Corrigendum: Structural Models for Roseolovirus U20 And U21: Non-Classical MHC-I Like Proteins From HHV-6A, HHV-6B, and HHV-7.

[This corrects the article DOI: 10.3389/fimmu.2022.864898.].

Structural Models for Roseolovirus U20 And U21: Non-Classical MHC-I Like Proteins From HHV-6A, HHV-6B, and HHV-7.

Human roseolovirus U20 and U21 are type I membrane glycoproteins that have been implicated in immune evasion by interfering with recognition of classical and non-classical MHC proteins. U20 and U21 are predicted to be type I glycoproteins with extracytosolic immunoglobulin-like domains, but detailed structural information is lacking. AlphaFold and RoseTTAfold are next generation machine-learning-based prediction engines that recently have revolutionized the field of computational three-dimensional protein structure prediction. Here, we review the structural biology of viral immunoevasins and the current status of computational structure prediction algorithms. We use these computational tools to generate structural models for U20 and U21 proteins, which are predicted to adopt MHC-Ia-like folds with closed MHC platforms and immunoglobulin-like domains. We evaluate these structural models and place them within current understanding of the structural basis for viral immune evasion of T cell and natural killer cell recognition.

Regulation of PCSK9 Expression and Function: Mechanisms and Therapeutic Implications.

Proprotein convertase subtilisin/kexin type 9 (PCSK9) promotes degradation of low-density lipoprotein receptor (LDLR) and plays a central role in regulating plasma levels of LDL cholesterol levels, lipoprotein(a) and triglyceride-rich lipoproteins, increasing the risk of cardiovascular disease. Additionally, PCSK9 promotes degradation of major histocompatibility protein class I and reduces intratumoral infiltration of cytotoxic T cells. Inhibition of PCSK9 increases expression of LDLR, thereby reducing plasma levels of lipoproteins and the risk of cardiovascular disease. PCSK9 inhibition also increases cell surface levels of major histocompatibility protein class I in cancer cells and suppresses tumor growth. Therefore, PCSK9 plays a vital role in the pathogenesis of cardiovascular disease and cancer, the top two causes of morbidity and mortality worldwide. Monoclonal anti-PCSK9 antibody-based therapy is currently the only available treatment that can effectively reduce plasma LDL-C levels and suppress tumor growth. However, high expenses limit their widespread use. PCSK9 promotes lysosomal degradation of its substrates, but the detailed molecular mechanism by which PCSK9 promotes degradation of its substrates is not completely understood, impeding the development of more cost-effective alternative strategies to inhibit PCSK9. Here, we review our current understanding of PCSK9 and focus on the regulation of its expression and functions.

MHC-I peptide binding activity assessed by exchange after cleavage of peptide covalently linked to ß2-microglobulin.

A common approach to measuring binding constants involves combining receptor and ligand and measuring the distribution of bound and free states after equilibration. For class I major histocompatibility (MHC-I) proteins, which bind short peptides for presentation to T cells, this approach is precluded by instability of peptide-free protein. Here we develop a method wherein a weakly-binding peptide covalently attached to the N-terminus of the MHC-I ß2m subunit is released from the peptide binding site after proteolytic cleavage of the linker. The resultant protein is able to bind added peptide. A direct binding assay and method for estimation of peptide binding constant (Kd) are described, in which fluorescence polarization is used to follow peptide binding. A competition binding assay and method for estimation of inhibitor binding constant (Ki) using the same principle also are also described. The method uses a cubic equation to relate observed binding to probe concentration, probe Kd, inhibitor concentration, and inhibitor Ki under general reaction conditions without assumptions relating to relative binding affinities or concentrations. We also delineate advantages of this approach compared to the Cheng-Prusoff and Munson-Rodbard approaches for estimation of Ki using competition binding data.