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-ß 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.

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.

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.

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.

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.

Multistate design of influenza antibodies improves affinity and breadth against seasonal viruses.

Influenza is a yearly threat to global public health. Rapid changes in influenza surface proteins resulting from antigenic drift and shift events make it difficult to readily identify antibodies with broadly neutralizing activity against different influenza subtypes with high frequency, specifically antibodies targeting the receptor binding domain (RBD) on influenza HA protein. We developed an optimized computational design method that is able to optimize an antibody for recognition of large panels of antigens. To demonstrate the utility of this multistate design method, we used it to redesign an antiinfluenza antibody against a large panel of more than 500 seasonal HA antigens of the H1 subtype. As a proof of concept, we tested this method on a variety of known antiinfluenza antibodies and identified those that could be improved computationally. We generated redesigned variants of antibody C05 to the HA RBD and experimentally characterized variants that exhibited improved breadth and affinity against our panel. C05 mutants exhibited improved affinity for three of the subtypes used in design by stabilizing the CDRH3 loop and creating favorable electrostatic interactions with the antigen. These mutants possess increased breadth and affinity of binding while maintaining high-affinity binding to existing targets, surpassing a major limitation up to this point.

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.

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.

Antibodies: Computer-Aided Prediction of Structure and Design of Function.

With the advent of high-throughput sequencing, and the increased availability of experimental structures of antibodies and antibody-antigen complexes, comes the improvement of computational approaches to predict the structure and design the function of antibodies and antibody-antigen complexes. While antibodies pose formidable challenges for protein structure prediction and design due to their large size and highly flexible loops in the complementarity-determining regions, they also offer exciting opportunities: the central importance of antibodies for human health results in a wealth of structural and sequence information that-as a knowledge base-can drive the modeling algorithms by limiting the conformational and sequence search space to likely regions of success. Further, efficient experimental platforms exist to test predicted antibody structure or designed antibody function, thereby leading to an iterative feedback loop between computation and experiment. We briefly review the history of computer-aided prediction of structure and design of function in the antibody field before we focus on recent methodological developments and the most exciting application examples.