Bridging immunogenetics and immunoproteomics: Model positional scanning library analysis for Major Histocompatibility Complex class II DQ in Tursiops truncatus

Abstract

The Major Histocompatibility Complex (MHC) is a critical element in mounting an effective immune response in vertebrates against invading pathogens. Studies of MHC in wildlife populations have typically focused on assessing diversity within the peptide binding regions (PBR) of the MHC class II (MHC II) family, especially the DQ receptor genes. Such metrics of diversity, however, are of limited use to health risk assessment since functional analyses (where changes in the PBR are correlated to recognition/pathologies of known pathogen proteins), are difficult to conduct in wildlife species. Here we describe a means to predict the binding preferences of MHC proteins: We have developed a model positional scanning library analysis (MPSLA) by harnessing the power of mixture based combinatorial libraries to probe the peptide landscapes of distinct MHC II DQ proteins. The algorithm provided by NNAlign was employed to predict the binding affinities of sets of peptides generated for DQ proteins. These binding affinities were then used to retroactively construct a model Positional Scanning Library screen. To test the utility of the approach, a model screen was compared to physical combinatorial screens for human MHC II DP. Model library screens were generated for DQ proteins derived from sequence data from bottlenose dolphins from the Indian River Lagoon (IRL) and the Atlantic coast of Florida, and compared to screens of DQ proteins from Genbank for dolphin and three other cetaceans. To explore the peptide binding landscape for DQ proteins from the IRL, combinations of the amino acids identified as active were compiled into peptide sequence lists that were used to mine databases for representation in known proteins. The frequency of which peptide sequences predicted to bind the MHC protein are found in proteins from pathogens associated with marine mammals was found to be significant (p values <0.0001). Through this analysis, genetic variation in MHC (classes I and II) can now be associated with the binding repertoires of the expressed MHC proteins and subsequently used to identify target pathogens. This approach may be eventually applied to evaluate individual population and species risk for outbreaks of emerging diseases.

Fig 1. Method for model positional scanning library analysis (MPSLA).

Nine steps that take the researcher from genetic sequence data, through MHC binding analysis, to protein and pathogen prediction.

Fig 2. Predicted binding affinities of peptide sequences derived from proteins encoded by cetacean MHC II DQ alleles.

Binding affinities of 7,634 peptide sequences predicted by NNAlign were compared by counting the number of peptides below 100, 500, 1000, 5000 and 10,000nM thresholds. The algorithm was supplied with a 7,647 amino acid sequence and the DQA and B protein sequences from cetaceans (killer whale, sperm whale, finless dolphin) obtained from Genbank and from bottlenose dolphins in the Indian River Lagoon (IRL) and adjacent Atlantic coast (ATL) (DQ1-1; Protein derived from DQA 1*01 DQB 1*01 DQ1-8; Protein derived from DQA 1*01 DQB 1*08 DQ1-10; Protein derived from DQA 1*01 DQB 1*10 and DQ2-4; Protein derived from DQA 1*02 DQB 1*04).

Fig 3. Distribution of active sequences derived from dolphin DQ proteins from the IRL.

Sequences with a binding affinity below 10,000nM for each of the datasets for the four proteins (DQ1-1, DQ1-8, DQ1-10 and DQ2-4) from dolphins in the Indian River Lagoon and adjacent Atlantic coast were compiled and the frequency of sequences identified uniquely binding to one protein or shared by 2, 3 or all 4 proteins is shown.

Fig 4. Comparison of amino acid ranking from physical and model positional scanning libraries.

Binding affinities for the (19 x 9 = 171) mixtures obtained from the screening of the physical protein or modeled protein encoded by HLA-DP2 (HsDPA1*0103, HsDPB1*0201) were ranked from 1–19, where 1 represents the lowest value and therefore highest binding affinity. Correlations were performed on scatterplots, Coefficients of determination (r²) derived from Pearson coefficients (r) are recorded in upper right corner.

Fig 5. Comparison of rankings for dolphin (standard) to four IRL dolphins and 3 other cetaceans.

Correlations were performed on scatterplots of amino acid ranking obtained from our standard DQ protein of Bottlenose dolphin (Tursiops truncatus), with each of the four proteins found in the IRL (DQ1-1, DQA 1–8, DQA1-10 and DQ2-4). Correlations were also performed comparing the amino acid ranking from DQ proteins for standard bottlenose dolphin to those of killer whale (Orcinus orca), Yangtze finless porpoise (Neophocaena phocaenoides) and sperm whale (Physeter microcephalus). Coefficients of determination (r²) derived from Pearson coefficients (r) are plotted for each of the 9 positions of the MPSLs.

Fig 6. Comparison of amino acid ranking from MPSLs derived for DQ1-10 and DQ2-4 in dolphin.

Predicted binding affinities for the (20 x 9 = 180) mixtures obtained from the model positional scanning libraries derived from proteins encoded by dolphin alleles DQA1*01DQB 1*10 (DQ1-10) and DQA1*02 DQB1*04 (DQ2-4) were ranked from 1–20, where 1 represents the lowest value and therefore highest binding affinity. Correlations were performed on scatterplots, coefficients of determination (r²) derived from Pearson coefficients (r) are recorded in upper right corner. Vertical and horizontal lines demark amino acids ranked below 5.

Fig 7. Selection of amino acids for combination sequences.

(A) To determine the highest ranked amino acids selective for, or common to MPSLs for 3 IRL dolphin proteins (DQ1-08 □, DQ1-10 Δ and DQ2-04 ○), each of the 9 positions the ranking data for all 3 MPSLs were superimposed on a single graph (sample graph for position 2 is shown). (B). The top 5 amino acids at each position were given a value 1–3 depending on their rank in the composite graph For example, Tyrosine (Y) in position 2 would be assigned a value of 1 for DQ2-4, 2 in DQ1-10, and is not in the top 5 for DQ1-8; Methionine (M) would be given a value of 1 for both DQ1-8 and DQ1-10. (C) Combinations of the common or most selective amino acids amino acids used to generate sequences a total of 13,392 sequences were generated.

Fig 8. Relative occurrence of genera in UniProt database and in current search for marine related pathogens.

A. Percentage of entries for named genera in the database compared to the percentage of sequences identified in named genera derived from MPSLA. B. Fold increase in identification of named genera.

Fig 9. Frequency of amino acids at each position of a decamer peptide sequence.

The frequency of each of the 20 amino acids (single letter code) was determined for each of the 10 positions of the peptide (one symbol for each position) from (A) a list of 616 sequences designed to have near equal distribution or (B) a list of 616 peptides derived from viral proteins.