Encoding Type and Position in Peptide QSAR: Application to Peptides Binding to Class I MHC Molecule HLA-A*0201

Pissurlenkar, Raghuvir R. S., Malde, Alpeshkumar K., Khedkar, Santosh A. and Coutinho , Evans C. (2007) Encoding Type and Position in Peptide QSAR: Application to Peptides Binding to Class I MHC Molecule HLA-A*0201. QSAR & Combinatorial Science, 26 2: 189-203. doi:10.1002/qsar.200530184


Author Pissurlenkar, Raghuvir R. S.
Malde, Alpeshkumar K.
Khedkar, Santosh A.
Coutinho , Evans C.
Title Encoding Type and Position in Peptide QSAR: Application to Peptides Binding to Class I MHC Molecule HLA-A*0201
Journal name QSAR & Combinatorial Science   Check publisher's open access policy
ISSN 1611-020X
Publication date 2007-02-01
Sub-type Article (original research)
DOI 10.1002/qsar.200530184
Volume 26
Issue 2
Start page 189
End page 203
Total pages 15
Place of publication Weinheim, Germany
Publisher Wiley-VCH Verlag
Language eng
Subject 030404 Cheminformatics and Quantitative Structure-Activity Relationships
030406 Proteins and Peptides
Abstract Various QSAR approaches have evolved over time to find a relationship between chemical structure and biological response by translating the information in the chemical structure into a quantitative (Hansch approach) or qualitative (Free-Wilson approach) description followed by a statistical modeling. The nature and position of every amino acid in the peptide sequence dictate its binding to the receptor. We have investigated an approach to peptide QSAR where the position and type of every amino acid in the sequence are encoded in the QSAR paradigm, and along with the principles of the Hansch (descriptor QSAR) and the Free-Wilson (binary QSAR) methodologies we were able to arrive at the optimal sequence of amino acids in the peptide. As an example, we illustrate the procedure on two datasets of nonamer peptides that bind to the class I major histocompatibility complex molecule HLA-A*0201. The models generated have statistically significant co-relation coefficient (r2) and predictive r2 (r2pred). The cross-validated r2 (q2) for descriptor QSAR approach and the binary QSAR approach are in an acceptable range. The QSAR analysis is able to identify the preferential and detrimental amino acids at every position of the nonamer peptide. This QSAR formalism as applied to peptides is a simple and effective way to explain structure-activity relationships as well as to optimize the binding affinity.
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
School of Chemistry and Molecular Biosciences
 
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