Peptide binding at class I major histocompatibility complex scored with linear functions and support vector machines.
We explore two different methods to predict the binding ability of nonapeptides at the class I major histocompatibility complex using a general linear scoring function that defines a separating hyperplane in the feature space of sequences. In absence of suitable data on non-binding nonapeptides we generated sequences randomly from a selected set of proteins from the protein data bank. The parameters of the scoring function were determined by a generalized least square optimization (LSM) and alternatively by the support vector machine (SVM). With the generalized LSM impaired data for learning with a small set of binding peptides and a large set of non-binding peptides can be treated in a balanced way rendering LSM more successful than SVM, while for symmetric data sets SVM has a slight advantage compared to LSM.
Riedesel
Henning
H
Institute of Chemistry, Free University of Berlin, Takustrasse 6, Berlin 14195, Germany. riedesel@chemie.fu-berlin.de
Kolbeck
Björn
B
Schmetzer
Oliver
O
Knapp
Ernst-Walter
EW
D000595:Amino Acid Sequence; D000818:Animals; D003198:Computer Simulation; D030562:Databases, Protein; D005805:Genes, MHC Class I; D015395:Histocompatibility Antigens Class I / Q000235:genetics*; D016018:Least-Squares Analysis; D008285:Major Histocompatibility Complex; D010455:Peptides / Q000737:chemistry / Q000276:immunology
D015395:Histocompatibility Antigens Class I; D010455:Peptides