1/ 🌟 We've added more results to our preprint‼️ TLDR: diatom in vivo assembly, RNP delivery via electroporation, and a new PEG transformation method... (1/10) biorxiv.org/content/10.1101/…

Nature may have explored only a tiny fraction of the proteins that could actually work. That sounds obvious when you consider the size of protein sequence space. But experimentally demonstrating it is much harder. A paper from Lawrence Livermore National Laboratory tested thousands of natural and new-to-nature proteins across three protein families, asking a surprisingly fundamental question: How much functional sequence space exists beyond the proteins evolution has already shown us? The answer appears to be: a lot. Across all three families, the researchers found many experimentally functional proteins far from known natural orthologs. In total, the study measured fitness for roughly 3,000 natural proteins and 10,000 new-to-nature designs, creating an unusually large experimental map of distant protein sequence space. But the second result may be even more important for protein AI. The experimental fitness landscapes were rugged. Protein language models and evolutionary sequence models could often capture broad signals of function, but they did not consistently reproduce either the local shape or the global trends of the measured landscapes. In some comparisons, simpler approaches such as Potts models and HMMs performed as well as or better than much larger AI models. That creates an interesting tension. Most protein foundation models learn heavily from the proteins that evolution happened to preserve. But the set of proteins that exist in nature is not necessarily the same as the set of proteins that could function. Evolution had historical constraints, ecological constraints, mutational paths and billions of contingent events. It never performed an exhaustive search of sequence space. So perhaps natural protein databases should not be treated as a map of functional protein space. They are a sparse evolutionary archive of it. And if functional proteins occupy regions that nature never sampled, the next frontier of protein design is not simply learning the distribution of existing proteins more accurately. It is learning where that distribution ends — and where functional biology continues beyond it. The proteins we know may be islands. The functional sequence space around them may be an ocean. biorxiv.org/content/10.64898…

Bridge editing is a technology for inserting, excising, or inverting any two pieces of DNA. Delighted to report in @Nature today a new paper with @hnisimasu on the mechanism of bridge RNAs and how these programmable gene editors cut themselves out of the genome!

Huge faculty recruitment effort at the University of Chicago -- a division-wide, open-rank search looking to hire about 20 faculty across 5 research areas in biology. Come be our colleague! Full announcement: uchicago.app.box.com/s/ckih9…

Interested in how sequence-to-expression AI models can be used to predict synthetic biology and genome engineering designs and what the failure modes might be? > Timon has posted a new benchmark set on our GitHub. See his thread below.

Benchmark alert! 📏 Today, @liv_soro and I launch the first comprehensive benchmark for yeast-focussed sequence-to-function models. github.com/Tom-Ellis-Lab/yea…

Deep Mutational Scanning at Genome Scale

Our lab’s latest on synthetic genomics “Synthetic Combinatorial Minimisation of Cell Cycle Control” is up now on BioRxiv - covering ambitious yeast #synbio cell cycle work led by Anastasiya Malyshava and co-supervised by @UniOfSurrey’s Matteo Barberis.

A new method to do mutagenesis inside of living cells, using libraries of retron editors. You put your gene of interest onto an F-plasmid and then coax the cells to pass it around by conjugation. Each recipient cell carries an editor, so the gene picks up a mutation with each transfer. The trick to keeping this "relay" going is a set of three selection markers that you rotate through: at every step you can select for those cells that just received the gene, and those cells become the senders for the next round. So the cycle repeats indefinitely, for as long as you need. They used the method to engineer a pyrrolysyl-tRNA synthetase enzyme to incorporate "unnatural" amino acids into proteins. Normally, if you want to make a library of genetic variants, you'd do it by mutating DNA in a test tube (or synthesizing pseudo-random sequences) and then transforming that into cells. The problem is that cells only take up a fraction of the DNA variants; not all the genetic diversity in the library actually makes it inside the cells. And if you instead express editors directly in cells to edit over and over, those editors tend to make unwanted mutations "in the background," or on the genome, which is usually bad. This new method solves both problems. First, there's no hard cap set by transformation: because the diversity is built up inside the population as the gene travels from cell to cell, the ceiling is set by how many cells you have to work with, not by how much DNA you can transform. And second, because the plasmid is constantly moving into a fresh batch of cells, the gene goes into a "clean" host each time. You are basically resetting the genetic background during each cycle to eliminate those off-target mutations. Clever.

Today in @Nature we report how AI-guided redesign enhances protein evolution. Integrating ProteinMPNN sequence design with autonomous laboratory evolution, we establish a workflow to engineer enzymes with improved properties over those evolved from natural proteins. Redesigned starting points consistently evolve an expanded fitness landscape, reaching new function with higher activity, specificity, and stability than their natural counterparts. drive.google.com/file/d/1C-Q… 1/14

Engineered plasmids from thousands of labs share almost no consolidated design language — until now. PlasmidGPT is a generative language model pretrained on 153,208 engineered plasmid sequences from Addgene, learning embeddings rich enough to visualize research topics across labs and map plasmid diversity across vector types. Those embeddings do real work: state-of-the-art lab-of-origin prediction for engineered plasmids, plus generalization to natural plasmids for host taxonomy prediction at both phylum and genus level. On the generative side, PlasmidGPT can be steered by a seed sequence or explicit design constraints to produce new functional plasmid sequences that recapitulate the part co-occurrence and synteny patterns seen in real plasmids — not just plausible-looking DNA, but structurally coherent designs. science.org/doi/10.1126/scia…

5 days left now to apply for the postdoc opportunity in my lab at Imperial in London 🇬🇧 - there’s a chance that we can hire 2 people into the team on this synthetic biology and materials theme. Application link is here - imperial.ac.uk/jobs/search-j…

Huge new work from Japan - radically engineered E.coli genome now half the size of the usual genome. 🧬 🦠 Impressive work… minimising genomes is on the march!

1/ Here's an angle to LySE I couldn't show fully in my @NatureMicrobiol paper: Combining my passion for DIY bio & directed evolution, LySE is extremely low-cost. The workflow is just mixing phage lysates and cells. All you need is a pipette & incubator. No epPCR or bioreactors!

This is the culmination of >10 years of work by generations of students and postdocs. Many false starts. And we know that others have tried too. Structural insights into the activate-and-release mechanism of NLR activation. And we can engineer it too! Kudos Team 🙏🔥

Excited to share our latest paper, out today @CellCellPress. We found that large pieces of the human genome can transfer between cells upon direct contact, endowing recipient cells with heritable phenotypic changes. (1/7) cell.com/cell/fulltext/S0092…

おかげさまで、gbdrawのGitHub starsが100個になりました。ありがとうございます

Excited to share our discovery of a new programmable RNA-guided DNA-targeting system hiding inside bacteriophages that predates CRISPR. We call it VIPR (Viral Interference Programmable Repeat), and it uses an entirely new logic to find its targets. Thread + link below.

Newest paper from our lab lead by @Daniel_Nucifora. We show that the entire genome of A. laidlawii can be transferred directly into yeast by cell fusion. Cloning whole genomes in yeast is a key step toward building and engineering synthetic cells. pubs.acs.org/doi/10.1021/acs…

The smallest bacterial genome, further blurring the bacteria-organelle boundary! Convergent extreme reductive evolution in ancient planthopper symbioses nature.com/articles/s41467-0…

Excited that our latest diatom engineering research @SchulichMedDent was highlighted by CTV News London! Congrats @ejlwalker and all authors. ctvnews.ca/london/article/fr…