identify paratope candidates that bind to a selected epitope, and rescoring and refining antibody-antigen docking poses.

Structure-guided engineering and molecular simulations to design a potent monoclonal antibody to target aP2 antigen for adaptive immune response instigation against type 2 diabetes.

Diabetes mellitus (DM) is recognized as one of the oldest chronic diseases and has become a significant public health issue, necessitating innovative therapeutic strategies to enhance patient outcomes. Traditional treatments have provided limited success, highlighting the need for novel approaches in managing this complex disease. In our study, we employed graph signature-based methodologies in conjunction with molecular simulation and free energy calculations. The objective was to engineer the CA33 monoclonal antibody for effective targeting of the aP2 antigen, aiming to elicit a potent immune response. This approach involved screening a mutational landscape comprising 57 mutants to identify modifications that yield significant enhancements in binding efficacy and stability. Analysis of the mutational landscape revealed that only five substitutions resulted in noteworthy improvements. Among these, mutations T94M, A96E, A96Q, and T94W were identified through molecular docking experiments to exhibit higher docking scores compared to the wild-type. Further validation was provided by calculating the dissociation constant (KD), which showed a similar trend in favor of these mutations. Molecular simulation analyses highlighted T94M as the most stable complex, with reduced internal fluctuations upon binding. Principal components analysis (PCA) indicated that both the wild-type and T94M mutant displayed similar patterns of constrained and restricted motion across principal components. The free energy landscape analysis underscored a single metastable state for all complexes, indicating limited structural variability and potential for high therapeutic efficacy against aP2. Total binding free energy (TBE) calculations further supported the superior performance of the T94M mutation, with TBE values demonstrating the enhanced binding affinity of selected mutants over the wild-type. Our findings suggest that the T94M substitution, along with other identified mutations, significantly enhances the therapeutic potential of the CA33 antibody against DM by improving its binding affinity and stability. These results not only contribute to a deeper understanding of antibody-antigen interactions in the context of DM but also provide a valuable framework for the rational design of antibodies aimed at targeting this disease more effectively.

Rational design of antibodies and development of a novel method for (1-3)-β-D glucan detection as an alternative to Limulus amebocyte lysate assay.

With advances in medicine, increasing medical interventions have increased the risk of invasive fungal disease development. (1-3)-β-D glucan (BDG) is a common fungal biomarker in serological tests. However, the scarcity of Limulus resources for BDG detection poses a challenge. This study addresses the need for an alternative to Limulus amebocyte lysate by using BDG mutant antibody for chemiluminescence detection. The wild-type BDG antibody was obtained by immunizing rabbits. An optimal V52HI/N34L Y mutant antibody, which has increased 3.7-fold of the testing efficiency compared to the wild-type antibody, was first achieved by mutating "hot-spot" residues that contribute to strong non-covalent bonds, as determined by alanine scanning and molecular dynamics simulation. The mutant was then applied to develop the magnetic particle chemiluminescence method. 574 clinical samples were tested using the developed method, with a cutoff value of 95 pg/mL set by Limulus amebocyte lysate. The receiver operating characteristic curve demonstrated an area under the curve of 0.905 (95% CI: 0.880-0.929). Chemiluminescence detected an antigen concentration of 89.98 pg/mL, exhibiting a sensitivity of 83.33% and specificity of 89.76%. In conclusion, the results showed a good agreement with Limulus amebocyte lysate and demonstrated the feasibility of using BDG mutant antibodies for invasive fungal disease diagnosis. The new method based on chemiluminescence for detecting BDG could shorten the sample-to-result time to approximately 30 min, rescue Limulus from being endangered and is resource efficient in terms of equipment and the non-use of a skilled technician.

Application of Computational Techniques in Antibody Fc-Fused Molecule Design for Therapeutics.

Since the advent of hybridoma technology in the year 1975, it took a decade to witness the first approved monoclonal antibody Orthoclone OKT39 (muromonab-CD3) in the year 1986. Since then, continuous strides have been made to engineer antibodies for specific desired effects. The engineering efforts were not confined to only the variable domains of the antibody but also included the fragment crystallizable (Fc) region that influences the immune response and serum half-life. Engineering of the Fc fragment would have a profound effect on the therapeutic dose, antibody-dependent cell-mediated cytotoxicity as well as antibody-dependent cellular phagocytosis. The integration of computational techniques into antibody engineering designs has allowed for the generation of testable hypotheses and guided the rational antibody design framework prior to further experimental evaluations. In this article, we discuss the recent works in the Fc-fused molecule design that involves computational techniques. We also summarize the usefulness of in silico techniques to aid Fc-fused molecule design and analysis for the therapeutics application.

Specific attributes of the VL domain influence both the structure and structural variability of CDR-H3 through steric effects.

Antibodies, through their ability to target virtually any epitope, play a key role in driving the adaptive immune response in jawed vertebrates. The binding domains of standard antibodies are their variable light (VL) and heavy (VH) domains, both of which present analogous complementarity-determining region (CDR) loops. It has long been known that the VH CDRs contribute more heavily to the antigen-binding surface (paratope), with the CDR-H3 loop providing a major modality for the generation of diverse paratopes. Here, we provide evidence for an additional role of the VL domain as a modulator of CDR-H3 structure, using a diverse set of antibody crystal structures and a large set of molecular dynamics simulations. We show that specific attributes of the VL domain such as subtypes, CDR canonical forms and genes can influence the structural diversity of the CDR-H3 loop, and provide a physical model for how this effect occurs through inter-loop contacts and packing of CDRs against each other. Our results indicate that the rigid minor loops fine-tune the structure of CDR-H3, thereby contributing to the generation of surfaces complementary to the vast number of possible epitope topologies, and provide insights into the interdependent nature of CDR conformations, an understanding of which is important for the rational antibody design process.

Combination of High-Resolution Structures for the B Cell Receptor and Co-Receptors Provides an Understanding of Their Interactions with Therapeutic Antibodies.

B cells are central to the adaptive immune response, providing long lasting immunity after infection. B cell activation is mediated by a cell surface B cell receptor (BCR) following recognition of an antigen. BCR signaling is modulated by several co-receptors including CD22 and a complex that contains CD19 and CD81. Aberrant signaling through the BCR and co-receptors promotes the pathogenesis of several B cell malignancies and autoimmune diseases. Treatment of these diseases has been revolutionized by the development of monoclonal antibodies that bind to B cell surface antigens, including the BCR and its co-receptors. However, malignant B cells can escape targeting by several mechanisms and until recently, rational design of antibodies has been limited by the lack of high-resolution structures of the BCR and its co-receptors. Herein we review recently determined cryo-electron microscopy (cryo-EM) and crystal structures of the BCR, CD22, CD19 and CD81 molecules. These structures provide further understanding of the mechanisms of current antibody therapies and provide scaffolds for development of engineered antibodies for treatment of B cell malignancies and autoimmune diseases.

Structural mechanism of Fab domain dissociation as a measure of interface stability

Therapeutic antibodies should not only recognize antigens specifically, but also need to be free from developability issues, such as poor stability. Thus, the mechanistic understanding and characterization of stability are critical determinants for rational antibody design. In this study, we use molecular dynamics simulations to investigate the melting process of 16 antigen binding fragments (Fabs). We describe the Fab dissociation mechanisms, showing a separation in the VH–VL and in the CH1–CL domains. We found that the depths of the minima in the free energy curve, corresponding to the bound states, correlate with the experimentally determined melting temperatures. Additionally, we provide a detailed structural description of the dissociation mechanism and identify key interactions in the CDR loops and in the CH1–CL interface that contribute to stabilization. The dissociation of the VH–VL or CH1–CL domains can be represented by conformational changes in the bend angles between the domains. Our findings elucidate the melting process of antigen binding fragments and highlight critical residues in both the variable and constant domains, which are also strongly germline dependent. Thus, our proposed mechanisms have broad implications in the development and design of new and more stable antigen binding fragments.

Conformational Entropy as a Potential Liability of Computationally Designed Antibodies.

In silico antibody discovery is emerging as a viable alternative to traditional in vivo and in vitro approaches. Many challenges, however, remain open to enabling the properties of designed antibodies to match those produced by the immune system. A major question concerns the structural features of computer-designed complementarity determining regions (CDRs), including the role of conformational entropy in determining the stability and binding affinity of the designed antibodies. To address this problem, we used enhanced-sampling molecular dynamics simulations to compare the free energy landscapes of single-domain antibodies (sdAbs) designed using structure-based (DesAb-HSA-D3) and sequence-based approaches (DesAbO), with that of a nanobody derived from llama immunization (Nb10). Our results indicate that the CDR3 of DesAbO is more conformationally heterogeneous than those of both DesAb-HSA-D3 and Nb10, and the CDR3 of DesAb-HSA-D3 is slightly more dynamic than that of Nb10, which is the original scaffold used for the design of DesAb-HSA-D3. These differences underline the challenges in the rational design of antibodies by revealing the presence of conformational substates likely to have different binding properties and to generate a high entropic cost upon binding.

Nanobody Paratope Ensembles in Solution Characterized by MD Simulations and NMR.

Variable domains of camelid antibodies (so-called nanobodies or VHH) are the smallest antibody fragments that retain complete functionality and therapeutic potential. Understanding of the nanobody-binding interface has become a pre-requisite for rational antibody design and engineering. The nanobody-binding interface consists of up to three hypervariable loops, known as the CDR loops. Here, we structurally and dynamically characterize the conformational diversity of an anti-GFP-binding nanobody by using molecular dynamics simulations in combination with experimentally derived data from nuclear magnetic resonance (NMR) spectroscopy. The NMR data contain both structural and dynamic information resolved at various timescales, which allows an assessment of the quality of protein MD simulations. Thus, in this study, we compared the ensembles for the anti-GFP-binding nanobody obtained from MD simulations with results from NMR. We find excellent agreement of the NOE-derived distance maps obtained from NMR and MD simulations and observe similar conformational spaces for the simulations with and without NOE time-averaged restraints. We also compare the measured and calculated order parameters and find generally good agreement for the motions observed in the ps–ns timescale, in particular for the CDR3 loop. Understanding of the CDR3 loop dynamics is especially critical for nanobodies, as this loop is typically critical for antigen recognition.

Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies

Antibodies have the capacity to bind a diverse set of antigens and have become critical therapeutics and diagnostic molecules. The binding of antibodies is facilitated by a set of six hypervariable loops that are diversified through genetic recombination and mutation. Accurate structural modeling of these loops is critical for rational design of antibodies, but remains an expensive, time-consuming endeavor using traditional experimental methods. Even with recent advances, accurate computational prediction of these hypervariable loops remains a challenge.

Antibody structure prediction using interpretable deep learning

SummaryTherapeutic antibodies make up a rapidly growing segment of the biologics market. However, rational design of antibodies is hindered by reliance on experimental methods for determining antibody structures. Here, we present DeepAb, a deep learning method for predicting accurate antibody FV structures from sequence. We evaluate DeepAb on a set of structurally diverse, therapeutically relevant antibodies and find that our method consistently outperforms the leading alternatives. Previous deep learning methods have operated as “black boxes” and offered few insights into their predictions. By introducing a directly interpretable attention mechanism, we show our network attends to physically important residue pairs (e.g., proximal aromatics and key hydrogen bonding interactions). Finally, we present a novel mutant scoring metric derived from network confidence and show that for a particular antibody, all eight of the top-ranked mutations improve binding affinity. This model will be useful for a broad range of antibody prediction and design tasks.

Germline-Dependent Antibody Paratope States and Pairing Specific VH-VL Interface Dynamics.

Antibodies have emerged as one of the fastest growing classes of biotherapeutic proteins. To improve the rational design of antibodies, we investigate the conformational diversity of 16 different germline combinations, which are composed of 4 different kappa light chains paired with 4 different heavy chains. In this study, we systematically show that different heavy and light chain pairings strongly influence the paratope, interdomain interaction patterns and the relative VH-VL interface orientations. We observe changes in conformational diversity and substantial population shifts of the complementarity determining region (CDR) loops, resulting in distinct dominant solution structures and differently favored canonical structures. Additionally, we identify conformational changes in the structural diversity of the CDR-H3 loop upon different heavy and light chain pairings, as well as upon changes in sequence and structure of the neighboring CDR loops, despite having an identical CDR-H3 loop amino acid sequence. These results can also be transferred to all CDR loops and to the relative VH-VL orientation, as certain paratope states favor distinct interface angle distributions. Furthermore, we directly compare the timescales of sidechain rearrangements with the well-described transition kinetics of conformational changes in the backbone of the CDR loops. We show that sidechain flexibilities are strongly affected by distinct heavy and light chain pairings and decipher germline-specific structural features co-determining stability. These findings reveal that all CDR loops are strongly correlated and that distinct heavy and light chain pairings can result in different paratope states in solution, defined by a characteristic combination of CDR loop conformations and VH-VL interface orientations. Thus, these results have broad implications in the field of antibody engineering, as they clearly show the importance of considering paired heavy and light chains to understand the antibody binding site, which is one of the key aspects in the design of therapeutics.

Alemtuzumab scFv fragments and CD52 interaction study through molecular dynamics simulation and binding free energy

Specific antibody-antigen recognition is crucial for the immune response. Knowledge of molecular interaction details in the recognition process is fundamental for the rational design of antibodies with improved properties. We used state-of-the-art computer simulation tools to deepen the molecular-level understanding of the interactions between the monoclonal antibody Alemtuzumab and its antigen, the CD52 membrane receptor, of great biotechnological importance. Thus, we seek such responses by modeling the interaction of native and known mutants single-chain fragment variable (scFv) of Alemtuzumab with CD52 inserted in a membrane model to mimic the physiological conditions of antibody-antigen binding. Extensive molecular dynamics simulations of the interaction between Alemtuzumab's scFvs and CD52 promoted greater understanding of the structural and energetic bases, which can be translated into the biological action and affinity of this antibody. The quantification of the scFv-CD52 complexes binding free energy (ΔGbind) by Molecular Mechanics–Poisson–Boltzmann Surface Area (MM-PBSA) correlated with the experimental binding energies described before. Thus, the mutants D53K, K54D, and K56D resulted in less attractive ΔGbind, therefore lower scFv-CD52 affinity than the native scFv. On the other hand, K56D and K54D/K56D showed lower binding to CD52. These Results revealed that the model system mimicking an environment close to the physiological with the presence of the CD52 in a membrane model proved essential for this system's study. The present study allowed to unveil the molecular mechanisms involved in antigen-antibody interaction and the effects of mutations. Thus, these mechanisms may be explored in the Alemtuzumab variants' rational design with enhanced properties.

Rational antibody design for undruggable targets using kinetically controlled biomolecular probes.

Several important drug targets, e.g., ion channels and G protein-coupled receptors, are extremely difficult to approach with current antibody technologies. To address these targets classes, we explored kinetically controlled proteases as structural dynamics-sensitive druggability probes in native-state and disease-relevant proteins. By using low-Reynolds number flows, such that a single or a few protease incisions are made, we could identify antibody binding sites (epitopes) that were translated into short-sequence antigens for antibody production. We obtained molecular-level information of the epitope-paratope region and could produce high-affinity antibodies with programmed pharmacological function against difficult-to-drug targets. We demonstrate the first stimulus-selective monoclonal antibodies targeting the transient receptor potential vanilloid 1 (TRPV1) channel, a clinically validated pain target widely considered undruggable with antibodies, and apoptosis-inducing antibodies selectively mediating cytotoxicity in KRAS-mutated cells. It is our hope that this platform will widen the scope of antibody therapeutics for the benefit of patients.

Mutation of Framework Residue H71 Results in Different Antibody Paratope States in Solution.

Characterizing and understanding the antibody binding interface have become a pre-requisite for rational antibody design and engineering. The antigen-binding site is formed by six hypervariable loops, known as the complementarity determining regions (CDRs) and by the relative interdomain orientation (VH–VL). Antibody CDR loops with a certain sequence have been thought to be limited to a single static canonical conformation determining their binding properties. However, it has been shown that antibodies exist as ensembles of multiple paratope states, which are defined by a characteristic combination of CDR loop conformations and interdomain orientations. In this study, we thermodynamically and kinetically characterize the prominent role of residue 71H (Chothia nomenclature), which does not only codetermine the canonical conformation of the CDR-H2 loop but also results in changes in conformational diversity and population shifts of the CDR-H1 and CDR-H3 loop. As all CDR loop movements are correlated, conformational rearrangements of the heavy chain CDR loops also induce conformational changes in the CDR-L1, CDR-L2, and CDR-L3 loop. These overall conformational changes of the CDR loops also influence the interface angle distributions, consequentially leading to different paratope states in solution. Thus, the type of residue of 71H, either an alanine or an arginine, not only influences the CDR-H2 loop ensembles, but co-determines the paratope states in solution. Characterization of the functional consequences of mutations of residue 71H on the paratope states and interface orientations has broad implications in the field of antibody engineering.

Construction and evaluation of an antibody phage display library targeting heparan sulfate

Heparan sulfate (HS) is a linear polysaccharide with high structural diversity. Different HS epitopes have been detected and localized using single chain variable fragment (scFv) antibodies from a ‘single pot’ phage display library containing a randomized complementarity determining region of the heavy chain (CDR3). In this study, we created a new library containing anti-HS scFvs that all harbor a dp-38 heavy chain segment where the CDR3 region was engineered to contain the XBBXBX heparin binding consensus site (X = any amino acid, B = R, K or H). The library contained ~1.73 × 106 unique antibodies and was biopanned against HS from several sources. The selected antibodies were sequenced and chemically/immunohistologically characterized. A number of 67 anti-HS scFv antibodies were selected, of which 31 contained a XBBXBX CDR3 sequence. There was a clear preference for glycine at the first and proline at the fourth position of the CDR3. The sequence GZZP(R/K)X (Z = R, K or H, but may also contain N, S, or Q) was unusually overrepresented. Selected antibodies reacted with HS/heparin, but not with other glycosaminoglycans. Antibodies reacted differentially with respect to N-, 2-O, or 6-O-desulfated heparin preparations, and showed distinct topologies of HS epitopes in rat kidney sections. The library may be instrumental in the selection of a large pool of HS epitope-specific antibodies, and - since all antibodies differ only in their 6 amino acid CDR region - may be a tool for a rational design of antibodies recognizing specific HS sulfation patterns.

MCSM-AB2: guiding rational antibody design using graph-based signatures.

A lack of accurate computational tools to guide rational mutagenesis has made affinity maturation a recurrent challenge in antibody (Ab) development. We previously showed that graph-based signatures can be used to predict the effects of mutations on Ab binding affinity. Here we present an updated and refined version of this approach, mCSM-AB2, capable of accurately modelling the effects of mutations on Ab-antigen binding affinity, through the inclusion of evolutionary and energetic terms. Using a new and expanded database of over 1800 mutations with experimental binding measurements and structural information, mCSM-AB2 achieved a Pearson's correlation of 0.73 and 0.77 across training and blind tests, respectively, outperforming available methods currently used for rational Ab engineering. mCSM-AB2 is available as a user-friendly and freely accessible web server providing rapid analysis of both individual mutations or the entire binding interface to guide rational antibody affinity maturation at http://biosig.unimelb.edu.au/mcsm_ab2. Supplementary data are available at Bioinformatics online.

Toward rational antibody design: recent advancements in molecular dynamics simulations.

Because antibodies have become an important therapeutic tool, rational antibody design is a challenging issue involving various science and technology fields. From the computational aspect, many types of design-assist methods have been developed, but their accuracy is not fully satisfactory. Because of recent advancements in computational power, molecular dynamics (MD) simulation has become a helpful tool to trace the motion of proteins and to characterize their properties. Thus, MD simulation has been applied to various systems involving antigen-antibody complexes and has been shown to provide accurate insight into antigen-antibody interactions and dynamics at an atomic resolution. Therefore, it is highly possible that MD simulation will play several roles complementing the conventional antibody design. In this review, we address several important features of MD simulation in the context of rational antibody design.

Third generation antibody discovery methods: in silico rational design.

Owing to their outstanding performances in molecular recognition, antibodies are extensively used in research and applications in molecular biology, biotechnology and medicine. Recent advances in experimental and computational methods are making it possible to complement well-established in vivo (first generation) and in vitro (second generation) methods of antibody discovery with novel in silico (third generation) approaches. Here we describe the principles of computational antibody design and review the state of the art in this field. We then present Modular, a method that implements the rational design of antibodies in a modular manner, and describe the opportunities offered by this approach.

Computational Tools for Aiding Rational Antibody Design.

Antibodies are a group of proteins responsible for mediating immune reactions in vertebrates. They are able to bind a variety of structural motifs on noxious molecules tagging them for elimination from the organism. As a result of their versatile binding properties, antibodies are currently one of the most important classes of biopharmaceuticals. In this chapter, we discuss how knowledge-based computational methods can aid experimentalists in the development of potent antibodies. When using common experimental methods for antibody development, we often know the sequence of an antibody that binds to our molecule, antigen, of interest. We may also have a structure or model of the antigen. In these cases, computational methods can help by both modeling the antibody and identifying the antibody-antigen contact residues. This information can then play a key role in the rational design of more potent antibodies.