Modeling of the TCR-MHC-peptide complex

CD4+ effector T cells accelerate Alzheimer's disease in mice

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by pathological deposition of misfolded self-protein amyloid beta (Aβ) which in kind facilitates tau aggregation and neurodegeneration. Neuroinflammation is accepted as a key disease driver caused by innate microglia activation. Recently, adaptive immune alterations have been uncovered that begin early and persist throughout the disease. How these occur and whether they can be harnessed to halt disease progress is unclear. We propose that self-antigens would induct autoreactive effector T cells (Teffs) that drive pro-inflammatory and neurodestructive immunity leading to cognitive impairments. Here, we investigated the role of effector immunity and how it could affect cellular-level disease pathobiology in an AD animal model.

METHODS

In this report, we developed and characterized cloned lines of amyloid beta (Aβ) reactive type 1 T helper (Th1) and type 17 Th (Th17) cells to study their role in AD pathogenesis. The cellular phenotype and antigen-specificity of Aβ-specific Th1 and Th17 clones were confirmed using flow cytometry, immunoblot staining and Aβ T cell epitope loaded haplotype-matched major histocompatibility complex II IAb (MHCII-IAb-KLVFFAEDVGSNKGA) tetramer binding. Aβ-Th1 and Aβ-Th17 clones were adoptively transferred into APP/PS1 double-transgenic mice expressing chimeric mouse/human amyloid precursor protein and mutant human presenilin 1, and the mice were assessed for memory impairments. Finally, blood, spleen, lymph nodes and brain were harvested for immunological, biochemical, and histological analyses.

RESULTS

The propagated Aβ-Th1 and Aβ-Th17 clones were confirmed stable and long-lived. Treatment of APP/PS1 mice with Aβ reactive Teffs accelerated memory impairment and systemic inflammation, increased amyloid burden, elevated microglia activation, and exacerbated neuroinflammation. Both Th1 and Th17 Aβ-reactive Teffs progressed AD pathology by downregulating anti-inflammatory and immunosuppressive regulatory T cells (Tregs) as recorded in the periphery and within the central nervous system.

CONCLUSIONS

These results underscore an important pathological role for CD4+ Teffs in AD progression. We posit that aberrant disease-associated effector T cell immune responses can be controlled. One solution is by Aβ reactive Tregs.

DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor

Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http://jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https://github.com/jiangBiolab/DLpTCR.

Using T Cell Receptor Repertoires to Understand the Principles of Adaptive Immune Recognition

Adaptive immune recognition is mediated by antigen receptors on B and T cells generated by somatic recombination during lineage development. The high level of diversity resulting from this process posed technical limitations that previously limited the comprehensive analysis of adaptive immune recognition. Advances over the last ten years have produced data and approaches allowing insights into how T cells develop, evolutionary signatures of recombination and selection, and the features of T cell receptors that mediate epitope-specific binding and T cell activation. The size and complexity of these data have necessitated the generation of novel computational and analytical approaches, which are transforming how T cell immunology is conducted. Here we review the development and application of novel biological, theoretical, and computational methods for understanding T cell recognition and discuss the potential for improved models of receptor:antigen interactions.

Peptide Vaccines: New Trends for Avoiding the Autoimmune Response

Background:

Several marketed antiviral vaccines (such as that against hepatitis virus A and/or B, influenza virus, human papillomavirus, yellow fever virus, measles, rubella and mumps viruses) may elicit various autoimmune reactions.

Results:

The cause of autoimmune response due to vaccination may be: 1. the adjuvant which is regularly added to the vaccine (especially in the case of various oil substrates), 2. the specific viral component itself (a protein or glycoprotein potentially possessing cross-reactive epitopes) and/or 3. contamination of the vaccine with traces of non-viral proteins mostly cellular in origin. Believing that peptide vaccines might represent an optimal solution for avoiding the above-mentioned problems, we discuss the principles of rational design of a typical peptide vaccine which should contain oligopeptides coming either from the selected structural virion components (i.e.capsid proteins and/or envelop glycoproteins or both) or from the virus-coded non-structural polypeptides. The latter should be equally immunogenic as the structural virus proteins. Describing the feasibility of identification and design of immunogenic epitopes, our paper also deals with possible problems of peptide vaccine manufacturing. The presented data are in part based on the experience of our own, in part, they are coming from the results published by others.

Conclusion:

Any peptide vaccine should be able to elicit relevant and specific antibody formation, as well as an efficient cell-mediated immune response. Consequently, the properly designed peptide vaccine is expected to consist of carefully selected viral peptides, which should stimulate the receptors of helper T/CD4 cells as well as of cytotoxic (T/CD8) lymphocytes.

DynaDom: structure-based prediction of T cell receptor inter-domain and T cell receptor-peptide-MHC (class I) association angles

Background

T cell receptor (TCR) molecules are involved in the adaptive immune response as they distinguish between self- and foreign-peptides, presented in major histocompatibility complex molecules (pMHC). Former studies showed that the association angles of the TCR variable domains (Vα/Vβ) can differ significantly and change upon binding to the pMHC complex. These changes can be described as a rotation of the domains around a general Center of Rotation, characterized by the interaction of two highly conserved glutamine residues.

Methods

We developed a computational method, DynaDom, for the prediction of TCR Vα/Vβ inter-domain and TCR/pMHC orientations in TCRpMHC complexes, which allows predicting the orientation of multiple protein-domains. In addition, we implemented a new approach to predict the correct orientation of the carboxamide endgroups in glutamine and asparagine residues, which can also be used as an external, independent tool.

Results

The approach was evaluated for the remodeling of 75 and 53 experimental structures of TCR and TCRpMHC (class I) complexes, respectively. We show that the DynaDom method predicts the correct orientation of the TCR Vα/Vβ angles in 96 and 89% of the cases, for the poses with the best RMSD and best interaction energy, respectively. For the concurrent prediction of the TCR Vα/Vβ and pMHC orientations, the respective rates reached 74 and 72%. Through an exhaustive analysis, we could show that the pMHC placement can be further improved by a straightforward, yet very time intensive extension of the current approach.

Conclusions

The results obtained in the present remodeling study prove the suitability of our approach for interdomain-angle optimization. In addition, the high prediction rate obtained specifically for the energetically highest ranked poses further demonstrates that our method is a powerful candidate for blind prediction. Therefore it should be well suited as part of any accurate atomistic modeling pipeline for TCRpMHC complexes and potentially other large molecular assemblies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12900-016-0071-7) contains supplementary material, which is available to authorized users.

Computational Modeling of T Cell Receptor Complexes

T-cell receptor (TCR) binding to peptide/MHC determines specificity and initiates signaling in antigen-specific cellular immune responses. Structures of TCR-pMHC complexes have provided enormous insight to cellular immune functions, permitted a rational understanding of processes such as pathogen escape, and led to the development of novel approaches for the design of vaccines and other therapeutics. As production, crystallization, and structure determination of TCR-pMHC complexes can be challenging, there is considerable interest in modeling new complexes. Here we describe a rapid approach to TCR-pMHC modeling that takes advantage of structural features conserved in known complexes, such as the restricted TCR binding site and the generally conserved diagonal docking mode. The approach relies on the powerful Rosetta suite and is implemented using the PyRosetta scripting environment. We show how the approach can recapitulate changes in TCR binding angles and other structural details, and highlight areas where careful evaluation of parameters is needed and alternative choices might be made. As TCRs are highly sensitive to subtle structural perturbations, there is room for improvement. Our method nonetheless generates high-quality models that can be foundational for structure-based hypotheses regarding TCR recognition.

Quantitative Analysis of the Association Angle between T-cell Receptor Vα/Vβ Domains Reveals Important Features for Epitope Recognition

T-cell receptors (TCR) play an important role in the adaptive immune system as they recognize pathogen- or cancer-based epitopes and thus initiate the cell-mediated immune response. Therefore there exists a growing interest in the optimization of TCRs for medical purposes like adoptive T-cell therapy. However, the molecular mechanisms behind T-cell signaling are still predominantly unknown. For small sets of TCRs it was observed that the angle between their Vα- and Vβ-domains, which bind the epitope, can vary and might be important for epitope recognition. Here we present a comprehensive, quantitative study of the variation in the Vα/Vβ interdomain-angle and its influence on epitope recognition, performing a systematic bioinformatics analysis based on a representative set of experimental TCR structures. For this purpose we developed a new, cuboid-based superpositioning method, which allows a unique, quantitative analysis of the Vα/Vβ-angles. Angle-based clustering led to six significantly different clusters. Analysis of these clusters revealed the unexpected result that the angle is predominantly influenced by the TCR-clonotype, whereas the bound epitope has only a minor influence. Furthermore we could identify a previously unknown center of rotation (CoR), which is shared by all TCRs. All TCR geometries can be obtained by rotation around this center, rendering it a new, common TCR feature with the potential of improving the accuracy of TCR structure prediction considerably. The importance of Vα/Vβ rotation for signaling was confirmed as we observed larger variances in the Vα/Vβ-angles in unbound TCRs compared to epitope-bound TCRs. Our results strongly support a two-step mechanism for TCR-epitope: First, preformation of a flexible TCR geometry in the unbound state and second, locking of the Vα/Vβ-angle in a TCR-type specific geometry upon epitope-MHC association, the latter being driven by rotation around the unique center of rotation.

The recognition of antigenic peptides by cytotoxic T-cells is one of the crucial steps during the adaptive immune response. Thus a detailed understanding of this process is not only important for elucidating the mechanism behind T-cell signaling, but also for various emerging new medical applications like T-cell based immunotherapies and designed bio-therapeutics. However, despite the fast growing interest in this field, the mechanistic basis of the immune response is still largely unknown. Previous qualitative studies suggested that the T-cell receptor (TCR) Vα/Vβ-interdomain angle plays a crucial role in epitope recognition as it predetermines the relative position of its antigen-recognizing CDR1-3 loops and thus TCR specificity. In the manuscript we present a systematic bioinformatic analysis of the structural characteristics of bound and unbound TCR molecules focusing on the Vα/Vβ-angle. Our results demonstrate the importance of this angle for signaling, as several distinct Vα/Vβ-angle based structural clusters could be observed and larger angle flexibilities exist for unbound TCRs than for bound TCRs, providing quantitative proof for a two-step locking mechanism upon epitope recognition. In this context, we could identify a unique rotational point, which allows a quantitative, yet intuitive description of all observed angle variations and the structural changes upon epitope binding.

Peptide Vaccine: Progress and Challenges

Conventional vaccine strategies have been highly efficacious for several decades in reducing mortality and morbidity due to infectious diseases. The bane of conventional vaccines, such as those that include whole organisms or large proteins, appear to be the inclusion of unnecessary antigenic load that, not only contributes little to the protective immune response, but complicates the situation by inducing allergenic and/or reactogenic responses. Peptide vaccines are an attractive alternative strategy that relies on usage of short peptide fragments to engineer the induction of highly targeted immune responses, consequently avoiding allergenic and/or reactogenic sequences. Conversely, peptide vaccines used in isolation are often weakly immunogenic and require particulate carriers for delivery and adjuvanting. In this article, we discuss the specific advantages and considerations in targeted induction of immune responses by peptide vaccines and progresses in the development of such vaccines against various diseases. Additionally, we also discuss the development of particulate carrier strategies and the inherent challenges with regard to safety when combining such technologies with peptide vaccines.

Structure-Based, Rational Design of T Cell Receptors

Adoptive cell transfer using engineered T cells is emerging as a promising treatment for metastatic melanoma. Such an approach allows one to introduce T cell receptor (TCR) modifications that, while maintaining the specificity for the targeted antigen, can enhance the binding and kinetic parameters for the interaction with peptides (p) bound to major histocompatibility complexes (MHC). Using the well-characterized 2C TCR/SIYR/H-2K(b) structure as a model system, we demonstrated that a binding free energy decomposition based on the MM-GBSA approach provides a detailed and reliable description of the TCR/pMHC interactions at the structural and thermodynamic levels. Starting from this result, we developed a new structure-based approach, to rationally design new TCR sequences, and applied it to the BC1 TCR targeting the HLA-A2 restricted NY-ESO-1_157–165 cancer-testis epitope. Fifty-four percent of the designed sequence replacements exhibited improved pMHC binding as compared to the native TCR, with up to 150-fold increase in affinity, while preserving specificity. Genetically engineered CD8⁺ T cells expressing these modified TCRs showed an improved functional activity compared to those expressing BC1 TCR. We measured maximum levels of activities for TCRs within the upper limit of natural affinity, K_D = ∼1 − 5 μM. Beyond the affinity threshold at K_D < 1 μM we observed an attenuation in cellular function, in line with the “half-life” model of T cell activation. Our computer-aided protein-engineering approach requires the 3D-structure of the TCR-pMHC complex of interest, which can be obtained from X-ray crystallography. We have also developed a homology modeling-based approach, TCRep 3D, to obtain accurate structural models of any TCR-pMHC complexes when experimental data is not available. Since the accuracy of the models depends on the prediction of the TCR orientation over pMHC, we have complemented the approach with a simplified rigid method to predict this orientation and successfully assessed it using all non-redundant TCR-pMHC crystal structures available. These methods potentially extend the use of our TCR engineering method to entire TCR repertoires for which no X-ray structure is available. We have also performed a steered molecular dynamics study of the unbinding of the TCR-pMHC complex to get a better understanding of how TCRs interact with pMHCs. This entire rational TCR design pipeline is now being used to produce rationally optimized TCRs for adoptive cell therapies of stage IV melanoma.

Prediction of cross-recognition of peptide-HLA A2 by Melan-A-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationships

The cross-recognition of peptides by cytotoxic T lymphocytes is a key element in immunology and in particular in peptide based immunotherapy. Here we develop three-dimensional (3D) quantitative structure-activity relationships (QSARs) to predict cross-recognition by Melan-A-specific cytotoxic T lymphocytes of peptides bound to HLA A*0201 (hereafter referred to as HLA A2). First, we predict the structure of a set of self- and pathogen-derived peptides bound to HLA A2 using a previously developed ab initio structure prediction approach [Fagerberg et al., J. Mol. Biol., 521–46 (2006)]. Second, shape and electrostatic energy calculations are performed on a 3D grid to produce similarity matrices which are combined with a genetic neural network method [So et al., J. Med. Chem., 4347–59 (1997)] to generate 3D-QSAR models. The models are extensively validated using several different approaches. During the model generation, the leave-one-out cross-validated correlation coefficient (q²) is used as the fitness criterion and all obtained models are evaluated based on their q² values. Moreover, the best model obtained for a partitioned data set is evaluated by its correlation coefficient (r = 0.92 for the external test set). The physical relevance of all models is tested using a functional dependence analysis and the robustness of the models obtained for the entire data set is confirmed using y-randomization. Finally, the validated models are tested for their utility in the setting of rational peptide design: their ability to discriminate between peptides that only contain side chain substitutions in a single secondary anchor position is evaluated. In addition, the predicted cross-recognition of the mono-substituted peptides is confirmed experimentally in chromium-release assays. These results underline the utility of 3D-QSARs in peptide mimetic design and suggest that the properties of the unbound epitope are sufficient to capture most of the information to determine the cross-recognition.

Peptide-based vaccinology: experimental and computational approaches to target hypervariable viruses through the fine characterization of protective epitopes recognized by monoclonal antibodies and the identification of T-cell-activating peptides

Defining immunogenic domains of viral proteins capable of eliciting a protective immune response is crucial in the development of novel epitope-based prophylactic strategies. This is particularly important for the selective targeting of conserved regions shared among hypervariable viruses. Studying postinfection and postimmunization sera, as well as cloning and characterization of monoclonal antibodies (mAbs), still represents the best approach to identify protective epitopes. In particular, a protective mAb directed against conserved regions can play a key role in immunogen design and in human therapy as well. Experimental approaches aiming to characterize protective mAb epitopes or to identify T-cell-activating peptides are often burdened by technical limitations and can require long time to be correctly addressed. Thus, in the last decade many epitope predictive algorithms have been developed. These algorithms are continually evolving, and their use to address the empirical research is widely increasing. Here, we review several strategies based on experimental techniques alone or addressed by in silico analysis that are frequently used to predict immunogens to be included in novel epitope-based vaccine approaches. We will list the main strategies aiming to design a new vaccine preparation conferring the protection of a neutralizing mAb combined with an effective cell-mediated response.

T-cell receptors binding orientation over peptide/MHC class I is driven by long-range interactions

Crystallographic data about T-Cell Receptor - peptide - major histocompatibility complex class I (TCRpMHC) interaction have revealed extremely diverse TCR binding modes triggering antigen recognition. Understanding the molecular basis that governs TCR orientation over pMHC is still a considerable challenge. We present a simplified rigid approach applied on all non-redundant TCRpMHC crystal structures available. The CHARMM force field in combination with the FACTS implicit solvation model is used to study the role of long-distance interactions between the TCR and pMHC. We demonstrate that the sum of the coulomb interactions and the electrostatic solvation energies is sufficient to identify two orientations corresponding to energetic minima at 0° and 180° from the native orientation. Interestingly, these results are shown to be robust upon small structural variations of the TCR such as changes induced by Molecular Dynamics simulations, suggesting that shape complementarity is not required to obtain a reliable signal. Accurate energy minima are also identified by confronting unbound TCR crystal structures to pMHC. Furthermore, we decompose the electrostatic energy into residue contributions to estimate their role in the overall orientation. Results show that most of the driving force leading to the formation of the complex is defined by CDR1,2/MHC interactions. This long-distance contribution appears to be independent from the binding process itself, since it is reliably identified without considering neither short-range energy terms nor CDR induced fit upon binding. Ultimately, we present an attempt to predict the TCR/pMHC binding mode for a TCR structure obtained by homology modeling. The simplicity of the approach and the absence of any fitted parameters make it also easily applicable to other types of macromolecular protein complexes.

TCRep 3D: an automated in silico approach to study the structural properties of TCR repertoires

TCRep 3D is an automated systematic approach for TCR-peptide-MHC class I structure prediction, based on homology and ab initio modeling. It has been considerably generalized from former studies to be applicable to large repertoires of TCR. First, the location of the complementary determining regions of the target sequences are automatically identified by a sequence alignment strategy against a database of TCR Vα and Vβ chains. A structure-based alignment ensures automated identification of CDR3 loops. The CDR are then modeled in the environment of the complex, in an ab initio approach based on a simulated annealing protocol. During this step, dihedral restraints are applied to drive the CDR1 and CDR2 loops towards their canonical conformations, described by Al-Lazikani et. al. We developed a new automated algorithm that determines additional restraints to iteratively converge towards TCR conformations making frequent hydrogen bonds with the pMHC. We demonstrated that our approach outperforms popular scoring methods (Anolea, Dope and Modeller) in predicting relevant CDR conformations. Finally, this modeling approach has been successfully applied to experimentally determined sequences of TCR that recognize the NY-ESO-1 cancer testis antigen. This analysis revealed a mechanism of selection of TCR through the presence of a single conserved amino acid in all CDR3β sequences. The important structural modifications predicted in silico and the associated dramatic loss of experimental binding affinity upon mutation of this amino acid show the good correspondence between the predicted structures and their biological activities. To our knowledge, this is the first systematic approach that was developed for large TCR repertoire structural modeling.

The autoimmune TCR-Ob.2F3 can bind to MBP85-99/HLA-DR2 having an unconventional mode as in TCR-Ob.1A12

The generation of T cell receptor (TCR) sequence diversity can produce 'forbidden' clones able to recognize self-antigens. Here, the structure of the complex between a myelin basic protein peptide (MBP85-99), human leukocyte antigen (HLA)-DR2 (DRB1*1501/DRA) and TCR-Ob.2F3, the dominant autoimmune clone obtained from a multiple sclerosis (MS) patient, has been determined using structural docking simulation and dynamics in silico and compared to the structure of TCR-Ob.1A12 complexes with the same MHC/peptide determined by X-ray crystallography. The two TCRs differ by three amino acids in the CDR3 α and β loops. As the result different hydrogen bonds are formed between the two CDR3β loops and the peptide in the complexes of the simulated structures, with three hydrogen bonds seen in the TCR-Ob.2F3 complex and five in the TCR-Ob.1A12 complex. The two TCRs, each located near the N-terminal end of the HLA-DR2 binding groove and both had an orthogonal binding axis but they deviated by about 10°. Simulation methods, such as structural docking and molecular dynamics as used here, provide an avenue to understand molecular binding mode efficiently and more rapidly than obtaining multiple crystal structures when a large structural database is already available.

T-cell epitope prediction and immune complex simulation using molecular dynamics: state of the art and persisting challenges

Atomistic Molecular Dynamics provides powerful and flexible tools for the prediction and analysis of molecular and macromolecular systems. Specifically, it provides a means by which we can measure theoretically that which cannot be measured experimentally: the dynamic time-evolution of complex systems comprising atoms and molecules. It is particularly suitable for the simulation and analysis of the otherwise inaccessible details of MHC-peptide interaction and, on a larger scale, the simulation of the immune synapse. Progress has been relatively tentative yet the emergence of truly high-performance computing and the development of coarse-grained simulation now offers us the hope of accurately predicting thermodynamic parameters and of simulating not merely a handful of proteins but larger, longer simulations comprising thousands of protein molecules and the cellular scale structures they form. We exemplify this within the context of immunoinformatics.

Immunoinformatics and modeling perspective of T cell epitope-based cancer immunotherapy: a holistic picture

Cancer immunotherapy is fast gaining global attention with its unique position as a potential therapy showing promise in cancer prevention and cure. It utilizes the natural system of immunity as opposed to chemotherapy and radiotherapy that utilize chemical drugs and radiation, respectively. Cancer immunotherapy essentially involves treatment and/or prevention with vaccines in the form of peptide vaccines (T and B cell epitopes), DNA vaccines and vaccination using whole tumor cells, dendritic cells, viral vectors, antibodies and adoptive transfer of T cells to harness the body's own immune system towards the targeting of cancer cells for destruction. Given the time, cost and labor involved in the vaccine discovery and development, researchers have evinced interest in the novel field of immunoinformatics to cut down the escalation of these critical resources. Immunoinformatics is a relatively new buzzword in the scientific circuit that is showing its potential and delivering on its promise in expediting the development of effective cancer immunotherapeutic agents. This review attempts to present a holistic picture of our race against cancer and time using the science and technology of immunoinformatics and molecular modeling in T cell epitope-based cancer immunotherapy. It also attempts to showcase some problem areas as well as novel ones waiting to be explored where development of novel immunoinformatics tools and simulations in the context of cancer immunotherapy would be highly welcome.

A modeled structure for amidase-03 from Bacillus anthracis

Homology models of amidase-03 from Bacillus anthracis were constructed using Modeller (9v2). Modeller constructs protein models using an automated approach for comparative protein structure modeling by the satisfaction of spatial restraints. A template structure of Listeria monocytogenes bacteriophage PSA endolysin PlyPSA (PDB ID: 1XOV) was selected from protein databank (PDB) using BLASTp with BLOSUM62 sequence alignment scoring matrix. We generated five models using the Modeller default routine in which initial coordinates are randomized and evaluated by pseudo-energy parameters. The protein models were validated using PROCHECK and energy minimized using the steepest descent method in GROMACS 3.2 (flexible SPC water model in cubic box of size 1 Å instead of rigid SPC model). We used G43a1 force field in GROMACS for energy calculations and the generated structure was subsequently analyzed using the VMD software for stereo-chemistry, atomic clash and misfolding. A detailed analysis of the amidase-03 model structure from Bacillus anthracis will provide insight to the molecular design of suitable inhibitors as drug candidates.

An introduction to epitope prediction methods and software

In this paper, current prediction methods and algorithms for both T- and B cell epitopes are reviewed, and a comprehensive summary of epitope prediction software and databases currently available online is also provided. This review can offer researchers in this field a sense of direction and insights for future work. However, our main purpose is to introduce clinical and basic biomedical researchers who are not familiar with these biological analysis tools and databases to these online resources and to provide guidance on how to use them effectively.

Modeling the bound conformation of Pemphigus vulgaris-associated peptides to MHC Class II DR and DQ alleles

Background

Pemphigus vulgaris (PV) is a severe autoimmune blistering disorder characterized by the presence of pathogenic autoantibodies directed against desmoglein-3 (Dsg3), involving specific DR4 and DR6 alleles in Caucasians and DQ5 allele in Asians. The development of sequence-based predictive algorithms to identify potential Dsg3 epitopes has encountered limited success due to the paucity of PV-associated allele-specific peptides as training data.

Results

In this work we constructed atomic models of ten PV associated, non-associated and protective alleles. Nine previously identified stimulatory Dsg3 peptides, Dsg3 96–112, Dsg3 191–205, Dsg3 206–220, Dsg3 252–266, Dsg3 342–356, Dsg3 380–394, Dsg3 763–777, Dsg3 810–824 and Dsg3 963–977, were docked into the binding groove of each model to analyze the structural aspects of allele-specific binding.

Conclusion

Our docking simulations are entirely consistent with functional data obtained from in vitro competitive binding assays and T cell proliferation studies in DR4 and DR6 PV patients. Our findings ascertain that DRB1*0402 plays a crucial role in the selection of specific self-peptides in DR4 PV. DRB1*0402 and DQB1*0503 do not necessarily share the same core residues, indicating that both alleles may have different binding specificities. In addition, our results lend credence to the hypothesis that the alleles DQB1*0201 and *0202 play a protective role by binding Dsg3 peptides with greater affinity than the susceptible alleles, allowing for efficient deletion of autoreactive T cells.

Structural prediction of peptides bound to MHC class I

An ab initio structure prediction approach adapted to the peptide-major histocompatibility complex (MHC) class I system is presented. Based on structure comparisons of a large set of peptide-MHC class I complexes, a molecular dynamics protocol is proposed using simulated annealing (SA) cycles to sample the conformational space of the peptide in its fixed MHC environment. A set of 14 peptide-human leukocyte antigen (HLA) A0201 and 27 peptide-non-HLA A0201 complexes for which X-ray structures are available is used to test the accuracy of the prediction method. For each complex, 1000 peptide conformers are obtained from the SA sampling. A graph theory clustering algorithm based on heavy atom root-mean-square deviation (RMSD) values is applied to the sampled conformers. The clusters are ranked using cluster size, mean effective or conformational free energies, with solvation free energies computed using Generalized Born MV 2 (GB-MV2) and Poisson-Boltzmann (PB) continuum models. The final conformation is chosen as the center of the best-ranked cluster. With conformational free energies, the overall prediction success is 83% using a 1.00 Angstroms crystal RMSD criterion for main-chain atoms, and 76% using a 1.50 Angstroms RMSD criterion for heavy atoms. The prediction success is even higher for the set of 14 peptide-HLA A0201 complexes: 100% of the peptides have main-chain RMSD values < or =1.00 Angstroms and 93% of the peptides have heavy atom RMSD values < or =1.50 Angstroms. This structure prediction method can be applied to complexes of natural or modified antigenic peptides in their MHC environment with the aim to perform rational structure-based optimizations of tumor vaccines.

Modeling the structure of bound peptide ligands to major histocompatibility complex

In this article, we present a new technique for the rapid and precise docking of peptides to MHC class I and class II receptors. Our docking procedure consists of three steps: (1) peptide residues near the ends of the binding groove are docked by using an efficient pseudo-Brownian rigid body docking procedure followed by (2) loop closure of the intervening backbone structure by satisfaction of spatial constraints, and subsequently, (3) the refinement of the entire backbone and ligand interacting side chains and receptor side chains experiencing atomic clash at the MHC receptor-peptide interface. The method was tested by remodeling of 40 nonredundant complexes of at least 3.00 A resolution for which three-dimensional structural information is available and independently for docking peptides derived from 15 nonredundant complexes into a single template structure. In the first test, 33 out of 40 MHC class I and class II peptides and in the second test, 11 out of 15 MHC-peptide complexes were modeled with a Calpha RMSD < 1.00 A.

Evolution of the variable gene segments and recombination signal sequences of the human T-cell receptor alpha/delta locus

The T-cell receptor (TCR) alpha and delta loci are particularly interesting because of their unique genomic structure, in that the gene segments for each locus are interspersed. The origin of this remarkable gene segment arrangement is obscure. In this report, we investigated the evolution of the TCRalpha and delta variable loci and their respective recombination signal sequences (RSSs). Our phylogenetic analyses divided the alpha and delta variable gene segments into two major groups each with distinguishing motifs in both the framework and complementarity determining regions (CDRs). Sequence analyses revealed that TCRdelta variable segments share similar CDR2 sequences with immunoglobulin light chain variable segments, possibly revealing similar evolutionary histories. Maximum likelihood analysis of the region on Chromosome 14q11.2 containing the loci revealed two possible ancestral TCR alpha/delta variable segments, TRDV2 and TRAV1-1/ 1-2, respectively. Maximum parsimony revealed different evolutionary patterns between the variable segment and RSS of the same variable gene arguing for dissimilar evolutionary origins. Two models could account for this difference: a V(D)J recombination activity involving embedded heptamer-like motifs in the germline genome, or, more plausibly, an unequal sister chromatid crossing-over. Either mechanism would have resulted in increased diversity for the adaptive immune system.

Binding free energy differences in a TCR-peptide-MHC complex induced by a peptide mutation: a simulation analysis

Recognition by the T-cell receptor (TCR) of immunogenic peptides presented by class I major histocompatibility complexes (MHCs) is the determining event in the specific cellular immune response against virus-infected cells or tumor cells. It is of great interest, therefore, to elucidate the molecular principles upon which the selectivity of a TCR is based. These principles can in turn be used to design therapeutic approaches, such as peptide-based immunotherapies of cancer. In this study, free energy simulation methods are used to analyze the binding free energy difference of a particular TCR (A6) for a wild-type peptide (Tax) and a mutant peptide (Tax P6A), both presented in HLA A2. The computed free energy difference is 2.9 kcal/mol, in good agreement with the experimental value. This makes possible the use of the simulation results for obtaining an understanding of the origin of the free energy difference which was not available from the experimental results. A free energy component analysis makes possible the decomposition of the free energy difference between the binding of the wild-type and mutant peptide into its components. Of particular interest is the fact that better solvation of the mutant peptide when bound to the MHC molecule is an important contribution to the greater affinity of the TCR for the latter. The results make possible identification of the residues of the TCR which are important for the selectivity. This provides an understanding of the molecular principles that govern the recognition. The possibility of using free energy simulations in designing peptide derivatives for cancer immunotherapy is briefly discussed.