The distribution of CTL epitopes in HIV-1 appears to be random, and similar to that of other proteomes

Background  

HIV-1 viruses are highly capable of mutating their proteins to escape the presentation of CTL epitopes in their current host. Upon transmission to another host, some escape mutations revert, but other remain stable in the virus sequence for at least several years. Depending on the rate of accumulation and reversion of escape mutations, HIV-1 could reach a high level of adaptation to the human population. Yusim et. al. hypothesized that the apparent clustering of CTL epitopes in the conserved regions of HIV-1 proteins could be an evolutionary signature left by large-scale adaptation of HIV-1 to its human/simian host.     

Frequent associations between CTL and T-Helper epitopes in HIV-1 genomes and implications for multi-epitope vaccine designs

Background  

Epitope vaccines have been suggested as a strategy to counteract viral escape and development of drug resistance. Multiple studies have shown that Cytotoxic T-Lymphocyte (CTL) and T-Helper (Th) epitopes can generate strong immune responses in Human Immunodeficiency Virus (HIV-1). However, not much is known about the relationship among different types of HIV epitopes, particularly those epitopes that can be considered potential candidates for inclusion in the multi-epitope vaccines.     

Prediction of antigenic epitopes on protein surfaces by consensus scoring

Background  

Prediction of antigenic epitopes on protein surfaces is important for vaccine design. Most existing epitope prediction methods focus on protein sequences to predict continuous epitopes linear in sequence. Only a few structure-based epitope prediction algorithms are available and they have not yet shown satisfying performance.     

NetCTLpan: pan-specific MHC class I pathway epitope predictions

Reliable predictions of immunogenic peptides are essential in rational vaccine design and can minimize the experimental effort needed to identify epitopes. In this work, we describe a pan-specific major histocompatibility complex (MHC) class I epitope predictor, NetCTLpan. The method integrates predictions of proteasomal cleavage, transporter associated with antigen processing (TAP) transport efficiency, and MHC class I binding affinity into a MHC class I pathway likelihood score and is an improved and extended version of NetCTL. The NetCTLpan method performs predictions for all MHC class I molecules with known protein sequence and allows predictions for 8-, 9-, 10-, and 11-mer peptides. In order to meet the need for a low false positive rate, the method is optimized to achieve high specificity. The method was trained and validated on large datasets of experimentally identified MHC class I ligands and cytotoxic T lymphocyte (CTL) epitopes. It has been reported that MHC molecules are differentially dependent on TAP transport and proteasomal cleavage. Here, we did not find any consistent signs of such MHC dependencies, and the NetCTLpan method is implemented with fixed weights for proteasomal cleavage and TAP transport for all MHC molecules. The predictive performance of the NetCTLpan method was shown to outperform other state-of-the-art CTL epitope prediction methods. Our results further confirm the importance of using full-type human leukocyte antigen restriction information when identifying MHC class I epitopes. Using the NetCTLpan method, the experimental effort to identify 90% of new epitopes can be reduced by 15% and 40%, respectively, when compared to the NetMHCpan and NetCTL methods. The method and benchmark datasets are available at .     

Detailed estimation of bioinformatics prediction reliability through the Fragmented Prediction Performance Plots

Background  

An important and yet rather neglected question related to bioinformatics predictions is the estimation of the amount of data that is needed to allow reliable predictions. Bioinformatics predictions are usually validated through a series of figures of merit, like for example sensitivity and precision, and little attention is paid to the fact that their performance may depend on the amount of data used to make the predictions themselves.     

Development of a Novel In Silico Docking Simulation Model for the Fine HIV-1 Cytotoxic T Lymphocyte Epitope Mapping

Introduction

Class I HLA''s polymorphism has hampered CTL epitope mapping with laborious experiments. Objectives are 1) to evaluate the novel in silico model in predicting previously reported epitopes in comparison with existing program, and 2) to apply the model to predict optimal epitopes with HLA using experimental results.

Materials and Methods

We have developed a novel in silico epitope prediction method, based on HLA crystal structure and a peptide docking simulation model, calculating the peptide-HLA binding affinity at four amino acid residues in each terminal. It was applied to predict 52 HIV best–defined CTL epitopes from 15-mer overlapping peptides, and its predictive ability was compared with the HLA binding motif-based program of HLArestrictor. It was then used to predict HIV-1 Gag optimal epitopes from previous ELISpot results.

Results

43/52 (82.7%) epitopes were detected by the novel model, whereas 37 (71.2%) by HLArestrictor. We also found a significant reduction in epitope detection rates for longer epitopes in HLArestrictor (p = 0.027), but not in the novel model. Improved epitope prediction was also found by introducing both models, especially in specificity (p<0.001). Eight peptides were predicted as novel, immunodominant epitopes in both models.

Discussion

This novel model can predict optimal CTL epitopes, which were not detected by an existing program. This model is potentially useful not only for narrowing down optimal epitopes, but predicting rare HLA alleles with less information. By introducing different principal models, epitope prediction will be more precise.     

PEPOP: Computational design of immunogenic peptides

Background  

Most methods available to predict protein epitopes are sequence based. There is a need for methods using 3D information for prediction of discontinuous epitopes and derived immunogenic peptides.     

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background  

The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities.     

A Novel Liposome-Based Adjuvant CAF01 for Induction of CD8+ Cytotoxic T-Lymphocytes (CTL) to HIV-1 Minimal CTL Peptides in HLA-A*0201 Transgenic Mice

Background

Specific cellular cytotoxic immune responses (CTL) are important in combating viral diseases and a highly desirable feature in the development of targeted HIV vaccines. Adjuvants are key components in vaccines and may assist the HIV immunogens in inducing the desired CTL responses. In search for appropriate adjuvants for CD8⁺ T cells it is important to measure the necessary immunological features e.g. functional cell killing/lysis in addition to immunological markers that can be monitored by simple immunological laboratory methods.

Methodology/Principal Findings

We tested the ability of a novel two component adjuvant, CAF01, consisting of the immune stimulating synthetic glycolipid TDB (Trehalose-Dibehenate) incorporated into cationic DDA (Dimethyldioctadecylammonium bromide) liposomes to induce CD8⁺ T-cell restricted cellular immune responses towards subdominant minimal HLA-A0201-restricted CTL epitopes from HIV-1 proteins in HLA-A*0201 transgenic HHD mice. CAF01 has an acceptable safety profile and is used in preclinical development of vaccines against HIV-1, malaria and tuberculosis.

Conclusions/Significance

We found that CAF01 induced cellular immune responses against HIV-1 minimal CTL epitopes in HLA-A*0201 transgenic mice to levels comparable with that of incomplete Freund''s adjuvant.     

Biotic interactions influence the projected distribution of a specialist mammal under climate change

Aim

To measure the effects of including biotic interactions on climate‐based species distribution models (SDMs) used to predict distribution shifts under climate change. We evaluated the performance of distribution models for an endangered marsupial, the northern bettong (Bettongia tropica), comparing models that used only climate variables with models that also took into account biotic interactions.

Location

North‐east Queensland, Australia.

Methods

We developed separate climate‐based distribution models for the northern bettong, its two main resources and a competitor species. We then constructed models for the northern bettong by including climate suitability estimates for the resources and competitor as additional predictor variables to make climate + resource and climate + resource + competition models. We projected these models onto seven future climate scenarios and compared predictions of northern bettong distribution made by these differently structured models, using a ‘global’ metric, the I similarity statistic, to measure overlap in distribution and a ‘local’ metric to identify where predictions differed significantly.

Results

Inclusion of food resource biotic interactions improved model performance. Over moderate climate changes, up to 3.0 °C of warming, the climate‐only model for the northern bettong gave similar predictions of distribution to the more complex models including interactions, with differences only at the margins of predicted distributions. For climate changes beyond 3.0 °C, model predictions diverged significantly. The interactive model predicted less contraction of distribution than the simpler climate‐only model.

Main conclusions

Distribution models that account for interactions with other species, in particular direct resources, improve model predictions in the present‐day climate. For larger climate changes, shifts in distribution of interacting species cause predictions of interactive models to diverge from climate‐only models. Incorporating interactions with other species in SDMs may be needed for long‐term prediction of changes in distribution of species under climate change, particularly for specialized species strongly dependent on a small number of biotic interactions.     

EPSVR and EPMeta: prediction of antigenic epitopes using support vector regression and multiple server results

Background  

Accurate prediction of antigenic epitopes is important for immunologic research and medical applications, but it is still an open problem in bioinformatics. The case for discontinuous epitopes is even worse - currently there are only a few discontinuous epitope prediction servers available, though discontinuous peptides constitute the majority of all B-cell antigenic epitopes. The small number of structures for antigen-antibody complexes limits the development of reliable discontinuous epitope prediction methods and an unbiased benchmark to evaluate developed methods.     

MultiRTA: A simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes

Background

The binding of peptide fragments of antigens to class II MHC is a crucial step in initiating a helper T cell immune response. The identification of such peptide epitopes has potential applications in vaccine design and in better understanding autoimmune diseases and allergies. However, comprehensive experimental determination of peptide-MHC binding affinities is infeasible due to MHC diversity and the large number of possible peptide sequences. Computational methods trained on the limited experimental binding data can address this challenge. We present the MultiRTA method, an extension of our previous single-type RTA prediction method, which allows the prediction of peptide binding affinities for multiple MHC allotypes not used to train the model. Thus predictions can be made for many MHC allotypes for which experimental binding data is unavailable.

Results

We fit MultiRTA models for both HLA-DR and HLA-DP using large experimental binding data sets. The performance in predicting binding affinities for novel MHC allotypes, not in the training set, was tested in two different ways. First, we performed leave-one-allele-out cross-validation, in which predictions are made for one allotype using a model fit to binding data for the remaining MHC allotypes. Comparison of the HLA-DR results with those of two other prediction methods applied to the same data sets showed that MultiRTA achieved performance comparable to NetMHCIIpan and better than the earlier TEPITOPE method. We also directly tested model transferability by making leave-one-allele-out predictions for additional experimentally characterized sets of overlapping peptide epitopes binding to multiple MHC allotypes. In addition, we determined the applicability of prediction methods like MultiRTA to other MHC allotypes by examining the degree of MHC variation accounted for in the training set. An examination of predictions for the promiscuous binding CLIP peptide revealed variations in binding affinity among alleles as well as potentially distinct binding registers for HLA-DR and HLA-DP. Finally, we analyzed the optimal MultiRTA parameters to discover the most important peptide residues for promiscuous and allele-specific binding to HLA-DR and HLA-DP allotypes.

Conclusions

The MultiRTA method yields competitive performance but with a significantly simpler and physically interpretable model compared with previous prediction methods. A MultiRTA prediction webserver is available at http://bordnerlab.org/MultiRTA.

     

Species distributions models may predict accurately future distributions but poorly how distributions change: A critical perspective on model validation

Aim

Species distribution models (SDMs) are widely used to make predictions on how species distributions may change as a response to climatic change. To assess the reliability of those predictions, they need to be critically validated with respect to what they are used for. While ecologists are typically interested in how and where distributions will change, we argue that SDMs have seldom been evaluated in terms of their capacity to predict such change. Instead, typical retrospective validation methods estimate model's ability to predict to only one static time in future. Here, we apply two validation methods, one that predicts and evaluates a static pattern, while the other measures change and compare their estimates of predictive performance.

Location

Fennoscandia.

Methods

We applied a joint SDM to model the distributions of 120 bird species in four model validation settings. We trained models with a dataset from 1975 to 1999 and predicted species' future occurrence and abundance in two ways: for one static time period (2013–2016, ‘static validation’) and for a change between two time periods (difference between 1996–1999 and 2013–2016, ‘change validation’). We then measured predictive performance using correlation between predicted and observed values. We also related predictive performance to species traits.

Results

Even though static validation method evaluated predictive performance as good, change method indicated very poor performance. Predictive performance was not strongly related to any trait.

Main Conclusions

Static validation method might overestimate predictive performance by not revealing the model's inability to predict change events. If species' distributions remain mostly stable, then even an unfit model can predict the near future well due to temporal autocorrelation. We urge caution when working with forecasts of changes in spatial patterns of species occupancy or abundance, even for SDMs that are based on time series datasets unless they are critically validated for forecasting such change.     

Cross-protective peptide vaccine against influenza A viruses developed in HLA-A*2402 human immunity model

Background

The virus-specific cytotoxic T lymphocyte (CTL) induction is an important target for the development of a broadly protective human influenza vaccine, since most CTL epitopes are found on internal viral proteins and relatively conserved. In this study, the possibility of developing a strain/subtype-independent human influenza vaccine was explored by taking a bioinformatics approach to establish an immunogenic HLA-A24 restricted CTL epitope screening system in HLA-transgenic mice.

Methodology/Principal Findings

HLA-A24 restricted CTL epitope peptides derived from internal proteins of the H5N1 highly pathogenic avian influenza A virus were predicted by CTL epitope peptide prediction programs. Of 35 predicted peptides, six peptides exhibited remarkable cytotoxic activity in vivo. More than half of the mice which were subcutaneously vaccinated with the three most immunogenic and highly conserved epitopes among three different influenza A virus subtypes (H1N1, H3N2 and H5N1) survived lethal influenza virus challenge during both effector and memory CTL phases. Furthermore, mice that were intranasally vaccinated with these peptides remained free of clinical signs after lethal virus challenge during the effector phase.

Conclusions/Significance

This CTL epitope peptide selection system can be used as an effective tool for the development of a cross-protective human influenza vaccine. Furthermore this vaccine strategy can be applicable to the development of all intracellular pathogens vaccines to induce epitope-specific CTL that effectively eliminate infected cells.     

Improved method for predicting linear B-cell epitopes

Background

B-cell epitopes are the sites of molecules that are recognized by antibodies of the immune system. Knowledge of B-cell epitopes may be used in the design of vaccines and diagnostics tests. It is therefore of interest to develop improved methods for predicting B-cell epitopes. In this paper, we describe an improved method for predicting linear B-cell epitopes.

Results

In order to do this, three data sets of linear B-cell epitope annotated proteins were constructed. A data set was collected from the literature, another data set was extracted from the AntiJen database and a data sets of epitopes in the proteins of HIV was collected from the Los Alamos HIV database. An unbiased validation of the methods was made by testing on data sets on which they were neither trained nor optimized on. We have measured the performance in a non-parametric way by constructing ROC-curves.

Conclusion

The best single method for predicting linear B-cell epitopes is the hidden Markov model. Combining the hidden Markov model with one of the best propensity scale methods, we obtained the BepiPred method. When tested on the validation data set this method performs significantly better than any of the other methods tested. The server and data sets are publicly available at http://www.cbs.dtu.dk/services/BepiPred.     

The Effect of HLA Polymorphisms on the Recognition of Gag Epitopes in HIV-1 CRF01_AE Infection

Introduction

The design of a globally effective vaccine rests on the identification of epitopes capable of eliciting effective cytotoxic T lymphocyte (CTL) responses across multiple HIV clades in different populations. This study aims to discern the effect of HLA polymorphisms and the cross-clade reactivity or clade-specificity of epitopes in Thailand where HIV-1 CRF01_AE is circulating.

Materials and Methods

14 peptides based on consensus HIV-1 CRF01_AE amino acid sequences were designed for use in IFN-γ ELISpot assays and ⁵¹Cr release assays among 66 HIV-1 CRF01_AE-infected Thai patients. For ELISpot responders carrying HLA alleles currently unknown to restrict CRF01_AE epitopes, in silico epitope-HLA prediction was performed.

Results

29/66 (43.9%) patients recognized at least one peptide. In total 79 responses were seen against all 14 peptides. 28/79 (35.4%) of the responses were in patients with HLA alleles previously reported to restrict CRF01_AE epitopes, 24/79 (30.4%) responses were in individuals with HLA alleles previously reported to restrict epitopes of HIV clades other than CRF01_AE, and the remaining 27/79 (34.2%) responses were not associated with HLA alleles previously known to restrict HIV epitopes. In silico epitope prediction detected 19 novel, epitope-HLA combinations, and 11/19 (57.9%) were associated with HLA-C alleles. We further confirmed a novel HLA restriction of a previously identified HIV-1 Gag epitope [p24_122–130: PPIPVGDIY (PY9)] by HLA-B*40:01 with a standard ⁵¹Cr release assay.

Discussion

CTL recognition sites in HIV-1 Gag were similar among different clades but the HLA restriction differed in Thai patients. This disparity in HLA restriction along different populations illustrated the importance of clade- and population-specific HLA analysis prior to CTL vaccine design.     

Computational verification of protein-protein interactions by orthologous co-expression

Background  

High-throughput methods identify an overwhelming number of protein-protein interactions. However, the limited accuracy of these methods results in the false identification of many spurious interactions. Accordingly, the resulting interactions are regarded as hypothetical and computational methods are needed to increase their confidence. Several methods have recently been suggested for this purpose including co-expression as a confidence measure for interacting proteins, but their performance is still quite poor.     

Combining gene prediction methods to improve metagenomic gene annotation

Background  

Traditional gene annotation methods rely on characteristics that may not be available in short reads generated from next generation technology, resulting in suboptimal performance for metagenomic (environmental) samples. Therefore, in recent years, new programs have been developed that optimize performance on short reads. In this work, we benchmark three metagenomic gene prediction programs and combine their predictions to improve metagenomic read gene annotation.     

Population coverage by HLA class-I restricted cytotoxic T-lymphocyte epitopes

Vaccination using cytotoxic T-lymphocyte (CTL) epitopes has become a widely used immunization strategy, especially because their structure makes them an attractive alternative to the delivery of whole proteins as immunogens. Nonetheless, their use is limited, in particular because of their specificity, being recognized only by cognate HLA alleles. The potential for immunizing a substantial portion of an ethnically diverse population using a modest number of peptides has been aided by the identification of HLA supertypes. However, the derivation of epitopes is often guided by methods that do not guarantee cross-reactivity, so we consider the feasibility of providing vaccine coverage to a multi-ethnic population under different assumptions. In particular, two large datasets are used to estimate the number of peptides needed to provide > or =90% group-specific coverage of a multiethnic population, when specificity is assumed to be either to a single serologic or molecular type. These assumptions are evaluated utilizing a clinically important epitope repertoire derived from two human cytomegalovirus proteins, and data on the in vitro memory response elicited by these peptides is presented. In summary, our combined theoretical and empiric studies suggest that 90% coverage of some ethnic groups is attainable with 11 uniquely defined HLA-restricted CTL epitopes. The derivation of four or more additional CTL epitopes is needed to attain 90% coverage of Blacks or Asians, the minimally covered groups. Ninety percent coverage of all major ethnic groups in a multi-ethnic population appears feasible without relying on cross-reactivity, but may require two to three times more CTL epitopes than estimated for serologic data, homogenous populations, or HLA alleles grouped as supertypes.     

GOPET: A tool for automated predictions of Gene Ontology terms

Background  

Vast progress in sequencing projects has called for annotation on a large scale. A Number of methods have been developed to address this challenging task. These methods, however, either apply to specific subsets, or their predictions are not formalised, or they do not provide precise confidence values for their predictions.