Title

Empirical prediction of peptide octanol-water partition coefficients

Authors

Channa K. Hattotuwagama & Darren R. Flower *

Affiliation

*The Jenner Institute, University of Oxford, Compton, Newbury, Berkshire, RG20 7NN, UK

Email

darren.flower@jenner.ac.uk; * Corresponding author

Phone

+44 1635 577954

Fax

+44 1635 577908

Article Type

Prediction Model

Date

received November 11,2006; accepted November 22, 2006; published online November 24, 2006

Abstract

Peptides are of great therapeutic potential as vaccines and drugs. Knowledge of physicochemical descriptors, including the partition coefficient P (commonly expressed in logarithm form: logP), is useful for screening out unsuitable molecules and also for the development of predictive Quantitative Structure-Activity Relationships (QSARs). In this paper we develop a new approach to the prediction of LogP values for peptides based on an empirical relationship between global molecular properties and measured physical properties. Our method was successful in terms of peptide prediction (total r² = 0.641). The final model consisted of 5 physicochemical descriptors (molecular weight, number of single bonds, 2D-VDW volume, 2D-VSA hydrophobic and 2D-VSA polar). The approach is peptide specific and its predictive accuracy was high. Overall, 67% of the peptides were able to be predicted within +/-0.5 log units from the experimental values. Our method thus represents a novel prediction method with proven predictive ability.

Keywords

Peptide; log P; partition coefficient; octanol-water; regression; physicochemical descriptor; hydrophobicity

Citation

Edited by

P. Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.