Is T Cell Negative Selection a Learning Algorithm?
Abstract
:1. Introduction
2. Results
2.1. Problem Definition and Model Design
2.2. An Artificial Immune System Discriminates Self from Foreign after Negative Selection
2.3. Discrimination Relies on Moderate Cross-Reactivity and Sequence Dissimilarity
2.4. Sequence Similarity Hampers Discrimination between Self- and Foreign Peptides
2.5. Selection on Non-Random Peptides Greatly Improves Self-Foreign Discrimination
3. Materials and Methods
3.1. Data and Code Availability
3.2. Simulation of Negative Selection
- Generation of an unbiased TCR repertoire containing all possible motifs of length 6. For details, see Repertoire model of negative selection (Appendix A.2).
- Selection of a training set of either n English strings or n self peptides. See Sequences (Appendix A.1) for details on the sequences used, and Training set selection (Appendix A.3) for details on the manners in which training sets are sampled. The training set selection method was random unless mentioned otherwise in the figure legend. The value of n can also be found in the figure legend.
- Negative selection of TCRs on the training set. All TCR motifs that match any of the training sequences in at least t adjacent positions are removed from the repertoire. Unless mentioned otherwise, negative selection was performed with an affinity threshold t = 3 for strings and t = 4 for peptides (see figure legends). All TCRs that remain make up the post-selection repertoire. For details on computational methods, see Repertoire model of negative selection (Appendix A.2).
- Analysis of the recognition of test sequences by the post-selection repertoire. Test sets always consist of “unseen” sequences that were not part of the training set used for negative selection. See figure legends for details on the number and source of the test sequences used. See Post-selection repertoire analysis (Appendix A.5) for details on specific analysis metrics used.
3.3. Supporting Methods
4. Discussion
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
AA | Amino acid |
AIS | Artificial immune system |
ANN | Artificial neural network |
HCMV | Human cytomegalovirus |
HIV | Human immunodeficiency virus |
MHC | Major histocompatibility complex |
SD | Standard deviation |
SEM | Standard error of the mean |
TCR | T cell receptor |
Appendix A. Supplementary Methods
Appendix A.1. Sequences
Strings
Peptides
Organism | Proteome Details | Proteins | ID | Download Date (d/m/y) | Unique 6-mers (#) |
---|---|---|---|---|---|
Ebola virus | Mayinga, Zaire, 1976 | 9 | UP000007209 | 27/09/2017 | 140 |
Human cyto- megalovirus (HCMV) | Human herpesvirus 5 AD169 Isolate Unknown X17403 | 190 | UP000008991 | 27/09/2017 | 2090 |
Hepatitis B virus | Genotype D subtype ayw (isolate France/Tiollais/1979) | 7 | UP000007930 | 27/09/2017 | 65 |
Hepatitis C virus | H77 isolate Unknown AF009606 | 2 | UP000000518 | 27/09/2017 | 112 |
Human immuno- deficiency virus (HIV) | Type 1 group M subtype B (isolate HXB2) | 9 | UP000002241 | 27/09/2017 | 69 |
Vaccinia virus | Strain Copenhagen | 257 | UP000008269 | 27/09/2017 | 1955 |
Zika virus | MR 766 Isolate Unknown AY632535 | 1 | UP000054557 | 27/09/2017 | 118 |
Listeria monocytogenes | serovar 1/2a (strain ATCC BAA-679/EGD-e ) | 2844 | UP000000817 | 27/09/2017 | 31,251 |
Plasmodium ovale (Malaria) | Wallikeri | 8636 | UP000078550 | 27/09/2017 | 89,408 |
Homo sapiens (human) | - | 20,230 | UP000005640 | 01/06/2017 | 263,216 |
Appendix A.2. Repertoire Model of Negative Selection
Appendix A.3. Training Set Selection
Optimal Training Peptide Selection
- List the self-reactive TCR motifs that still remain in the repertoire;
- Select the self peptide that deletes the most of these remaining self-reactive TCRs. If multiple self peptides delete an equal number of remaining TCRs, we pick only those self peptides that do not overlap in the TCRs they delete.
Biased Training Peptide Selection
Appendix A.4. Sequence Analysis
String Graphs
Peptide Graphs
Concordance
AA Enrichment
Exchangeability
Appendix A.5. Post-Selection Repertoire Analysis
Sequence Recognition
Self-Foreign Discrimination
Affinity Distribution
TCR Survival/Deletion
Appendix A.6. Statistical Analysis
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Wortel, I.M.N.; Keşmir, C.; de Boer, R.J.; Mandl, J.N.; Textor, J. Is T Cell Negative Selection a Learning Algorithm? Cells 2020, 9, 690. https://doi.org/10.3390/cells9030690
Wortel IMN, Keşmir C, de Boer RJ, Mandl JN, Textor J. Is T Cell Negative Selection a Learning Algorithm? Cells. 2020; 9(3):690. https://doi.org/10.3390/cells9030690
Chicago/Turabian StyleWortel, Inge M. N., Can Keşmir, Rob J. de Boer, Judith N. Mandl, and Johannes Textor. 2020. "Is T Cell Negative Selection a Learning Algorithm?" Cells 9, no. 3: 690. https://doi.org/10.3390/cells9030690
APA StyleWortel, I. M. N., Keşmir, C., de Boer, R. J., Mandl, J. N., & Textor, J. (2020). Is T Cell Negative Selection a Learning Algorithm? Cells, 9(3), 690. https://doi.org/10.3390/cells9030690