The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method
Open accessAbstract
Background: Many processes in molecular biology involve the recognition of short sequences of nucleic-or amino acids, such as the binding of immunogenic peptides to major histocompatibility complex (MHC) molecules. From experimental data, a model of the sequence specificity of these processes can be constructed, such as a sequence motif, a scoring matrix or an artificial neural network. The purpose of these models is two-fold. First, they can provide a summary of experimental results, allowing for a deeper understanding of the mechanisms involved in sequence recognition. Second, such models can be used to predict the experimental outcome for yet untested sequences. In the past we reported the development of a method to generate such models called the Stabilized Matrix Method (SMM). This method has been successfully applied to predicting peptide binding to MHC molecules, peptide transport by the transporter associated with antigen presentation (TAP) and proteasomal cleavage of protein sequences. Results: Herein we report the implementation of the SMM algorithm as a publicly available software package. Specific features determining the type of problems the method is most appropriate for are discussed. Advantageous features of the package are: (1) the output generated is easy to interpret, (2) input and output are both quantitative, (3) specific computational strategies to handle experimental noise are built in, (4) the algorithm is designed to effectively handle bounded experimental data, (5) experimental data from randomized peptide libraries and conventional peptides can easily be combined, and (6) it is possible to incorporate pair interactions between positions of a sequence. Conclusion: Making the SMM method publicly available enables bioinformaticians and experimental biologists to easily access it, to compare its performance to other prediction methods, and to extend it to other applications.
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Cites
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SYFPEITHI: database for MHC ligands and peptide motifs
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Reliable prediction of T‐cell epitopes using neural networks with novel sequence representations
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Self-citationIdentifying MHC Class I Epitopes by Predicting the TAP Transport Efficiency of Epitope Precursors
Peters, B., Bulik, S., Tampe, R., van Endert, P. M., & Holzhütter, H.-G. (2003) · The Journal of Immunology · doi:10.4049/jimmunol.171.4.1741 · Cited by 339
Self-citationRecognition principle of the TAP transporter disclosed by combinatorial peptide libraries
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Methods for Prediction of Peptide Binding to MHC Molecules: A Comparative Study
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Cited by
Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens
Gubin, M. M., Zhang, X., Schuster, H., Caron, E., Ward, J. P., Noguchi, T., Ivanova, Y., Hundal, J., Arthur, C. D., Krebber, W.-J., et al. (2014) · Nature · doi:10.1038/nature13988 · Cited by 1,974
“The Stabilized Matrix Method (SMM) algorithm 32 , the Artificial Neural Network (ANN) algorithm 33 , and the NetMHCpan algorithm 34 provided by the Immune Epitope Database and Analysis Resource (http:// www.immuneepitope.org) was used to predict epitope processing and binding affinities and the…”
Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas
Cancer Genome Atlas Research Network, Campbell, J. D., Alexandrov, A., Kim, J., Wala, J., Berger, A. H., Pedamallu, C. S., Shukla, S. A., Guo, G., Brooks, A. N., et al. (2016) · Nature Genetics · doi:10.1038/ng.3564 · Cited by 1,200
Turtle, C. J., Hanafi, L.-A., Berger, C., Hudecek, M., Pender, B., Robinson, E., Hawkins, R., Chaney, C., Cherian, S., Chen, X., et al. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aaf8621 · Cited by 1,016
“Predictions of major histocompatibility complex class I binding by peptides shared between the candidate pools were made using the Immune Epitope Database and Analysis Resource Consensus tool (32), which combines predictions from NetMHC (3.4) (33, 34), SMM (35), and Comblib (36).”
InfluentialIEDB-AR: immune epitope database-analysis resource in 2019
Dhanda, S. K., Mahajan, S., Paul, S., Yan, Z., Kim, H., Jespersen, M. C., Jurtz, V., Andreatta, M., Greenbaum, J. A., Marcatili, P., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz452 · Cited by 427
A Community Resource Benchmarking Predictions of Peptide Binding to MHC-I Molecules
Peters, B., Bui, H.-H., Frankild, S., Nielsen, M., Lundegaard, C., Kostem, E., Basch, D., Lamberth, K., Harndahl, M., Fleri, W., et al. (2006) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0020065 · Cited by 288
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Metadata
- Authors
- Peters, B., & Sette, A.
- Venue
- BMC Bioinformatics
- Published
- 2005-05-31
- Volume
- 6
- Issue
- 1
- Pages
- 132-132
- ISSN
- 1471-2105 (ISSN-L)
- OpenAlex
- W2110574412
- Cited by
- 512
- References
- 14
- FWCI
- 4.61
- Type
- journal article
- Language
- en
- Open access
- gold
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