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Jacques Neefjes

Researcher at Leiden University Medical Center

Publications -  352
Citations -  34927

Jacques Neefjes is an academic researcher from Leiden University Medical Center. The author has contributed to research in topics: MHC class I & Antigen presentation. The author has an hindex of 95, co-authored 331 publications receiving 31500 citations. Previous affiliations of Jacques Neefjes include University of Amsterdam & Netherlands Cancer Institute.

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Human VAPome Analysis Reveals MOSPD1 and MOSPD3 as Membrane Contact Site Proteins Interacting with FFAT-Related FFNT Motifs.

TL;DR: MOSPD1 and MOSPD3 are described as ER-localized tethers interacting with FFAT motif-containing proteins that expand the VAP family of proteins and highlight two separate complexes in control of interactions between intracellular compartments.
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PKA phosphorylation redirects ERα to promoters of a unique gene set to induce tamoxifen resistance.

TL;DR: It is demonstrated that activation of the PKA signaling pathway alters the transcriptome by redirecting ERα to new transcriptional start sites, resulting in altered transcription and TAM resistance.
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Cutting Edge: HLA-B27 Acquires Many N-Terminal Dibasic Peptides: Coupling Cytosolic Peptide Stability to Antigen Presentation

TL;DR: It is suggested that HLA-B27 can present peptides from Ags present in fewer copies than required for successful peptide generation for other MHC class I molecules.
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Multidrug resistance-associated protein 9 (ABCC12) is present in mouse and boar sperm

TL;DR: In mouse and boar sperm, Mrp9 localizes to the midpiece, a structure containing all sperm mitochondria, however, immunolocalization microscopy and cell fractionation studies with transfected HEK-293 cells and mouse testis show that MRP9/Mrp9 does not localize to mitochondria.
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Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome–Inhibitor Interaction Landscapes

TL;DR: Drug Discovery Maps (DDM) is a machine learning model that maps the activity profile of compounds across an entire protein family, based on the t-distributed stochastic neighbor embedding algorithm to generate a visualization of molecular and biological similarity.