The rule based classification models for MHC binding prediction and identification of the most relevant physicochemical properties for the individual allele
Abstract
Binding of proteolyzed fragments of proteins to MHC molecules is essential and the most selective step that determines T-cell epitopes. Therefore, the prediction of MHC-peptide binding is principal for anticipating potential T cell epitopes and is of immense relevance in vaccine design. Despite numerous methods for predicting MHC binding ligands, there still exist limitations that affect the reliability of a prevailing number of methods. Certain important methods based on physicochemical properties have very low reported accuracy. The aim of this paper is to present a new approach of extracting the most important physicochemical properties that influence the classification of MHC-binding ligands. In this study, we have developed rule based classification models which take into account the physicochemical properties of amino acids and their frequencies. The models use k-means clustering technique for extracting the relevant physicochemical properties. The results of the study indicate t...hat the physicochemical properties of amino acids contribute significantly to the peptide-binding and that the different alleles are characterized by a different set of the physicochemical properties.
Keywords:
The rule based classification / MHC - peptide binding / K - mean clusteringSource:
The University Thought - Publication in Natural Sciences, 2016, 6, 1, 60-66Publisher:
- Univerzitet u Prištini - Prirodno-matematički fakultet, Kosovska Mitrovica
Funding / projects:
- Methods of Numerical and Nonlinear Analysis with Applications (RS-MESTD-Basic Research (BR or ON)-174002)
Collections
Institution/Community
Mašinski fakultetTY - JOUR AU - Jandrlić, Davorka PY - 2016 UR - https://machinery.mas.bg.ac.rs/handle/123456789/2261 AB - Binding of proteolyzed fragments of proteins to MHC molecules is essential and the most selective step that determines T-cell epitopes. Therefore, the prediction of MHC-peptide binding is principal for anticipating potential T cell epitopes and is of immense relevance in vaccine design. Despite numerous methods for predicting MHC binding ligands, there still exist limitations that affect the reliability of a prevailing number of methods. Certain important methods based on physicochemical properties have very low reported accuracy. The aim of this paper is to present a new approach of extracting the most important physicochemical properties that influence the classification of MHC-binding ligands. In this study, we have developed rule based classification models which take into account the physicochemical properties of amino acids and their frequencies. The models use k-means clustering technique for extracting the relevant physicochemical properties. The results of the study indicate that the physicochemical properties of amino acids contribute significantly to the peptide-binding and that the different alleles are characterized by a different set of the physicochemical properties. PB - Univerzitet u Prištini - Prirodno-matematički fakultet, Kosovska Mitrovica T2 - The University Thought - Publication in Natural Sciences T1 - The rule based classification models for MHC binding prediction and identification of the most relevant physicochemical properties for the individual allele EP - 66 IS - 1 SP - 60 VL - 6 DO - 10.5937/univtho6-10768 ER -
@article{ author = "Jandrlić, Davorka", year = "2016", abstract = "Binding of proteolyzed fragments of proteins to MHC molecules is essential and the most selective step that determines T-cell epitopes. Therefore, the prediction of MHC-peptide binding is principal for anticipating potential T cell epitopes and is of immense relevance in vaccine design. Despite numerous methods for predicting MHC binding ligands, there still exist limitations that affect the reliability of a prevailing number of methods. Certain important methods based on physicochemical properties have very low reported accuracy. The aim of this paper is to present a new approach of extracting the most important physicochemical properties that influence the classification of MHC-binding ligands. In this study, we have developed rule based classification models which take into account the physicochemical properties of amino acids and their frequencies. The models use k-means clustering technique for extracting the relevant physicochemical properties. The results of the study indicate that the physicochemical properties of amino acids contribute significantly to the peptide-binding and that the different alleles are characterized by a different set of the physicochemical properties.", publisher = "Univerzitet u Prištini - Prirodno-matematički fakultet, Kosovska Mitrovica", journal = "The University Thought - Publication in Natural Sciences", title = "The rule based classification models for MHC binding prediction and identification of the most relevant physicochemical properties for the individual allele", pages = "66-60", number = "1", volume = "6", doi = "10.5937/univtho6-10768" }
Jandrlić, D.. (2016). The rule based classification models for MHC binding prediction and identification of the most relevant physicochemical properties for the individual allele. in The University Thought - Publication in Natural Sciences Univerzitet u Prištini - Prirodno-matematički fakultet, Kosovska Mitrovica., 6(1), 60-66. https://doi.org/10.5937/univtho6-10768
Jandrlić D. The rule based classification models for MHC binding prediction and identification of the most relevant physicochemical properties for the individual allele. in The University Thought - Publication in Natural Sciences. 2016;6(1):60-66. doi:10.5937/univtho6-10768 .
Jandrlić, Davorka, "The rule based classification models for MHC binding prediction and identification of the most relevant physicochemical properties for the individual allele" in The University Thought - Publication in Natural Sciences, 6, no. 1 (2016):60-66, https://doi.org/10.5937/univtho6-10768 . .