Interleukin-25 recognition by its unique receptor IL-17Rb via two discrete linear and cyclic epitopes

Molecular design and systematic optimization of a halogen-bonding system between the asthma interleukin-5 receptor and its cyclic peptide ligand

Human interleukin-5 (IL-5) functions as an important pro-inflammatory factor by binding to its specific receptor, IL-5Rα, which has been implicated in the pathogenesis of asthma. Previously, a disulfide-bonded cyclic peptide AF17121 obtained from random library screening and sequence variation was found to competitively disrupt the cognate IL-5Rα/IL-5 interaction with moderate potency. In this study, the crystal complex of IL-5Rα with AF17121 was investigated at structural and energetic levels. It is revealed that the side-chain indole moiety of the AF17121 Trp5 residue is a potential site for a stem putative halogen bond (X-bond) with IL-5Rα, which is just located within the key 3 EXXR6 motif region recognized specifically by IL-5Rα. We systematically examined four halogen substitution types at five positions of the indole moiety; QM/MM calculations theoretically unraveled that only halogenations at 5 and 6 positions can form effective X-bonds with the side-chain hydroxyl oxygen of the IL-5Rα Thr21 residue and the backbone carbonyl oxygen of Ala66 residue, respectively. Binding assays observed that I-substitution at the 5 position and Br-substitution at the 6 position can result in two potent halogenated peptides, [5I]AF17121 and [6Br]AF17121, which are improved by 1.6-fold and 3.5-fold relative to the native AF17121, respectively. 5I/6Br-double substitution, resulting in [5I/6Br]AF17121, can further enhance the peptide affinity by 7.5-fold. Structural analysis revealed that the X-bond stemming from 6Br-substitution is also involved in an orthogonal interaction system with a H-bond; they share a common backbone carbonyl oxygen acceptor of IL-5Rα Ala66 residue and exhibit a significant synergistic effect between them.

Rational design of stapled helical peptides as antidiabetic PPARγ antagonists to target coactivator site by decreasing unfavorable entropy penalty instead of increasing favorable enthalpy contribution

Peroxisome proliferator-activated receptor γ (PPARγ) is a ligand-activated transcription factor belonging to the nuclear hormone receptor and has been exploited as a well-established druggable target for the treatment of diabetes mellitus (DM). Traditionally, small-molecule compounds have been developed to attack at the ligand site and Ser273 phosphorylation site of PPARγ. In this study, we derived helical peptide segments from the LXXLL motif region of coactivator proteins as antidiabetic PPARγ antagonists, which were expected to competitively disrupt the native interaction between PPARγ and its cognate coactivators by rebinding at PPARγ coactivator site. Structural analysis, dynamics simulation and energetics dissection revealed that these peptides cannot be well folded into active helical structure when splitting from the protein context of their parent coactivators and exhibit a large flexibility and intrinsic disorder in the free state, which would, therefore, incur a considerable entropy penalty upon rebinding to PPARγ. Hydrocarbon stapling strategy was employed to constrain these free coactivator peptides into ordered helical conformation, thus largely minimizing unfavorable entropy penalty but having only a moderate effect on favorable enthalpy contribution. The computational findings were further substantiated by fluorescence-based assays; the binding affinity of three potent SRC1, NCoA6 and p300 coactivator peptides to PPARγ was observed to be improved by 7.2-fold, 4.2-fold and 5.7-fold upon the stapling, which were also measured to have an efficient competitive potency with their unstapled counterparts for PPARγ coactivator site, with CC₅₀ = 0.096, 0.12 and 0.18 μM, respectively.

Construction of a Colorectal Cancer Prognostic Risk Model and Screening of Prognostic Risk Genes Using Machine-Learning Algorithms

This study is aimed at constructing a prognostic risk model for colorectal cancer (CRC) using machine-learning algorithms to provide accurate staging and screening of credible prognostic risk genes. We extracted CRC data from GSE126092 and GSE156355 of the Gene Expression Omnibus (GEO) database and datasets from TCGA to analyze the differentially expressed genes (DEGs) using bioinformatics analysis. Among the 330 shared DEGs related to CRC prognosis, we divided the analysis period into different phases and applied univariate COX regression, LASSO, and multivariate COX regression analysis. GO analysis and KEGG analysis revealed that the functions of these DEGs were primarily focused on cell cycle, DNA replication, cell mitosis, and other related functions, and this confirmed our results from a biological perspective. Finally, a prognostic risk model for CRC based on the CHGA, CLU, PLK1, AXIN2, NR3C2, IL17RB, GCG, and AJUBA genes was constructed, and the risk score enabled us to predict the prognosis for CRC. To obtain a comprehensive and accurate model, we used both internal and external evaluations, and the model was able to correctly differentiate patients with CRC into a high-risk group with poor prognosis and a low-risk group with good prognosis. The AUC values of the 3-, 5-, and 10-year survival ROC curves were 0.715, 0.721, and 0.777, respectively, according to the internal evaluation, and the AUC values were 0.606, 0.698, and 0.608, respectively, for the external evaluation using GSE39582 from the GEO database. We determined that CLU, PLK1, and IL17RB could be considered to be independent prognostic factors for CRC with significantly different expression (P < 0.05). Using machine-learning methods, a prognostic risk model comprised of eight genes was constructed. Not only does this model provide improved treatment guidance, but it also provides a novel perspective for analyzing survival conditions at a deeper biological level.

Interleukin-1α, Interleukin-1β and Interleukin-1 Receptor Antagonist Share a Common U-shaped Recognition Epitope on Interleukin-1 Receptor Surface

Interleukin-1 (IL-1) plays a central role in the regulation of immune and inflammatory responses. There are two forms of IL-1 agonists (IL-1α and IL-1β) and one form of IL-1 antagonist (IL-1Ra); they share a similar binding mode to IL-1 receptor (IL-1R) but exhibit opposite biological functions on the receptor. In this study, the intermolecular interactions of IL-1R receptor with IL-1α, IL-1β and IL-1Ra ligands were systematically investigated at structural, energetic and dynamic levels. It was found that the receptor primarily adopts a U-shaped, double-stranded and linear/conformational-hybrid epitope to commonly interact with the three ligands. The epitope covers a common protein segment (residues 107-127), which is fully located within in the C2T2 subsdomain of IL-1R extracellular domain (ECD) and contributes ~40% to the total binding energy of IL-1R/ligand association. The epitope is natively folded into an ordered conformation in IL-1R protein context but would become largely disordered out of the context. Here, we adopted a disulfide bridge to staple U-shaped epitope-derived peptides, which can be effectively constrained into a native-like conformation and thus exhibit an improved affinity to ligands as compared to their unstapled counterpart, with affinity increase by up to ~15-fold. These disulfide bridges were designed to point out of ligand/peptide complex interface and thus would not disrupt the direct complex interaction. Energetic decomposition imparted that the stapling has only a modest influence on the interaction enthalpy and desolvation effect of ligand/peptide binding, but can substantially reduce entropy penalty upon the binding. For a peptide, the stapling-addressed entropic reduction can be roughly regarded as a constant, which only improves peptide affinity to these ligands, but does not change peptide selectivity over different ligands. This article is protected by copyright. All rights reserved.