Knowledge-based grouping of modeled HLA peptide complexes

Pandjassarame Kangueane, Meena K. Sakharkar, Kuan S. Lim, Han Hao, Kui Lin, Ren E. Chee, Prasanna Kolatkar

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Human leukocyte antigens are the most polymorphic of human genes and multiple sequence alignment shows that such polymorphisms are clustered in the functional peptide binding domains. Because of such polymorphism among the peptide binding residues, the prediction of peptides that bind to specific HLA molecules is very difficult. In recent years two different types of computer based prediction methods have been developed and both the methods have their own advantages and disadvantages. The nonavailability of allele specific binding data restricts the use of knowledge-based prediction methods for a wide range of HLA alleles. Alternatively, the modeling scheme appears to be a promising predictive tool for the selection of peptides that bind to specific HLA molecules. The scoring of the modeled HLA-peptide complexes is a major concern. The use of knowledge based rules (van der Waals clashes and solvent exposed hydrophobic residues) to distinguish binders from nonbinders is applied in the present study. The rules based on (1) number of observed atomic clashes between the modeled peptide and the HLA structure, and (2) number of solvent exposed hydrophobic residues on the modeled peptide effectively discriminate experimentally known binders from poor/nonbinders. Solved crystal complexes show no vdW Clash (vdWC) in 95% cases and no solvent exposed hydrophobic peptide residues (SEHPR) were seen in 86% cases. In our attempt to compare experimental binding data with the predicted scores by this scoring scheme, 77% of the peptides are correctly grouped as good binders with a sensitivity of 71%. (C) 2000 American Society for Histocompatibility and Immunogenetics.

Original languageEnglish
Pages (from-to)460-466
Number of pages7
JournalHuman Immunology
Volume61
Issue number5
DOIs
Publication statusPublished - 1 May 2000
Externally publishedYes

Fingerprint

Peptides
Alleles
Sequence Alignment
HLA Antigens
Genes

Keywords

  • Grouping rules
  • HLA-peptide
  • Modeling
  • SEHPR
  • VdWC

ASJC Scopus subject areas

  • Immunology
  • Immunology and Allergy

Cite this

Kangueane, P., Sakharkar, M. K., Lim, K. S., Hao, H., Lin, K., Chee, R. E., & Kolatkar, P. (2000). Knowledge-based grouping of modeled HLA peptide complexes. Human Immunology, 61(5), 460-466. https://doi.org/10.1016/S0198-8859(00)00106-3

Knowledge-based grouping of modeled HLA peptide complexes. / Kangueane, Pandjassarame; Sakharkar, Meena K.; Lim, Kuan S.; Hao, Han; Lin, Kui; Chee, Ren E.; Kolatkar, Prasanna.

In: Human Immunology, Vol. 61, No. 5, 01.05.2000, p. 460-466.

Research output: Contribution to journalArticle

Kangueane, P, Sakharkar, MK, Lim, KS, Hao, H, Lin, K, Chee, RE & Kolatkar, P 2000, 'Knowledge-based grouping of modeled HLA peptide complexes', Human Immunology, vol. 61, no. 5, pp. 460-466. https://doi.org/10.1016/S0198-8859(00)00106-3
Kangueane P, Sakharkar MK, Lim KS, Hao H, Lin K, Chee RE et al. Knowledge-based grouping of modeled HLA peptide complexes. Human Immunology. 2000 May 1;61(5):460-466. https://doi.org/10.1016/S0198-8859(00)00106-3
Kangueane, Pandjassarame ; Sakharkar, Meena K. ; Lim, Kuan S. ; Hao, Han ; Lin, Kui ; Chee, Ren E. ; Kolatkar, Prasanna. / Knowledge-based grouping of modeled HLA peptide complexes. In: Human Immunology. 2000 ; Vol. 61, No. 5. pp. 460-466.
@article{8c0d09ddd0d14afcbb38a0637c1d52a7,
title = "Knowledge-based grouping of modeled HLA peptide complexes",
abstract = "Human leukocyte antigens are the most polymorphic of human genes and multiple sequence alignment shows that such polymorphisms are clustered in the functional peptide binding domains. Because of such polymorphism among the peptide binding residues, the prediction of peptides that bind to specific HLA molecules is very difficult. In recent years two different types of computer based prediction methods have been developed and both the methods have their own advantages and disadvantages. The nonavailability of allele specific binding data restricts the use of knowledge-based prediction methods for a wide range of HLA alleles. Alternatively, the modeling scheme appears to be a promising predictive tool for the selection of peptides that bind to specific HLA molecules. The scoring of the modeled HLA-peptide complexes is a major concern. The use of knowledge based rules (van der Waals clashes and solvent exposed hydrophobic residues) to distinguish binders from nonbinders is applied in the present study. The rules based on (1) number of observed atomic clashes between the modeled peptide and the HLA structure, and (2) number of solvent exposed hydrophobic residues on the modeled peptide effectively discriminate experimentally known binders from poor/nonbinders. Solved crystal complexes show no vdW Clash (vdWC) in 95{\%} cases and no solvent exposed hydrophobic peptide residues (SEHPR) were seen in 86{\%} cases. In our attempt to compare experimental binding data with the predicted scores by this scoring scheme, 77{\%} of the peptides are correctly grouped as good binders with a sensitivity of 71{\%}. (C) 2000 American Society for Histocompatibility and Immunogenetics.",
keywords = "Grouping rules, HLA-peptide, Modeling, SEHPR, VdWC",
author = "Pandjassarame Kangueane and Sakharkar, {Meena K.} and Lim, {Kuan S.} and Han Hao and Kui Lin and Chee, {Ren E.} and Prasanna Kolatkar",
year = "2000",
month = "5",
day = "1",
doi = "10.1016/S0198-8859(00)00106-3",
language = "English",
volume = "61",
pages = "460--466",
journal = "Human Immunology",
issn = "0198-8859",
publisher = "Elsevier Inc.",
number = "5",

}

TY - JOUR

T1 - Knowledge-based grouping of modeled HLA peptide complexes

AU - Kangueane, Pandjassarame

AU - Sakharkar, Meena K.

AU - Lim, Kuan S.

AU - Hao, Han

AU - Lin, Kui

AU - Chee, Ren E.

AU - Kolatkar, Prasanna

PY - 2000/5/1

Y1 - 2000/5/1

N2 - Human leukocyte antigens are the most polymorphic of human genes and multiple sequence alignment shows that such polymorphisms are clustered in the functional peptide binding domains. Because of such polymorphism among the peptide binding residues, the prediction of peptides that bind to specific HLA molecules is very difficult. In recent years two different types of computer based prediction methods have been developed and both the methods have their own advantages and disadvantages. The nonavailability of allele specific binding data restricts the use of knowledge-based prediction methods for a wide range of HLA alleles. Alternatively, the modeling scheme appears to be a promising predictive tool for the selection of peptides that bind to specific HLA molecules. The scoring of the modeled HLA-peptide complexes is a major concern. The use of knowledge based rules (van der Waals clashes and solvent exposed hydrophobic residues) to distinguish binders from nonbinders is applied in the present study. The rules based on (1) number of observed atomic clashes between the modeled peptide and the HLA structure, and (2) number of solvent exposed hydrophobic residues on the modeled peptide effectively discriminate experimentally known binders from poor/nonbinders. Solved crystal complexes show no vdW Clash (vdWC) in 95% cases and no solvent exposed hydrophobic peptide residues (SEHPR) were seen in 86% cases. In our attempt to compare experimental binding data with the predicted scores by this scoring scheme, 77% of the peptides are correctly grouped as good binders with a sensitivity of 71%. (C) 2000 American Society for Histocompatibility and Immunogenetics.

AB - Human leukocyte antigens are the most polymorphic of human genes and multiple sequence alignment shows that such polymorphisms are clustered in the functional peptide binding domains. Because of such polymorphism among the peptide binding residues, the prediction of peptides that bind to specific HLA molecules is very difficult. In recent years two different types of computer based prediction methods have been developed and both the methods have their own advantages and disadvantages. The nonavailability of allele specific binding data restricts the use of knowledge-based prediction methods for a wide range of HLA alleles. Alternatively, the modeling scheme appears to be a promising predictive tool for the selection of peptides that bind to specific HLA molecules. The scoring of the modeled HLA-peptide complexes is a major concern. The use of knowledge based rules (van der Waals clashes and solvent exposed hydrophobic residues) to distinguish binders from nonbinders is applied in the present study. The rules based on (1) number of observed atomic clashes between the modeled peptide and the HLA structure, and (2) number of solvent exposed hydrophobic residues on the modeled peptide effectively discriminate experimentally known binders from poor/nonbinders. Solved crystal complexes show no vdW Clash (vdWC) in 95% cases and no solvent exposed hydrophobic peptide residues (SEHPR) were seen in 86% cases. In our attempt to compare experimental binding data with the predicted scores by this scoring scheme, 77% of the peptides are correctly grouped as good binders with a sensitivity of 71%. (C) 2000 American Society for Histocompatibility and Immunogenetics.

KW - Grouping rules

KW - HLA-peptide

KW - Modeling

KW - SEHPR

KW - VdWC

UR - http://www.scopus.com/inward/record.url?scp=0034006333&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0034006333&partnerID=8YFLogxK

U2 - 10.1016/S0198-8859(00)00106-3

DO - 10.1016/S0198-8859(00)00106-3

M3 - Article

VL - 61

SP - 460

EP - 466

JO - Human Immunology

JF - Human Immunology

SN - 0198-8859

IS - 5

ER -