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

Abstract

Discovery of natural antioxidants has been carried out for decades relying mainly on experimental approaches that are commonly associated with time and cost demanding biochemical assays. The maturation of quantitative structure activity relationship (QSAR) modelling has provided an alternative approach for searching and designing antioxidant compounds with alleviated costs. As a contribution to this approach, this work aimed to establish a fragment‐based 3D‐QSAR procedure to discover and design potential antioxidants based on tryptophyllin L structures isolated from the red tree frog Litoria rubella. A force field and a Gaussian 3D‐QSAR model were built to screen for potential antioxidants from tripeptide fragments covering all sequences of tryptophyllin L database. Among those, PWY(NH2) corresponding tryptophyllin L 4.1 was predicted to have the highest 2,2′‐azino‐bis(3‐ethylbenzothiazoline‐6‐sulphonic acid) radical cation (ABTS+·) scavenging capability. Two newly designed peptides PYW and PYW(NH2) together with PWY(NH2), tryptophyllin L 4.1, and the reference peptide PWY were synthesized and subjected to two antioxidant assays including ABTS scavenging and ferric reducing antioxidant power assays. Although the experimental TEAC values of the five peptides were roughly similar to those from predictions, the activity order was not in agreement with the predictions. The dissimilarities were accounted by the difference in the experimental procedures, the deviation of modelling regression, and the synergetic effect of structural and experimental features. The ABTS radical scavenging assays revealed that all the tested peptides were strong ABTS+· scavengers with the antioxidant capabilities approximately twice as high as trolox and higher than glutathione. The ferric reducing activities of the peptides were, on the other hand, much weaker than that of trolox suggesting different antioxidant mechanisms inserted by trolox and the peptides. This work was a demonstration that 3D‐QSAR methods can be employed in conjunction with experimental methods to effectively detect and design antioxidant peptides.

Integration of machine learning in 3D-QSAR CoMSIA models for the identification of lipid antioxidant peptides

“…To assess the impact of two different force elds on the statistical performance of the models, we employed the OPLS_2005 and Tripos force elds on each set of aligned structures to generate CoMSIA models, referred to as OPLS-based and Triposbased CoMSIA models, respectively. The construction of the OPLS-based models followed the procedure outlined in, 24,25 while the construction of the Tripos-based models was guided by the Release Notes integrated into Sybyl X 2.1.…”

“…These models were subsequently employed to screen various tripeptide fragments derived from Tryptophyllin L peptides. 24,25 This is a peptide family that possesses a characteristic Pro-Trp sequence extracted from the dorsal skin of the frog Litoria rubella, an Australian frog occupying a large area of central and northern Australia. Details of the extraction, structures and antioxidant activities of some peptides from this peptide family can be found in the following ref.…”

“…26 and 27 and also in our recent publications. 24,25 In our previous study, a Gaussian-based 3D-QSAR model constructed using the dataset of ABTS + radical scavenging tripeptides was employed in a combined statistical and experimental approach to identify a range of antioxidant Tryptophyllin L peptides. These peptides exhibited excellent ABTS + , DPPH radical scavenging and reducing power.…”