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 that crosslinking interactions prior to cell lysis reveals protein interactions that are often lost upon cell lysis. We predict the structures of these protein interactions and others in the Subti Wiki database with AlphaFold‐Multimer and, after controlling for the false‐positive rate of the predictions, we propose novel structural models of 153 dimeric and 14 trimeric protein assemblies. Crosslinking MS data independently validates the AlphaFold predictions and scoring. We report and validate novel interactors of central cellular machineries that include the ribosome, RNA polymerase, and pyruvate dehydrogenase, assigning function to several uncharacterized proteins. Our approach uncovers protein–protein interactions inside intact cells, provides structural insight into their interaction interfaces, and is applicable to genetically intractable organisms, including pathogenic bacteria.

During 2010–2011, we investigated interspecies transmission of partetraviruses between predators (humans and chimpanzees) and their prey (colobus monkeys) in Côte d’Ivoire. Despite widespread infection in all species investigated, no interspecies transmission could be detected by PCR and genome analysis. All sequences identified formed species- or subspecies (chimpanzee)-specific clusters, which supports a co-evolution hypothesis.

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, co-fractionation mass spectrometry and the SubtiWiki database to identify protein-protein interactions in the model Gram-positive bacterium Bacillus subtilis. Pairing this with structure prediction by AIphaFold-Multimer, we identify novel interactors of central machineries that include the ribosome, RNA polymerase and pyruvate dehydrogenase, as well as interactions involving uncharacterized proteins, which we functionally validate. After controlling for the false-positive rate of the AlphaFold approach, we propose novel structural models of 153 dimeric and 14 trimeric protein assemblies. We show that crosslinking MS data can independently validate AlphaFold predictions in situ. Our approach uncovers protein-protein interactions inside cells, provides structural insight into their interaction interface, and is applicable to genetically intractable organisms, including pathogenic bacteria.

It can be challenging to effectively impart higher education content to students. We experienced such difficulty in a lecture series with invited senior scientists presenting their area of Biotech research. Instead of a vivid exchange with the expert, we observed limited and restrained student contributions. In qualitative interviews with these students we learned that they perceive their knowledge disparity as too big and the fear of being embarrassed by asking “stupid” questions obstructed their participation. This let us to radically rethink the course design resulting in our own interpretation of flipped classroom, peer learning and student empowerment. We designed an engineering course that focuses on providing master students with the best possible environment to gain theoretical knowledge in a new field within a limited time period (currently: six weeks - six topics) aiming to empower them in these topics by acquiring new knowledge on their own. Based on seed questions and tag words, students conduct background research and create a team presentation for an invited field expert, thereby getting prepared for a subsequent indepth discussion with the expert. The current layout is the product of an iterative process over the course of five years, and several rounds of fine-tuning within each year, based on extensive student and instructor feedback. Students particularly appreciate the positive in-course atmosphere with a focus on growth-mindset, the strong experience in teamwork, being taken seriously, and making contact with field experts and frontiers of current knowledge.

Listening to scientific presentations and reading scientific literature are core activities of any scientist, and frequent components of students' curricula. When employing these activities in teaching, finding the right balance between student instruction and autonomous learning is important for best learning outcomes and teachers’ workload. We here present our course design for a coordinated lecture series and journal club, that finds this balance by leveraging modern learning concepts in a digital environment. Participating students were tasked to read a landmark scientific paper every week ahead of a lecture by a scientist with practical experience on the topic of that paper, often an author of that week’s paper. Students then had to hand in written answers to three questions probing their understanding of the topic and the paper. In a subsequent seminar, activating questions were discussed by the students in break-out rooms and then answered by randomly chosen students in class, followed by a broad discussion that included the homework questions. Students gave weekly feedback on their learning progress and experience, and the course was then dynamically adapted accordingly. This yielded a course with largely increased course capacity, reduced teachers’ workload, and substantially enhanced learning outcomes, qualitatively and quantitatively compared to previous implementations of the course.