Antibody H3 Structure Prediction

EuDockScore: Euclidean graph neural networks for scoring protein-protein interfaces

MOTIVATION

Protein-protein interactions are essential for a variety of biological phenomena including mediating biochemical reactions, cell signaling, and the immune response. Proteins seek to form interfaces which reduce overall system energy. Although determination of single polypeptide chain protein structures has been revolutionized by deep learning techniques, complex prediction has still not been perfected. Additionally, experimentally determining structures is incredibly resource and time expensive. An alternative is the technique of computational docking, which takes the solved individual structures of proteins to produce candidate interfaces (decoys). Decoys are then scored using a mathematical function that assess the quality of the system, known as scoring functions. Beyond docking, scoring functions are a critical component of assessing structures produced by many protein generative models. Scoring models are also used as a final filtering in many generative deep learning models including those that generate antibody binders, and those which perform docking.

RESULTS

In this work, we present improved scoring functions for protein-protein interactions which utilizes cutting-edge Euclidean graph neural network architectures, to assess protein-protein interfaces. These Euclidean docking score models are known as EuDockScore, and EuDockScore-Ab with the latter being antibody-antigen dock specific. Finally, we provided EuDockScore-AFM a model trained on antibody-antigen outputs from AlphaFold-Multimer (AFM) which proves useful in reranking large numbers of AFM outputs.

AVAILABILITY AND IMPLEMENTATION

The code for these models is available at https://gitlab.com/mcfeemat/eudockscore.

Towards the accurate modelling of antibody-antigen complexes from sequence using machine learning and information-driven docking

MOTIVATION

Antibody-antigen complex modelling is an important step in computational workflows for therapeutic antibody design. While experimentally determined structures of both antibody and the cognate antigen are often not available, recent advances in machine learning-driven protein modelling have enabled accurate prediction of both antibody and antigen structures. Here, we analyse the ability of protein-protein docking tools to use machine learning generated input structures for information-driven docking.

RESULTS

In an information-driven scenario, we find that HADDOCK can generate accurate models of antibody-antigen complexes using an ensemble of antibody structures generated by machine learning tools and AlphaFold2 predicted antigen structures. Targeted docking using knowledge of the complementary determining regions on the antibody and some information about the targeted epitope allows the generation of high-quality models of the complex with reduced sampling, resulting in a computationally cheap protocol that outperforms the ZDOCK baseline.

AVAILABILITY AND IMPLEMENTATION

The source code of HADDOCK3 is freely available at github.com/haddocking/haddock3. The code to generate and analyse the data is available at github.com/haddocking/ai-antibodies. The full runs, including docking models from all modules of a workflow have been deposited in our lab collection (data.sbgrid.org/labs/32/1139) at the SBGRID data repository.

Antibody-SGM, a Score-Based Generative Model for Antibody Heavy-Chain Design

Traditional computational methods for antibody design involved random mutagenesis followed by energy function assessment for candidate selection. Recently, diffusion models have garnered considerable attention as cutting-edge generative models, lauded for their remarkable performance. However, these methods often focus solely on the backbone or sequence, resulting in the incomplete depiction of the overall structure and necessitating additional techniques to predict the missing component. This study presents Antibody-SGM, an innovative joint structure-sequence diffusion model that addresses the limitations of existing protein backbone generation models. Unlike previous models, Antibody-SGM successfully integrates sequence-specific attributes and functional properties into the generation process. Our methodology generates full-atom native-like antibody heavy chains by refining the generation to create valid pairs of sequences and structures, starting with random sequences and structural properties. The versatility of our method is demonstrated through various applications, including the design of full-atom antibodies, antigen-specific CDR design, antibody heavy chains optimization, validation with Alphafold3, and the identification of crucial antibody sequences and structural features. Antibody-SGM also optimizes protein function through active inpainting learning, allowing simultaneous sequence and structure optimization. These improvements demonstrate the promise of our strategy for protein engineering and significantly increase the power of protein design models.

Highly Accurate and Efficient Deep Learning Paradigm for Full-Atom Protein Loop Modeling with KarmaLoop

Protein loop modeling is a challenging yet highly nontrivial task in protein structure prediction. Despite recent progress, existing methods including knowledge-based, ab initio, hybrid, and deep learning (DL) methods fall substantially short of either atomic accuracy or computational efficiency. To overcome these limitations, we present KarmaLoop, a novel paradigm that distinguishes itself as the first DL method centered on full-atom (encompassing both backbone and side-chain heavy atoms) protein loop modeling. Our results demonstrate that KarmaLoop considerably outperforms conventional and DL-based methods of loop modeling in terms of both accuracy and efficiency, with the average RMSDs of 1.77 and 1.95 Å for the CASP13+14 and CASP15 benchmark datasets, respectively, and manifests at least 2 orders of magnitude speedup in general compared with other methods. Consequently, our comprehensive evaluations indicate that KarmaLoop provides a state-of-the-art DL solution for protein loop modeling, with the potential to hasten the advancement of protein engineering, antibody-antigen recognition, and drug design.

De Novo Synthesis and Structural Elucidation of CDR-H3 Loop Mimics

The binding affinity of antibodies to specific antigens stems from a remarkably broad repertoire of hypervariable loops known as complementarity-determining regions (CDRs). While recognizing the pivotal role of the heavy-chain 3 CDRs (CDR-H3s) in maximizing antibody-antigen affinity and specificity, the key structural determinants responsible for their adaptability to diverse loop sequences, lengths, and noncanonical structures are hitherto unknown. To address this question, we achieved a de novo synthesis of bulged CDR-H3 mimics excised from their full antibody context. CD and NMR data revealed that these stable standalone β-hairpin scaffolds are well-folded and retain many of the native bulge CDR-H3 features in water. In particular, the tryptophan residue, highly conserved across CDR-H3 sequences, was found to extend the kinked base of these β-bulges through a combination of stabilizing intramolecular hydrogen bond and CH/π interaction. The structural ensemble consistent with our NMR observations exposed the dynamic nature of residues at the base of the loop, suggesting that β-bulges act as molecular hinges connecting the rigid stem to the more flexible loops of CDR-H3s. We anticipate that this deeper structural understanding of CDR-H3s will lay the foundation to inform the design of antibody drugs broadly and engineer novel CDR-H3 peptide scaffolds as therapeutics.

Accurate prediction of antibody function and structure using bio-inspired antibody language model

In recent decades, antibodies have emerged as indispensable therapeutics for combating diseases, particularly viral infections. However, their development has been hindered by limited structural information and labor-intensive engineering processes. Fortunately, significant advancements in deep learning methods have facilitated the precise prediction of protein structure and function by leveraging co-evolution information from homologous proteins. Despite these advances, predicting the conformation of antibodies remains challenging due to their unique evolution and the high flexibility of their antigen-binding regions. Here, to address this challenge, we present the Bio-inspired Antibody Language Model (BALM). This model is trained on a vast dataset comprising 336 million 40% nonredundant unlabeled antibody sequences, capturing both unique and conserved properties specific to antibodies. Notably, BALM showcases exceptional performance across four antigen-binding prediction tasks. Moreover, we introduce BALMFold, an end-to-end method derived from BALM, capable of swiftly predicting full atomic antibody structures from individual sequences. Remarkably, BALMFold outperforms those well-established methods like AlphaFold2, IgFold, ESMFold and OmegaFold in the antibody benchmark, demonstrating significant potential to advance innovative engineering and streamline therapeutic antibody development by reducing the need for unnecessary trials. The BALMFold structure prediction server is freely available at https://beamlab-sh.com/models/BALMFold.

Convergent evolution and B-cell recirculation in germinal centers in a human lymph node

Germinal centers (GCs) play a central role in generating an effective immune response against infectious pathogens, and failures in their regulating mechanisms can lead to the development of autoimmune diseases and cancer. Although previous works study experimental systems of the immune response with mouse models that are immunized with specific antigens, our study focused on a real-life situation, with an ongoing GC response in a human lymph node (LN) involving multiple asynchronized GCs reacting simultaneously to unknown antigens. We combined laser capture microdissection of individual GCs from human LN with next-generation repertoire sequencing to characterize individual GCs as distinct evolutionary spaces. In line with well-characterized GC responses in mice, elicited by immunization with model antigens, we observe a heterogeneous clonal diversity across individual GCs from the same human LN. Still, we identify shared clones in several individual GCs, and phylogenetic tree analysis combined with paratope modeling suggest the re-engagement and rediversification of B-cell clones across GCs and expanded clones exhibiting shared antigen responses across distinct GCs, indicating convergent evolution of the GCs.

Structure and Dynamics Guiding Design of Antibody Therapeutics and Vaccines

Antibodies and other new antibody-like formats have emerged as one of the most rapidly growing classes of biotherapeutic proteins. Understanding the structural features that drive antibody function and, consequently, their molecular recognition is critical for engineering antibodies. Here, we present the structural architecture of conventional IgG antibodies alongside other formats. We emphasize the importance of considering antibodies as conformational ensembles in solution instead of focusing on single-static structures because their functions and properties are strongly governed by their dynamic nature. Thus, in this review, we provide an overview of the unique structural and dynamic characteristics of antibodies with respect to their antigen recognition, biophysical properties, and effector functions. We highlight the numerous technical advances in antibody structure prediction and design, enabled by the vast number of experimentally determined high-quality structures recorded with cryo-EM, NMR, and X-ray crystallography. Lastly, we assess antibody and vaccine design strategies in the context of structure and dynamics.

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.

Structural modeling of antibody variable regions using deep learning-progress and perspectives on drug discovery

AlphaFold2 has hallmarked a generational improvement in protein structure prediction. In particular, advances in antibody structure prediction have provided a highly translatable impact on drug discovery. Though AlphaFold2 laid the groundwork for all proteins, antibody-specific applications require adjustments tailored to these molecules, which has resulted in a handful of deep learning antibody structure predictors. Herein, we review the recent advances in antibody structure prediction and relate them to their role in advancing biologics discovery.

The ClusPro AbEMap web server for the prediction of antibody epitopes

Antibodies play an important role in the immune system by binding to molecules called antigens at their respective epitopes. These interfaces or epitopes are structural entities determined by the interactions between an antibody and an antigen, making them ideal systems to analyze by using docking programs. Since the advent of high-throughput antibody sequencing, the ability to perform epitope mapping using only the sequence of the antibody has become a high priority. ClusPro, a leading protein-protein docking server, together with its template-based modeling version, ClusPro-TBM, have been re-purposed to map epitopes for specific antibody-antigen interactions by using the Antibody Epitope Mapping server (AbEMap). ClusPro-AbEMap offers three different modes for users depending on the information available on the antibody as follows: (i) X-ray structure, (ii) computational/predicted model of the structure or (iii) only the amino acid sequence. The AbEMap server presents a likelihood score for each antigen residue of being part of the epitope. We provide detailed information on the server's capabilities for the three options and discuss how to obtain the best results. In light of the recent introduction of AlphaFold2 (AF2), we also show how one of the modes allows users to use their AF2-generated antibody models as input. The protocol describes the relative advantages of the server compared to other epitope-mapping tools, its limitations and potential areas of improvement. The server may take 45-90 min depending on the size of the proteins.

Mapping of antibody epitopes based on docking and homology modeling

Antibodies are key proteins produced by the immune system to target pathogen proteins termed antigens via specific binding to surface regions called epitopes. Given an antigen and the sequence of an antibody the knowledge of the epitope is critical for the discovery and development of antibody based therapeutics. In this work, we present a computational protocol that uses template-based modeling and docking to predict epitope residues. This protocol is implemented in three major steps. First, a template-based modeling approach is used to build the antibody structures. We tested several options, including generation of models using AlphaFold2. Second, each antibody model is docked to the antigen using the fast Fourier transform (FFT) based docking program PIPER. Attention is given to optimally selecting the docking energy parameters depending on the input data. In particular, the van der Waals energy terms are reduced for modeled antibodies relative to x-ray structures. Finally, ranking of antigen surface residues is produced. The ranking relies on the docking results, that is, how often the residue appears in the docking poses' interface, and also on the energy favorability of the docking pose in question. The method, called PIPER-Map, has been tested on a widely used antibody-antigen docking benchmark. The results show that PIPER-Map improves upon the existing epitope prediction methods. An interesting observation is that epitope prediction accuracy starting from antibody sequence alone does not significantly differ from that of starting from unbound (i.e., separately crystallized) antibody structure.

Structure-Based Optimization of Antibody-Based Biotherapeutics for Improved Developability: A Practical Guide for Molecular Modelers

A great effort to avoid known developability risks is now more often being made earlier during the lead candidate discovery and optimization phase of biotherapeutic drug development. Predictive computational strategies, used in the early stages of antibody discovery and development, to mitigate the risk of late-stage failure of antibody candidates, are highly valuable. Various structure-based methods exist for accurately predicting properties critical to developability, and, in this chapter, we discuss the history of their development and demonstrate how they can be used to filter large sets of candidates arising from target affinity screening and to optimize lead candidates for developability. Methods for modeling antibody structures from sequence and detecting post-translational modifications and chemical degradation liabilities are also discussed.

A machine learning strategy for the identification of key in silico descriptors and prediction models for IgG monoclonal antibody developability properties

Identification of favorable biophysical properties for protein therapeutics as part of developability assessment is a crucial part of the preclinical development process. Successful prediction of such properties and bioassay results from calculated in silico features has potential to reduce the time and cost of delivering clinical-grade material to patients, but nevertheless has remained an ongoing challenge to the field. Here, we demonstrate an automated and flexible machine learning workflow designed to compare and identify the most powerful features from computationally derived physiochemical feature sets, generated from popular commercial software packages. We implement this workflow with medium-sized datasets of human and humanized IgG molecules to generate predictive regression models for two key developability endpoints, hydrophobicity and poly-specificity. The most important features discovered through the automated workflow corroborate several previous literature reports, and newly discovered features suggest directions for further research and potential model improvement.

Antigen binding by conformational selection in near-germline antibodies

Conformational flexibility in antibody-combining sites has been hypothesized to facilitate polyspecificity toward multiple unique epitopes and enable the limited germline repertoire to match an overwhelming diversity of potential antigens; however, elucidating the mechanisms of antigen recognition by flexible antibodies has been understandably challenging. Here, multiple liganded and unliganded crystal structures of the near-germline anticarbohydrate antibodies S25–2 and S25–39 are reported, which reveal an unprecedented diversity of complementarity-determining region H3 conformations in apparent equilibrium. These structures demonstrate that at least some germline or near-germline antibodies are flexible entities sensitive to their chemical environments, with conformational selection available as an evolved mechanism that preserves the inherited ability to recognize common pathogens while remaining adaptable to new threats.

Challenges and Advantages of Accounting for Backbone Flexibility in Prediction of Protein-Protein Complexes

Predicting protein binding is a core problem of computational biophysics. That this objective can be partly achieved with some amount of success using docking algorithms based on rigid protein models is remarkable, although going further requires allowing for protein flexibility. However, accurately capturing the conformational changes upon binding remains an enduring challenge for docking algorithms. Here, we adapt our Upside folding model, where side chains are represented as multi-position beads, to explore how flexibility may impact predictions of protein-protein complexes. Specifically, the Upside model is used to investigate where backbone flexibility helps, which types of interactions are important, and what is the impact of coarse graining. These efforts also shed light on the relative challenges posed by folding and docking. After training the Upside energy function for docking, the model is competitive with the established all-atom methods. However, allowing for backbone flexibility during docking is generally detrimental, as the presence of comparatively minor (3-5 Å) deviations relative to the docked structure has a large negative effect on performance. While this issue appears to be inherent to current forcefield-guided flexible docking methods, systems involving the co-folding of flexible loops such as antibody-antigen complexes represent an interesting exception. In this case, binding is improved when backbone flexibility is allowed using the Upside model.

Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods

Although antibodies have become the fastest-growing class of therapeutics on the market, it is still challenging to develop them for therapeutic applications, which often require these molecules to withstand stresses that are not present in vivo. We define developability as the likelihood of an antibody candidate with suitable functionality to be developed into a manufacturable, stable, safe, and effective drug that can be formulated to high concentrations while retaining a long shelf life. The implementation of reliable developability assessments from the early stages of antibody discovery enables flagging and deselection of potentially problematic candidates, while focussing available resources on the development of the most promising ones. Currently, however, thorough developability assessment requires multiple in vitro assays, which makes it labor intensive and time consuming to implement at early stages. Furthermore, accurate in vitro analysis at the early stage is compromised by the high number of potential candidates that are often prepared at low quantities and purity. Recent improvements in the performance of computational predictors of developability potential are beginning to change this scenario. Many computational methods only require the knowledge of the amino acid sequences and can be used to identify possible developability issues or to rank available candidates according to a range of biophysical properties. Here, we describe how the implementation of in silico tools into antibody discovery pipelines is increasingly offering time- and cost-effective alternatives to in vitro experimental screening, thus streamlining the drug development process. We discuss in particular the biophysical and biochemical properties that underpin developability potential and their trade-offs, review various in vitro assays to measure such properties or parameters that are predictive of developability, and give an overview of the growing number of in silico tools available to predict properties important for antibody development, including the CamSol method developed in our laboratory.

Redesigning an antibody H3 loop by virtual screening of a small library of human germline-derived sequences

The design of superior biologic therapeutics, including antibodies and engineered proteins, involves optimizing their specific ability to bind to disease-related molecular targets. Previously, we developed and applied the Assisted Design of Antibody and Protein Therapeutics (ADAPT) platform for virtual affinity maturation of antibodies (Vivcharuk et al. in PLoS One 12(7):e0181490, 10.1371/journal.pone.0181490, 2017). However, ADAPT is limited to point mutations of hot-spot residues in existing CDR loops. In this study, we explore the possibility of wholesale replacement of the entire H3 loop with no restriction to maintain the parental loop length. This complements other currently published studies that sample replacements for the CDR loops L1, L2, L3, H1 and H2. Given the immense sequence space theoretically available to H3, we focused on the virtual grafting of over 5000 human germline-derived H3 sequences from the IGMT/LIGM database increasing the diversity of the sequence space when compared to using crystalized H3 loop sequences. H3 loop conformations are generated and scored to identify optimized H3 sequences. Experimental testing of high-ranking H3 sequences grafted into the framework of the bH1 antibody against human VEGF-A led to the discovery of multiple hits, some of which had similar or better affinities relative to the parental antibody. In over 75% of the tested designs, the re-designed H3 loop contributed favorably to overall binding affinity. The hits also demonstrated good developability attributes such as high thermal stability and no aggregation. Crystal structures of select re-designed H3 variants were solved and indicated that although some deviations from predicted structures were seen in the more solvent accessible regions of the H3 loop, they did not significantly affect predicted affinity scores.

Accurate determination of epitope for antibodies with unknown 3D structures

MAbTope is a docking-based method for the determination of epitopes. It has been used to successfully determine the epitopes of antibodies with known 3D structures. However, during the antibody discovery process, this structural information is rarely available. Although we already have evidence that homology models of antibodies could be used instead of their 3D structure, the choice of the template, the methodology for homology modeling and the resulting performance still have to be clarified. Here, we show that MAbTope has the same performance when working with homology models of the antibodies as compared to crystallographic structures. Moreover, we show that even low-quality models can be used. We applied MAbTope to determine the epitope of dupilumab, an anti- interleukin 4 receptor alpha subunit therapeutic antibody of unknown 3D structure, that we validated experimentally. Finally, we show how the MAbTope-determined epitopes for a series of antibodies targeting the same protein can be used to predict competitions, and demonstrate the accuracy with an experimentally validated example.

3D: three-dimensionalRMSD: root mean square deviationCDR: complementary-determining regionCPU: central processing unitsVH: heavy chain variable regionVL: light chain variable regionscFv: single-chain variable fragmentsVHH: single-chain antibody variable regionIL4Rα: Interleukin 4 receptor alpha chainSPR: surface plasmon resonancePDB: protein data bankHEK293: Human embryonic kidney 293 cellsEDTA: Ethylenediaminetetraacetic acidFBS: Fetal bovine serumANOVA: Analysis of varianceEGFR: Epidermal growth factor receptorPE: PhycoerythrinAPC: AllophycocyaninFSC: forward scatterSSC: side scatterWT: wild type

Keywords: MAbTope, Epitope Mapping, Molecular docking, Antibody modeling, Antibody-antigen docking

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.

Investigation of anomalous charge variant profile reveals discrete pH-dependent conformations and conformation-dependent charge states within the CDR3 loop of a therapeutic mAb

During the development of a therapeutic monoclonal antibody (mAb-1), the charge variant profile obtained by pH-gradient cation exchange chromatography (CEX) contained two main peaks, each of which exhibited a unique intrinsic fluorescence profile and demonstrated inter-convertibility upon reinjection of isolated peak fractions. Domain analysis of mAb-1 by CEX and liquid chromatography-mass spectrometry indicated that the antigen-binding fragment chromatographed as two separate peaks that had identical mass. Surface plasmon resonance binding analysis to antigen demonstrated comparable kinetics/affinity between these fractionated peaks and unfractionated starting material. Subsequent molecular modeling studies revealed that the relatively long and flexible complementarity-determining region 3 (CDR3) loop on the heavy chain could adopt two discrete pH-dependent conformations: an “open” conformation at neutral pH where the HC-CDR3 is largely solvent exposed, and a “closed” conformation at lower pH where the solvent exposure of a neighboring tryptophan in the light chain is reduced and two aspartic acid residues near the ends of the HC-CDR3 loop have atypical pKa values. The pH-dependent equilibrium between “open” and “closed” conformations of the HC-CDR3, and its proposed role in the anomalous charge variant profile of mAb-1, were supported by further CEX and hydrophobic interaction chromatography studies. This work is an example of how pH-dependent conformational changes and conformation-dependent changes to net charge can unexpectedly contribute to perceived instability and require thorough analytical, biophysical, and functional characterization during biopharmaceutical drug product development.

Recombinant Antibody Engineering Enables Reversible Binding for Continuous Protein Biosensing

Engineering antibodies to improve target specificity, reduce detection limits, or introduce novel functionality is an important research area for biosensor development. While various affinity biosensors have been developed to generate an output signal upon varying analyte concentrations, reversible and continuous protein monitoring in complex biological samples remains challenging. Herein, we explore the concept of directed evolution to modulate dissociation kinetics of a high affinity anti-epidermal growth factor receptor (EGFR) single-chain variable antibody fragment (scFv) to enable continuous protein sensing in a label-free binding assay. A mutant scFv library was generated from the wild type (WT) fragment via targeted permutation of four residues in the antibody-antigen-binding interface. A single round of phage display biopanning complemented with high-throughput screening methods then permitted isolation of a specific binder with fast reaction kinetics. We were able to obtain ∼30 times faster dissociation rates when compared to the WT without appreciably affecting overall affinity and specificity by targeting a single paratope that is known to contribute to the binding interaction. Suitability of a resulting mutant fragment to sense varying antigen concentrations in continuous mode was demonstrated in a modified label-free binding assay, achieving low nanomolar detection limits (K_D = 8.39 nM). We also confirmed these results using an independent detection mechanism developed previously by our group, incorporating a polarity-dependent fluorescent dye into the scFv and reading out EGFR binding based on fluorescence wavelength shifts. In future, this generic approach could be employed to generate improved or novel binders for proteins of interest, ready for deployment in a broad range of assay platforms.

Antibody CDR loops as ensembles in solution vs. canonical clusters from X-ray structures

In the past decade, the relevance of antibodies as therapeutics has increased substantially. Therefore, structural and functional characterization, in particular of the complementarity-determining regions (CDRs), is crucial to the design and engineering of antibodies with unique binding properties. Various studies have focused on classifying the CDR loops into a small set of main-chain conformations to facilitate antibody design by assuming that certain sequences can only adopt a limited number of conformations. Here, we present a kinetic classification of CDR loop structures as ensembles in solution. Using molecular dynamics simulations in combination with strong experimental structural information, we observe conformational transitions between canonical clusters and additional dominant solution structures in the micro-to-millisecond timescale for all CDR loops, independent of length and sequence composition. Besides identifying all relevant conformations in solution, our results revealed that various canonical cluster medians actually belong to the same kinetic minimum. Additionally, we reconstruct the kinetics and probabilities of the conformational transitions between canonical clusters, and thereby extend the model of static canonical structures to reveal a dynamic conformational ensemble in solution as a new paradigm in the field of antibody structure design.

Abbreviations: CDR: Complementary-determining region; Fv: Antibody variable fragment; PCCA: Perron cluster analysis; tICA: Time-lagged independent component analysis; V_H: Heavy chain variable region; V_L: Light chain variable region

Homology Model of a Catalytically Competent Bifunctional Rel Protein

Bacteria have developed different bet hedging strategies to survive hostile environments and stressful conditions with persistency being maybe the most elegant yet still poorly understood one. Persisters' temporary tolerance to antibiotic treatment hints at their role not only in chronic and recurrent infections but also in the insurgence of resistant strains. Therefore, hampering persisters formation might represent an innovative strategy in the quest for new effective antimicrobial compounds. Among the molecular mechanisms postulated for the persister phenotypic switch, we decided to focus our attention on the stringent response and, in particular, on the upstream triggering step that is the accumulation of guanosine tetra- and pentaphosphate, collectivity called (p)ppGpp. Intracellular levels of (p)ppGpp are regulated by a superfamily of enzymes called RSH (RelA/SpoT homologue) that are able to promote its synthesis via pyrophosphate transfer from an ATP molecule to the 3' position of either GDP or GTP. These enzymes are classified based on the structural domain(s) present (only synthetase, only hydrolase, or both). Here we present our work on Rel Seq (from S. equisimilis), still the only bifunctional Rel protein for which a GDP-bound "synthetase-ON" structure is available. Analysis of the synthetase site, occupied only by GDP, revealed a partially active state, where the supposed ATP binding region is not conformationally apt to accommodate it. In order to achieve a protein model that gets closer to a fully active state, we generated a chimera structure of Rel Seq by homology modeling, starting from the crystal structure of the catalytically competent state of RelP, a smaller, single-domain, Rel protein from S. aureus. Molecular dynamics simulations allowed verifying the stability of the generated chimera model. Virtual screening and ligand design studies are underway.

Computational approaches to therapeutic antibody design: established methods and emerging trends

Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.

Local and Global Rigidification Upon Antibody Affinity Maturation

During the affinity maturation process the immune system produces antibodies with higher specificity and activity through various rounds of somatic hypermutations in response to an antigen. Elucidating the affinity maturation process is fundamental in understanding immunity and in the development of biotherapeutics. Therefore, we analyzed 10 pairs of antibody fragments differing in their specificity and in distinct stages of affinity maturation using metadynamics in combination with molecular dynamics (MD) simulations. We investigated differences in flexibility of the CDR-H3 loop and global changes in plasticity upon affinity maturation. Among all antibody pairs we observed a substantial rigidification in flexibility and plasticity reflected in a substantial decrease of conformational diversity. To visualize and characterize these findings we used Markov-states models to reconstruct the kinetics of CDR-H3 loop dynamics and for the first time provide a method to define and localize surface plasticity upon affinity maturation.

Methods for sequence and structural analysis of B and T cell receptor repertoires

B cell receptors (BCRs) and T cell receptors (TCRs) make up an essential network of defense molecules that, collectively, can distinguish self from non-self and facilitate destruction of antigen-bearing cells such as pathogens or tumors. The analysis of BCR and TCR repertoires plays an important role in both basic immunology as well as in biotechnology. Because the repertoires are highly diverse, specialized software methods are needed to extract meaningful information from BCR and TCR sequence data. Here, we review recent developments in bioinformatics tools for analysis of BCR and TCR repertoires, with an emphasis on those that incorporate structural features. After describing the recent sequencing technologies for immune receptor repertoires, we survey structural modeling methods for BCR and TCRs, along with methods for clustering such models. We review downstream analyses, including BCR and TCR epitope prediction, antibody-antigen docking and TCR-peptide-MHC Modeling. We also briefly discuss molecular dynamics in this context.

How repertoire data are changing antibody science

Antibodies are vital proteins of the immune system that recognize potentially harmful molecules and initiate their removal. Mammals can efficiently create vast numbers of antibodies with different sequences capable of binding to any antigen with high affinity and specificity. Because they can be developed to bind to many disease agents, antibodies can be used as therapeutics. In an organism, after antigen exposure, antibodies specific to that antigen are enriched through clonal selection, expansion, and somatic hypermutation. The antibodies present in an organism therefore report on its immune status, describe its innate ability to deal with harmful substances, and reveal how it has previously responded. Next-generation sequencing technologies are being increasingly used to query the antibody, or B-cell receptor (BCR), sequence repertoire, and the amount of BCR data in public repositories is growing. The Observed Antibody Space database, for example, currently contains over a billion sequences from 68 different studies. Repertoires are available that represent both the naive state (i.e. antigen-inexperienced) and that after immunization. This wealth of data has created opportunities to learn more about our immune system. In this review, we discuss the many ways in which BCR repertoire data have been or could be exploited. We highlight its utility for providing insights into how the naive immune repertoire is generated and how it responds to antigens. We also consider how structural information can be used to enhance these data and may lead to more accurate depictions of the sequence space and to applications in the discovery of new therapeutics.

Structural diversity of B-cell receptor repertoires along the B-cell differentiation axis in humans and mice

Most current analysis tools for antibody next-generation sequencing data work with primary sequence descriptors, leaving accompanying structural information unharnessed. We have used novel rapid methods to structurally characterize the complementary-determining regions (CDRs) of more than 180 million human and mouse B-cell receptor (BCR) repertoire sequences. These structurally annotated CDRs provide unprecedented insights into both the structural predetermination and dynamics of the adaptive immune response. We show that B-cell types can be distinguished based solely on these structural properties. Antigen-unexperienced BCR repertoires use the highest number and diversity of CDR structures and these patterns of naïve repertoire paratope usage are highly conserved across subjects. In contrast, more differentiated B-cells are more personalized in terms of CDR structure usage. Our results establish the CDR structure differences in BCR repertoires and have applications for many fields including immunodiagnostics, phage display library generation, and “humanness” assessment of BCR repertoires from transgenic animals. The software tool for structural annotation of BCR repertoires, SAAB+, is available at https://github.com/oxpig/saab_plus.

B-cell receptors (BCR) are the major components of the adaptive immune system. These are immunoglobulin molecules that bind to foreign substances known as antigens. Each individual has a huge BCR repertoire, where each individual BCR has a specific binding site composed of the complementary-determining regions (CDRs) capable of recognising a specific antigen. Drug discovery and immunodiagnostics inspired by the adaptive immune system rely on our ability to accurately interrogate the structural diversity of the binding sites of the BCR repertoire. Here we report our novel rapid pipeline, SAAB+, which has enabled us to interrogate how the structure of the CDR changes in BCR repertoires along the B-cell differentiation axis. By analysing human and mouse BCR repertoires at an unprecedented scale, we observed species-specific structural predetermination and detected CDR dynamics across multiple stages of B-cell differentiation. We showed that naïve repertoires share the highest number and diversity of CDR structures, a pattern which was highly conserved in all B-cell donors. Our results suggest that increased B-cell differentiation is associated with a personalization of CDR structure usages. Finally, we established the differences in CDR usages between humans and mice, analysis with immediate relevance for BCR repertoire “humanness” assessment and rational immunotherapeutic engineering.

Comparative Analysis of the CDR Loops of Antigen Receptors

The adaptive immune system uses two main types of antigen receptors: T-cell receptors (TCRs) and antibodies. While both proteins share a globally similar β-sandwich architecture, TCRs are specialized to recognize peptide antigens in the binding groove of the major histocompatibility complex, while antibodies can bind an almost infinite range of molecules. For both proteins, the main determinants of target recognition are the complementarity-determining region (CDR) loops. Five of the six CDRs adopt a limited number of backbone conformations, known as the "canonical classes"; the remaining CDR (β3in TCRs and H3 in antibodies) is more structurally diverse. In this paper, we first update the definition of canonical forms in TCRs, build an auto-updating sequence-based prediction tool (available at http://opig.stats.ox.ac.uk/resources) and demonstrate its application on large scale sequencing studies. Given the global similarity of TCRs and antibodies, we then examine the structural similarity of their CDRs. We find that TCR and antibody CDRs tend to have different length distributions, and where they have similar lengths, they mostly occupy distinct structural spaces. In the rare cases where we found structural similarity, the underlying sequence patterns for the TCR and antibody version are different. Finally, where multiple structures have been solved for the same CDR sequence, the structural variability in TCR loops is higher than that in antibodies, suggesting TCR CDRs are more flexible. These structural differences between TCR and antibody CDRs may be important to their different biological functions.

Human adaptive immune receptor repertoire analysis-Past, present, and future

The genes encoding adaptive immune antigen receptors, namely the immunoglobulins expressed in membrane-bound or secreted forms by B cells, and the cell surface T cell receptors, are unique in human biology because they are generated by combinatorial rearrangement of the genomic DNA. The diversity of receptors so generated in populations of lymphocytes enables the human immune system to recognize antigens expressed by pathogens, but also underlies the pathological specificity of autoimmune diseases and the mistargeted immunity in allergies. Several recent technological developments, foremost among them the invention of high-throughput DNA sequencing instruments, have enabled much deeper and thorough evaluation of clones of human B cells and T cells and the antigen receptors they express during physiological and pathogenic immune responses. The evolutionary struggles between host adaptive immune responses and populations of pathogens are now open to greater scrutiny, elucidation of the underlying reasons for successful or failed immunity, and potential predictive modeling, than ever before. Here we give an overview of the foundations, recent progress, and future prospects in this dynamic area of research.

Can We Improve Vaccine Efficacy by Targeting T and B Cell Repertoire Convergence?

Traditional vaccine development builds on the assumption that healthy individuals have virtually unlimited antigen recognition repertoires of receptors in B cells and T cells [the B cell receptor (BCR) and TCR respectively]. However, there are indications that there are "holes" in the breadth of repertoire diversity, where no or few B or T cell are able to bind to a given antigen. Repertoire diversity may in these cases be a limiting factor for vaccine efficacy. Assuming that it is possible to predict which B and T cell receptors will respond to a given immunogen, vaccine strategies could be optimized and personalized. In addition, vaccine testing could be simplified if we could predict responses through sequencing BCR and TCRs. Bulk sequencing has shown putatively specific converging sequences after infection or vaccination. However, only single cell technologies have made it possible to capture the sequence of both heavy and light chains of a BCR or the alpha and beta chains the TCR. This has enabled the cloning of receptors and the functional validation of a predicted specificity. This review summarizes recent evidence of converging sequences in infectious diseases. Current and potential future applications of single cell technology in immune repertoire analysis are then discussed. Finally, possible short- and long- term implications for vaccine research are highlighted.

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.

DynBench3D, a Web-Resource to Dynamically Generate Benchmark Sets of Large Heteromeric Protein Complexes

Multi-protein machines are responsible for most cellular tasks, and many efforts have been invested in the systematic identification and characterization of thousands of these macromolecular assemblies. However, unfortunately, the (quasi) atomic details necessary to understand their function are available only for a tiny fraction of the known complexes. The computational biology community is developing strategies to integrate structural data of different nature, from electron microscopy to X-ray crystallography, to model large molecular machines, as it has been done for individual proteins and interactions with remarkable success. However, unlike for binary interactions, there is no reliable gold-standard set of three-dimensional (3D) complexes to benchmark the performance of these methodologies and detect their limitations. Here, we present a strategy to dynamically generate non-redundant sets of 3D heteromeric complexes with three or more components. By changing the values of sequence identity and component overlap between assemblies required to define complex redundancy, we can create sets of representative complexes with known 3D structure (i.e., target complexes). Using an identity threshold of 20% and imposing a fraction of component overlap of <0.5, we identify 495 unique target complexes, which represent a real non-redundant set of heteromeric assemblies with known 3D structure. Moreover, for each target complex, we also identify a set of assemblies, of varying degrees of identity and component overlap, that can be readily used as input in a complex modeling exercise (i.e., template subcomplexes). We hope that resources like this will significantly help the development and progress assessment of novel methodologies, as docking benchmarks and blind prediction contests did. The interactive resource is accessible at https://DynBench3D.irbbarcelona.org.

Construction and Characterization of a Humanized Anti-Epstein-Barr Virus gp350 Antibody with Neutralizing Activity in Cell Culture

Acute Epstein-Barr virus (EBV) infection in immunosuppressed transplant patients can give rise to a malignant B-cell proliferation known as post-transplant lymphoproliferative disease (PTLD). The EBV major virion surface glycoprotein (gp)350 is a principal target of naturally occurring neutralizing antibodies and is viewed as the best target to prevent acute infection and PTLD in at-risk transplant recipients. We have constructed a humanized (hu) version of the murine anti-gp350 neutralizing monoclonal antibody 72a1. The hu72a1 IgG1 antibody displayed no significant anti-mouse activity, recognized both gp350 and its splice variant gp220 as well as a gp350 peptide that was shown to constitute the principal EBV gp350 neutralizing epitope when tested in immunoassays. Hu72a1 antibody blocked in vitro EBV infection of B cells at a level which equaled that of a mouse-human chimeric 72a1 antibody construct. This work provides a further structural and immunological understanding of the 72a1 antibody interaction with EBV gp350, and constitutes a launch point for future anti-EBV therapeutic antibodies designed to block EBV infection and prevent PTLD while eliminating the deleterious antigenic murine features of the original 72a1 antibody.

How B-Cell Receptor Repertoire Sequencing Can Be Enriched with Structural Antibody Data

Next-generation sequencing of immunoglobulin gene repertoires (Ig-seq) allows the investigation of large-scale antibody dynamics at a sequence level. However, structural information, a crucial descriptor of antibody binding capability, is not collected in Ig-seq protocols. Developing systematic relationships between the antibody sequence information gathered from Ig-seq and low-throughput techniques such as X-ray crystallography could radically improve our understanding of antibodies. The mapping of Ig-seq datasets to known antibody structures can indicate structurally, and perhaps functionally, uncharted areas. Furthermore, contrasting naïve and antigenically challenged datasets using structural antibody descriptors should provide insights into antibody maturation. As the number of antibody structures steadily increases and more and more Ig-seq datasets become available, the opportunities that arise from combining the two types of information increase as well. Here, we review how these data types enrich one another and show potential for advancing our knowledge of the immune system and improving antibody engineering.

Computationally-driven identification of antibody epitopes

Understanding where antibodies recognize antigens can help define mechanisms of action and provide insights into progression of immune responses. We investigate the extent to which information about binding specificity implicitly encoded in amino acid sequence can be leveraged to identify antibody epitopes. In computationally-driven epitope localization, possible antibody–antigen binding modes are modeled, and targeted panels of antigen variants are designed to experimentally test these hypotheses. Prospective application of this approach to two antibodies enabled epitope localization using five or fewer variants per antibody, or alternatively, a six-variant panel for both simultaneously. Retrospective analysis of a variety of antibodies and antigens demonstrated an almost 90% success rate with an average of three antigen variants, further supporting the observation that the combination of computational modeling and protein design can reveal key determinants of antibody–antigen binding and enable efficient studies of collections of antibodies identified from polyclonal samples or engineered libraries.