Tesei, Pesce & Lindorff-Larsen (2026) Computational design of intrinsically disordered proteins. Curr Opin Struct Biol 96:103210 #IDP #biophys #CompBiol #StructBiol #biophys #CompChem doi.org/10.1016/j.sbi.2025.1…

Metadiffusion: Inference-Time Meta-Energy Biasing of Biomolecular Diffusion Models 1. The authors introduce metadiffusion, a method that adds an inference-time meta-energy biasing layer on top of pretrained biomolecular diffusion models like Boltz-2, enabling diverse conformational ensemble generation without any retraining or fine-tuning. 2. The approach supports three complementary modes: optimisation (pushing collective variables to extrema), steering (targeting specific values), and exploration (maximizing inter-sample diversity through Gaussian repulsion potentials). 3. Metadiffusion generates conformational ensembles whose residue-level flexibility patterns closely match molecular dynamics simulations, achieving mean Pearson's R of 0.81 with ATLAS MD data—approaching the reproducibility ceiling between independent MD runs themselves. 4. The method enables controlled exploration of collective variables including radius of gyration, hinge angles, solvent-accessible surface area, and pairwise RMSD, allowing researchers to traverse alternative binding poses across proteins, nucleic acids, and ligands. 5. Through gradient-guided denoising, metadiffusion can steer ensembles to match experimental observables including SAXS pair distance distributions and NMR chemical shifts, as demonstrated with class V GTP aptamer and human calmodulin. 6. The technique operates orders of magnitude faster than microsecond-scale MD simulations while maintaining physical plausibility, with generated structures readily improvable through energy minimization to resolve occasional bond breaks and steric clashes. 7. As a model-agnostic framework, metadiffusion can theoretically extend to other diffusion-based structure predictors including AlphaFold3, OpenFold, and Chai-1, bridging the gap between single-structure prediction and ensemble-level experimental constraints. 💻Code: github.com/metadiffusion/met… 📜Paper: biorxiv.org/content/10.64898… #compbiol #structuralbiology #proteindynamics #diffusionmodels #machinelearning #alphafold #boltz2 #conformationalensemble #SAXS #NMR #metadynamics #biomolecularsimulation

My group is looking to recruit 2 postdocs👩‍💻👨‍🏫 on the "A-SOuRCCE" project: 1⃣ AI for Knowledge Graphs in Regulatory Genomics 2⃣ Agentic AI for Scientific Discovery in Genomics Ideal positions for recent bioinformatics/genomics/compbiol PhDs looking to pivot towards AI 🖥️🧠 & to do impactful science. Also great for computer science people looking to get into genomics. Apply via links below! 👇 Deadline in ~2 weeks (10.2.)

Opportunity in machine learning for #biologics #DrugDiscovery at Roche (Basel Switzerland) ref: 202507-117925 #ML #AI #CompBiol #CompChem #cheminformatics #ChemJobs roche.wd3.myworkdayjobs.com/…

Happy to share our latest work "Large-scale energy decomposition for the analysis of protein stability". We use simple simulations to predict the impact of mutations on stability of more than 250 mutants in different folds. doi.org/10.1016/j.cstres.202… #CompChem #CompBiol #AI #Biotech

#PostDoc opportunity in molecular modelling & machine learning at AstraZeneca (Gothenburg Sweden) ref: R-207702 | closing: 15-Sep-2024 | #ML #AI #cheminformatics #CompChem #CompBiol #PostDocJobs #ChemPostDoc careers.astrazeneca.com/job/…

A productive day of #networking and fostering new connections with @LivUniHDS at the HDS/CBF away day 🧬👩🏽‍💻 Looking forward to working more closely together and building successful #collaborations! 💖 #teamscience #datascience #compbiol #bioinformatics

Come and join a really exciting project - a collaboration between @cavignone_r @GSK and us here at Strathclyde on #Streptomyces #EngBio #SynBio #CompBiol #MtabolicModelling pls pass on to any interested modellers/computational biologist pls RT

We'd welcome DPhil applicants to our #SPACE_Study lab through @OxfordCancer progr. We use #CuttingEdge spatial genomics to unpack #ProstateCancer lethality •Translational science •CompBiol •Wet lab Apply 1st Dec youtu.be/YdzF0-PFXhc cancer.ox.ac.uk/study(No.52 in booklet)

Opportunity for computational biologist to investigate #aging and age-related diseases at AbbVie (Lake County IL USA) #CompBiol #bioinformatics #CompBiolJobs careers.abbvie.com/en/job/la…

See how to use #compchem #compbiol #AI #MachineLearning to Design Antibodies, Antigens and Immunogens for vaccinology. Huge work by @FedericaGuarra in Journal of Chemical Theory and Computation @JCIM_JCTC pubs.acs.org/doi/10.1021/acs…

Fantastic opportunity to join a fantastic joung CompBiol group with a great dedicated PI! 👇🏽👇🏽👇🏽👇🏽

We'd welcome applications from aspiring #ClinicianScientists through @OxfordCancer DPhil programme to our #SPACE_Study lab where we use exciting #CuttingEdge spatial genomics to unpack #ProstateCancer lethality •Translational science •CompBiol •Wet lab youtu.be/YdzF0-PFXhc

👆@ClaireLonsdale2 @RJABuggs @gawbul @coregenomics @JosiahSeaman @jomcinerney @Metallophyte @ja_kirkpatrick ? #biofinformatics #compbiol #agile #scidevops

So, this is the paper we talked about at our @compbiol journal club today: Precise Prediction of Promoter Strength Based on a De Novo Synthetic Promoter Library Coupled with Machine Learning pubs.acs.org/doi/10.1021/acs… 1/?

Submit your #ComputationalBiology research to @thePeerJ between Nov 1st and Nov 10th and receive an APC waiver! Find out more: peerj.com/blog/post/11528488… #ADecadeOfPeerJ #CompBiol

Thanks for the support and publicity @SOBLA_BIOF!!! We feel like walking on the red carpet 🤩🎉 1day away from the Mini-symposium: Building Bridges in CompBiol 👏👏👏 @PatSoto_BioPhys @CeciliaBores n best team ever 💜 🇧🇴🇨🇴🇪🇸

Very happy to welcome the full score of our new #masterstudents in #compbiol & #bioinformatics to @thegoecampus and @uniGoettingen

FWIW, I don’t personally think that all bioinformatics is computational biology nor all CompBiol is really bioinformatics. But most of both is also the other. Both are so broad that if you care about any difference you need to be more specific anyway.

In my opinion differentiating Bioinfo and CompBio has proven to be difficult and not that useful (what is the purpose?) From the examples below: Protein function prediction is Bioinfo & protein interactions CompBiol?