A Classic Pattern of Type 1 Brugada Syndrome on ECG: A Case Report
2023

Sherif Elkattawy
Jesus Romero email@example.com
Ana L Romero
Omar Elkattawy
Sohail Sodhi
Sherif ; Elkattawy
Jesus ; Romero
Ana L ; Romero
Omar ; Elkattawy
Sodhi
Sherif Elkattawy
Jesus Romero
Ana L Romero
Omar Elkattawy
Sohail Sodhi
Fayez Shamoon
Sherif Elkattawy
Cardiology Department, St. Joseph's University Medical Center
PatersonNJUSA

Jesus Romero
Internal Medicine Department
RWJBarnabas Health/Trinitas Regional Medical Center
ElizabethNJUSA

Ana L Romero
Omar Elkattawy
Internal Medicine Department
RWJBarnabas Health/Trinitas Regional Medical Center
ElizabethNJUSA

Internal Medicine Department
Rutgers University-New Brunswick, Jersey City
NJUSA

Sohail Sodhi
Internal Medicine Department
RWJBarnabas Health/Trinitas Regional Medical Center
ElizabethNJUSA

Fayez Shamoon
Cardiology Department, St. Joseph's University Medical Center
PatersonNJUSA

Journal of Community Hospital Internal Medicine Perspectives Journal of Community Hospital Internal Medicine Perspectives Volume 13 Issue 4
Cardiology Department, St. Joseph's University Medical Center
Internal Medicine Department
RWJBarnabas Health/Trinitas Regional Medical Center
Internal Medicine Department
RWJBarnabas Health/Trinitas Regional Medical Center
Internal Medicine Department
Internal Medicine Department
RWJBarnabas Health/Trinitas Regional Medical Center
Rutgers University-New Brunswick
Paterson, Elizabeth, Elizabeth, Jersey City, ElizabethNew Jersey, New Jersey, New Jersey, New Jersey, New JerseyUSA, USA, USA., USA., USA

A Classic Pattern of Type 1 Brugada Syndrome on ECG: A Case Report

Journal of Community Hospital Internal Medicine Perspectives
13202310.55729/2000-9666.1187Article 20 2023See next page for additional authors Follow this and additional works at: https://scholarlycommons.gbmc.org/jchimp Available at: https://scholarlycommons.gbmc.org/jchimp/vol13/iss4/20 This Case Report is brought to you for free and open access by the Journal at GBMC Healthcare Scholarly Commons. It has been accepted for inclusion in Journal of Community Hospital Internal Medicine Perspectives by an authorized editor of GBMC Healthcare Scholarly Commons. For more information, please contact firstname.lastname@example.com. This case report is available in Journal of Community Hospital Internal Medicine Perspectives: https://scholarlycommons.gbmc.org/jchimp/vol13/iss4/20Brugada syndromeChannelopathyAutomatic implantable cardioverter defibrillatorSudden cardiac death
Brugada syndrome is an inherited disorder characterized by a channelopathy of cardiac sodium, potassium, and calcium channel. The pathophysiology of this disorder is not completely elucidated yet, however, most of the reported cases are caused by a pathogenic alteration in the SCN5A gene, leading to the malfunction of cardiac sodium channels. Several stressors are well known to unmask this pathology including fever and electrolytes imbalance.Three ECG patterns are frequently described in the literature, type 1, type 2, and type 3. However, only the type 1 pattern is considered diagnostic of Brugada syndrome in the appropriate clinical context. Therapeutic strategies can range from conservative medical management with antiarrhythmic medications to Automatic Implantable Cardioverter Defibrillator (AICD) placement. Prompt recognition is of utmost importance since this pathology can rapidly evolve into life-threatening arrhythmias and sudden cardiac death.Here we present a case of a 22-year-old male who presented after a syncopal episode and was found to have Brugada syndrome in the setting of Influenza A infection.

Introduction

B rugada syndrome is an inherited cardiac disorder often associated with mutations in the SCN5A gene which encodes for cardiac sodium channels. It has been found that this pathology is more prevalent in Southeast Asians with a strong male correlation with a 4:1 ratio as compared to females. There are three ECG patterns, type 1 is characterized by a coved-shaped ST-segment elevation along with a negative T-wave in the right precordial leads. Type 2 pattern is characterized by a saddleback morphology and ST-segment elevation. Type 3 pattern can be identified by a coved pattern or a saddleback along with an ST-segment elevation of less than 1 mm. This syndrome is estimated to be responsible for approximately 4% of sudden cardiac death in the general population, and around 20% of deaths among individuals with no known structural heart disease. 1 An implantable cardioverter defibrillator remains to be a safe therapeutic strategy and reduces cardiovascular mortality in patients with Brugada syndrome. 2,3 Here we present a case of a young gentleman who presented with Brugada syndrome in the setting of Influenza A infection.

Case presentation

The patient is a 22-year-old male with a past medical history of schizophrenia who presented to the Emergency Department (ED) after an unwitnessed syncopal episode. The patient described the presence of prodromal symptoms characterized by flushing, palpitations, lightheadedness, and dizziness prior to the syncopal episode the morning of the presentation. He is not aware of the exact time duration of the episode, but he denied confusion or disorientation after the event. Afterward, the patient went to work when he started to experience headaches, myalgias, nasal congestion, sore throat, nausea, and 2e3 episodes of non-bloody, nonbilious emesis. He recalled a similar episode 6 years prior to the presentation.

He denied chest pain, shortness of breath, orthopnea, paroxysmal nocturnal dyspnea, tongue biting, abdominal pain, diarrhea, fever, or chills. He had a significant family history of unspecified heart conditions. He had a paternal uncle who died suddenly at the age of 16, and a sister who had pacemaker placement at 16 who is currently 24 years old and recently diagnosed with heart failure.

In the ED, the patient was febrile with a temperature of 100.7 Fahrenheit, blood pressure of 136/ 82 mmHg, heart rate of 98 beats per min, respiratory rate of 18 per min, and saturating at 98% on room air. On physical examination, the patient had a regular rate and rhythm, S1 and S2 were present without extra sounds, and no murmurs, rubs, or gallops were appreciated. Pertinent labs included mild hypokalemia of 3.4 Chest X-ray was negative for any acute or chronic cardiopulmonary disease. ECG showed Brugada Type 1 pattern raising concern for Brugada Syndrome (Fig. 1).

The patient was admitted for further management with Tamiflu and Tylenol. Arrangements were made and the patient was transferred to Newark Beth Israel for an electrophysiology study and assessment for possible AICD placement.

Discussion

Brugada syndrome is an unusual cardiac disorder with an autosomal dominant pattern of inheritance associated with a mutation in multiple genes encoding for calcium, sodium, and potassium cardiac channels. Most of the reported cases are caused by a pathogenic alteration in the SCN5A gene, leading to the malfunction of cardiac sodium channels. However, other less frequent mutations of genes encoding for sodium channel subunits (SCN1B, SCN2B, SCN3B) have been reported, as well as, in genes associated with potassium channels (KCNE3, KCNJ8, KCND3, and KCNE5). 4 Three patterns have been described in the literature that can be differentiated by EKG findings. Type 1 pattern is characterized by a coved-type STsegment elevation (>2 mm) associated with a negative T wave in two or more right precordial leads (V1eV3). Type 2 pattern is recognized by the evidence of saddleback morphology followed by an ST-segment elevation of at least 1 mm along with a 2 mm J-point elevation. 4 Type 3 pattern can be identified by a coved pattern or a saddleback along with an ST-segment elevation of less than 1 mm. 3 However, only the type 1 pattern is diagnostic of Brugada syndrome while the presence of type 2 or 3 patterns is suggestive of this disorder. 1 The ECG changes in this pathology may be triggered by a wide variety of stressors (electrolyte imbalances, fever, drug intoxication) or medications (anesthetics, sodium channel blockers, psychotropics). Similar to our case, Manohar et al., reported a case of an elderly lady who had a type 1 Brugada pattern on ECG after she was found febrile in the setting of sepsis secondary to urinary tract infection. 5 Furthermore, Adler et al., found that this ECG pattern was 20 times more likely to be evidenced in febrile vs afebrile patients; Amin et al., reported that fever was a relevant precipitating factor (18%) of the cardiac arrests in patients with Brugada syndrome. 6,7 Currently, there are multiple therapeutic strategies for patients with Brugada syndrome from conservative management with antiarrhythmic medications (quinidine, bepridil) or phosphodiesterase III inhibitors (cilostazol) to more invasive management such as an AICD insertion. AICD placement has been proven to be a safe long-term therapeutic tool that reduces cardiovascular mortality in these patients, however, it is associated with complications including infections or lead failure. 2 Clinicians should remain vigilant and have a low threshold for diagnosing this pathology since these patients are at increased risk for sudden cardiac death despite the absence of structural heart abnormality. Even though this syndrome has no known cure, the prevention of life-threatening arrhythmias is of utmost importance.

[3.6e5.1 MMOL/L], troponin of <0.01 [0.5 ng/ml], and lactic acid 1.05 [0.5e2.2 mmol/L]. He tested positive for Influenza A.

Fig. 1 .
1ECG showing coved ST-elevation in anterior leads.
Conflict of interestThere is no conflict of interest.
Risk stratification of sudden cardiac death in Brugada syndrome: an updated review of literature. C Darar, E A Mohammed, B Mohammed, E O Noha, B Zakaria, Egypt Heart J. Darar C, Mohammed EA, Mohammed B, Noha EO, Zakaria B. Risk stratification of sudden cardiac death in Brugada syndrome: an updated review of literature. Egypt Heart J. 2022.

Brugada syndrome: a comprehensive review of pathophysiological mechanisms and risk stratification strategies. IJC Heart & Vasculature. K H Li, S Lee, C Yin, Li KH, Lee S, Yin C, et al. Brugada syndrome: a comprehensive review of pathophysiological mechanisms and risk stratification strategies. IJC Heart & Vasculature; 2020.

Worldwide prevalence of Brugada syndrome: a systematic review and meta-analysis. W Vutthikraivit, P Rattanawong, P Putthapiban, Acta Cardiol Sin. Vutthikraivit W, Rattanawong P, Putthapiban P, et al. World- wide prevalence of Brugada syndrome: a systematic review and meta-analysis. Acta Cardiol Sin. 2018.

Brugada syndrome: clinical and genetic findings. G S Brugada, O Campuzano, E Arbelo, J Brugada, Ramon Brugada, Genet Med. Brugada GS, Campuzano O, Arbelo E, Brugada J, Ramon Brugada. Brugada syndrome: clinical and genetic findings. Genet Med. 2016.

Fever-Induced Brugada syndrome. J Invest Med High Impact Case Rep. S Manohar, B R Dahal, B Gitler, Manohar S, Dahal BR, Gitler B. Fever-Induced Brugada syn- drome. J Invest Med High Impact Case Rep. 2015.

Fever Increases the risk for cardiac arrest in the Brugada syndrome. A S Amin, P G Meregalli, A Bardai, Aam Wilde, H L Tan, Ann Intern Med. Amin AS, Meregalli PG, Bardai A, Wilde AAM, Tan HL. Fever Increases the risk for cardiac arrest in the Brugada syndrome. Ann Intern Med. 2008.

Fever-induced Brugada pattern: how common is it and what does it mean? Heart Rhythm. A Adler, G Topaz, K Heller, D Zeltser, T Ohayon, U Rozovski, Adler A, Topaz G, Heller K, Zeltser D, Ohayon T, Rozovski U. Fever-induced Brugada pattern: how common is it and what does it mean? Heart Rhythm. 2013.


Peptide binding predictions for HLA DR, DP and DQ molecules

Peng Wang
John Sidney
Yohan Kim
Alessandro Sette
Ole Lund
Morten Nielsen
Bjoern Peters
Peptide binding predictions for HLA DR, DP and DQ molecules
R E S E A R C H A R T I C L E Open Access
Background: MHC class II binding predictions are widely used to identify epitope candidates in infectious agents, allergens, cancer and autoantigens. The vast majority of prediction algorithms for human MHC class II to date have targeted HLA molecules encoded in the DR locus. This reflects a significant gap in knowledge as HLA DP and DQ molecules are presumably equally important, and have only been studied less because they are more difficult to handle experimentally. Results: In this study, we aimed to narrow this gap by providing a large scale dataset of over 17,000 HLA-peptide binding affinities for a set of 11 HLA DP and DQ alleles. We also expanded our dataset for HLA DR alleles resulting in a total of 40,000 MHC class II binding affinities covering 26 allelic variants. Utilizing this dataset, we generated prediction tools utilizing several machine learning algorithms and evaluated their performance.Conclusion:We found that 1) prediction methodologies developed for HLA DR molecules perform equally well for DP or DQ molecules. 2) Prediction performances were significantly increased compared to previous reports due to the larger amounts of training data available.3) The presence of homologous peptides between training and testing datasets should be avoided to give real-world estimates of prediction performance metrics, but the relative ranking of different predictors is largely unaffected by the presence of homologous peptides, and predictors intended for end-user applications should include all training data for maximum performance. 4) The recently developed NN-align prediction method significantly outperformed all other algorithms, including a naïve consensus based on all prediction methods. A new consensus method dropping the comparably weak ARB prediction method could outperform the NN-align method, but further research into how to best combine MHC class II binding predictions is required.

Background

HLA class II molecules are expressed by human professional antigen presenting cells (APCs) and can display peptides derived from exogenous antigens to CD4 + T cells [1]. The molecules are heterodimers consisting of an alpha chain and a beta chain encoded in one of three loci: HLA DR, DP and DQ [2,3]. The DR locus can encode two beta chains DRB1 and DRB3-5 which are in linkage disequilibrium [4]. The genes encoding class II molecules are highly polymorphic, as evidenced by the IMGT/HLA database [5] which lists 1,190 known sequences of HLA class II alleles for HLA-DR, HLA-DP and HLA-DQ molecules (Table 1). Both alpha and beta chains can impact the distinct peptide binding specificity of an HLA class II molecule [6]. HLA class II peptide ligands that are recognized by T cells and trigger an immune response are referred to as immune epitopes [7]. Identifying such epitopes can help detect and modulate immune responses in infectious diseases, allergy, autoimmune diseases and cancer.

Computational predictions of peptide binding to HLA molecules are a powerful tool to identify epitope candidates. These predictions can generalize experimental findings from peptide binding assays, sequencing of naturally presented HLA ligands, and three dimensional structures of HLA peptide complexes solved by X-ray crystallography (for a review on MHC class II prediction algorithms see [8] and references herein). Several databases have been established to document the results of such experiments including Antijen [9], MHCBN [10], MHCPEP [11], FIMM [12], SYFPEITHI [13] and the Immune Epitope Database (IEDB) [14,15]. IEDB currently documents 12,577 peptides tested for binding to one of more of 158 MHC class II allelic variants of which 114 are human (HLA). It is possible to develop binding prediction methods for HLA molecules for which no experimental data are available by extrapolating what is known for related molecules [16][17][18][19]. However, the quality of these extrapolations decreases for molecules that are very different from the experimentally characterized ones, and completely ab initio predictions have not been successful [20]. It is therefore a major gap in knowledge that little binding data are available for HLA DP and DQ molecules, which are more difficult to work with experimentally, but are equally relevant as HLA DR molecules. Resulting from this lack of data, the vast majority of HLA class II binding predictions to date are only available for DR molecules. We here address this gap by providing a consistent, large scale dataset of binding affinities for HLA DR, DP and DQ molecules which we use to establish and evaluate peptide binding prediction tools.

It is our goal to include a variety of binding prediction algorithms in the IEDB Analysis Resource (IEDB-AR) [21], identify the best performing ones, and ideally combine multiple algorithms into a superior consensus prediction. In this study, we implemented two methods in addition to the previously incorporated ones. The first method is based on the use of combinatorial peptide libraries to characterize HLA class II molecules. Such libraries consist of mixtures of peptides of the same length, all sharing one residue at one position. Determining the affinity of a panel of such peptide libraries to an HLA molecule provides an unbiased and comprehensive assessment of its binding specificity. This approach is also time and cost effective, as the same panel of peptide libraries can be scanned for all HLA molecules of interest, and has been applied successfully for multiple applications [22][23][24], The second method we newly implemented was NN-align [25]. This neural network based approach combines the peptide sequence representation used in the NetMHC algorithm [26,27] that was highly successful in predicting the binding specificity of HLA class I molecules [28,29] with the representation of peptide flanking residues and peptide length used in NetMHCIIpan method [19]. Both the NN-align and the combinatorial peptide library method were evaluated in terms of their prediction performance and ability to improve a consensus prediction approach.

Finally, we wanted to address the impact of homologous peptides in our datasets on evaluating prediction results. The presence of homologous peptides in our dataset is primarily due to the strategies that were utilized in the peptide selection process. For comprehensive epitope mapping studies in individual antigens, we typically utilize 15-mer peptides overlapping by 10 residues that span entire protein sequences. Another strategy utilized to define classical binding motifs is to systematically introduce point mutations in a reference ligand to map essential residues for peptide:MHC interaction. Finally, for identified epitopes, additional variants from homologous proteins are often tested to predict potential cross-reactivity. All of these strategies introduce multiple peptides with significant sequence similarity into the dataset. This could affect the assessment of binding prediction in two distinct manners: 1) peptides in the testing set for which a homolog is present in the training may be easier to predict and thereby lead to overestimates of performance compared to real life applications; 2) the presence of multiple homologous peptides during training may bias prediction methods leading to reduced prediction performance when testing. To examine these issues, we compared evaluations with different approaches to removing similar peptides.

Results

Derivation and assembly of a novel MHC class II binding affinity dataset

In a previous report, we described the release of 10,017 MHC class II binding affinities experimentally measured by our group [30]. The data included measured binding affinities for a total of 17 different mouse and human allelic variants. This dataset was at the time the largest collection of homogenous MHC class II binding affinities available to the public and remains a valuable asset for the immunology research community. However, it was apparent that this dataset could be expanded and its utility improved in several regards. First, coverage of human HLA DP and DQ molecules was limited or non-existing. Secondly, for several molecules, relatively few data points existed, in spite of the fact that we and others [30,31] have shown that several hundred data points are desirable to derive accurate predictive algorithms. We have now compiled a new set of 44,541 experimentally measured, MHC class II peptide binding affinities covering 26 allelic variants (Table 2). This set includes and expands the previous set, and is the result of our general ongoing efforts to map epitopes in infectious agents and allergens. These data represent an over four fold increase in binding affinity measurements and ã 60% increase in allelic variant coverage. Importantly, the alleles included were selected for their high frequency in the human population (see Table 2). As a result, the combined allele frequency of this set of 26 MHC class II molecules results in >99% population coverage (Table 2). Overall, an average of 1,713 data points and 858 binders (peptides with measured IC 50 < 1000 nM) are included for each molecule, ranging from a minimum of 577 data points for HLA DRB1*0404 and 180 binders for H-2-IAb, to the highest values of 6,427 data points and 4,519 binders for the HLA-DRB1*0101 molecule. This uniformly large number of more than 500 affinity measurements for each included allelic variant was previously found to be required to consistently generate reliable predictions [30]. To the best of our knowledge, this is the first publicly available dataset of HLA-DP and HLA-DQ binding affinities of significant size.

Evaluation of previously reported methods with the new dataset

In our previous evaluation of MHC class II binding prediction algorithms, we tested the performance of a large number of publicly available methods. Among those methods, ARB, SMM-align and PROPRED (based on the matrices constructed by Sturniolo et al. [16] on which also the TEPITOPE predictions are based) were the top performing ones and were incorporated into the MHC class II binding prediction component of the IEDB analysis resource [21]. Here, we re-evaluated their performance on the new dataset. As in the previous evaluation, we performed 5-fold cross validation for ARB and SMM-align and direct prediction for PROPRED over the entire data set, and quantified the performance of the various methods by calculating the AUC values using an IC 50 cutoff of 1000 nM, as shown in Table 3 under the "current" columns. On average, the performance of the various methods was 0.784 for ARB (range 0.702 to 0.871), 0.849 for SMM-align (range 0.741 to 0.932), and 0.726 for PROPRED (range 0.600 to 0.804). Importantly, the cross-validated prediction performance for the newly included allelic variants was comparable to that of the previously included ones. Thus, the ARB and SMM-align machine learning approaches can be successfully applied to HLA DP and DQ allelic variants. The previously reported prediction performance data taken from [30] is also shown in Table 3 under the "old" columns. Compared to the average evaluation results reported previously, ARB (0.784 vs. 0.706) and SMM-align (0.849 vs. 0.727) showed markedly improved performance. As the training algorithms were unchanged, this most likely can be attributed to the increase in dataset sizes. In contrast, PROPRED achieved virtually the same AUC value (0.726 vs. 0.731). As the PROPRED approach is fixed and not retrained based on additional data, it is not surprising that the predictive performance on the new dataset did not differ substantially from the previously reported performance. Also, as the new data set cannot be utilized to train new PROPRED predictions, its predictions can now be generated for only a minority of the molecules considered.

Incorporating novel prediction algorithms into the MHC class II binding prediction arsenal

In addition to the previously implemented prediction methods, we integrated two new approaches into the Table 3 Comparison of ARB, SMM-align and PROPRED's performance on current and old dataset.  IEDB analysis resource. We used combinatorial peptide libraries to experimentally characterize the binding specificity of each HLA molecule for which new assays were established, including all HLA-DP and HLA-DQ allelic variants. The affinity of 180 libraries of 13-mer peptides, each sharing one amino acid residue in one of the positions from 3-11 was determined. The ability of these matrices to predict binding of individual peptides was evaluated with the entire new dataset, and the resulting AUC values are shown in Table 4 in the "ALL" column. It was found that the combinatorial library performed with AUC similar or better than the PROPRED method, which is similarly constructed based on affinity measurements for a library of single residue substitution peptides. Similar results were obtained when performance was measured with Spearman's rank correlation coefficient (Additional file 1, Table S1). This confirms that combinatorial peptide libraries are an efficient experimental approach to derive MHC class II binding profiles. Also, these predictions provide an alternative for those molecules for which the PROPRED method is not available.

The second new method we added to the IEDB analysis resource was NN-align [25]. This method differs  2. The Consensus-best3 method is based on NN-align, SMM-align and combinatorial peptide library. PROPRED was used for allelic variants when combinatorial peptide library was not available Best prediction performance for each allelic variant was highlighted. The best performing method for "ALL" dataset was highlighted with underline while the best performing method for "SR" dataset was highlighted in bold.

from previous approaches in that NN-align is neural network based and can hence take into account higher order sequence correlations. Furthermore, NN-align incorporates peptide flanking residues and peptide length directly into the training of the method. This is in contrast to the SMM-align method, where the peptide flanking residues and peptide length are dealt with in an ad-hoc manner. We evaluated the performance of NN-align using the same 5-fold data separations used for the ARB and SMM-align methods. The AUC values derived from this cross validation are shown in Table 4 under the "ALL" columns and the Spearman's rank correlation coefficients were shown in Additional file 1, Table S1. The NN-align method stands out as having by far the best performance, with an average AUC value of 0.882 and average Spearman's rank correlation coefficient of 0.758.

A novel homology reduction approach for unbiased cross validation

Some peptides in our dataset have significant homology to each other which could bias the cross-validation results if similar peptides are present in both the training and the testing sets. Previous studies have attempted to address this issue and several strategies have been proposed to generate sequence similarity reduced datasets for cross-validation purpose. One such approach is to remove similar peptides from the entire dataset [32]. We call this a 'random selection' strategy as the order in which peptides are removed is not defined. We applied the algorithm to our dataset and for any two peptides that shared an identical 9-mer core region, or that had more than 80% overall sequence identity, one peptide was removed. The results are shown in Additional file 1, Table S2 and highlight that this strategy selected a different number of peptides in repeated runs. To avoid this, we applied a Hobohm 1 like selection strategy that deterministically selects a set of peptides, and also maximizes the number of peptides included in the data. This was done by a forward selection procedure described in the methods section. Briefly, for each peptide the number of similar peptides was recorded and peptides were sorted according to this number. Peptides were selected from this ordered list starting with those with the smallest number of similar peptides. If a peptide was encountered for which a similar matching one was already selected, it was discarded. As shown in Additional file 1, Table S2, this strategy indeed resulted in a stable selection of peptides and always selected a higher number of peptides than the random selection algorithm.

Using the forward selection algorithm, we derived sequence Similarity Reduced (SR) datasets and used them in five-fold cross validation to evaluate the performance of our panel of MHC class II binding prediction tools. The results are shown in Table 4 under columns titled SR. Clearly, reducing sequence similarity had a significant impact on the observed classifier performance, which is consistent with previous findings [32]. At the same time, the order of performance of the different prediction methods was unchanged when using the reduced dataset, with NN-align performing the best, SMM-align second, ARB third, and PROPRED and the combinatorial libraries last. The order of performance determined by Spearman's rank correlation coefficient analysis (Additional file 1, Table S1) was largely identical except that ARB and PROPRED switched position. The largest drop in performance was observed for NN-align and SMM-align, where the average AUC value was reduced by 0.100 and 0.087 (0.151 and 0.130 in terms of Spearman's rank correlation coefficient) when tested with similarity reduced datasets, respectively. The smallest reduction was observed for PROPRED with an average AUC reduction of 0.023 (0.036 in terms of Spearman's rank correlation coefficient) followed by the combinatorial peptide library with a reduction in AUC of 0.060 (0.099 in terms of Spearman's rank correlation coefficient). As the latter two methods do not utilize the training dataset to make their prediction, it is expected that they show less of a drop in performance than the others. The fact that a reduction in performance was observed at all indicates that removing similar peptides from the testing set alone makes the prediction benchmark harder. This can be explained by the fact that homologous peptides removed because they are single residue substitutions of known epitopes or reference ligands are often 'easy' to predict, as they carry strong and straightforward signals to discover binding motifs.

Training with peptides of significant sequence similarity doesn't negatively influence the prediction of unrelated sequences

An important question arising from the sequence similarity reduction and cross validation evaluation is whether inclusion of similar sequences will have a negative impact on the prediction of unrelated sequences. An excessive amount of peptides with similar sequences may bias a classifier such that the performances on sequences without significant similarity to the training data are negatively influenced. This was demonstrated in [32] in which a classifier displayed better performance than others when evaluated on a dataset that contained similar sequences, but which completely failed when evaluated on a dataset with no homology between peptides. It is unclear though how relevant this finding is in practice, specifically as the inclusion of single residue substitutions can contain particularly useful information demonstrated by the fact that this is how the MHC binding motifs were originally defined [33].

We developed a simple strategy to test if the inclusion of homologous peptides in the training data can affect the prediction of unrelated peptides. For each allelic variant, we selected a subset of singular peptides (SP) set, which share no sequence similarity with any other peptides in the set (Figure 1). The similarity reduced (SR) set is a superset of the SP set, which in addition to the SP peptides also contains one peptide from each cluster of similar peptides. For each peptide in the SP set, there exist two blinded binding predictions obtained in the previous cross validations: One where the training set included all peptides including homologs (the ALL set), the other where only nonhomologous peptides were included in the training (the SR set). By comparing the performance of the two predictions, we evaluated if inclusion of homologous peptides in the training negatively impacts the prediction of non-homologous peptides. We performed this test on all implemented machine learning methods with similar results, and are showing the resulting AUCs for the top performing method NNalign in Table 5. On average, the performance of methods trained including homologues was higher than methods trained leaving out those peptides. While the difference is not significant (paired two tailed t-test, p-value = 0.259), this alleviates concerns for the tested methods that predictions will actually get worse when including homologous peptides in the training. Thus, it is advisable that the ultimate classifiers for public use should be trained using all available binding data.

A consensus approach of selected methods outperforms a generalized consensus approach and individual methods In our previous study, a median rank based consensus approach gave the best prediction performance. In this study, we updated the consensus approach with the new methods (NN-align and combinatorial peptide library) and evaluated its performance on the similarity reduced as well as entire dataset ( Table 4). The result showed that while the consensus method remains a competitive approach, it does not outperform the best available individual approach NN-align (paired one tailed t-test, p-value = 0.135) on the similarity reduced dataset.

We next investigated optimized approaches for deriving consensus predictions. We reasoned that simply increasing the number of methods included in a consensus prediction might not be optimal, especially if certain methods are underperforming, or simply if multiple methods are conceptually redundant (based on identical or similar approaches). To determine the benefit of including individual methods in the consensus, we tested the performance of the consensus approach while removing each of the five methods (Additional file 1, Table S3) using the similarity-reduced dataset. The results indicated that removing NN-align, SMM-align, the combinatorial peptide library and PROPRED reduced prediction performance. In contrast, removing ARB actually had a positive impact on consensus performance. Based on this, we tested the performance of a consensus approach on the SR dataset utilizing NN-align, SMMalign and the combinatorial library, or substituted PROPRED for the combinatorial library for those alleles for which it is not available (labeled consensus-best3). The resulting average AUC on the SR set (0.786) is significantly improved over consensus using all methods (paired, one sided t-test, p-value = 0.033). Also, the prediction performance of consensus-best3 in comparison to NN-align is significantly better in the SR set (paired, one sided t-test, p-value = 0.0034). When performance was measured with Spearman's rank correlation coefficient, very similar results were obtained though the performance of NN-align and consensus-best3 were virtually identical on the SR set. Thus, a combination of selected subsets of methods for a consensus could achieve better performance than the naïve consensus approach in which all methods were utilized. Figure 1 A Venn diagram illustrating the relationship among "ALL", "SR' and "SP" datasets. The simulated dataset illustrated the superset relationships among the "ALL", "SR" and "SP" sets. The "ALL" dataset contains all three peptides. The "SR" dataset contains two peptides with one of the similar peptide being removed and the "SP" dataset only contains a single peptide that shares no similarity with any other peptides.

Inclusion of the novel dataset into the IEDB and integration of the algorithms in the IEDB analysis resource

We have updated the MHC class II portion of the IEDB analysis resource http://tools.immuneepitope.org/analyze/html/mhc_II_binding.html to reflect the progress in data accumulation and algorithm development. There are now six algorithms available to predict MHC class II epitope: the previously established ARB, SMM-align and PROPRED methods, the newly established combinatorial library and NN-align predictions, and the combined consensus approach. The ARB algorithm has been reimplemented in Python to allow better integration with the website and future development. The machine learning based approaches (ARB, NN-align and SMM-align) have been retrained with the complete dataset described in this article to provide improved performance. The collection of algorithms has also been implemented as a standalone command line application that provides identical functionality as the website. This package can be downloaded from the IEDB analysis resource along with the MHC class II binding affinity datasets, the prediction scores, and the combinatorial peptide library matrices.

Discussion and Conclusions

Computational algorithms to predict epitope candidates have become an essential tool for genomic screens of pathogens for T cell response targets [34][35][36][37]. The majority of these algorithms rely on experimental The "ALL" column indicates 5-fold cross validation performance of this subset trained with entire dataset. The "SR" indicates 5-fold cross validation performance of this subset trained with sequence similarity reduced dataset. 1. AUC reduction = AUC all -AUC SR 2. # peptide reduction = # peptide all -# peptide SR 3. % peptide reduction = (# peptide all -# peptide SR)/# peptide all binding affinities to generate predictive models. The data presented in this study provides a large scale and homogenous dataset of experimental binding affinities for HLA class II molecules, along with a comprehensive evaluation of prediction performances for a number of algorithms. The binding dataset made available here is about four-fold larger than the one in our previous report [30]. The increased number of peptides per allele resulted in a significantly improved performance of machine learning methods, ARB and SMM-align. This reinforces the idea that the prediction performance of a machine learning method is greatly dependent on the amount of learning data available. This present dataset is not only significantly larger than what was previously available, but also for the first time covers HLA-DP and HLA-DQ molecules in depth. Lack of data for these alleles was identified in previous studies as one of the challenges facing HLA class II binding predictions [30,31]. The significant increase (i.e. over 40%) in the number of allelic variants results in a > 99% population coverage which could be very valuable for the development of T-cell epitope based vaccine. This dataset will also be useful in improving pan-like approaches that take advantage of binding pocket similarities among different MHC molecules to generate binding predictors for allelic variants without binding data [19].

We added two new methods to our panel of prediction algorithms. Combinatorial peptide libraries were used to experimentally characterize HLA class II alleles for which no PROPRED predictions were available. Data from such libraries have successfully been used to predict proteasomal cleavage [22], TAP transport [23] and MHC class I binding [24]. The performance of the libraries for class II predictions was comparable to that of PROPRED, and in general inferior to the machine learning approaches. The main value of the combinatorial library approach lies in its experimental efficiency, and in that its predictions can be considered completely independent of those from machine learning algorithms. The combinatorial library approach increases its value when combined with machine learning methods for consensus prediction approaches.

The second method added was NN-align, which showed a remarkably high prediction performance in the benchmark. This repeats the dominating performance of the related NetMHC prediction methods in a number of recent MHC class I prediction benchmarks [28,29,38].

One of the challenges for evaluating the MHC class II binding prediction performances is how to deal with the presence of homologous peptides in the available data [32]. One concern is that peptides in the testing set for which a homolog is present in the training data may lead to artificially high prediction performances. To address this, we generated sequence similarity reduced dataset from the entire available data using a forward selection approach such that no homologous peptides are present in the subset. The prediction performance on this similarity reduced dataset shows that the absolute AUC values of the compared methods is indeed significantly lower than that of the entire dataset. However, the rank-order of the different prediction methods was largely unchanged between datasets. This leads us to conclude that 1) the impact of homologous peptides shared between training and testing datasets has a minor impact on rankings of prediction methods at least for large scale datasets, but should nevertheless be corrected for. 2) Prediction performance comparisons between different methods cannot be made based on absolute AUC values unless both training and testing datasets are identical.

A second concern when dealing with homologous peptides in the training dataset is that the presence of a large number of similar peptides may bias the classifier such that the prediction performance of unrelated peptides is negatively affected. We performed a direct comparison of the predictive performance on novel peptides based on classifiers trained in the presence and absence of similar peptides. The comparison showed that there is a performance gain for classifiers trained with the larger dataset including similar peptides. Thus we recommend that classifiers created for end user applications should be trained with all available data to gain maximum predictive power for epitope identification.

Constructing meta-classifiers is a popular approach to improve predictive performance. We previously reported a median rank based consensus approach that outperforms individual MHC class II binding prediction methods. With the addition of new methods, we found that consensus methods including all available methods failed to outperform the best available individual method. On the other hand, when only methods that contributed positively to the consensus approach were included, the consensus approach outperformed the best individual method (0.786 vs. 0.782) on the "SR" dataset. The absolute values of improved average AUC is much smaller than that was reported in our previous study (0.004 vs. 0.033). This suggested that simple median rank based approach is less effective as individual method's performance improves and more sophisticated consensus approaches are needed to capitalize on a large array of MHC class II binding prediction methods. Also, the best individual method (NN-align) still outperformed the consensus with selected methods when they were tested with the "ALL" dataset. Since there are significant peptide similarities in the "ALL" dataset, this could be due to overfitting. We plan to systematically examine how to best construct consensus predictions for MHC binding in the future, building on work done by us and others in the past [30,39,40].

Methods

Positional scanning combinatorial libraries and peptide binding assays

The combinatorial libraries were synthesized as previously described [24,41]. Peptides in each library are 13-mers with Alanine residues in positions 1, 2, 12 and 13. The central 9 residues in the peptides are equal mixtures of all 20 naturally occurring residues except for a single position per library which contains a fixed amino acid residue. A total of 180 libraries were used to cover all possible fixed residues at all positions in the 9-mer core. The IC50 values for an example peptide library (HLA-DPA1*0103-DPB1*0201) are shown in Additional file 1, Table S4.

The binding assay methods for MHC class II molecules in general [42,43] as well as HLA-DP [44] and HLA-DQ [45] molecules have been described in detail previously.

Deriving scoring matrix for positional scanning combinatorial peptide libraries IC50 values for each mixture were standardized as a ratio to the geometric mean IC50 value of the entire set of 180 mixtures, and then normalized at each position so that the value associated with the optimal value at each position corresponds to 1. For each position, an average (geometric) relative binding affinity (ARB) was calculated, and then the ratio of the ARB for the entire library to the ARB for each position was derived. The final results are a set of 9 × 20 scoring matrices were used to predict the binding of novel peptides to MHC molecules by multiplying the matrix values corresponding to the sequence of 9-mer cores in the peptide of interest. An example scoring matrix (HLA-DPA1*0103-DPB1*0201) is shown in Additional file 1, Table S5.

Generation of similarity reduced datasets for cross validation

Several previous studies have proposed measurements to determine peptide similarity [32,[46][47][48][49]. Here we adopted the similarity measure described by El-Manzalawy et al. [32]. Two peptides were defined as similar if they satisfied one of the following conditions: (1) The two peptides share a 9-mer subsequence. (2) The two peptides have more than 80% sequence identity. The sequence identity was calculated as follows. For peptide p1 with length L 1 and peptide p2 with length L 2 , all non-gap alignments between p1 and p2 were examined. The number of identical residues in each alignment was compared and the maximum M was taken as the number of identical residues between the two peptides. The sequence identity was then calculated as M/min(L 1 , L 2 ).

In order to derive the similarity reduced (SR) dataset, we first partitioned the dataset into binder and non-binder using an IC 50 cutoff of 1000 nM. The cutoff of 1000 nM was chosen for its biological relevance as a previous study showed that a cutoff of 1000 nM captured near 97% DR-restricted epitopes [50]. For each peptide in a partition, we first determined its similarity with the rest of peptides in the dataset and the number of peptides sharing similarity with each peptide (N similarity ) was recorded. We then sorted the peptides according to their N similarity in ascending order and stored the sorted peptides in a list L all . The forward step-wise Hobohm 1 algorithm [51] consisting of the following three steps was next applied to generate a similarity reduced:

2. The peptide on top of L all (P top ) is removed from L all and compared with all peptides in Set SR . If the peptide P top is not similar with any peptide in Set SR , then P top is stored in Set SR otherwise P top is discarded.

3. Repeat step 2 until L all is empty. The peptides selected by this procedure for the binder and non-binder partitions were then combined to generate the final SR dataset.

In order to test whether the inclusion of homologous peptides in the training data can affect the prediction of unrelated peptides, we generated a singular peptides (SP) set. For each allelic variant, we selected a subset of peptides, which share no sequence similarity with any other peptides in the set.

The three sets of peptides used in the study have a simple superset relationship in that the "ALL" set is a superset of "SR" set and the "SR" set is a superset of the "SP" set. The relationship was further illustrated in Figure 1.

Cross validation and performance evaluation with ROC

Two types of performance evaluation were carried out. For the combinatorial library and the PROPRED predictions which are not trained on peptide binding data, the entire dataset was used to measure prediction performance. For the ARB, SMM-align and NN-align predictions which require peptide binding data for training, five-fold cross validations were performed to measure classifier performance. For the consensus approach, the predictions were generated for each method as described above and then combined to generate the consensus.

Receiver operating characteristic (ROC) curves [52] were used to measure the performance of MHC class II binding prediction tools. For binding assays, the peptides were classified into binders (experimental IC 50 < 1000 nM) and nonbinders (experimental IC 50 ≥1000 nM) as described previously [30]. For a given prediction method and a given cutoff for the predicted scores, the rate of true positive and false positive predictions can be calculated. An ROC curve is generated by varying the cutoff from the highest to the lowest predicted scores, and plotting the true positive rate against the false positive rate at each cutoff. The area under ROC curve is a measure of prediction algorithm performance where 0.5 is random prediction and 1.0 is perfect prediction. The plotting of ROC curve and calculation of AUC were carried out with the ROCR [53] package for R [54]. In addition, the predictive performance was also evaluated via Spearman's rank correlation coefficient.

Additional material

Additional file 1: Supplementary Tables. Description: five supplementary tables that contain additional analysis described in the paper.

871 0.777 0.932 0.830 0.804 0.779 0.867 0.756 0.956 0.847 0.942 0.850 0.946 0.854 1. SR 1 stands for similarity reduced.

Table 1
1Overview of human MHC class II loci, allele and polymorphism.Locus
Gene
Chain
# of alleles

HLA-DP
HLA-DPA1
alpha
28

HLA-DP
HLA-DPB1
beta
138

HLA-DQ
HLA-DQA1
alpha
35

HLA-DQ
HLA-DQB1
beta
108

HLA-DR
HLA-DRA
alpha
3

HLA-DR
HLA-DRB1
beta
785

HLA-DR
HLA-DRB2
beta
1

HLA-DR
HLA-DRB3
beta
52

HLA-DR
HLA-DRB4
beta
14

HLA-DR
HLA-DRB5
beta
19

HLA-DR
HLA-DRB6
beta
3

HLA-DR
HLA-DRB7
beta
2

HLA-DR
HLA-DRB8
beta
1

HLA-DR
HLA-DRB9
beta
1

Table 2
2Overview of MHC class II binding dataset utilized in the present study.Allelic variant
# of binding affinities
# of binders 1
% of binders
Allele frequency 2

HLA-DPA1*0201-DPB1*0101
1399
702
0.5
16.0

HLA-DPA1*0103-DPB1*0201
1404
635
0.45
17.5
HLA-DPA1*01-DPB1*0401
1337
540
0.4
36.2

HLA-DPA1*0301-DPB1*0402
1407
621
0.44
41.6

HLA-DPA1*0201-DPB1*0501
1410
528
0.37
21.7

HLA-DQA1*0501-DQB1*0201
1658
742
0.45
11.3

HLA-DQA1*0501-DQB1*0301
1689
1023
0.61
35.1

HLA-DQA1*0301-DQB1*0302
1719
670
0.39
19.0

HLA-DQA1*0401-DQB1*0402
1701
731
0.43
12.8

HLA-DQA1*0101-DQB1*0501
1739
687
0.4
14.6
HLA-DQA1*0102-DQB1*0602
1629
974
0.6
14.6

HLA-DRB1*0101
6427
4519
0.7
5.4

HLA-DRB1*0301
1715
553
0.32
13.7

HLA-DRB1*0401
1769
978
0.55
4.6

HLA-DRB1*0404
577
396
0.69
3.6

HLA-DRB1*0405
1582
806
0.51
6.2

HLA-DRB1*0701
1745
1033
0.59
13.5

HLA-DRB1*0802
1520
591
0.39
4.9
HLA-DRB1*0901
1520
815
0.54
6.2

HLA-DRB1*1101
1794
957
0.53
11.8

HLA-DRB1*1302
1580
656
0.42
7.7

HLA-DRB1*1501
1769
909
0.51
12.2

HLA-DRB3*0101
1501
426
0.28
26.1

HLA-DRB4*0101
1521
654
0.43
41.8

HLA-DRB5*0101
1769
992
0.56
16.0
H-2-IAb
660
180
0.27
-

Total
44541
22318

Min
577
180

Max
6427
4519

DP
92.6

DQ
81.6

DRB1
71.0

DRB3/4/5
70.9

Total
99.9

1. Binder defined as IC50 <1000 nM.
2. Average haplotype and phenotype frequencies for individual alleles are based on data available at dbMHC. dbMHC data considers prevalence in Europe, North
Africa, North-East Asia, the South Pacific (Australia and Oceania), Hispanic North and South America, American Indian, South-East Asia, South-West Asia, and Sub-
Saharan Africa populations. DP, DRB1 and DRB3/4/5 frequencies consider only the beta chain frequency, given that the DRA chain is largely monomorphic, and
that differences in DPA are not hypothesized to significantly influence binding. Frequency data are not available for DRB3/4/5 alleles. However, because of
linkage with DRB1 alleles, coverage for these specificities may be assumed as follows: DRB3 with DR3, DR11, DR12, DR13 and DR14; DRB4 with DR4, DR7 and
DR9; DRB5 with DR15 and DR16. Specific allele frequencies at each B3/B4/B5 locus is based on published associations with various DRB1 alleles, and assumes
only limited variation at the indicated locus.

Table 4
4Cross validation prediction performances of all methods on complete and similarity reduced datasets measured with AUC.Allelic variant
ARB
SMM-align PROPRED
combinatorial library NN-align
Consensus
Consensus-best3 2

ALL SR 1 ALL SR 1 ALL SR 1 ALL
SR 1
ALL SR 1
ALL SR 1
ALL
SR 1

Table 5
5Prediction performance on singular peptide set (SP) using training sets with and without homologs.Allelic variant
SR
AUC

ALL
AUC

AUC reduction 1
# peptide reduction 2
% peptide reduction 3

HLA-DPA1*0103-DPB1*0201
0.787
0.797
0.010
801
0.571

HLA-DPA1*01-DPB1*0401
0.809
0.801
-0.008
797
0.596

HLA-DPA1*0201-DPB1*0101
0.764
0.735
-0.029
795
0.568

HLA-DPA1*0201-DPB1*0501
0.587
0.640
0.053
824
0.584

HLA-DPA1*0301-DPB1*0402
0.744
0.772
0.028
805
0.572

HLA-DQA1*0101-DQB1*0501
0.850
0.821
-0.029
1155
0.664

HLA-DQA1*0102-DQB1*0602
0.667
0.719
0.052
1036
0.636

HLA-DQA1*0301-DQB1*0302
0.569
0.756
0.187
1123
0.653

HLA-DQA1*0401-DQB1*0402
0.632
0.551
-0.081
1116
0.656

HLA-DQA1*0501-DQB1*0201
0.587
0.652
0.065
1069
0.645

HLA-DQA1*0501-DQB1*0301
0.764
0.766
0.002
1087
0.644

HLA-DRB1*0101
0.777
0.781
0.004
2923
0.455

HLA-DRB1*0301
0.782
0.786
0.004
579
0.338

HLA-DRB1*0401
0.682
0.709
0.027
548
0.310

HLA-DRB1*0404
0.805
0.818
0.013
103
0.179

HLA-DRB1*0405
0.765
0.748
-0.017
533
0.337

HLA-DRB1*0701
0.793
0.810
0.017
570
0.327

HLA-DRB1*0802
0.672
0.622
-0.050
503
0.331

HLA-DRB1*0901
0.669
0.651
-0.018
478
0.314

HLA-DRB1*1101
0.809
0.799
-0.010
590
0.329

HLA-DRB1*1302
0.712
0.733
0.021
510
0.323

HLA-DRB1*1501
0.712
0.719
0.007
598
0.338

HLA-DRB3*0101
0.829
0.838
0.009
514
0.342

HLA-DRB4*0101
0.762
0.745
-0.017
510
0.335

HLA-DRB5*0101
0.774
0.798
0.024
571
0.323

H-2-IAb
0.816
0.833
0.017
114
0.173

Average
0.737
0.748
0.011
779
0.444

Wang et al. BMC Bioinformatics 2010, 11:568 http://www.biomedcentral.com/1471-2105/11/568 Page 9 of 12
AcknowledgementsThis work was supported by NIH contracts HHSN26620040006C and HHSN272200700048C.Submit your next manuscript to BioMed Central and take full advantage of:
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10.1186/1471-2105-11-568Team RDC: R: A Language and Environment for Statistical Computing. Vienna, AustriaTeam RDC: R: A Language and Environment for Statistical Computing. Vienna, Austria 2006. doi:10.1186/1471-2105-11-568

Peptide binding predictions for HLA DR, DP and DQ molecules. Wang, BMC Bioinformatics. 11568Cite this article asCite this article as: Wang et al.: Peptide binding predictions for HLA DR, DP and DQ molecules. BMC Bioinformatics 2010 11:568.