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... Show moreThe 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-1b 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-1b complexes require inflammatory signals for sur-face expression in situ, despite the broad presence of Qa-1b 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. Show less
Cabukusta, B.; Berlin, I.; Elsland, D.M. van; Forkink, I.; Spits, M.; Jong, A.W.M. de; ... ; Neefjes, J. 2020
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... Show moreMembrane 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 BiolD, 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. Show less
Janssen, A.P.A.; Grimm, S.H.; Wijdeven, R.H.M.; Lenselink, E.B.; Neefjes, J.; Boeckel, C.A.A. van; ... ; Stelt, M. van der 2019
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 ... Show moreThe 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. Show less
Janssen, A.P.A.; Grimm, S.H.; Wijdeven, R.H.M.; Lenselink, E.B.; Neefjes, J.J.C.; Boeckel, C.A.A. van; ... ; Stelt, M. van der 2019
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 ... Show moreThe 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. Show less
Janssen, A.P.A.; Grimm, S.H.; Wijdeven, R.H.M.; Lenselink, E.B.; Neefjes, J.J.C.; Boeckel, C.A.A. van; ... ; Stelt, M. van der 2018
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 ... Show moreThe 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. Show less
Janssen, A.P.A.; Grimm, S.H.; Wijdeven, R.H.M.; Lenselink, E.B.; Neefjes, J.; Boeckel, C.A.A. van; ... ; Stelt, M. van der 2018
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 ... Show moreThe 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. Show less
Janssen, A.P.A.; Grimm, S.H.; Wijdeven, R.H.M.; Lenselink, E.B.; Neefjes, J.; Boeckel, C.A.A. van; ... ; Stelt, M. van der 2018
The aim of my work was to identify novel proteins that are involved in different aspects of disease biology, in the hope that we can target these to treat the disease. Using large-scale screening... Show moreThe aim of my work was to identify novel proteins that are involved in different aspects of disease biology, in the hope that we can target these to treat the disease. Using large-scale screening technologies, I found a novel regulator of chemosensitivity to the anti-cancer drugs etoposide and doxorubicin. Furthermore, novel proteins and drugs that target MHCII antigen presentation were picked up. Interestingly, one of these drugs is currently used in the clinic to treat MHCII-dependent autoimmune diseases, but for which the mechanism of action is unknown. Lastly, these screens yielded enzymes that control the dynamics of endosomes in the cell, which can potentially be targeted in cancers that rely on overactivation of growth signaling receptors. Taken together, my work has provided new potential targets for the treatment or classification of several tumor types, as well as a potential mechanism of action for a widely used drug. Show less
