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Human leukocyte antigen (HLA) class-I molecules present fragments of the cellular proteome to the T cell receptor (TCR) of cytotoxic T cells to control infectious diseases and cancer. The large number of combinations of HLA class-I allotypes and peptides allows for highly specific and dedicated low-affinity interactions to a diverse array of TCRs and natural killer (NK) cell receptors. Whether the divergent HLA class-I peptide complex is exclusive for interactions with these proteins is unknown. Using genome-wide CRISPR-Cas9 activation and knockout screens, we identified peptide-specific HLA-C∗07 combinations that can interact with the surface molecules CD55 and heparan sulfate. These interactions closely resemble the HLA class-I interaction with the TCR regarding both the affinity range and the specificity of the peptide and HLA allele. These findings indicate that various proteins can specifically bind HLA class-I peptide complexes due to their polymorphic nature, which suggests there are more interactions like the ones we describe here., Competing Interests: The authors declare no competing interest., (© 2024 The Author(s).)

The immune checkpoint NKG2A/CD94 is a promising target for cancer immunotherapy, and its ligand major histocompatibility complex E (MHC-E) is frequently upregulated in cancer. NKG2A/CD94-mediated inhibition of lymphocytes depends on the presence of specific leader peptides in MHC-E, but when and where they are presented in situ is unknown. We apply a nanobody specific for the Qdm/Qa-1 b complex, the NKG2A/CD94 ligand in mouse, and find that presentation of Qdm peptide depends on every member of the endoplasmic reticulum-resident peptide loading complex. With a turnover rate of 30 min, the Qdm peptide reflects antigen processing capacity in real time. Remarkably, Qdm/Qa-1 b complexes require inflammatory signals for surface expression in situ, despite the broad presence of Qa-1 b molecules in homeostasis. Furthermore, we identify LILRB1 as a functional inhibition receptor for MHC-E in steady state. These data provide a molecular understanding of NKG2A blockade in immunotherapy and assign MHC-E as a convergent ligand for multiple immune checkpoints., Competing Interests: Declaration of interests J.W. was co-founder and chief scientist at Abexxa Biologics, Inc., during the study and had ownership interest (including stock and patents) of Abexxa. J.W. is a consultant for Boehringer-Ingelheim International GmbH. T.v.H. reports receiving a commercial grant from Abexxa and was an advisory board member for the same., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Membrane contact sites (MCS) are intracellular regions where two organelles come closer to exchange information and material. The majority of the endoplasmic reticulum (ER) MCS are attributed to the ER-localized tether proteins VAPA, VAPB, and MOSPD2. These recruit other proteins to the ER by interacting with their FFAT motifs. Here, we describe MOSPD1 and MOSPD3 as ER-localized tethers interacting with FFAT motif-containing proteins. Using BioID, we identify proteins interacting with VAP and MOSPD proteins and find that MOSPD1 and MOSPD3 prefer unconventional FFAT-related FFNT (two phenylalanines [FF] in a neutral tract) motifs. Moreover, VAPA/VAPB/MOSPD2 and MOSPD1/MOSPD3 assemble into two separate ER-resident complexes to interact with FFAT and FFNT motifs, respectively. Because of their ability to interact with FFNT motifs, MOSPD1 and MOSPD3 could form MCS between the ER and other organelles. Collectively, these findings expand the VAP family of proteins and highlight two separate complexes in control of interactions between intracellular compartments., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

The interpretation of high-dimensional structure-activity data sets in drug discovery to predict ligand-protein interaction landscapes is a challenging task. Here we present Drug Discovery Maps (DDM), a machine learning model that maps the activity profile of compounds across an entire protein family, as illustrated here for the kinase family. DDM is based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm to generate a visualization of molecular and biological similarity. DDM maps chemical and target space and predicts the activities of novel kinase inhibitors across the kinome. The model was validated using independent data sets and in a prospective experimental setting, where DDM predicted new inhibitors for FMS-like tyrosine kinase 3 (FLT3), a therapeutic target for the treatment of acute myeloid leukemia. Compounds were resynthesized, yielding highly potent, cellularly active FLT3 inhibitors. Biochemical assays confirmed most of the predicted off-targets. DDM is further unique in that it is completely open-source and available as a ready-to-use executable to facilitate broad and easy adoption.