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MHC Class II Binding Prediction-A Little Help from a Friend.

Dimitrov I, Garnev P, Flower DR, Doytchinova I - J. Biomed. Biotechnol. (2010)

Bottom Line: The accurate and reliable prediction of peptide-MHC binding is fundamental to the robust identification of T-cell epitopes and thus the successful design of peptide- and protein-based vaccines.This number increases in the top 10% and 15% and then does not change significantly.We show that while existing caveats are well founded, nonetheless use of computational models of class II binding can still offer viable help to the work of the immunologist and vaccinologist.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Pharmacy, Medical University of Sofia, 2 Dunav st., 1000 Sofia, Bulgaria.

ABSTRACT
Vaccines are the greatest single instrument of prophylaxis against infectious diseases, with immeasurable benefits to human wellbeing. The accurate and reliable prediction of peptide-MHC binding is fundamental to the robust identification of T-cell epitopes and thus the successful design of peptide- and protein-based vaccines. The prediction of MHC class II peptide binding has hitherto proved recalcitrant and refractory. Here we illustrate the utility of existing computational tools for in silico prediction of peptides binding to class II MHCs. Most of the methods, tested in the present study, detect more than the half of the true binders in the top 5% of all possible nonamers generated from one protein. This number increases in the top 10% and 15% and then does not change significantly. For the top 15% the identified binders approach 86%. In terms of lab work this means 85% less expenditure on materials, labour and time. We show that while existing caveats are well founded, nonetheless use of computational models of class II binding can still offer viable help to the work of the immunologist and vaccinologist.

No MeSH data available.


Related in: MedlinePlus

Number of identified binders (sensitivity) in the top 5%, 10%, 15%, 20% and 25% of all overlapping nonamers generated from a protein: (a) DRB1*0101, (b) DRB1*0301, (c) DRB1*0401, (d) DRB1*0404, (e) DRB1*0405, (f) DRB1*0701, (g) DRB1*0802, (h) DRB1*0901, (i) DRB1*1101, (j) DRB1*1201, (k) DRB1*1302, (l) DRB1*1501.
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Related In: Results  -  Collection


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fig1: Number of identified binders (sensitivity) in the top 5%, 10%, 15%, 20% and 25% of all overlapping nonamers generated from a protein: (a) DRB1*0101, (b) DRB1*0301, (c) DRB1*0401, (d) DRB1*0404, (e) DRB1*0405, (f) DRB1*0701, (g) DRB1*0802, (h) DRB1*0901, (i) DRB1*1101, (j) DRB1*1201, (k) DRB1*1302, (l) DRB1*1501.

Mentions: The test subset of peptides binding to HLA-DRB1*0101 consisted of 2051 binders. Four of the servers (NetMHCII, NetMHCpan, RANKPEP, and EpiTOP) recognize more than 60% of them in the top 10% (Figure 1(a)). The number of the identified binders increases in the next cutoff steps reaching 93% by NetMHCpan, 91% by EpiTOP, and 88% by NetMHCII and RANKPEP at the top 25%.


MHC Class II Binding Prediction-A Little Help from a Friend.

Dimitrov I, Garnev P, Flower DR, Doytchinova I - J. Biomed. Biotechnol. (2010)

Number of identified binders (sensitivity) in the top 5%, 10%, 15%, 20% and 25% of all overlapping nonamers generated from a protein: (a) DRB1*0101, (b) DRB1*0301, (c) DRB1*0401, (d) DRB1*0404, (e) DRB1*0405, (f) DRB1*0701, (g) DRB1*0802, (h) DRB1*0901, (i) DRB1*1101, (j) DRB1*1201, (k) DRB1*1302, (l) DRB1*1501.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2875769&req=5

fig1: Number of identified binders (sensitivity) in the top 5%, 10%, 15%, 20% and 25% of all overlapping nonamers generated from a protein: (a) DRB1*0101, (b) DRB1*0301, (c) DRB1*0401, (d) DRB1*0404, (e) DRB1*0405, (f) DRB1*0701, (g) DRB1*0802, (h) DRB1*0901, (i) DRB1*1101, (j) DRB1*1201, (k) DRB1*1302, (l) DRB1*1501.
Mentions: The test subset of peptides binding to HLA-DRB1*0101 consisted of 2051 binders. Four of the servers (NetMHCII, NetMHCpan, RANKPEP, and EpiTOP) recognize more than 60% of them in the top 10% (Figure 1(a)). The number of the identified binders increases in the next cutoff steps reaching 93% by NetMHCpan, 91% by EpiTOP, and 88% by NetMHCII and RANKPEP at the top 25%.

Bottom Line: The accurate and reliable prediction of peptide-MHC binding is fundamental to the robust identification of T-cell epitopes and thus the successful design of peptide- and protein-based vaccines.This number increases in the top 10% and 15% and then does not change significantly.We show that while existing caveats are well founded, nonetheless use of computational models of class II binding can still offer viable help to the work of the immunologist and vaccinologist.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Pharmacy, Medical University of Sofia, 2 Dunav st., 1000 Sofia, Bulgaria.

ABSTRACT
Vaccines are the greatest single instrument of prophylaxis against infectious diseases, with immeasurable benefits to human wellbeing. The accurate and reliable prediction of peptide-MHC binding is fundamental to the robust identification of T-cell epitopes and thus the successful design of peptide- and protein-based vaccines. The prediction of MHC class II peptide binding has hitherto proved recalcitrant and refractory. Here we illustrate the utility of existing computational tools for in silico prediction of peptides binding to class II MHCs. Most of the methods, tested in the present study, detect more than the half of the true binders in the top 5% of all possible nonamers generated from one protein. This number increases in the top 10% and 15% and then does not change significantly. For the top 15% the identified binders approach 86%. In terms of lab work this means 85% less expenditure on materials, labour and time. We show that while existing caveats are well founded, nonetheless use of computational models of class II binding can still offer viable help to the work of the immunologist and vaccinologist.

No MeSH data available.


Related in: MedlinePlus