Structural Framework for Analysis of CD4+ T-Cell Epitope Dominance in Viral Fusion Proteins.

Antigen conformation shapes CD4+ T-cell specificity through mechanisms of antigen processing, and the consequences for immunity may rival those from conformational effects on antibody specificity. CD4+ T cells initiate and control immunity to pathogens and cancer and are at least partly responsible for immunopathology associated with infection, autoimmunity, and allergy. The primary trigger for CD4+ T-cell maturation is the presentation of an epitope peptide in the MHC class II antigen-presenting protein (MHCII), most commonly on an activated dendritic cell, and then the T-cell responses are recalled by subsequent presentations of the epitope peptide by the same or other antigen-presenting cells. Peptide presentation depends on the proteolytic fragmentation of the antigen in an endosomal/lysosomal compartment and concomitant loading of the fragments into the MHCII, a multistep mechanism called antigen processing and presentation. Although the role of peptide affinity for MHCII has been well studied, the role of proteolytic fragmentation has received less attention. In this Perspective, we will briefly summarize evidence that antigen resistance to unfolding and proteolytic fragmentation shapes the specificity of the CD4+ T-cell response to selected viral envelope proteins, identify several remarkable examples in which the immunodominant CD4+ epitopes most likely depend on the interaction of processing machinery with antigen conformation, and outline how knowledge of antigen conformation can inform future efforts to design vaccines.

CD4+ T-Cell Epitope Prediction by Combined Analysis of Antigen Conformational Flexibility and Peptide-MHCII Binding Affinity.

Antigen processing in the class II MHC pathway depends on conventional proteolytic enzymes, potentially acting on antigens in native-like conformational states. CD4+ epitope dominance arises from a competition among antigen folding, proteolysis, and MHCII binding. Protease-sensitive sites, linear antibody epitopes, and CD4+ T-cell epitopes were mapped in plague vaccine candidate F1-V to evaluate the various contributions to CD4+ epitope dominance. Using X-ray crystal structures, antigen processing likelihood (APL) predicts CD4+ epitopes with significant accuracy for F1-V without considering peptide-MHCII binding affinity. We also show that APL achieves excellent performance over two benchmark antigen sets. The profiles of conformational flexibility derived from the X-ray crystal structures of the F1-V proteins, Caf1 and LcrV, were similar to the biochemical profiles of linear antibody epitope reactivity and protease sensitivity, suggesting that the role of structure in proteolysis was captured by the analysis of the crystal structures. The patterns of CD4+ T-cell epitope dominance in C57BL/6, CBA, and BALB/c mice were compared to epitope predictions based on APL, MHCII binding, or both. For a sample of 13 diverse antigens, the accuracy of epitope prediction by the combination of APL and I-Ab-MHCII-peptide affinity reached 36%. When MHCII allele specificity was also diverse, such as in human immunity, prediction of dominant epitopes by APL alone reached 42% when using a stringent scoring threshold. Because dominant CD4+ epitopes tend to occur in conformationally stable antigen domains, crystal structures typically are available for analysis by APL, and thus, the requirement for a crystal structure is not a severe limitation.

The Serpin-like Loop Insertion of Ovalbumin Increases the Stability and Decreases the OVA 323-339 Epitope Processing Efficiency.

Chicken ovalbumin (cOVA) has been studied for decades primarily due to the robust genetic and molecular resources that are available for experimental investigations. cOVA is a member of the serpin superfamily of proteins that function as protease inhibitors, although cOVA does not exhibit this activity. As a serpin, cOVA possesses a protease-sensitive reactive center loop that lies adjacent to the OVA 323-339 CD4+ T-cell epitope. We took advantage of the previously described single-substitution variant, OVA R339T, which can undergo the dramatic structural transition observed in serpins, to study how changes in loop size and protein stability influence the processing and presentation of the OVA 323-339 epitope. We observed that the OVA R339T loop insertion increases the stability and protease resistance, resulting in the reduced presentation of the OVA 323-339 epitope in vitro. These findings have implications for the design of more effective vaccines for the treatment of infectious diseases and cancer as well as the development of more robust CD4+ T-cell epitope prediction tools.

Deciphering and predicting CD4+ T cell immunodominance of influenza virus hemagglutinin.

The importance of CD4+ T helper (Th) cells is well appreciated in view of their essential role in the elicitation of antibody and cytotoxic T cell responses. However, the mechanisms that determine the selection of immunodominant epitopes within complex protein antigens remain elusive. Here, we used ex vivo stimulation of memory T cells and screening of naive and memory T cell libraries, combined with T cell cloning and TCR sequencing, to dissect the human naive and memory CD4+ T cell repertoire against the influenza pandemic H1 hemagglutinin (H1-HA). We found that naive CD4+ T cells have a broad repertoire, being able to recognize naturally processed as well as cryptic peptides spanning the whole H1-HA sequence. In contrast, memory Th cells were primarily directed against just a few immunodominant peptides that were readily detected by mass spectrometry-based MHC-II peptidomics and predicted by structural accessibility analysis. Collectively, these findings reveal the presence of a broad repertoire of naive T cells specific for cryptic H1-HA peptides and demonstrate that antigen processing represents a major constraint determining immunodominance.

Deimmunizing substitutions in Pseudomonas exotoxin domain III perturb antigen processing without eliminating T-cell epitopes.

Effective adaptive immune responses depend on activation of CD4+ T cells via the presentation of antigen peptides in the context of major histocompatibility complex (MHC) class II. The structure of an antigen strongly influences its processing within the endolysosome and potentially controls the identity of peptides that are presented to T cells. A recombinant immunotoxin, comprising exotoxin A domain III (PE-III) from Pseudomonas aeruginosa and a cancer-specific antibody fragment, has been developed to manage cancer, but its effectiveness is limited by the induction of neutralizing antibodies. Here, we observed that this immunogenicity is substantially reduced by substituting six residues within PE-III. Although these substitutions targeted T-cell epitopes, we demonstrate that reduced conformational stability and protease resistance were responsible for the reduced antibody titer. Analysis of mouse T-cell responses coupled with biophysical studies on single-substitution versions of PE-III suggested that modest but comprehensible changes in T-cell priming can dramatically perturb antibody production. The most strongly responsive PE-III epitope was well-predicted by a structure-based algorithm. In summary, single-residue substitutions can drastically alter the processing and immunogenicity of PE-III but have only modest effects on CD4+ T-cell priming in mice. Our findings highlight the importance of structure-based processing constraints for accurate epitope prediction.

Structural Basis for CD4+ T Cell Epitope Dominance in Arbo-Flavivirus Envelope Proteins: A Meta-Analysis.

A meta-analysis of CD4+ T cell epitope maps reveals clusters and gaps in envelope-protein (E protein) immunogenicity that can be explained by the likelihood of epitope processing, as determined by E protein three-dimensional structures. Differential processing may be at least partially responsible for variations in disease severity among arbo-flaviruses and points to structural features that modulate protection from disease.

CD4+ T-cell epitope prediction using antigen processing constraints.

T-cell CD4+ epitopes are important targets of immunity against infectious diseases and cancer. State-of-the-art methods for MHC class II epitope prediction rely on supervised learning methods in which an implicit or explicit model of sequence specificity is constructed using a training set of peptides with experimentally tested MHC class II binding affinity. In this paper we present a novel method for CD4+ T-cell eptitope prediction based on modeling antigen-processing constraints. Previous work indicates that dominant CD4+ T-cell epitopes tend to occur adjacent to sites of initial proteolytic cleavage. Given an antigen with known three-dimensional structure, our algorithm first aggregates four types of conformational stability data in order to construct a profile of stability that allows us to identify regions of the protein that are most accessible to proteolysis. Using this profile, we then construct a profile of epitope likelihood based on the pattern of transitions from unstable to stable regions. We validate our method using 35 datasets of experimentally measured CD4+ T cell responses of mice bearing I-Ab or HLA-DR4 alleles as well as of human subjects. Overall, our results show that antigen processing constraints provide a significant source of predictive power. For epitope prediction in single-allele systems, our approach can be combined with sequence-based methods, or used in instances where little or no training data is available. In multiple-allele systems, sequence-based methods can only be used if the allele distribution of a population is known. In contrast, our approach does not make use of MHC binding prediction, and is thus agnostic to MHC class II genotypes.

Comprehensive analysis of contributions from protein conformational stability and major histocompatibility complex class II-peptide binding affinity to CD4+ epitope immunogenicity in HIV-1 envelope glycoprotein.

UNLABELLED: Helper T-cell epitope dominance in human immunodeficiency virus type 1 (HIV-1) envelope glycoprotein gp120 is not adequately explained by peptide binding to major histocompatibility complex (MHC) proteins. Antigen processing potentially influences epitope dominance, but few, if any, studies have attempted to reconcile the influences of antigen processing and MHC protein binding for all helper T-cell epitopes of an antigen. Epitopes of gp120 identified in both humans and mice occur on the C-terminal flanks of flexible segments that are likely to be proteolytic cleavage sites. In this study, the influence of gp120 conformation on the dominance pattern in gp120 from HIV strain 89.6 was examined in CBA mice, whose MHC class II protein has one of the most well defined peptide-binding preferences. Only one of six dominant epitopes contained the most conserved element of the I-Ak binding motif, an aspartic acid. Destabilization of the gp120 conformation by deletion of single disulfide bonds preferentially enhanced responses to the cryptic I-Ak motif-containing sequences, as reported by T-cell proliferation or cytokine secretion. Conversely, inclusion of CpG in the adjuvant with gp120 enhanced responses to the dominant CD4+ T-cell epitopes. The gp120 destabilization affected secretion of some cytokines more than others, suggesting that antigen conformation could modulate T-cell functions through mechanisms of antigen processing. IMPORTANCE: CD4+ helper T cells play an essential role in protection against HIV and other pathogens. Thus, the sites of helper T-cell recognition, the dominant epitopes, are targets for vaccine design; and the corresponding T cells may provide markers for monitoring infection and immunity. However, T-cell epitopes are difficult to identify and predict. It is also unclear whether CD4+ T cells specific for one epitope are more protective than T cells specific for other epitopes. This work shows that the three-dimensional (3D) structure of an HIV protein partially determines which epitopes are dominant, most likely by controlling the breakdown of HIV into peptides. Moreover, some types of signals from CD4+ T cells are affected by the HIV protein 3D structure; and thus the protectiveness of a particular peptide vaccine could be related to its location in the 3D structure.

Protein identification using receptor arrays and mass spectrometry.

Mass spectrometry is one of the main tools for protein identification in complex mixtures. When the sequence of the protein is known, we can check to see if the known mass distribution of peptides for a given protein is present in the recorded mass distribution of the mixture being analyzed. Unfortunately, this general approach suffers from high false-positive rates, since in a complex mixture, the likelihood that we will observe any particular mass distribution is high, whether or not the protein of interest is in the mixture. In this paper, we propose a scoring methodology and algorithm for protein identification that make use of a new experimental technique, which we call receptor arrays, for separating a mixture based on another differentiating property of peptides called isoelectric point (pI). We perform extensive simulation experiments on several genomes and show that additional information about peptides can achieve an average 30% reduction in false-positive rates over existing methods, while achieving very high true-positive identification rates.

A polynomial-time algorithm for de novo protein backbone structure determination from nuclear magnetic resonance data.

We describe an efficient algorithm for protein backbone structure determination from solution Nuclear Magnetic Resonance (NMR) data. A key feature of our algorithm is that it finds the conformation and orientation of secondary structure elements as well as the global fold in polynomial time. This is the first polynomial-time algorithm for de novo high-resolution biomacromolecular structure determination using experimentally recorded data from either NMR spectroscopy or X-ray crystallography. Previous algorithmic formulations of this problem focused on using local distance restraints from NMR (e.g., nuclear Overhauser effect [NOE] restraints) to determine protein structure. This approach has been shown to be NP-hard, essentially due to the local nature of the constraints. In practice, approaches such as molecular dynamics and simulated annealing, which lack both combinatorial precision and guarantees on running time and solution quality, are used routinely for structure determination. We show that residual dipolar coupling (RDC) data, which gives global restraints on the orientation of internuclear bond vectors, can be used in conjunction with very sparse NOE data to obtain a polynomial-time algorithm for structure determination. Furthermore, an implementation of our algorithm has been applied to six different real biological NMR data sets recorded for three proteins. Our algorithm is combinatorially precise, polynomialtime, and uses much less NMR data to produce results that are as good or better than previous approaches in terms of accuracy of the computed structure as well as running time.

High-throughput inference of protein-protein interfaces from unassigned NMR data.

SUMMARY: We cast the problem of identifying protein-protein interfaces, using only unassigned NMR spectra, into a geometric clustering problem. Identifying protein-protein interfaces is critical to understanding inter- and intra-cellular communication, and NMR allows the study of protein interaction in solution. However it is often the case that NMR studies of a protein complex are very time-consuming, mainly due to the bottleneck in assigning the chemical shifts, even if the apo structures of the constituent proteins are known. We study whether it is possible, in a high-throughput manner, to identify the interface region of a protein complex using only unassigned chemical shifts and residual dipolar coupling (RDC) data. We introduce a geometric optimization problem where we must cluster the cells in an arrangement on the boundary of a 3-manifold, where the arrangement is induced by a spherical quadratic form [corrected] The arrangement is induced by a spherical quadratic form, which in turn is parameterized by a SO(3)xR2. We show that this formalism derives directly from the physics of RDCs. We present an optimal algorithm for this problem that runs in O(n3 log n) time for an n-residue protein. We then use this clustering algorithm as a subroutine in a practical algorithm for identifying the interface region of a protein complex from unassigned NMR data. We present the results of our algorithm on NMR data for seven proteins from five protein complexes, and show that our approach is useful for high-throughput applications in which we seek to rapidly identify the interface region of a protein complex. AVAILABILITY: Contact authors for source code.

An algebraic geometry approach to protein structure determination from NMR data.

Our paper describes the first provably-efficient algorithm for determining protein structures de novo, solely from experimental data. We show how the global nature of a certain kind of NMR data provides quantifiable complexity-theoretic benefits, allowing us to classify our algorithm as running in polynomial time. While our algorithm uses NMR data as input, it is the first polynomial-time algorithm to compute high-resolution structures de novo using any experimentally-recorded data, from either NMR spectroscopy or X-Ray crystallography. Improved algorithms for protein structure determination are needed, because currently, the process is expensive and time-consuming. For example, an area of intense research in NMR methodology is automated assignment of nuclear Overhauser effect (NOE) restraints, in which structure determination sits in a tight inner-loop (cycle) of assignment/refinement. These algorithms are very time-consuming, and typically require a large cluster. Thus, algorithms for protein structure determination that are known to run in polynomial time and provide guarantees on solution accuracy are likely to have great impact in the long-term. Methods stemming from a technique called "distance geometry embedding" do come with provable guarantees, but the NP-hardness of these problem formulations implies that in the worst case these techniques cannot run in polynomial time. We are able to avoid the NP-hardness by (a) some mild assumptions about the protein being studied, (b) the use of residual dipolar couplings (RDCs) instead of a dense network of NOEs, and (c) novel algorithms and proofs that exploit the biophysical geometry of (a) and (b), drawing on a variety of computer science, computational geometry, and computational algebra techniques. In our algorithm, RDC data, which gives global restraints on the orientation of internuclear bond vectors, is used in conjunction with very sparse NOE data to obtain a polynomial-time algorithm for protein structure determination. An implementation of our algorithm has been applied to 6 different real biological NMR data sets recorded for 3 proteins. Our algorithm is combinatorially precise, polynomial-time, and uses much less NMR data to produce results that are as good or better than previous approaches in terms of accuracy of the computed structure as well as running time. In practice approaches such as restrained molecular dynamics and simulated annealing, which lack both combinatorial precision and guarantees on running time and solution quality, are commonly used. Our results show that by using a different "slice" of the data, an algorithm that is polynomial time and that has guarantees about solution quality can be obtained. We believe that our techniques can be extended and generalized for other structure-determination problems such as computing side-chain conformations and the structure of nucleic acids from experimental data.