Minimum analogue peptide sets (MAPS) for quantitative structure‐activity relationships

Abstract

The information contents in previously published peptide sets was compared with smaller sets of peptides selected according to statistical designs. It was found that minimum analogue peptide sets (MAPS) constructed by factorial or fractional factorial designs in physicochemical properties contained substantial structure‐activity information. Although five to six times smaller than the originally published peptide sets the MAPS resulted in QSAR models able to predict biological activity. The QSARs derived from a MAPS of nine dipeptides, and from a set of 58 dipeptides inhibiting angiotensin converting enzyme were compared and found to be of equal strength. Furthermore, for a set of bitter tasting dipeptides it was found that an incomplete MAPS of 10 dipeptides gave just as good a model as the model based on a set of 48 dipeptides. By comparison other non‐designed sets of peptides gave QSARs with poor predictive power. It was also demonstrated how MAPS centered on a lead peptide can be constructed as to specifically explore the physicochemical and biological properties in the vicinity of the lead. It was concluded that small information‐rich peptide sets MAPS can be constructed on the basis of statistical designs with principal properties of amino acids as design variables.

Learnings from quantitative structure–activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: a review

“…The peptide descriptors used to construct the QSAR models were either based on physicochemical characteristics 32,45 or scalar amino acid descriptors. [30][31][32]46 Most of the QSAR studies have successfully proposed the structural requirements linked with the ACE inhibitory properties of peptides. These consisted of specic amino acids located at different positions in the peptide sequence (Table 3).…”

“…24,26,30,31 For comparative purposes, it is recommended that the bioactivity output should be obtained under similar experimental conditions. 18,27,32 The peptide database may be restricted to sequences which originate from a single protein 26 or a group of proteins found within certain species (e.g., Homo sapiens, Bos taurus, etc.).…”

“…The lead peptide may be used to design specic analogs which are then employed to construct minimum analogue peptide sets (MAPS). 31,34 The sequence of the peptide candidates to be included in the peptide library and subsequently tested to generate biological activity data may be dened using a full or fractional factorial design. The MAPS are designed in such a way as to incorporate the minimum number of peptides in the dataset while covering a wide range of amino acid physicochemical properties and while simultaneously varying their positions within the peptide sequences.…”

Predicting the Activity of Peptides Based on Amino Acid Information

“…[6][7][8] The parameters are hydrophobicity, STERIMOL minimum width of the side chain, loss of side chain hydropathy by helix formation, optical rotation, side chain molecular volume, frequency of the 4th residue in turn, AA composition of EXT of multi-spanning proteins and net charge index, and they were encoded as G1~G8 (Table 1). For a set of peptide analogues, the chemical structures would be now characterized by describing each varied amino acid position with 8 G-scale values.…”

A pipeline for improved QSAR analysis of peptides: physiochemical property parameter selection via BMSF, near-neighbor sample selection via semivariogram, and weighted SVR regression and prediction

“…Since Kidera et al (1985) first coded 10 orthogonal factors from 188 reported physicochemical properties through factor analysis, a series of inductive descriptors have been constructed and applied in peptide computational study. Some examples are Z-scales (Hellberg et al 1991;Sandberg et al 1998), ISA-ECI (Collantes and Dunn 1995), SZOTT (Liang et al 2006), T-scales (Tian et al 2007), ATS-QTMS (Yousefinejad et al 2012), etc. However, these inductive descriptors are the linear combinations of the multiple physicochemical property parameters selected for the amino acids and hence, the QSAR models established using these descriptors could not clearly elucidate the correlation between the initial physicochemical properties and the bioactivity of peptides.…”