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Understanding and predicting binding between human leukocyte antigens (HLAs ) and peptides by network analysis. Luo H, Ye H, Ng H, Shi L.
Table of contents
- SignalP-5.0 Server
- ExPASy: SIB Bioinformatics Resource Portal - Visual Guidance
- Custom Peptide Synthesis
- 1 Introduction
In all the hypotheses except the danger hypothesis, the HLAs on the surface of antigen-presenting cells APCs present peptides to T-cell receptors TCRs on the surfaces of T-cells and the drug molecules interfere with the system through covalent binding to the peptides, instable interaction, or insertion into the binding grooves of HLAs. Ultimately, it is beneficial to predict ADR occurrences of drugs before patients take the drugs.
However, the mechanisms for ADRs are complicated and each of the players in the system has a large number of variations in their structures, making it very challenging to study HLA related ADRs. Our previous molecular modeling study showed the drug-HLA binding prediction was improved when the binding peptide was incorporated in the modeling system [ 19 ].
Therefore, better understanding and accurately predicting HLA-peptide binding could facilitate predicting ADRs related to genetic predisposition. However, lacking enough experimental HLA-peptide binding data to train machine learning models for many HLAs limits the capability of this approach. In addition, a significant part of machine learning models uses parameters that are derived from peptides with the same length but experiments showed the same HLA can bind peptides with different lengths, making predicting HLA-peptide binding using these methods very challenging.
New methods for accurately predicting HLA-peptide binding that overcome the challenges of the reported machine learning models are in urgent need. Therefore, in this study we conducted network analysis to understand the binding characteristics between HLAs and peptides and developed a new method named neighbor-edges based and unbiased leverage algorithm Nebula to predict HLA-peptide binding. Through analyzing the HLA-peptide binding network, we identified nine modules that are densely connected regions in the network [ 23 ].
Modularity is the measurement of goodness of a division of a network into modules [ 24 ] and was used to yield nine modules. The modularity of the real HLA-peptide binding network was compared to the modularity values yielded from random networks.
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Peptides and HLAs in the same modules shared similar properties. We further developed Nebula to predict HLA-peptide binding. To our best knowledge, this study is the first one to use network analysis for understanding and predicting HLA-peptide binding.
An overview of this study's workflow is shown in Figure 1. We first harvested qualitative Class I HLA-peptide binding data from four major databases that collected and curated HLA binding assays from the literature. The HLA-peptide binding network was then constructed from the harvested data. Thereafter, the fast greedy modularity optimization algorithm was used to identify modules.
The modularity analysis on 1, randomly generated networks was conducted to verify that the modules yielded from the HLA-peptide binding network could not be generated by chance. Finally, we implemented Nebula to make predictions and evaluated its performance via leave-one-out LOO validations.
- Download Analysis, Networks, Peptides 1985.
- Original Research ARTICLE.
- PRIMA 2013: Principles and Practice of Multi-Agent Systems: 16th International Conference, Dunedin, New Zealand, December 1-6, 2013. Proceedings.
A flowchart of this study's workflow. The HLA-peptide binding network was generated from the binding data.
ExPASy: SIB Bioinformatics Resource Portal - Visual Guidance
The modularity analysis was then conducted on the HLA-peptide binding network and the 1, randomly generated networks. Finally, Nebula was used to predict HLA-peptide binding. For IEDB data, "positive-high", "positive-intermediate" and "positive-low" were all considered as positives. For AntiJen data, the "weak binders" are considered as negatives according to the paper's description [ 28 ].
We harvested the qualitative binding data of Class I HLA-peptide binding assays from the four databases by Aug 25, and combined all data into a single dataset. The dataset contains three columns: HLA, peptide and binding category positive or negative. For an HLA-peptide pair with multiple entries in the databases, we calculated the proportion of positives.
If the proportion is larger than or equal to 0. We removed the peptides and HLAs that contain only one binding datum for two reasons: 1 the datum may be in low quality because only the HLA or the peptide shows binding among such a large number of peptides or HLAs; and 2 it would not be able to be predicted by Nebula because no data for the HLAs or peptides could be used for the predictions in the LOO validations. The data filtering process was run through several iterations to make sure all the peptides and HLAs had more than one binding datum. To enable the calculations in the network analysis, we used 2 and 1 to represent positive and negative, respectively.
Finally, the HLA-peptide binding network was constructed using the igraph package version 0. We used the fast greedy modularity optimization algorithm by Clauset et al. This algorithm is well-known for its advantage to fast detect modules from large networks [ 29 ]. To examine whether the yielded modules really have binding characteristics for HLAs and peptides or could be generated by chance, we generated 1, random networks using three criteria: 1 the network topology, both nodes and edges, in the random networks remain the same as in the real HLA-peptide binding network; 2 only weights positive or negative were randomly shuffled, while keeping the same amount of positives and negatives; and 3 the modules from random networks were generated using the same algorithm and parameters.
The modularity values [ 24 ] were then compared between the real HLA-peptide binding network and its randomly permutated networks. The modules yielded from modularity analysis of the HLA-peptide binding network were compared in terms of both the HLAs and the peptides.
Chelvanayagam [ 8 ] identified a uniform list of HLA residues that specifically interact with each amino acid AA position in 9-mer peptides. For each AA position, we extracted the corresponding residues from all the HLA sequences and put them together as position-specific pseudo-sequences. The pairwise sequence identities of the pseudo-sequences were calculated using Clustal Omega 1.
Custom Peptide Synthesis
Nebula was developed through modification of the collaborative filtering algorithm [ 32 ] and the network-based inference NBI method [ 33 , 34 ]. The HLA-peptide binding data can be constructed into a weighted bipartite network, where an edge is drawn between HLA h i and peptide p x if there is a binding datum positive or negative between them. The edge weight is given by w h i , p x in equation 1. If HLA h i and peptide p x do not have a binding datum, a prediction value for the edge between h i and p x can be calculated from the edges neighboring to the edge in prediction using equations 2 Likewise, P p x , h i can be calculated using equation 4.
Nebula treats the contributions from the edges connected to the two nodes of the edge equally in prediction, that is P h i , p x and P p x , h i.
Therefore, the final prediction value between HLA h i and peptide p x as F h i ,p x is calculated using equation 6. F h i ,p x is a continuous value which is converted into a categorical prediction value C h i ,p x in Nebula using the unbiased leverage UL as presented by equation 7. Since we assigned the weights for positive binding as 2 and negative as 1, the UL was set to be 1.
To evaluate the performance of Nebula, we used LOO validations. Each of the edges was taken out one at a time from the HLA-peptide binding network, and the remaining network was used to predict the weight of the taken-away edge. A receiver operating characteristic ROC curve was generated using the continuous final prediction values F h i ,p x against the binding labels using AUC package in R version 0. Sensitivity, specificity and accuracy were calculated by comparing the categorical prediction values C h i ,p x against the labels determined from HLA-peptide binding assays.
We did a similar evaluation for NBI method [ 34 ] as a comparison. The author of NBI, Dr. Feixiong Cheng, provided the NBI codes to us. Two-fold cross-validations were also conducted to eliminate potential over-fitting from the LOO validations. Each time the entire HLA-peptide binding network was randomly divided into two even portions and each portion was used to predict HLA-peptide binding in the other portion.
We ran iterations and calculated the sensitivity, specificity, accuracy and area under the ROC curve AUC values to measure the performance of Nebula. After data pre-processing, we obtained , binding data points Nine modules were identified from the HLA-peptide binding network using the fast greedy modularity optimization algorithm as shown in Figure 2a. A modularity value of 0. The calculated results of the peptides and HLAs in the nine modules are given in Table 1. Results of modularity analysis. Nine modules were generated from the HLA-peptide binding network and plotted via Cytoscape 3.
The HLAs are shown in red, peptides in cyan and edges in grey. Modularity values of 1, randomly permutated networks are given in the histogram b. As a comparison, the modularity value yield from the real HLA-peptide binding network is indicated by the red arrow. Using the same modularity analysis algorithm, we analyzed the 1, randomly generated networks.
The 1, modularity values were plotted as a histogram shown in Figure 2b. All the 1, modularity values are lower than the modularity yielded from the real HLA-peptide binding network the red arrow in Figure 2b , indicating the nine modules harvested by the algorithm is not likely some result obtained by chance.
In order to discover potential signals buried in the nine modules, we analyzed the peptide and HLA properties across the modules. In this modularity comparison, we used very strict criteria to generate the random networks not even altering the topology of the original network. Another way is to generate random networks by reconnecting edges while keeping the same amount of nodes, positive and negative edges, which resulted in modularity values with even larger differences data not shown.
The distributions of peptides and HLAs in the nine modules are listed in Table 1. Modules and show a higher specificity on peptide lengths while module 4 is a mixture of 8-mers, 9-mers and mers. The results indicate peptides in different modules may have different binding interactions with HLAs. Modules are the major modules that contain more than 1, peptides and 9-mers are the majority of peptides We further analyzed 9-mer peptides across these modules to explore HLA-peptide binding characteristics of the modules.
According to Hong et al. For each module, we categorized the AA residues within the 9-mer peptides into the three groups from Position 1 to 9. The result is shown in Figure 3. The distribution of amino acid residues by position from 9-mer peptides in each module. Each bar chart shows the 9-mer peptides from a module indicated at the top of the bar chart.
The x-axis marks P1 to P9 represent positions 1 to 9 in 9-mers. At each position, 20 possible amino acid residues were categoried into three groups: polar charged green , polar uncharged red and apolar blue. The percentages of the three groups are shown at each position. We found the distributions of AA residue categories are similar across different modules for most positions.
However, two positions showed very distinct characteristics across modules.