Who needs AI? @Nature charged $11K to publish this howler. #NatureRipoffs forbetterscience.com/2026/06…

CryoEM has pretty low resolution, you can see things on the order of protein domains, and you need millions of particles to see individual proteins. This enables us to get an order of magnitude higher resolution, where we're able to _see_ the secondary structures in a single particle! This is important because protein structural measurement is one of the main bottlenecks for understanding structural biology. Really exciting work from the imaging institute!!

Laser phase plate improves structure determination of small proteins by cryo-EM | Science science.org/doi/10.1126/scie…

Plant-parasitic #nematodes have evolved specialized effector proteins, which can suppress reactive oxygen species (ROS) bursts, detoxify reactive molecules, or manipulate host pathways to reduce immune responses. Anil Kumar, Chunoti Changwal, and Thomas J. Baum review the current knowledge on these effector-driven strategies—their discovery using advanced genomics; their specific molecular mechanism of ROS suppression; and the critical interplay between ROS signaling and plant hormone pathways during infection—and provide an overview of the key techniques used to detect and quantify ROS in plant–nematode interactions: doi.org/10.1094/MPMI-11-25-0…

The structure of endo-1,4-β-xylanase A from Anaerobacterium chartisolvens is the first from the newly defined glycoside hydrolase family 30 subfamily 12 @UChicago @ActaCrystD @IUCr #GlycosideHydrolaseFamily30 #GH30 #GH3012 doi.org/10.1107/S20597983260…

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…

Susceptible and resistant corn to southern corn rust @UNL_PlantPath @UNLincoln @UNLresearch

Beautiful rainbow 🌈 in Norwich

Whether apoplastic small RNAs regulate plant immunity? Check our work in tomato identified their roles in priming plant defence to Phytophthora. Thanks for the wonderful suggestion from @wenboEffector and the professional processing of the editors at MP

I’ve officially resigned as Associate Editor for Frontiers in Systems Neuroscience (part of @FrontNeurosci). It used to be a reputable journal, but became a case study in how forced automation destroys academic integrity. 👇

OrthoFinder: improved phylogenetic orthology inference with enhanced accuracy and scalability | Nature Methods nature.com/articles/s41592-0…

Mitochondria tethered to the nucleus secure its energy supply nature.com/articles/d41586-0…

The first crystal structures of the luminal domain of Fsc1 reveal an elongated, modular architecture composed of five tandem fasciclin domains @michiganstateu @ActaCrystD @IUCr #Fsc1 #FasciclinDomain #AutophagosomeVacuoleFusion doi.org/10.1107/S20597983260…

ipTM and ipSAE don't predict binding affinity. A-alpha Bio measured 7M interactions: almost no correlation. Dug into this with Michael Holden in Ep 1 of Protein Engineering in Practice (by @ranomics): youtu.be/cVmGeFGsVA0

AlloGen: Conformation-Selective Binder Generation with Differential State Scoring 1. AlloGen targets a core limitation in protein binder design: optimizing affinity to a single receptor structure can yield binders that engage both active/inactive (apo/holo) states, providing little functional specificity for allosteric systems (kinases, nuclear receptors, GPCRs). 2. The framework decouples generation from evaluation: any backbone generator proposes candidates for the desired state, then a learned scorer Qθ ranks or guides designs by a differential selectivity margin between goal (holo) and undesired (apo) conformations. 3. Qθ is an SE(3)-invariant interface graph transformer that scores receptor–binder interface geometry in a rigid-motion-invariant way, using interface graphs (8 Å cutoff) with residue-local frames, geometric edge features (distance RBFs, directions, relative orientations), and optional ESM-2 embeddings. 4. Training uses a two-phase curriculum to avoid degenerate “ignore-the-conformation” solutions: Phase 1 regresses to DockQ (interface quality grounding), then Phase 2 applies paired InfoNCE fine-tuning on (holo, apo, binder) triplets with cross-target negatives to force true conformational discrimination rather than receptor identity bias. 5. On 8 held-out OOD targets, Qθ shows consistent rank correlation with DockQ (mean Spearman ρ ≈ 0.520), while contact/energy proxies (PRODIGY, interface size, edge density) largely fail to track docking quality and cannot provide a differential state signal. 6. Qθ appears to encode target- and conformation-specific information: cross-target scoring shows strong diagonal dominance (designs score best on their intended target/state), and on calmodulin it produces a monotonic score increase along an interpolated apo→holo conformational path, suggesting it learns a continuous landscape rather than a binary label. 7. Because Qθ is differentiable and generator-agnostic, it supports multiple integration modes without retraining generators: passive best-of-K reranking and active guidance (classifier guidance, twisted diffusion sampling, SMC resampling, and post-generation Langevin refinement). 8. Across 15 generator×guidance combinations (RFdiffusion, PXDesign, Proteina-ComplexA), resampling-based guidance (TDS/SMC) is broadly strongest; Langevin refinement helps structure-only generators but can harm sequence-aware priors (e.g., PXDesign), emphasizing that guidance interacts with generator assumptions. 9. Experimental validation on calmodulin (a challenging ~30 Å apo↔holo rearrangement) supports the computational selectivity signal: 5/10 synthesized de novo peptides bound holo CaM (KD 46.6 nM to 1.06 µM) with no detectable apo binding, while a low-∆q negative control showed no binding—linking predicted differential scoring to measurable state specificity. 10. The study positions conformational selectivity as a learnable, transferable design objective: a modular scorer trained on paired states can retrofit existing binder-generation pipelines to design molecules that recognize functional states rather than static structures. 💻Code: huggingface.co/ChatterjeeLab… 📜Paper: arxiv.org/abs/2606.05474 #ComputationalBiology #ProteinDesign #GenerativeAI #MachineLearning #StructuralBiology #Allostery #ProteinEngineering #DiffusionModels #GNN #Calmodulin

Very useful website to score PPI. Shows why you should NOT use ipTM - p53 MDM2 scores 0.3 in ipTM, but 0.75 in ipSAE, 0.67 in iLIS, 0.99 in actifpTM. Well-known interaction, already in AF2 training data. ipTM is low bc of large irrelevant domains and IDRs in BOTH proteins.

Potential unlocked: an atlas of cloned wheat genes for genome engineering and breeding nph.onlinelibrary.wiley.com/… ♻️

🚀 New tool out! LIVIA (Local Interaction Visualization and Analysis) — a browser-based tool for assessing and visualizing predicted protein-protein interactions. Drop in a prediction from AlphaFold-Multimer, AF3, ColabFold, Boltz-1/2, Chai-1, or OpenFold3 (ZIP or folder, auto-detected) and LIVIA answers the two questions you actually care about: ▸ Do these proteins interact? ▸ Which residues form the interface? What you get: ▸ Interface confidence scores — iLIS (our local metric), ipSAE, actifpTM, ipTM ▸ Interaction interface heatmaps (PAE, LIS, cLIS) ▸ Sequence viewer linear & circular contact maps highlighting Local Interaction Residues (LIR) and contact LIR (cLIR) ▸ Embedded Mol* 3D viewer ▸ Downloadable ChimeraX & PyMOL scripts Also fetches dimers directly from the AlphaFold Database — adding the interface annotations AFDB doesn't provide. Everything runs locally in your browser. No install, no upload. LIVIA started as a personal tool — I built it with Claude Code and used it to make every structure figure in our FlyPredictome preprint (Kim et al., 2026). (Claude Code is truly insane...) Along the way I realized it could be useful for others, too. If you have a favorite color palette for structure visualization, please let me know — happy to add it as a preset 🎨 🔗 LIVIA tool: flyark.github.io/LIVIA 🐍 iLIS / batch CLI: github.com/flyark/AFM-LIS 📄 LIVIA preprint: biorxiv.org/content/10.64898… 📄 FlyPredictome preprint: biorxiv.org/content/10.64898…

#TansleyInsight: Cross-kingdom communication between plants and parasitic #nematodes @Chris_Bell6, @lderevnina & @Seb_EvdA 👇 📖 nph.onlinelibrary.wiley.com/… #LatestIssue #PlantScience