MHC-II dynamics are maintained in HLA-DR allotypes to ensure catalyzed peptide exchange

Structural insights into human MHC-II association with invariant chain

The loading of processed peptides on to major histocompatibility complex II (MHC-II) molecules for recognition by T cells is vital to cell-mediated adaptive immunity. As part of this process, MHC-II associates with the invariant chain (Ii) during biosynthesis in the endoplasmic reticulum to prevent premature peptide loading and to serve as a scaffold for subsequent proteolytic processing into MHC-II-CLIP. Cryo-electron microscopy structures of full-length Human Leukocyte Antigen-DR (HLA-DR) and HLA-DQ complexes associated with Ii, resolved at 3.0 to 3.1 Å, elucidate the trimeric assembly of the HLA/Ii complex and define atomic-level interactions between HLA, Ii transmembrane domains, loop domains, and class II-associated invariant chain peptides (CLIP). Together with previous structures of MHC-II peptide loading intermediates DO and DM, our findings complete the structural path governing class II antigen presentation.

PANDORA v2.0: Benchmarking peptide-MHC II models and software improvements

T-cell specificity to differentiate between self and non-self relies on T-cell receptor (TCR) recognition of peptides presented by the Major Histocompatibility Complex (MHC). Investigations into the three-dimensional (3D) structures of peptide:MHC (pMHC) complexes have provided valuable insights of MHC functions. Given the limited availability of experimental pMHC structures and considerable diversity of peptides and MHC alleles, it calls for the development of efficient and reliable computational approaches for modeling pMHC structures. Here we present an update of PANDORA and the systematic evaluation of its performance in modelling 3D structures of pMHC class II complexes (pMHC-II), which play a key role in the cancer immune response. PANDORA is a modelling software that can build low-energy models in a few minutes by restraining peptide residues inside the MHC-II binding groove. We benchmarked PANDORA on 136 experimentally determined pMHC-II structures covering 44 unique αβ chain pairs. Our pipeline achieves a median backbone Ligand-Root Mean Squared Deviation (L-RMSD) of 0.42 Å on the binding core and 0.88 Å on the whole peptide for the benchmark dataset. We incorporated software improvements to make PANDORA a pan-allele framework and improved the user interface and software quality. Its computational efficiency allows enriching the wealth of pMHC binding affinity and mass spectrometry data with 3D models. These models can be used as a starting point for molecular dynamics simulations or structure-boosted deep learning algorithms to identify MHC-binding peptides. PANDORA is available as a Python package through Conda or as a source installation at https://github.com/X-lab-3D/PANDORA.

ITGB4 Serves as an Identification and Prognosis Marker Associated with Immune Infiltration in Small Cell Lung Carcinoma

Integrin beta 4 (ITGB4) is a vital factor for numerous cancers. However, no reports regarding ITGB4 in small cell lung carcinoma (SCLC) have been found in the existing literature. This study systematically investigated the expression and clinical value of ITGB4 in SCLC using multi-center and large-sample (n = 963) data. The ITGB4 expression levels between SCLC and control tissues were compared using standardized mean difference and Wilcoxon rank-sum test. The clinical significance of the gene in SCLC was observed using Cox regression and Kaplan-Meier curves. ITGB4 is overexpressed in multiple cancers and represents significant value in distinguishing among cancer samples (AUC = 0.91) and predicting the prognoses (p < 0.05) of patients with different cancers. In contrast, decreased ITGB4 mRNA expression was determined in SCLC (SMD < 0), and this finding was further confirmed at protein levels using in-house specimens (p < 0.05). This decrease in expression may be attributed to the regulatory role of estrogen receptor 1. ITGB4 may participate in the progression of SCLC by affecting several signaling pathways (e.g., tumor necrosis factor signaling pathway) and a series of immune cells (e.g., dendritic cells) (p < 0.05). The gene may serve as a potential marker for predicting the disease status (AUC = 0.97) and prognoses (p < 0.05) of patients with SCLC. Collectively, ITGB4 was identified as an identification and prognosis marker associated with immune infiltration in SCLC.