Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design

Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved computational methods for both prediction and design. Constant improvements in performance within the Rosetta software suite for biomolecular modeling have given rise to a greater breadth of structure prediction, including docking and design application cases for antibody and antigen modeling. Here, we present an overview of current protocols for antibody and antigen modeling using Rosetta and exemplify those by detailed tutorials originally developed for a Rosetta workshop at Vanderbilt University. These tutorials cover antibody structure prediction, docking, and design and antigen design strategies, including the addition of glycans in Rosetta. We expect that these materials will allow novice users to apply Rosetta in their own projects for modeling antibodies and antigens.

Structure- and sequence-based design of synthetic single-domain antibody libraries

Single-domain antibody fragments known as VHH have emerged in the pharmaceutical industry as useful biotherapeutics. These molecules, which are naturally produced by camelids, share the characteristics of high affinity and specificity with traditional human immunoglobulins, while consisting of only a single heavy chain. Currently, the most common method for generating VHH is via animal immunization, which can be costly and time-consuming. Here we describe the development of a synthetic VHH library for in vitro selection of single domain binders. We combine structure-based design and next-generation sequencing analysis to build a library with characteristics that closely mimic the natural repertoire. To validate the performance of our synthetic library, we isolated VHH against three model antigens (soluble mouse PD-1 ectodomain, amyloid-β peptide, and MrgX1 GPCR) of different sizes and characteristics. We were able to isolate diverse binders targeting different epitopes with high affinity (as high as 5 nM) against all three targets. We then show that anti-mPD-1 binders have functional activity in a receptor blocking assay.

Identification of Structurally Related Antibodies in Antibody Sequence Databases Using Rosetta-Derived Position-Specific Scoring

The amount of antibody (Ab) variable gene sequence information is expanding rapidly, but our ability to predict the function of Abs from sequence alone is limited. Here, we describe a sequence-to-function prediction method that couples structural data for a single Ab/antigen (Ag) complex with repertoire data. We used a position-specific structure-scoring matrix (P3SM) incorporating structure-prediction scores from Rosetta to identify Ab variable loops that have predicted structural similarity to the influenza virus-specific human Ab CH65. The P3SM approach identified new members of this Ab class. Recombinant Ab expression, crystallography, and virus inhibition assays showed that the HCDR3 loops of the newly identified Abs possessed similar structure and antiviral activity as the comparator CH65. This approach enables discovery of new human Abs with desired structure and function using cDNA repertoires that are obtained readily with current amplicon sequencing techniques.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Computationally Designed Cyclic Peptides Derived from an Antibody Loop Increase Breadth of Binding for Influenza Variants

The influenza hemagglutinin (HA) glycoprotein is the target of many broadly neutralizing antibodies. However, influenza viruses can rapidly escape antibody recognition by mutation of hypervariable regions of HA that overlap with the binding epitope. We hypothesized that by designing peptides to mimic antibody loops, we could enhance breadth of binding to HA antigenic variants by reducing contact with hypervariable residues on HA that mediate escape. We designed cyclic peptides that mimic the heavy-chain complementarity-determining region 3 (CDRH3) of anti-influenza broadly neutralizing antibody C05 and show that these peptides bound to HA molecules with <100 nM affinity, comparable with that of the full-length parental C05 IgG. In addition, these peptides exhibited increased breadth of recognition to influenza H4 and H7 subtypes by eliminating clashes between the hypervariable antigenic regions and the antibody CDRH1 loop. This approach can be used to generate antibody-derived peptides against a wide variety of targets.

Multi-state design of flexible proteins predicts sequences optimal for conformational change

Computational protein design of an ensemble of conformations for one protein–i.e., multi-state design–determines the side chain identity by optimizing the energetic contributions of that side chain in each of the backbone conformations. Sampling the resulting large sequence-structure search space limits the number of conformations and the size of proteins in multi-state design algorithms. Here, we demonstrated that the REstrained CONvergence (RECON) algorithm can simultaneously evaluate the sequence of large proteins that undergo substantial conformational changes. Simultaneous optimization of side chain conformations across all conformations increased sequence conservation when compared to single-state designs in all cases. More importantly, the sequence space sampled by RECON MSD resembled the evolutionary sequence space of flexible proteins, particularly when confined to predicting the mutational preferences of limited common ancestral descent, such as in the case of influenza type A hemagglutinin. Additionally, we found that sequence positions which require substantial changes in their local environment across an ensemble of conformations are more likely to be conserved. These increased conservation rates are better captured by RECON MSD over multiple conformations and thus multiple local residue environments during design. To quantify this rewiring of contacts at a certain position in sequence and structure, we introduced a new metric designated ‘contact proximity deviation’ that enumerates contact map changes. This measure allows mapping of global conformational changes into local side chain proximity adjustments, a property not captured by traditional global similarity metrics such as RMSD or local similarity metrics such as changes in φ and ψ angles.

Multi-state design can be used to engineer proteins that need to exist in multiple conformations or that bind to multiple partner molecules. In essence, multi-state design selects a compromise of protein sequences that allow for an ensemble of protein conformations, or states, associated with a particular biological function. In this paper, we used the REstrained CONvergence (RECON) algorithm with Rosetta to show that multi-state design of flexible proteins predicts sequences optimal for conformational change, mimicking mutation preferences sampled in evolution. Modeling optimal local side chain physicochemical environments within an ensemble selected significantly more native-like sequences than selections performed when all conformations states are designed independently. This outcome was particularly true for amino acids whose local side chain environment change between conformations. To quantify such contact map changes, we introduced a novel metric to show that sequence conservation is dependent on protein flexibility, i.e., changes in local side chain environments between stated limit the space of tolerated mutations. Additionally, such positions in sequence and structure are more likely to be energetically frustrated, at least in some states. Importantly, we showed that multi-state design over an ensemble of conformations (space) can explore evolutionary tolerated sequence space (time), thus enabling RECON to not only design proteins that require multiple states for function but also predict mutations that might be tolerated in native proteins but have not yet been explored by evolution. The latter aspect can be important to anticipate escape mutations, for example in pathogens or oncoproteins.

Immune repertoire fingerprinting by principal component analysis reveals shared features in subject groups with common exposures

BACKGROUND

Advances in next-generation sequencing (NGS) of antibody repertoires have led to an explosion in B cell receptor sequence data from donors with many different disease states. These data have the potential to detect patterns of immune response across populations. However, to this point it has been difficult to interpret such patterns of immune response between disease states in the absence of functional data. There is a need for a robust method that can be used to distinguish general patterns of immune responses at the antibody repertoire level.

RESULTS

We developed a method for reducing the complexity of antibody repertoire datasets using principal component analysis (PCA) and refer to our method as "repertoire fingerprinting." We reduce the high dimensional space of an antibody repertoire to just two principal components that explain the majority of variation in those repertoires. We show that repertoires from individuals with a common experience or disease state can be clustered by their repertoire fingerprints to identify common antibody responses.

CONCLUSIONS

Our repertoire fingerprinting method for distinguishing immune repertoires has implications for characterizing an individual disease state. Methods to distinguish disease states based on pattern recognition in the adaptive immune response could be used to develop biomarkers with diagnostic or prognostic utility in patient care. Extending our analysis to larger cohorts of patients in the future should permit us to define more precisely those characteristics of the immune response that result from natural infection or autoimmunity.

High frequency of shared clonotypes in human B cell receptor repertoires

The human genome contains approximately 20 thousand protein-coding genes¹, but the size of the collection of antigen receptors of the adaptive immune system that is generated by the recombination of gene segments with non-templated junctional additions (on B cells) is unknown-although it is certainly orders of magnitude larger. It has not been established whether individuals possess unique (or private) repertoires or substantial components of shared (or public) repertoires. Here we sequence recombined and expressed B cell receptor genes in several individuals to determine the size of their B cell receptor repertoires, and the extent to which these are shared between individuals. Our experiments revealed that the circulating repertoire of each individual contained between 9 and 17 million B cell clonotypes. The three individuals that we studied shared many clonotypes, including between 1 and 6% of B cell heavy-chain clonotypes shared between two subjects (0.3% of clonotypes shared by all three) and 20 to 34% of λ or κ light chains shared between two subjects (16 or 22% of λ or κ light chains, respectively, were shared by all three). Some of the B cell clonotypes had thousands of clones, or somatic variants, within the clonotype lineage. Although some of these shared lineages might be driven by exposure to common antigens, previous exposure to foreign antigens was not the only force that shaped the shared repertoires, as we also identified shared clonotypes in umbilical cord blood samples and all adult repertoires. The unexpectedly high prevalence of shared clonotypes in B cell repertoires, and identification of the sequences of these shared clonotypes, should enable better understanding of the role of B cell immune repertoires in health and disease.

Multifunctional Pan-ebolavirus Antibody Recognizes a Site of Broad Vulnerability on the Ebolavirus Glycoprotein

Ebolaviruses cause severe disease in humans, and identification of monoclonal antibodies (mAbs) that are effective against multiple ebolaviruses are important for therapeutics development. Here we describe a distinct class of broadly neutralizing human mAbs with protective capacity against three ebolaviruses infectious for humans: Ebola (EBOV), Sudan (SUDV), and Bundibugyo (BDBV) viruses. We isolated mAbs from human survivors of ebolavirus disease and identified a potent mAb, EBOV-520, which bound to an epitope in the glycoprotein (GP) base region. EBOV-520 efficiently neutralized EBOV, BDBV, and SUDV and also showed protective capacity in relevant animal models of these infections. EBOV-520 mediated protection principally by direct virus neutralization and exhibited multifunctional properties. This study identified a potent naturally occurring mAb and defined key features of the human antibody response that may contribute to broad protection. This multifunctional mAb and related clones are promising candidates for development as broadly protective pan-ebolavirus therapeutic molecules.

Integrating linear optimization with structural modeling to increase HIV neutralization breadth

Computational protein design has been successful in modeling fixed backbone proteins in a single conformation. However, when modeling large ensembles of flexible proteins, current methods in protein design have been insufficient. Large barriers in the energy landscape are difficult to traverse while redesigning a protein sequence, and as a result current design methods only sample a fraction of available sequence space. We propose a new computational approach that combines traditional structure-based modeling using the Rosetta software suite with machine learning and integer linear programming to overcome limitations in the Rosetta sampling methods. We demonstrate the effectiveness of this method, which we call BROAD, by benchmarking the performance on increasing predicted breadth of anti-HIV antibodies. We use this novel method to increase predicted breadth of naturally-occurring antibody VRC23 against a panel of 180 divergent HIV viral strains and achieve 100% predicted binding against the panel. In addition, we compare the performance of this method to state-of-the-art multistate design in Rosetta and show that we can outperform the existing method significantly. We further demonstrate that sequences recovered by this method recover known binding motifs of broadly neutralizing anti-HIV antibodies. Finally, our approach is general and can be extended easily to other protein systems. Although our modeled antibodies were not tested in vitro, we predict that these variants would have greatly increased breadth compared to the wild-type antibody.

In this article, we report a new approach for protein design, which combines traditional structural modeling with machine learning and integer programming. Using this method, we are able to design antibodies that are predicted to bind large panels of antigenically diverse HIV variants. The combination of methods from these fields allows us to surpass protein design limitations that have been seen up to this point. We predict that if we tested these modified antibodies against HIV variants they would have greater neutralization breadth than any antibodies seen to this point.

Structural basis for nonneutralizing antibody competition at antigenic site II of the respiratory syncytial virus fusion protein

Palivizumab was the first antiviral monoclonal antibody (mAb) approved for therapeutic use in humans, and remains a prophylactic treatment for infants at risk for severe disease because of respiratory syncytial virus (RSV). Palivizumab is an engineered humanized version of a murine mAb targeting antigenic site II of the RSV fusion (F) protein, a key target in vaccine development. There are limited reported naturally occurring human mAbs to site II; therefore, the structural basis for human antibody recognition of this major antigenic site is poorly understood. Here, we describe a nonneutralizing class of site II-specific mAbs that competed for binding with palivizumab to postfusion RSV F protein. We also describe two classes of site II-specific neutralizing mAbs, one of which escaped competition with nonneutralizing mAbs. An X-ray crystal structure of the neutralizing mAb 14N4 in complex with F protein showed that the binding angle at which human neutralizing mAbs interact with antigenic site II determines whether or not nonneutralizing antibodies compete with their binding. Fine-mapping studies determined that nonneutralizing mAbs that interfere with binding of neutralizing mAbs recognize site II with a pose that facilitates binding to an epitope containing F surface residues on a neighboring protomer. Neutralizing antibodies, like motavizumab and a new mAb designated 3J20 that escape interference by the inhibiting mAbs, avoid such contact by binding at an angle that is shifted away from the nonneutralizing site. Furthermore, binding to rationally and computationally designed site II helix-loop-helix epitope-scaffold vaccines distinguished neutralizing from nonneutralizing site II antibodies.

Protocols for Molecular Modeling with Rosetta3 and RosettaScripts

Previously, we published an article providing an overview of the Rosetta suite of biomacromolecular modeling software and a series of step-by-step tutorials [Kaufmann, K. W., et al. (2010) Biochemistry 49, 2987–2998]. The overwhelming positive response to this publication we received motivates us to here share the next iteration of these tutorials that feature de novo folding, comparative modeling, loop construction, protein docking, small molecule docking, and protein design. This updated and expanded set of tutorials is needed, as since 2010 Rosetta has been fully redesigned into an object-oriented protein modeling program Rosetta3. Notable improvements include a substantially improved energy function, an XML-like language termed “RosettaScripts” for flexibly specifying modeling task, new analysis tools, the addition of the TopologyBroker to control conformational sampling, and support for multiple templates in comparative modeling. Rosetta’s ability to model systems with symmetric proteins, membrane proteins, noncanonical amino acids, and RNA has also been greatly expanded and improved.

Design of Protein Multi-specificity Using an Independent Sequence Search Reduces the Barrier to Low Energy Sequences

Computational protein design has found great success in engineering proteins for thermodynamic stability, binding specificity, or enzymatic activity in a 'single state' design (SSD) paradigm. Multi-specificity design (MSD), on the other hand, involves considering the stability of multiple protein states simultaneously. We have developed a novel MSD algorithm, which we refer to as REstrained CONvergence in multi-specificity design (RECON). The algorithm allows each state to adopt its own sequence throughout the design process rather than enforcing a single sequence on all states. Convergence to a single sequence is encouraged through an incrementally increasing convergence restraint for corresponding positions. Compared to MSD algorithms that enforce (constrain) an identical sequence on all states the energy landscape is simplified, which accelerates the search drastically. As a result, RECON can readily be used in simulations with a flexible protein backbone. We have benchmarked RECON on two design tasks. First, we designed antibodies derived from a common germline gene against their diverse targets to assess recovery of the germline, polyspecific sequence. Second, we design "promiscuous", polyspecific proteins against all binding partners and measure recovery of the native sequence. We show that RECON is able to efficiently recover native-like, biologically relevant sequences in this diverse set of protein complexes.

Epitope mapping of inhibitory antibodies targeting the C2 domain of coagulation factor VIII by hydrogen-deuterium exchange mass spectrometry

BACKGROUND

The development of anti-factor VIII antibodies (inhibitors) is a significant complication in the management of patients with hemophilia A, leading to significant increases in morbidity and treatment cost. Using a panel of mAbs against different epitopes on FVIII, we have recently shown that epitope specificity, inhibitor kinetics and time to maximum inhibition are more important than inhibitor titer in predicting responses to FVIII and the combination of FVIII and recombinant FVIIa. In particular, a subset of high-titer inhibitors responded to high-dose FVIII, which would not be predicted on the basis of their inhibitor titer alone. Thus, the ability to quickly map the epitope spectrum of patient plasma with a clinically feasible assay may fundamentally change how clinicians approach the treatment of high-titer inhibitor patients.

OBJECTIVES

To map the epitopes of anti-FVIII mAbs, three of which are classic inhibitors and one of which is a non-classic inhibitor, by the use of hydrogen-deuterium exchange coupled with mass spectrometry (HDX-MS).

METHODS

The binding epitopes of four mAbs targeting the FVIII C2 domain were mapped with HDX-MS.

RESULTS

The epitopes determined with HDX-MS are consistent with those obtained earlier through structural characterization and antibody competition assays. In addition, classic and non-classic inhibitor epitopes could be distinguished by the use of a limited subset of C2 domain-derived peptic fragments.

CONCLUSION

Our results demonstrate the effectiveness and robustness of the HDX-MS method for epitope mapping, and suggest a potential role of rapid mapping of FVIII inhibitor epitopes in facilitating individualized treatment of inhibitor patients.