Length-independent structural similarities enrich the antibody CDR canonical class model

Repeated vaccination with homologous influenza hemagglutinin broadens human antibody responses to unmatched flu viruses

The on-going diversification of influenza virus necessicates annual vaccine updating. The vaccine antigen, the viral spike protein hemagglutinin (HA), tends to elicit strain-specific neutralizing activity, predicting that sequential immunization with the same HA strain will boost antibodies with narrow coverage. However, repeated vaccination with homologous SARS-CoV-2 vaccine eventually elicits neutralizing activity against highly unmatched variants, questioning this immunological premise. We evaluated a longitudinal influenza vaccine cohort, where each year the subjects received the same, novel H1N1 2009 pandemic vaccine strain. Repeated vaccination gradually enhanced receptor-blocking antibodies (HAI) to highly unmatched H1N1 strains within individuals with no initial memory recall against these historical viruses. An in silico model of affinity maturation in germinal centers integrated with a model of differentiation and expansion of memory cells provides insight into the mechanisms underlying these results and shows how repeated exposure to the same immunogen can broaden the antibody response against diversified targets.

Investigating the ability of deep learning-based structure prediction to extrapolate and/or enrich the set of antibody CDR canonical forms

Deep learning models have been shown to accurately predict protein structure from sequence, allowing researchers to explore protein space from the structural viewpoint. In this paper we explore whether "novel" features, such as distinct loop conformations can arise from these predictions despite not being present in the training data. Here we have used ABodyBuilder2, a deep learning antibody structure predictor, to predict the structures of ~1.5M paired antibody sequences. We examined the predicted structures of the canonical CDR loops and found that most of these predictions fall into the already described CDR canonical form structural space. We also found a small number of "new" canonical clusters composed of heterogeneous sequences united by a common sequence motif and loop conformation. Analysis of these novel clusters showed their origins to be either shapes seen in the training data at very low frequency or shapes seen at high frequency but at a shorter sequence length. To evaluate explicitly the ability of ABodyBuilder2 to extrapolate, we retrained several models whilst withholding all antibody structures of a specific CDR loop length or canonical form. These "starved" models showed evidence of generalisation across CDRs of different lengths, but they did not extrapolate to loop conformations which were highly distinct from those present in the training data. However, the models were able to accurately predict a canonical form even if only a very small number of examples of that shape were in the training data. Our results suggest that deep learning protein structure prediction methods are unable to make completely out-of-domain predictions for CDR loops. However, in our analysis we also found that even minimal amounts of data of a structural shape allow the method to recover its original predictive abilities. We have made the ~1.5 M predicted structures used in this study available to download at https://doi.org/10.5281/zenodo.10280181.

Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind to the same epitope

The function of an antibody is intrinsically linked to the epitope it engages. Clonal clustering methods, based on sequence identity, are commonly used to group antibodies that will bind to the same epitope. However, such methods neglect the fact that antibodies with highly diverse sequences can exhibit similar binding site geometries and engage common epitopes. In a previous study, we described SPACE1, a method that structurally clustered antibodies in order to predict their epitopes. This methodology was limited by the inaccuracies and incomplete coverage of template-based modeling. In addition, it was only benchmarked at the level of domain-consistency on one virus class. Here, we present SPACE2, which uses the latest machine learning-based structure prediction technology combined with a novel clustering protocol, and benchmark it on binding data that have epitope-level resolution. On six diverse sets of antigen-specific antibodies, we demonstrate that SPACE2 accurately clusters antibodies that engage common epitopes and achieves far higher dataset coverage than clonal clustering and SPACE1. Furthermore, we show that the functionally consistent structural clusters identified by SPACE2 are even more diverse in sequence, genetic lineage, and species origin than those found by SPACE1. These results reiterate that structural data improve our ability to identify antibodies that bind to the same epitope, adding information to sequence-based methods, especially in datasets of antibodies from diverse sources. SPACE2 is openly available on GitHub (https://github.com/oxpig/SPACE2).

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.

Structural insight to elucidate the binding specificity of the anti-cortisol Fab fragment with glucocorticoids

Cortisol is a steroid hormone that is produced by the adrenal gland. It is a primary stress hormone that increases glucose levels in the blood stream. High concentrations of cortisol in the body can be used as a biomarker for acute and chronic stress and related mental and physiological disorders. Therefore, the accurate quantification of cortisol levels in body fluids is essential for clinical diagnosis. In this article, we describe the isolation of recombinant anti-cortisol antibodies with high affinity for cortisol and discover their cross-reactivity with other glucocorticoids. To describe the cortisol binding site and elucidate the structural basis for the binding specificity, the high-resolution crystal structures of the anti-cortisol (17) Fab fragment in the absence of glucocorticoid (2.00 Å) and the presence of cortisol (2.26 Å), corticosterone (1.86 Å), cortisone (1.85 Å) and prednisolone (2.00 Å) were determined. To our knowledge, this is the first determined crystal structure of a cortisol-specific antibody. The recognition of cortisol is driven by hydrophobic interactions and hydrogen bonding at the protein-ligand interface coupled with a conformational transition. Comparison of ligand-free and ligand-bound structures showed that the side chains of residues Tyr58-H and Arg56-H can undergo local conformational changes at the binding site, most likely prior to the binding event via a conformational selection mechanism. Compared to other anti-steroid antibody-antigen complexes, (17) Fab possesses a structurally unique steroid binding site, as the H3 loop from the CDR area has only a minor contribution, but framework residues have a prominent contribution to hapten binding.

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 V_H-V_L and in the C_H1-C_L 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 C_H1-C_L interface that contribute to stabilization. The dissociation of the V_H-V_L or C_H1-C_L 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.

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.

Computational identification of HCV neutralizing antibodies with a common HCDR3 disulfide bond motif in the antibody repertoires of infected individuals

Despite recent success in hepatitis C virus (HCV) treatment using antivirals, an HCV vaccine is still needed to prevent reinfections in treated patients, to avert the emergence of drug-resistant strains, and to provide protection for people with no access to the antiviral therapeutics. The early production of broadly neutralizing antibodies (bNAbs) associates with HCV clearance. Several potent bNAbs bind a conserved HCV glycoprotein E2 epitope using an unusual heavy chain complementarity determining region 3 (HCDR3) containing an intra-loop disulfide bond. Isolation of additional structurally-homologous bNAbs would facilitate the recognition of key determinants of such bNAbs and guide rational vaccine design. Here we report the identification of new antibodies containing an HCDR3 disulfide bond motif using computational screening with the Rosetta software. Using the newly-discovered and already-known members of this antibody family, we review the required HCDR3 amino acid composition and propose determinants for the bent versus straight HCDR3 loop conformation observed in these antibodies.

Unraveling a Conserved Conformation of the FG Loop upon the Binding of Natural Ligands to the Human and Murine PD1

The activation of T cells is normally accompanied by inhibitory mechanisms within which the PD1 receptor stands out. PD1 drives T cells to an unresponsive state called exhaustion, characterized by a markedly decreased capacity to exert effector functions upon binding the ligands PDL1 and PDL2. For this reason, PD1 has become one of the most important targets in cancer immunotherapy. Despite the numerous studies about PD1 signaling modulation, how the PD1 signaling pathway is activated upon the ligands' binding remains an open question. In this work, we used molecular dynamics simulations to assess the differences of the PD1 motion in the free state and in complex with the ligands. We found that, in both human and murine systems, the binding of PDL1 and PDL2 stabilizes the conformation of the FG loop similarly. This result, combined with the conservation of the FG loop residues across species, suggests that the conformation of the FG loop is somehow related to the signaling process. We also found a high similarity between the PD1-PDL1 structures with the variable region of an antibody structure, where the FG loop occupies a similar position to the CDR3 light chain.

In silico prediction of post-translational modifications in therapeutic antibodies

Monoclonal antibodies are susceptible to chemical and enzymatic modifications during manufacturing, storage, and shipping. Deamidation, isomerization, and oxidation can compromise the potency, efficacy, and safety of therapeutic antibodies. Recently, in silico tools have been used to identify liable residues and engineer antibodies with better chemical stability. Computational approaches for predicting deamidation, isomerization, oxidation, glycation, carbonylation, sulfation, and hydroxylation are reviewed here. Although liable motifs have been used to improve the chemical stability of antibodies, the accuracy of in silico predictions can be improved using machine learning and molecular dynamic simulations. In addition, there are opportunities to improve predictions for specific stress conditions, develop in silico prediction of novel modifications in antibodies, and predict the impact of modifications on physical stability and antigen-binding.

A novel framework for engineering protein loops exploring length and compositional variation

Insertions and deletions (indels) are known to affect function, biophysical properties and substrate specificity of enzymes, and they play a central role in evolution. Despite such clear significance, this class of mutation remains an underexploited tool in protein engineering with few available platforms capable of systematically generating and analysing libraries of varying sequence composition and length. We present a novel DNA assembly platform (InDel assembly), based on cycles of endonuclease restriction digestion and ligation of standardised dsDNA building blocks, that can generate libraries exploring both composition and sequence length variation. In addition, we developed a framework to analyse the output of selection from InDel-generated libraries, combining next generation sequencing and alignment-free strategies for sequence analysis. We demonstrate the approach by engineering the well-characterized TEM-1 β-lactamase Ω-loop, involved in substrate specificity, identifying multiple novel extended spectrum β-lactamases with loops of modified length and composition-areas of the sequence space not previously explored. Together, the InDel assembly and analysis platforms provide an efficient route to engineer protein loops or linkers where sequence length and composition are both essential functional parameters.

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

Discovery of Marburg virus neutralizing antibodies from virus-naïve human antibody repertoires using large-scale structural predictions

Marburg virus (MARV) disease is lethal, with fatality rates up to 90%. Neutralizing antibodies (Abs) are promising drug candidates to prevent or treat the disease. Current efforts are focused in part on vaccine development to induce such MARV-neutralizing Abs. We analyzed the antibody repertoire from healthy unexposed and previously MARV-infected individuals to assess if naïve repertoires contain suitable precursor antibodies that could become neutralizing with a limited set of somatic mutations. We computationally searched the human Ab variable gene repertoire for predicted structural homologs of the neutralizing Ab MR78 that is specific to the receptor binding site (RBS) of MARV glycoprotein (GP). Eight Ab heavy-chain complementarity determining region 3 (HCDR3) loops from MARV-naïve individuals and one from a previously MARV-infected individual were selected for testing as HCDR3 loop chimeras on the MR78 Ab framework. Three of these chimerized antibodies bound to MARV GP. We then tested a full-length native Ab heavy chain encoding the same 17-residue-long HCDR3 loop that bound to the MARV GP the best among the chimeric Abs tested. Despite only 57% amino acid sequence identity, the Ab from a MARV-naïve donor recognized MARV GP and possessed neutralizing activity against the virus. Crystallization of both chimeric and full-length native heavy chain-containing Abs provided structural insights into the mechanism of binding for these types of Abs. Our work suggests that the MARV GP RBS is a promising candidate for epitope-focused vaccine design to induce neutralizing Abs against MARV.

Antibodies exhibit multiple paratope states influencing VH-VL domain orientations

In the last decades, antibodies have emerged as one of the most important and successful classes of biopharmaceuticals. The highest variability and diversity of an antibody is concentrated on six hypervariable loops, also known as complementarity determining regions (CDRs) shaping the antigen-binding site, the paratope. Whereas it was assumed that certain sequences can only adopt a limited set of backbone conformations, in this study we present a kinetic classification of several paratope states in solution. Using molecular dynamics simulations in combination with experimental structural information we capture the involved conformational transitions between different canonical clusters and additional dominant solution structures occurring in the micro-to-millisecond timescale. Furthermore, we observe a strong correlation of CDR loop movements. Another important aspect when characterizing different paratope states is the relative VH/VL orientation and the influence of the distinct CDR loop states on the VH/VL interface. Conformational rearrangements of the CDR loops do not only have an effect on the relative VH/VL orientations, but also influence in some cases the elbow-angle dynamics and shift the respective distributions. Thus, our results show that antibodies exist as several interconverting paratope states, each contributing to the antibody's properties.

SCALOP: sequence-based antibody canonical loop structure annotation

MOTIVATION

Canonical forms of the antibody complementarity-determining regions (CDRs) were first described in 1987 and have been redefined on multiple occasions since. The canonical forms are often used to approximate the antibody binding site shape as they can be predicted from sequence. A rapid predictor would facilitate the annotation of CDR structures in the large amounts of repertoire data now becoming available from next generation sequencing experiments.

RESULTS

SCALOP annotates CDR canonical forms for antibody sequences, supported by an auto-updating database to capture the latest cluster information. Its accuracy is comparable to that of a standard structural predictor but it is 800 times faster. The auto-updating nature of SCALOP ensures that it always attains the best possible coverage.

AVAILABILITY AND IMPLEMENTATION

SCALOP is available as a web application and for download under a GPLv3 license at opig.stats.ox.ac.uk/webapps/scalop.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

Adaption of human antibody λ and κ light chain architectures to CDR repertoires

Monoclonal antibodies bind with high specificity to a wide range of diverse antigens, primarily mediated by their hypervariable complementarity determining regions (CDRs). The defined antigen binding loops are supported by the structurally conserved β-sandwich framework of the light chain (LC) and heavy chain (HC) variable regions. The LC genes are encoded by two separate loci, subdividing the entity of antibodies into kappa (LC_κ) and lambda (LC_λ) isotypes that exhibit distinct sequence and conformational preferences. In this work, a diverse set of techniques were employed including machine learning, force field analysis, statistical coupling analysis and mutual information analysis of a non-redundant antibody structure collection. Thereby, it was revealed how subtle changes between the structures of LC_κ and LC_λ isotypes increase the diversity of antibodies, extending the predetermined restrictions of the general antibody fold and expanding the diversity of antigen binding. Interestingly, it was found that the characteristic framework scaffolds of κ and λ are stabilized by diverse amino acid clusters that determine the interplay between the respective fold and the embedded CDR loops. In conclusion, this work reveals how antibodies use the remarkable plasticity of the beta-sandwich Ig fold to incorporate a large diversity of CDR loops.

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.

Antibody humanization-the Influence of the antibody framework on the CDR-H3 loop ensemble in solution

Antibody engineering of non-human antibodies has focused on reducing immunogenicity by humanization, being a major limitation in developing monoclonal antibodies. We analyzed four series of antibody binding fragments (Fabs) and a variable fragment (Fv) with structural information in different stages of humanization to investigate the influence of the framework, point mutations and specificity on the complementarity determining region (CDR)-H3 loop dynamics. We also studied a Fv without structural information of the anti-idiotypic antibody Ab2/3H6, because it completely lost its binding affinity upon superhumanization, as an example of a failed humanization. Enhanced sampling techniques in combination with molecular dynamics simulations allow to access micro- to milli-second timescales of the CDR-H3 loop dynamics and reveal kinetic and thermodynamic changes involved in the process of humanization. In most cases, we observe a reduced conformational diversity of the CDR-H3 loop when grafted on a human framework and find a conformational shift of the dominant CDR-H3 loop conformation in solution. A shallow side minimum of the conformational CDR-H3 loop ensemble attached to the murine framework becomes the dominant conformation in solution influenced by the human framework. Additionally, we observe in the case of the failed humanization that the potentially binding competent murine CDR-H3 loop ensemble in solution shows nearly no kinetical or structural overlap with the superhumanized variant, thus explaining the loss of binding.

Transitions of CDR-L3 Loop Canonical Cluster Conformations on the Micro-to-Millisecond Timescale

Sequence and structural diversity of antibodies are concentrated on six hypervariable loops, also known as the complementarity determining regions (CDRs). Five of six antibody CDR loops presumably adopt a so-called canonical structure out of a limited number of conformations. However, here we show for four antibody CDR-L3 loops differing in length and sequence, that each loop undergoes conformational transitions between different canonical structures. By extensive sampling in combination with Markov-state models we reconstruct the kinetics and probabilities of the transitions between canonical structures. Additionally, for these four CDR-L3 loops, we identify all relevant conformations in solution. Thereby we extend the model of static canonical structures to a dynamic conformational ensemble as a new paradigm in the field of antibody structure design.

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.

STCRDab: the structural T-cell receptor database

The Structural T–cell Receptor Database (STCRDab; http://opig.stats.ox.ac.uk/webapps/stcrdab) is an online resource that automatically collects and curates TCR structural data from the Protein Data Bank. For each entry, the database provides annotations, such as the α/β or γ/δ chain pairings, major histocompatibility complex details, and where available, antigen binding affinities. In addition, the orientation between the variable domains and the canonical forms of the complementarity-determining region loops are also provided. Users can select, view, and download individual or bulk sets of structures based on these criteria. Where available, STCRDab also finds antibody structures that are similar to TCRs, helping users explore the relationship between TCRs and antibodies.

CDR-H3 loop ensemble in solution - conformational selection upon antibody binding

We analyzed pairs of protein-binding, peptide-binding and hapten-binding antibodies crystallized as complex and in the absence of the antigen with and without conformational differences upon binding in the complementarity-determining region (CDR)-H3 loop. Here, we introduce a molecular dynamics-based approach to capture a diverse conformational ensemble of the CDR-H3 loop in solution. The results clearly indicate that the inherently flexible CDR-H3 loop indeed needs to be characterized as a conformational ensemble. The conformational changes of the CDR-H3 loop in all antibodies investigated follow the paradigm of conformation selection, because we observe the experimentally determined binding competent conformation without the presence of the antigen within the ensemble of pre-existing conformational states in solution before binding. We also demonstrate for several examples that the conformation observed in the antibody crystal structure without antigen present is actually selected to bind the carboxyterminal tail region of the antigen-binding fragment (Fab). Thus, special care must be taken when characterizing antibody CDR-H3 loops by Fab X-ray structures, and the possibility that pre-existing conformations are present should always be considered.

High-Throughput Antibody Structure Modeling and Design Using ABodyBuilder

Antibodies are proteins of the adaptive immune system; they can be designed to bind almost any molecule, and are increasingly being used as biotherapeutics. Experimental antibody design is an expensive and time-consuming process, and computational antibody design methods can now be used to help develop new therapeutics and diagnostics. Within the design pipeline, accurate antibody structure modeling is essential, as it provides the basis for antibody-antigen docking, binding affinity prediction, and estimating thermal stability. Ideally, models should be rapidly generated, allowing the exploration of the breadth of antibody space. This allows methods to replicate the natural processes of antibody diversification (e.g., V(D)J recombination and somatic hypermutation), and cope with large volumes of data that are typical of next-generation sequencing datasets. Here we describe ABodyBuilder and PEARS, algorithms that build and mutate antibody model structures. These methods take ~30 s to generate a model antibody structure.

Computational design of structured loops for new protein functions

The ability to engineer the precise geometries, fine-tuned energetics and subtle dynamics that are characteristic of functional proteins is a major unsolved challenge in the field of computational protein design. In natural proteins, functional sites exhibiting these properties often feature structured loops. However, unlike the elements of secondary structures that comprise idealized protein folds, structured loops have been difficult to design computationally. Addressing this shortcoming in a general way is a necessary first step towards the routine design of protein function. In this perspective, we will describe the progress that has been made on this problem and discuss how recent advances in the field of loop structure prediction can be harnessed and applied to the inverse problem of computational loop design.

Five computational developability guidelines for therapeutic antibody profiling

Immunogenicity, instability, self-association, high viscosity, polyspecificity, or poor expression can all preclude an antibody from becoming a therapeutic. Early identification of these negative characteristics is essential. Akin to the Lipinski guidelines, which measure druglikeness in small molecules, our Therapeutic Antibody Profiler highlights antibodies that possess characteristics that are rare/unseen in clinical-stage mAb therapeutics. The only required input is the variable domain sequence. We show examples where our approach would have advised against manufacturing antibodies that were found to aggregate or have poor expression.

Therapeutic mAbs must not only bind to their target but must also be free from “developability issues” such as poor stability or high levels of aggregation. While small-molecule drug discovery benefits from Lipinski’s rule of five to guide the selection of molecules with appropriate biophysical properties, there is currently no in silico analog for antibody design. Here, we model the variable domain structures of a large set of post-phase-I clinical-stage antibody therapeutics (CSTs) and calculate in silico metrics to estimate their typical properties. In each case, we contextualize the CST distribution against a snapshot of the human antibody gene repertoire. We describe guideline values for five metrics thought to be implicated in poor developability: the total length of the complementarity-determining regions (CDRs), the extent and magnitude of surface hydrophobicity, positive charge and negative charge in the CDRs, and asymmetry in the net heavy- and light-chain surface charges. The guideline cutoffs for each property were derived from the values seen in CSTs, and a flagging system is proposed to identify nonconforming candidates. On two mAb drug discovery sets, we were able to selectively highlight sequences with developability issues. We make available the Therapeutic Antibody Profiler (TAP), a computational tool that builds downloadable homology models of variable domain sequences, tests them against our five developability guidelines, and reports potential sequence liabilities and canonical forms. TAP is freely available at opig.stats.ox.ac.uk/webapps/sabdab-sabpred/TAP.php.

Non-H3 CDR template selection in antibody modeling through machine learning

Antibodies are proteins generated by the adaptive immune system to recognize and counteract a plethora of pathogens through specific binding. This adaptive binding is mediated by structural diversity in the six complementary determining region (CDR) loops (H1, H2, H3, L1, L2 and L3), which also makes accurate structural modeling of CDRs challenging. Both homology and de novo modeling approaches have been used; to date, the former has achieved greater accuracy for the non-H3 loops. The homology modeling of non-H3 CDRs is more accurate because non-H3 CDR loops of the same length and type can be grouped into a few structural clusters. Most antibody-modeling suites utilize homology modeling for the non-H3 CDRs, differing only in the alignment algorithm and how/if they utilize structural clusters. While RosettaAntibody and SAbPred do not explicitly assign query CDR sequences to clusters, two other approaches, PIGS and Kotai Antibody Builder, utilize sequence-based rules to assign CDR sequences to clusters. While the manually curated sequence rules can identify better structural templates, because their curation requires extensive literature search and human effort, they lag behind the deposition of new antibody structures and are infrequently updated. In this study, we propose a machine learning approach (Gradient Boosting Machine [GBM]) to learn the structural clusters of non-H3 CDRs from sequence alone. The GBM method simplifies feature selection and can easily integrate new data, compared to manual sequence rule curation. We compare the classification results using the GBM method to that of RosettaAntibody in a 3-repeat 10-fold cross-validation (CV) scheme on the cluster-annotated antibody database PyIgClassify and we observe an improvement in the classification accuracy of the concerned loops from 84.5% ± 0.24% to 88.16% ± 0.056%. The GBM models reduce the errors in specific cluster membership misclassifications when the involved clusters have relatively abundant data. Based on the factors identified, we suggest methods that can enrich structural classes with sparse data to further improve prediction accuracy in future studies.

Predicting loop conformational ensembles

Motivation

Protein function is often facilitated by the existence of multiple stable conformations. Structure prediction algorithms need to be able to model these different conformations accurately and produce an ensemble of structures that represent a target's conformational diversity rather than just a single state. Here, we investigate whether current loop prediction algorithms are capable of this. We use the algorithms to predict the structures of loops with multiple experimentally determined conformations, and the structures of loops with only one conformation, and assess their ability to generate and select decoys that are close to any, or all, of the observed structures.

Results

We find that while loops with only one known conformation are predicted well, conformationally diverse loops are modelled poorly, and in most cases the predictions returned by the methods do not resemble any of the known conformers. Our results contradict the often-held assumption that multiple native conformations will be present in the decoy set, making the production of accurate conformational ensembles impossible, and hence indicating that current methodologies are not well suited to prediction of conformationally diverse, often functionally important protein regions.

Contact

marks@stats.ox.ac.uk.

Supplementary information

Supplementary data are available at Bioinformatics online.

Structurally Mapping Antibody Repertoires

Every human possesses millions of distinct antibodies. It is now possible to analyze this diversity via next-generation sequencing of immunoglobulin genes (Ig-seq). This technique produces large volume sequence snapshots of B-cell receptors that are indicative of the antibody repertoire. In this paper, we enrich these large-scale sequence datasets with structural information. Enriching a sequence with its structural data allows better approximation of many vital features, such as its binding site and specificity. Here, we describe the structural annotation of antibodies pipeline that maps the outputs of large Ig-seq experiments to known antibody structures. We demonstrate the viability of our protocol on five separate Ig-seq datasets covering ca. 35 m unique amino acid sequences from ca. 600 individuals. Despite the great theoretical diversity of antibodies, we find that the majority of sequences coming from such studies can be reliably mapped to an existing structure.

Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction

Motivation

Loops are often vital for protein function, however, their irregular structures make them difficult to model accurately. Current loop modelling algorithms can mostly be divided into two categories: knowledge-based, where databases of fragments are searched to find suitable conformations and ab initio, where conformations are generated computationally. Existing knowledge-based methods only use fragments that are the same length as the target, even though loops of slightly different lengths may adopt similar conformations. Here, we present a novel method, Sphinx, which combines ab initio techniques with the potential extra structural information contained within loops of a different length to improve structure prediction.

Results

We show that Sphinx is able to generate high-accuracy predictions and decoy sets enriched with near-native loop conformations, performing better than the ab initio algorithm on which it is based. In addition, it is able to provide predictions for every target, unlike some knowledge-based methods. Sphinx can be used successfully for the difficult problem of antibody H3 prediction, outperforming RosettaAntibody, one of the leading H3-specific ab initio methods, both in accuracy and speed.

Availability and Implementation

Sphinx is available at http://opig.stats.ox.ac.uk/webapps/sphinx.

Supplementary information

Supplementary data are available at Bioinformatics online.

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.

Antibody Heavy Chain Variable Domains of Different Germline Gene Origins Diversify through Different Paths

B cells produce antibodies, key effector molecules in health and disease. They mature their properties, including their affinity for antigen, through hypermutation events; processes that involve, e.g., base substitution, codon insertion and deletion, often in association with an isotype switch. Investigations of antibody evolution define modes whereby particular antibody responses are able to form, and such studies provide insight important for instance for development of efficient vaccines. Antibody evolution is also used in vitro for the design of antibodies with improved properties. To better understand the basic concepts of antibody evolution, we analyzed the mutational paths, both in terms of amino acid substitution and insertions and deletions, taken by antibodies of the IgG isotype. The analysis focused on the evolution of the heavy chain variable domain of sets of antibodies, each with an origin in 1 of 11 different germline genes representing six human heavy chain germline gene subgroups. Investigated genes were isolated from cells of human bone marrow, a major site of antibody production, and characterized by next-generation sequencing and an in-house bioinformatics pipeline. Apart from substitutions within the complementarity determining regions, multiple framework residues including those in protein cores were targets of extensive diversification. Diversity, both in terms of substitutions, and insertions and deletions, in antibodies is focused to different positions in the sequence in a germline gene-unique manner. Altogether, our findings create a framework for understanding patterns of evolution of antibodies from defined germline genes.

Association between a common immunoglobulin heavy chain allele and rheumatic heart disease risk in Oceania

The indigenous populations of the South Pacific experience a high burden of rheumatic heart disease (RHD). Here we report a genome-wide association study (GWAS) of RHD susceptibility in 2,852 individuals recruited in eight Oceanian countries. Stratifying by ancestry, we analysed genotyped and imputed variants in Melanesians (607 cases and 1,229 controls) before follow-up of suggestive loci in three further ancestral groups: Polynesians, South Asians and Mixed or other populations (totalling 399 cases and 617 controls). We identify a novel susceptibility signal in the immunoglobulin heavy chain (IGH) locus centring on a haplotype of nonsynonymous variants in the IGHV4-61 gene segment corresponding to the IGHV4-61*02 allele. We show each copy of IGHV4-61*02 is associated with a 1.4-fold increase in the risk of RHD (odds ratio 1.43, 95% confidence intervals 1.27–1.61, P=4.1 × 10⁻⁹). These findings provide new insight into the role of germline variation in the IGH locus in disease susceptibility.

Rheumatic heart disease (RHD) is a chronic auto-inflammatory reaction to group A streptococcal infection, and frequently occurs in individuals from the South Pacific. This study finds a novel association between an immunoglobulin heavy chain allele and risk of RHD in Pacific Islanders and South Asians.

The H3 loop of antibodies shows unique structural characteristics

The H3 loop in the Complementarity Determining Region of antibodies plays a key role in their ability to bind the diverse space of potential antigens. It is also exceptionally difficult to model computationally causing a significant hurdle for in silico development of antibody biotherapeutics. In this article, we show that most H3s have unique structural characteristics which may explain why they are so challenging to model. We found that over 75% of H3 loops do not have a sub‐Angstrom structural neighbor in the non‐antibody world. Also, in a comparison with a nonredundant set of all protein fragments over 30% of H3 loops have a unique structure, with the average for all of other loops being less than 3%. We further observed that this structural difference can be seen at the level of four residue fragments where H3 loops present numerous novel conformations, and also at the level of individual residues with Tyrosine and Glycine often found in energetically unfavorable conformations. Proteins 2017; 85:1311–1318. © 2017 Wiley Periodicals, Inc.

Antibody H3 Structure Prediction

Antibodies are proteins of the immune system that are able to bind to a huge variety of different substances, making them attractive candidates for therapeutic applications. Antibody structures have the potential to be useful during drug development, allowing the implementation of rational design procedures. The most challenging part of the antibody structure to experimentally determine or model is the H3 loop, which in addition is often the most important region in an antibody's binding site. This review summarises the approaches used so far in the pursuit of accurate computational H3 structure prediction.

Modeling and docking of antibody structures with Rosetta

We describe Rosetta-based computational protocols for predicting the 3D structure of an antibody from sequence (RosettaAntibody) and then docking the antibody to protein antigens (SnugDock). Antibody modeling leverages canonical loop conformations to graft large segments from experimentally determined structures, as well as offering (i) energetic calculations to minimize loops, (ii) docking methodology to refine the V_L-V_H relative orientation and (iii) de novo prediction of the elusive complementarity determining region (CDR) H3 loop. To alleviate model uncertainty, antibody-antigen docking resamples CDR loop conformations and can use multiple models to represent an ensemble of conformations for the antibody, the antigen or both. These protocols can be run fully automated via the ROSIE web server (http://rosie.rosettacommons.org/) or manually on a computer with user control of individual steps. For best results, the protocol requires roughly 1,000 CPU-hours for antibody modeling and 250 CPU-hours for antibody-antigen docking. Tasks can be completed in under a day by using public supercomputers.