Dominik Hrebik

I am very happy to announce that we solved the in-virus #cryoEM structure of the #HIV matrix protein. The structure helped us to resolve a long mystery in HIV biology. A🧵1/5 Read more in @Nature : nature.com/articles/s4158… 🧪

Researchers killed Mycoplasma capricolum cells, confirmed they were dead, and then "revived" them by transplanting in a synthetic Mycoplasma mycoides genome. I'll plan to write a longer essay on this.

Our latest article at @AsimovPress is a step-by-step guide to designing antibodies with BoltzGen, AlphaFold3, and other tools.

Very happy to see this work from Fabian Rehm, Jason Chin, and team on the development of a high error-rate orthogonal DNA replication system in E. coli, thus supporting continuous hypermutation and evolution of target genes in vivo. The system is based on a protein-primed linear plasmid replication mechanism, which has become a reliable way of realizing the orthogonal replication concept through which hypermutation is durably targeted to an orthogonal plasmid while sparing the genome. Our original orthogonal DNA replication (OrthoRep) system in yeast, Rongzhen Tian's and Jason Chin's BacORep and EcORep systems in bacteria, and now this promising new system all repurpose protein-primed replication to achieve orthogonality. I look forward to seeing how these systems continue to be applied to gene and biomolecular evolution at scale! nature.com/articles/s4158…

Out Now! Structural insights into the assembly and evolution of a complex bacterial flagellar motor dlvr.it/TQG534 #BacterialResearch #Microbiology #FlagellarMotor

We just made an app that walks you through designing a novel protein with AI from scratch. Takes about 5 minutes, requires zero biology knowledge. ➡️ design-a-protein.com The best part: we will actually synthesize 1000 of those protein designs in the lab and test their real world function as a therapeutic.

@OdedRechavi I like to listen to this one before my talks. Now back to the lab again...

Preparing to ask a question when you raise your hand and wait for your turn to speak for 3 seconds

@DrHelenFry Thank you Dr. Fry, this was very interesting reading. I really like history and personal accounts like this one.

In the 1960s, Herman Rothman recognised a name on a list of accused Auschwitz war criminals. It was someone he once knew. He had Perry Broad's diary in his attic. What happened next took him to the courtroom of one of the most infamous Nazi trials in history: (🧵)

@slavov_n This is actually a good counterargument to people who claim that X-ray crystallography structures are non-physiological.

The protein concentration in the cytoplasm is so high that the average protein has a water hydration shell of only ≈ 10 water molecules separating it from the adjacent protein hydration shell.

IT'S HAPPENING! 💥 I'm psyched to launch the collaboration between @qedScience & @openrxiv @biorxivpreprint! Let's go back to concentrating on making discoveries, and not on “getting published”, because it’s not going to matter soon, when feedback & evaluation are detached from the journals. Preprint + q.e.d = your science is out there, and anyone can appreciate it 👇

Group Leader in Biomedical Engineering at the @MDC_Berlin in Berlin. It's a broad call for anyone doing exciting bioengineering; please apply or share. We have a great institute with amazing colleagues and core facilities, highly recommend! application.mdc-berlin.de/site/index.php…

Maximum Likelihood seems like such a natural idea, but it has historically been highly controversial, with an epic and turbulent history with numerous assaults on the idea, culminating in a beautiful and complicated theory. A highly entertaining read:

Rubisco is (arguably) the most abundant protein on Earth. (LPP surely comes close, right?) It’s an enzyme that fixes CO₂ into sugars during photosynthesis. Unfortunately, as most people learn in school, Rubisco is inefficient. Sometimes it confuses O₂ for CO₂ and wastes energy. Plants make up for this in raw concentration; up to half the soluble protein in a leaf is Rubisco. People have been trying to engineer better Rubiscos for many decades, but it's not easy because the proteins are big, do not fold easily (they need chaperone proteins to help out), are made from 16 subunits in land plants. But there's a new paper in Nature Plants that looks really interesting. The TL;DR is that a group in Australia figured out how to express plant Rubiscos (and all SEVEN of their folding chaperones) using a set of 3 plasmids inside of E. coli cells. This enabled them to do "directed evolution" of Rubisco in bacterial cells, and quickly find Rubisco mutants that have higher enzymatic efficiency or that fold better. In addition to the 3 plasmids, the researchers also coaxed E. coli to make ribulose-1,5-biphosphate, or RuBP, which is the 5-carbon sugar that Rubisco smashes into carbon dioxide to make molecules of 3-PGA for central metabolism. Now, the clever bit is that you RANDOMLY MUTATE the three plasmids encoding the Rubisco to make millions of variants. Then, you transform those mutated plasmids into E. coli. If the E. coli do NOT make a functional Rubisco, RuBP levels build up and kill the cell; the molecule becomes toxic. But if the E. coli DO make a functional Rubisco, then they keep the RuBP levels in check and live just fine. Using this "screening assay," the researchers found 46 fast-growing colonies of E. coli. Two of those colonies encoded really useful mutations. One mutation (M116L) makes Rubisco about 25–40% faster. The other (A242V) makes it fold and assemble much more efficiently. They put this mutation into a "hybrid Arabidopsis–tobacco Rubisco," put that into tobacco plants, and measured growth. The plants with M116L grew 75% faster than wildtype. No guarantees this will scale to more useful crops, like wheat and corn and soybeans etc. But it seems like a nice in vitro assay for faster prototyping!

ChimeraX daily builds can predict binding affinity of small molecules using Boltz 2 on your Mac, Windows or Linux computer. rbvi.ucsf.edu/chimerax/data/…

Looking for a deep dive into 2D classification in #cryoEM? These recent guide pages cover practical aspects & parameter choices when running these jobs in #CryoSPARC, along with the theory behind expectation maximization: guide.cryosparc.com/processing-dat… and guide.cryosparc.com/expectation-ma…

@MatejAdamy Deravý mohol dostať Darwinovu cenu. Ani jedným sposobom sa to nepodarilo. Takto nic no...

Keby Cintula mieril ešte horšie, Fico nemá žiadne pohlavie.

CodonTransformer: a multispecies codon optimizer using context-aware neural networks Scientists looking to produce proteins in new host organisms often face challenges in adjusting DNA sequences to match the host’s translation preferences. This customization, known as codon optimization, can improve expression of both natural and engineered proteins in bacteria, yeast, plants, and mammals. However, designing sequences that preserve long-range dependencies and avoid regulatory pitfalls requires sophisticated methods and extensive data. Fallahpour et al. developed a Transformer-based deep learning model, trained on over one million gene-protein pairs spanning 164 species, to address these complexities. Their architecture applies a BigBird Transformer variant with block-sparse attention, incorporating specialized tokens that represent both amino acids and codons. Through a masked language modeling strategy, the authors introduced a framework called STREAM that aligns codon and amino acid information, enabling the model to generate complete DNA sequences from partial inputs. They further fine-tuned the model using the top 10% of genes with the highest codon similarity index and demonstrated context-specific optimizations that captured organism-level preferences. The researchers found that their approach produces DNA sequences with a near-natural balance of rare and common codons, matching local frequency distributions and maintaining GC content. They also reported minimal disruption by unwanted cis-regulatory elements, making these outputs potentially suitable for high-yield protein production. This combination of accurate language modeling, comprehensive training data, and flexible fine-tuning illustrates the model’s potential for robust codon optimization across multiple species. Paper: nature.com/articles/s4146…

Introducing ATOMICA 💫 A model to universally represent molecular interactions (for proteins, nucleic acids, small molecules, and ions) at an all-atom scale 🧵

Did quantum mechanics for protein structures sneak up on everyone else this past year, or was it just me? Recently-impossible, 🔥protein-scale🔥 quantum accuracy is now feasible with AI-native, whole-protein quantum refinement

LigandMPNN is a deep-learning-based protein sequence design method that can explicitly model all nonprotein components of biomolecular systems. nature.com/articles/s4159…

Hey ChimeraX users! ChimeraX-CliX now supports color preview, quick menubar/toolbar search, and many other powerful features to support you. If you are interested in, please download the latest version -> cxtoolshed.rbvi.ucsf.edu/apps/chimeraxc…