A Benchmark Dataset Comprising Partition and Distribution Coefficients of Linear Peptides

Matthew N Davies,Darren R Flower

doi:10.7167/2013/976758

Abstract

Peptides have a dominant role in biology; yet the study of their physical properties is at best sporadic. Peptide quantitative structure-activity relationship (QSAR) lags far behind the QSAR analysis of drug-like organic small molecules. Traditionally, QSAR has focussed on experimentally determined partition coefficients as the main descriptor of hydrophobicity. A partition coefficient () is the ratio between the concentrations of an uncharged chemical substance in two immiscible phases: most typically water and an organic solvent, usually 1-octanol. A distribution coefficient () is the equivalent ratio for charged molecules. We report here a compilation of partition and distribution coefficients for linear peptides compiled from literature reports, suitable for the development and benchmarking of peptide and prediction algorithms.

Highlights

Peptides abound in nature, functioning as hormones, including bradykinins, insulin, gastrins, oxytocins, and various growth factors; as neuropeptides [1], such as encephalins and endorphins; as MHC-bound epitopes, the principal recognition element in cellular immunology [2]; as intermediates in the degradation of proteins [3]; as bacteriocins [4], such as microcins; as antimicrobial host defence peptides [5], such as dermcidins and defensins; and as venom peptides [6], such as α, μ, and ω-conotoxins, χ-conopeptides, conantokins, and contulakins; to name but a few of their many important and diverse roles and functions
We report a rigorous and scrupulous compilation of partition and distribution coefficients for linear peptides collated from literature reports, suitable for the development, and benchmarking, of peptide log P and log D prediction
As an example of our approach, the following was used to search for articles in PubMed: (LOGP∗ OR “ logP∗” OR “ log(P∗)” OR “ log(P)” OR LOGD∗ OR “ logd∗” OR “ log(D∗)” OR “ log(D)” OR “partition coefficient” OR “partition coefficients” OR “distribution coefficient” OR “distribution coefficients” OR octanol∗) AND

Summary

Introduction

Peptides abound in nature, functioning as hormones, including bradykinins, insulin, gastrins, oxytocins, and various growth factors; as neuropeptides [1], such as encephalins and endorphins; as MHC-bound epitopes, the principal recognition element in cellular immunology [2]; as intermediates in the degradation of proteins [3]; as bacteriocins [4], such as microcins; as antimicrobial host defence peptides [5], such as dermcidins and defensins; and as venom peptides [6], such as α-, μ-, and ω-conotoxins, χ-conopeptides, conantokins, and contulakins; to name but a few of their many important and diverse roles and functions. Peptides have historically been regarded by the pharmaceutical industry as poor drug candidates [7], not least as they are thought to lack desirable Lipinski-like qualities, such as possessing a low molecular weight [8]. Occurring peptides often have a limited half-life. If administered orally, they are rapidly broken down by endo- and exopeptidases within the gut, reducing oral bioavailability. They are rapidly broken down by endo- and exopeptidases within the gut, reducing oral bioavailability For this reason, therapeutic peptides are often delivered parenterally, which can be both impractical and expensive. Peptides can be highly specific, reducing unnecessary side effects; whilst naturally occurring peptides are likely to exhibit low toxicity

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