Antioxidant activities of major tryptophyllin L peptides: A joint investigation of Gaussian‐based 3D‐QSAR and radical scavenging experiments

Abstract

The red tree frog Litoria rubella from Australia has been studied for several decades showing that their dorsal skin glands secrete a number of small peptides containing a Pro–Trp sequence, known as tryptophyllin L peptides. Although peptides from many genera of Australian frogs have been reported to possess a variety of biological activities, the bioactivities of this peptide family have remained to be discovered. In this study, we investigated the antioxidant potency of a number of tryptophyllin L peptides for the first time using a joint statistical and experimental approach in which predictions based on Gaussian three‐dimensional quantitative structure–activity relationship (3D‐QSAR) models were employed to guide an in vitro experimental investigation. Two tryptophyllin tripeptides P–W–L (OH) and P–W–L (NH2) were predicted to have the Trolox equivalent antioxidant capacity (TEAC) values of 0.80 and 0.87 μM Trolox/μM peptide, respectively. With those promising results, antioxidant capabilities of five tryptophyllin L peptides with the common core Pro–Trp–Leu were synthesized and subjected to 1,1‐diphenyl‐2‐picrylhydrazyl (DPPH), ferric reducing ability of plasma (FRAP) and 2,2′‐azino‐bis(3‐ethylbenzothiazoline‐6‐sulphonic acid) radical cation (ABTS˙+) radical scavenging assays. The tests indicated that all the tested tryptophyllin L peptides, noticeably S–P–W–L (OH) and F–P–W–L (NH2), are strong ABTS˙+ radical scavengers and moderate scavengers in the other two assays. The results, thus, suggested that the tryptophyllin L peptides are likely to be a part of the skin antioxidant system helping the frog to cope with drastic change in oxygen exposure and humidity, as they inhabit over a large area of Australia with a wide climate variation.

Virtual screening and rational design of antioxidant peptides based on tryptophyllin L structures isolated from the Litoria rubella frog

“…The effect of amidation was found inconsistent with that of the previous study. 24 While predictions from the present models indicated that amidation moderately increased ABTS + Á scavenging ability (Table 4), experimental results showed an opposite trend for two pairs of tripeptides (PWY, PYW, and their amidated counterparts; Figure 5).…”

“…The results were not in agreement with those from the previous study as F-P-W-L(NH 2 ) and P-W-L(NH 2 ) displayed higher TEAC values than their acidic derivatives both experimentally and statistically. 24 Therefore, it can be concluded that amidation does not affect ABTS + Á scavenging abilities of peptides significantly or other factors may overrule this effect. The TEAC values of the five peptides and GSH over a concentration range from 2.5 to 10 μM at 6 min was compared in Figure 7.…”

“…37 In a recent study, we have investigated antioxidant potential of five major tryptophyllin L peptides. 24 Two tryptophyllin L including FPWL(NH 2 ) and SPWL showed comparable activities with those of glutathione in the three assays 2,2 0 -azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS + Á), 1,1-diphenyl-2-picrylhydrazyl (DPPH . ) radical scavenging, and ferric reducing, suggesting that they may be a part of the skin antioxidant system of the frog.…”

“…13,14 In the last decades, the databases of discovered antioxidant peptides have grown substantially with their major sources being cereals, 15,16 marine protein by-products, 17,18 meat, [19][20][21] editable plants, 22,23 and recently some of them were isolated from frog's dorsal skins. [24][25][26] The standard procedure for discovering antioxidants peptides normally started with protein hydrolysis by enzymes or microorganisms to obtain hydrolysates, which were then separated, purified, and identified using high performance liquid chromatography (HPLC) coupled with mass spectrometry (MS). The isolated peptides were finally subjected to a number of antioxidant assays to examine their antioxidant ability.…”

“…Among which, a variety of modelling algorithms including multiple linear regression (MLR), partial least square (PLS), random forest (RF), support vector machine (SVM), and recently convolutional neural network (CNN) have been used to generate predictive patterns, from which characteristics beneficial for antioxidant activity of peptides were extrapolated. [28][29][30][31][32] The features chosen for modelling also varied from geometric, physicochemical, electronic to comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) descriptors, 24,[28][29][30][31][32][33][34][35] from descriptors representing each peptide as a vector of individual amino acids 28,30,36 to descriptors representing the features of the whole peptide molecule. 29,32 Most of the predictive models for antioxidants were constructed with the first goal being to learn about which independent variables or features affect antioxidant activities of the peptides in the studied databases, and the second goal was to employ the constructed model to predict and finally design effective antioxidant peptides.…”