Anti-Inflammatory Activity and Mechanism of Hydrostatin-SN1 From Hydrophis cyanocinctus in Interleukin-10 Knockout Mice

A TNF-α blocking peptide that reduces NF-κB and MAPK activity for attenuating inflammation

Overexpression of tumor necrosis factor-α (TNF-α) is implicated in many inflammatory diseases, including septic shock, hepatitis, asthma, insulin resistance and autoimmune diseases, such as rheumatoid arthritis and Crohn's disease. The TNF-α signaling pathway is a valuable target, and anti-TNF-α drugs are successfully used to treat autoimmune and inflammatory diseases. Here, we study anti-inflammatory activity of an anti-TNF-α peptide (SN1-13, DEFHLELHLYQSW). In the cellular level assessment, SN1-13 inhibited TNF-α-induced cytotoxicity and blocks TNF-α-triggered signaling activities (IC50 = 15.40 μM). Moreover, the potential binding model between SN1-13 and TNF-α/TNFRs conducted through molecular docking revealed that SN1-13 could stunt TNF-α mediated signaling thought blocking TNF-α and its receptor TNFR1 and TNFR2. These results suggest that SN1-13 would be a potential lead peptide to treat TNF-α-mediated inflammatory diseases.

Prediction of anti-inflammatory peptides by a sequence-based stacking ensemble model named AIPStack

Accurate and efficient identification of anti-inflammatory peptides (AIPs) is crucial for the treatment of inflammation. Here, we proposed a two-layer stacking ensemble model, AIPStack, to effectively predict AIPs. At first, we constructed a new dataset for model building and validation. Then, peptide sequences were represented by hybrid features, which were fused by two amino acid composition descriptors. Next, the stacking ensemble model was constructed by random forest and extremely randomized tree as the base-classifiers and logistic regression as the meta-classifier to receive the outputs from the base-classifiers. AIPStack achieved an AUC of 0.819, accuracy of 0.755, and MCC of 0.510 on the independent set 3, which were higher than other AIP predictors. Furthermore, the essential sequence features were highlighted by the Shapley Additive exPlanation (SHAP) method. It is anticipated that AIPStack could be used for AIP prediction in a high-throughput manner and facilitate the hypothesis-driven experimental design.

•

AIPStack model was developed for the prediction of anti-inflammatory peptides

•

The hybrid features were used to describe the peptide sequences

•

The proposed model AIPStack outperformed existing ones

•

SHAP was used to highlight the essential features required for AIP prediction

The Screening of Therapeutic Peptides for Anti-Inflammation through Phage Display Technology

For the treatment of inflammatory illnesses such as rheumatoid arthritis and carditis, as well as cancer, several anti-inflammatory medications have been created over the years to lower the concentrations of inflammatory mediators in the body. Peptides are a class of medication with the advantages of weak immunogenicity and strong activity, and the phage display technique is an effective method for screening various therapeutic peptides, with a high affinity and selectivity, including anti-inflammation peptides. It enables the selection of high-affinity target-binding peptides from a complex pool of billions of peptides displayed on phages in a combinatorial library. In this review, we will discuss the regular process of using phage display technology to screen therapeutic peptides, and the peptides screened for anti-inflammation properties in recent years according to the target. We will describe how these peptides were screened and how they worked in vitro and in vivo. We will also discuss the current challenges and future outlook of using phage display to obtain anti-inflammatory therapeutic peptides.