Bogdan Toader

@btoader.com

Applied mathematician developing computational imaging tools #CryoET #TeamTomo Postdoc in Scheres & Bharat labs @ MRC Laboratory of Molecular Biology, Cambridge btoader.com

Disrupting phage liquid crystalline droplets restores antibiotic susceptibility in Pseudomonas aeruginosa biofilms out in @plosbiology.org by @abultarafder.bsky.social and team. Exciting collaboration with @geiselbiofilm.bsky.social @pearce-maths.bsky.social and others

PLOS Biology@plosbiology.org · 2mo ago

#Biofilm matrices containing filamentous phages help #Pseudomonas aeruginosa tolerate antibiotics. @abultarafder.bsky.social @tbharat-lab.bsky.social &co show that #nanobody disruption of #phage Pf4 #LiquidCrystalline droplets restores #antibiotic susceptibility @plosbiology.org 🧪 plos.io/4xkd6Mw

Top: Nanobody binders are potent inhibitors of Pf4 liquid crystalline droplet formation and disrupt preformed droplets. Cryo-ET of Pf4 liquid crystalline droplets incubated with Nb43. Tomographic slice of a Pf4 liquid crystalline droplet specimen incubated with (left) 0.1 μM and (right) 1 μM Nb43. Bottom: Schematic representation of nanobody action in abolishing antibiotic tolerance of P. aeruginosa biofilms. In untreated biofilms (left), cells show increased antibiotic tolerance due to Pf4 liquid crystalline droplets formed by depletion attraction in the biofilm EPS matrix, where encapsulated cells are protected by an antibiotic diffusion block. In nanobody treated biofilms, patchy binding of nanobody to Pf4 filaments reduces depletion attraction between the filaments preventing liquid crystalline droplet formation and encapsulation of cells, leading to increased antibiotic susceptibility of bacteria within the biofilm.

Oxygen gradients reshape cross-feeding through emergent spatial organization of gut commensal bacteria www.biorxiv.org/content/10.6... Use of isotope labels and cryo-CLEM-FIB-SIMS to study microbial communities by Hannah Ochner. Collaboration with @kiranrpatil.bsky.social @jmghigolab.bsky.social

Oxygen gradients reshape cross-feeding through emergent spatial organization of gut commensal bacteria

Microbial interactions unfold within environments structured by physical transport and chemical gradients. Yet most mechanistic studies rely on well-mixed systems that mask the reciprocal influences of environmental heterogeneity on metabolism and ecology. Here, we investigate how the physical environment modulates the interaction between the gut commensal Bacteroides thetaiotaomicron and Escherichia coli . In anoxic liquid culture, cell-resolved isotope imaging and genetic perturbations reveal exploitative cross-feeding, where E. coli consumes diffusible sugars released by B . thetaiotaomicron during starch degradation. When exposed to intestinal-like oxygen gradients in microfluidics, the interaction is restructured by spatial organization. The species self-organize into complementary niches: E. coli locally depletes sugars and oxygen, thereby expanding the anoxic niche required by B. thetaiotaomicron . A reactive transport model confirms that this organization arises from coupled feedback between physical transport and metabolic reaction rates. Together, our results reveal how physical structure and chemical gradients convert an exploitative cross-feeding interaction into a dynamic niche-construction process that generates emergent spatial organization and stabilizes coexistence. ### Competing Interest Statement The authors have declared no competing interest.

biorxiv.org

Check out our pre-print, where we train a protein and small molecule force field from scratch with a graph neural network. We show comparable performance to existing, manually-tuned force fields on a range of tasks including binding free energy prediction. (1/4) arxiv.org/abs/2603.16770

Training a force field for proteins and small molecules from scratch

Force fields for molecular dynamics are usually developed manually, limiting their transferability and making systematic exploration of functional forms challenging. We developed a graph neural networ...

arxiv.org

Please spread the word: the Structural Studies Division @mrclmb.bsky.social is looking for a new tenure-track, independent group leader with an exciting plan in any area of Structural (Molecular & Cell) biology, in discovery biology and/or methods development. 🥳 mrc.tal.net/vx/mobile-0/...

Research Group Leader Tenure Track - Structural Studies - LMB 2775 - Medical Research Council

Location: Cambridge. Vacancy: Research Group Leader Tenure Track - Structural Studies - LMB 2775. Closing Date: 16/03/2026, 23:55

mrc.tal.net

Very excited to be able to talk about something I've been working on for a while now - we're working with Commonwealth Fusion Systems, IMO the leading fusion startup in the world, to take our work on AI and tokamaks and make it work at the frontier of fusion energy. deepmind.google/discover/blo...

Google DeepMind is bringing AI to the next generation of fusion energy

We’re announcing our research partnership with Commonwealth Fusion Systems (CFS) to bring clean, safe, limitless fusion energy closer to reality with our advanced AI systems. This partnership...

deepmind.google

Introducing DINOv3 🦕🦕🦕 A SotA-enabling vision foundation model, trained with pure self-supervised learning (SSL) at scale. High quality dense features, combining unprecedented semantic and geometric scene understanding. Three reasons why this matters👇

Bild

Our entrance into protein design! Inverse folding steered by external sources of information, and for multiple conformations. By the amazing @kaiyi94.bsky.social and @kjamali.bsky.social! Download our code and try it yourself. 🥳

Kai Yi@kaiyi94.bsky.social · last yr.

Excited to share our paper at #ICML: All-atom inverse protein folding through discrete flow matching with @kjamali.bsky.social and @sjorsscheres.bsky.social : openreview.net/forum?id=8tQdw…. If you are at ICML, let’s connect and talk generative models&protein design!