Selected article for: "MHC class and percentile rank"
Author: Anamika Basu; Anasua Sarkar; Ujjwal Maulik
Title: Strategies for vaccine design for corona virus using Immunoinformatics techniques
  • Document date: 2020_3_2
    • ID: 618glydc_16
    Snippet: Linear T-cell epitopes for MHC-I binding for nonstructural protein NS4 protein in coronavirus are recognized by consensus methods using various methods such as Artificial neural network (ANN) [18] , Stabilized matrix method (SMM) [19] and Scoring Matrices Derived from Combinatorial Peptide Libraries (Comblib) from tools for MHC -I binding prediction methods of Immune Epitope Database (IEDB) (www.iedb.org) [20] . This serverbased method forecasts .....
    Document: Linear T-cell epitopes for MHC-I binding for nonstructural protein NS4 protein in coronavirus are recognized by consensus methods using various methods such as Artificial neural network (ANN) [18] , Stabilized matrix method (SMM) [19] and Scoring Matrices Derived from Combinatorial Peptide Libraries (Comblib) from tools for MHC -I binding prediction methods of Immune Epitope Database (IEDB) (www.iedb.org) [20] . This serverbased method forecasts the MHC class I binding predication to 26 MHC supertypes as percentile rank. SMM algorithm of MHC-1 binding, transporter of antigenic peptides (TAP) transport efficiency and proteasomal cleavage efficiency are also considered to determine the IC 50 values for processing prediction of epitopes by MHC-I molecules [21] . On the basis of low IC 50 values, 5 best epitopes bind with specific MHC-1 molecules are elected for further evaluation.

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