05/2023 - Two-component molecular motor driven by a GTPase cycle
ATPases are a group of enzymes that can cyclically convert the free energy of ATP hydrolysis into mechanical work. GTPases are another class of enzymes that are predominantly associated with signal transduction processes, but their role in mechanotransduction is less established. It was previously shown that the binding of the GTPase Rab5 to the tethering […]
03/2023 - Immune selection determines tumor antigenicity and influences response to checkpoint inhibitors
In cancer, evolutionary forces select for clones that evade the immune system. Here we analyzed >10,000 primary tumors and 356 immune-checkpoint-treated metastases using immune dN/dS, the ratio of nonsynonymous to synonymous mutations in the immunopeptidome, to measure immune selection in cohorts and individuals. We classified tumors as immune edited when antigenic mutations were removed by […]
03/2023 - Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning
While AlphaFold2 can predict accurate protein structures from the primary sequence, challenges remain for proteins that undergo conformational changes or for which few homologous sequences are known. Here we introduce AlphaLink, a modified version of the AlphaFold2 algorithm that incorporates experimental distance restraint information into its network architecture. By employing sparse experimental contacts as anchor […]
02/2023 - MicroFPGA: An affordable FPGA platform for microscope control
Modern microscopy relies increasingly on microscope automation to improve throughput, ensure reproducibility or observe rare events. Automation requires computer control of the important elements of the microscope. Furthermore, optical elements that are usually fixed or manually movable can be placed on electronically-controllable elements. In most cases, a central electronics board is necessary to generate the control signals they require and to […]
02/2023 - Protein complexes in cells by AI-assisted structural proteomics
Accurately modeling the structures of proteins and their complexes using artificial intelligence is revolutionizing molecular biology. Experimental data enable a candidate-based approach to systematically model novel protein assemblies. Here, we use a combination of in-cell crosslinking mass spectrometry and co-fractionation mass spectrometry (CoFrac-MS) to identify protein–protein interactions in the model Gram-positive bacterium Bacillus subtilis. We show […]
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