Fig 1.
Example of AIMS encoding for the analysis of TCR-peptide interactions.
(A) Rendering of a specific TCR-pMHC interaction (PDB ID: 1OGA) with TCRα shown in blue, TCRβ in orange, MHC in white. (B) Inset shows a zoom in on this TCR-peptide interface, with the MHC now translucent. Representative AIMS encoding of the single TCR CDR3β sequence (C, central-encoding) or the peptide sequence (D, bulge-encoding) in panel A. Below these single encodings are examples of full TCR repertoire (C) or immunopeptidome (D) AIMS-encoded matrices. Each amino acid in the structures, the single encoded sequences, and the matrices is represented by a unique color.
Fig 2.
Comparison of the purity of receptor clustering using paired chain (left) or single chain CDR3B (right) data.
Dimensionality reduction using UMAP, followed by density based OPTICS clustering subsects the data into biophysically similar paired chain (A) and single chain (B) receptors. The first ten of these clusters are then re-visualized in their AIMS-encoded matrix, with black lines marking different clusters (C, D). The antigen specificity of each of these clusters is then quantified by percentages (E, F), with the colors corresponding to specific peptides as shown in the key.
Fig 3.
Isolation of individual sequence clusters and subsequent position-sensitive biophysical characterization of these sequences highlights the details provided by AIMS analysis.
A subset of clusters identified in Fig 2 are isolated and shown as their AIMS matrix encoding for the paired chain (A) and single-chain (B) datasets. From these encodings, we can calculate the position sensitive biophysical properties for each cluster (C, D). Opaque lines with dots represent the averages over each cluster, while wider translucent regions centered on these lines give the variance as calculated by a bootstrapping procedure (Methods). Statistically significant differences (p < 0.05, calculated via non-parametric permutation test) are denoted by asterisks, with an asterisk over a solid bar representing extended regions of statistically significant differences.
Fig 4.
Statistical and information theoretic AIMS analysis of Influenza A- and Ebolavirus-derived peptides.
(A) Position sensitive amino acid frequency difference between the two peptide datasets. (B) Position sensitive Shannon entropy quantification and encoding coverage of the two peptide datasets. Statistically significant differences in the entropy (p < 0.05, calculated via non-parametric permutation test) are denoted by asterisks. The coverage applies to all position sensitive metrics, and highlights that differences in the entropy and mutual information at positions 5 and 9 are largely due to differences in coverage. (C) Position sensitive mutual information difference between the two peptide datasets. (D) Di-gram amino acid frequency difference calculated between the two peptide datasets. In all difference plots, a deeper shade of purple represents a higher quantity for the HLA-A*02 presented Influenza A peptide dataset, while a deeper shade of orange represents a higher quantity for the HLA-B*15 presented Ebolavirus peptide dataset. Raw distributions for each individual dataset (S9 Fig) and identification of statistically significant regions (S10 Fig) can be found in the supporting information.
Fig 5.
Comparison of AIMS TCR clustering analysis to GLIPH results.
(A) The Influenza-reactive TCRs identified by Glanville et al. [26] are encoded into an AIMS matrix using the bulge-encoding. (B) Each sequence is then processed using the standard AIMS pipeline, and then projected onto two dimensions using UMAP and clustered using the DBSCAN algorithm. (C) These clusters are then re-visualized as an AIMS matrix. (D) Finally, the motifs identified by GLIPH can be directly compared to the motifs identified by AIMS via the clustering in panel C. Biophysical properties of each amino acid in the motif are colored according to the key, and an “X” in the AIMS motif represents “any amino acid with this biophysical property”, i.e. the orange “X” can represent S, T, G, or A.
Fig 6.
Quantitative comparison of distance metrics used in AIMS and TCRdist.
Both the CDR3-only sequences (A) and the full-CDR sequences (B) of Dash et al. [24] are encoded into AIMS matrices. Sequence distances calculated via TCRdist and AIMS are then directly compared for these CDR3-only sequences (C) or full-CDR sequences (D) for the TCRα- and β-chains. Correlation coefficients between these distance metrics are reported for the full set of sequences and for closely related sequences, which are delineated by the dashed vertical lines.