Application of machine learning techniques in predicting MHC binders.
The machine learning techniques are playing a vital role in the field of immunoinformatics. In the past, a number of methods have been developed for predicting major histocompatibility complex (MHC)-binding peptides using machine learning techniques. These methods allow predicting MHC-binding peptides with high accuracy. In this chapter, we describe two machine learning technique-based methods, nHLAPred and MHC2Pred, developed for predicting MHC binders for class I and class II alleles, respectively. nHLAPred is a web server developed for predicting binders for 67 MHC class I alleles. This sever has two methods: ANNPred and ComPred. ComPred allows predicting binders for 67 MHC class I alleles, using the combined method [artificial neural network (ANN) and quantitative matrix] for 30 alleles and quantitative matrix-based method for 37 alleles. ANNPred allows prediction of binders for only 30 alleles purely based on the ANN. MHC2Pred is a support vector machine (SVM)-based method for prediction of promiscuous binders for 42 MHC class II alleles.
Lata
Sneh
S
Institute of Microbial Technology, Chandigarh, India.
Bhasin
Manoj
M
Raghava
Gajendra P S
GP
D000483:Alleles; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D001665:Binding Sites / Q000235:genetics; D019295:Computational Biology; D003198:Computer Simulation; D018984:Epitopes, T-Lymphocyte / Q000737:chemistry / Q000378:metabolism; D005805:Genes, MHC Class I; D005802:Genes, MHC Class II; D006680:HLA Antigens / Q000737:chemistry / Q000145:classification / Q000235:genetics / Q000378:metabolism; D006801:Humans; D007125:Immunogenetics; D020407:Internet; D008285:Major Histocompatibility Complex; D008958:Models, Molecular; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D011485:Protein Binding
D018984:Epitopes, T-Lymphocyte; D006680:HLA Antigens