Name
Abstract | ||
|---|---|---|
The ability to predict antigenic sites on proteins is crucial for the production of synthetic peptide vaccines and synthetic peptide probes of antibody structure. Large number of amino acid propensity scales based on various properties of the antigenic sites like hydrophilicity, flexibility/mobility, turns and bends have been proposed and tested previously. However these methods are not very accurate in predicting epitopes and non-epitope regions. We propose algorithms that combine 14 best performing individual propensity scales and give better prediction accuracy as compared to individual scales. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/CSBW.2005.109 | CSB Workshops |
Keywords | Field | DocType |
non-epitope region,synthetic peptide probe,amino acid propensity scale,synthetic peptide vaccine,better prediction accuracy,antibody structure,continuous epitopes,individual propensity scale,large number,antigenic site,individual scale,learning artificial intelligence,molecular biophysics,amino acid,proteins,statistical analysis | Epitope,Amino acid,Computer science,Peptide,Molecular biophysics,Bioinformatics,Statistical analysis | Conference |
ISBN | Citations | PageRank |
0-7695-2442-7 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Reeti Tandon | 1 | 3 | 1.04 |
| Sudeshna Adak | 2 | 16 | 6.53 |
| Brion Sarachan | 3 | 1 | 0.69 |
| William FitzHugh | 4 | 0 | 0.34 |
| J. Heil | 5 | 2 | 2.69 |
| Vaibhav A Narayan | 6 | 74 | 3.43 |