Dr. Armita Nourmohammad

#TheoBiophysicsPosition We are recruiting postdocs at @UW (Seattle)! Join us if interested in bridging Stat. Phys. with immunology, proteins & evolution. Our work is theoretical & computational with experimental collaborations. info: sites.google.com/uw.edu/statphy… Some recent work:

this work was initiated by Mike Pun (now at @vant_ai), and is built on his work on H-CNN: pnas.org/doi/10.1073/pn… fantastic collaboration with @Asya_Minervina @PGTimmune , and Phil Bradley

check out our new manuscript (led by @GMarcoVisani) structure-based machine learning model for TCR-pMHC complexes, predicting T-cell affinity to peptide-MHC complexes, quantifying T-cell receptor specificity, and designing de-novo immunogenic peptides: arxiv.org/abs/2503.00648

Curious about generative modeling for T and B cell receptor repertoires and its role in understanding immune system diversity? Watch my talk here youtu.be/HVUYG-UmRH8?t=…. Thanks to the @airr_community and @victorgreiff for the opportunity to share our work!

🚨 Job Alert! 🚨 Join the UC Berkeley Physics Department! We’re hiring an Assistant Professor in soft condensed matter (broadly defined). Both experimentalists and theorists are encouraged to apply! aprecruit.berkeley.edu/JPF04653

Are you an early career scientist who wants to pursue a research project in the lab of a Fred Hutch Computational Biologist mentor? If yes, then the #MahanFellowship is for you. Apply by Dec. 20, 2024! Learn more: apply.interfolio.com/157470

HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction 1. HERMES introduces a cutting-edge 3D rotationally equivariant neural network model to predict the effects of mutations on protein stability and binding, significantly improving performance compared to previous models. 2. A key feature of HERMES is its pre-training on atomic 3D structures to predict amino acid identities, which enhances accuracy when fine-tuned to predict mutation effects. 3. HERMES offers substantial improvements in speed (2.75× faster) over its predecessor H-CNN while retaining high accuracy in mutation effect predictions, making it a practical choice for large datasets. 4. The model’s architecture respects permutation anti-symmetry in mutations, ensuring that the effect of reversing a mutation is predicted as the negative of the forward mutation, a feature that most models achieve through data augmentation. 5. When tested on experimental and computational protein structures, HERMES outperformed state-of-the-art models, including Stability-Oracle and ProteinMPNN, in predicting protein stability and binding effects. 6. HERMES was rigorously benchmarked across multiple datasets, showing excellent performance even when using computationally predicted protein structures, such as those from ESMFold, for both training and testing. 7. The paper highlights HERMES' ability to predict not only stability effects but also the binding affinity changes of mutations using the SKEMPI dataset, showing high correlation with experimental data. 8. HERMES is open-source, enabling users to fine-tune the model on their specific datasets for customized mutation effect predictions, potentially accelerating discoveries in protein engineering and disease-related mutation analysis. @TheArmita @GMarcoVisani 💻Code: github.com/StatPhysBio/he… 📜Paper: doi.org/10.1101/2024.0…

Are you an undergraduate interested in pursuing graduate studies biological physics? Join us for a virtual panel “Applying to Graduate School in Biophysics” with panelists who have been on both sides of the application process! Oct. 3 at 3pm ET. Register: apsphysics.zoom.us/webinar/regist…

My 'Predicting the evolution of antibiotic resistance' is out now 🤓🥹 tinyurl.com/3pkyejwp I review a topic that's very dear to my ❤️- how quantitative models of resource allocation can help predict bacterial behavior under stress 🦠💊 #AntibioticResistance #compbio

We are hiring post-docs! Please do not hesitate to share. We are especially looking for candidates with strong theory and computational backgrounds, who dream about 1. applying their skills and 2. developing new ones to understand squishy, non-linear, evolving ... stuff ! 😉

Check out HERMES, our equivariant structure-based suit of models to predict stability and fitness effect of mutations, led by @GMarcoVisani arxiv.org/abs/2407.06703 bonus: fine-tune your own protein models. Google Colab: colab.research.google.com/drive/1JQxkXeG… Github: github.com/StatPhysBio/pr…

We don’t want this climate future for Europe - in my view it’s a risk we should avoid at all cost, as I explain in this video: youtu.be/ZHNNW8c_FaA?si…

Is the #AMOC approaching a tipping point? Here's my take after researching this topic for over 30 years. Open access, peer-reviewed, in full colour & understandable for non-experts. tos.org/oceanography/a…

🚨Wed 29/05 - @TLM_Cambridge online seminar🚨 Professor Hernan García @HernanGGarcia (UC Berkeley) on “Using Physics as a Microscope to Dissect Transcriptional Dynamics in Development” Please RT & to attend online please register to our 📧: lists.cam.ac.uk/sympa/subscrib…

Do you have an excellent idea for an NITMB Long Program? Learn how the NITMB can support you as you develop your idea into an exceptional program! 🧵(1/5)

very excited about the upcoming theory of living matter seminar @TLM_Cambridge

For postdocs and other early-career physicists, this is a great way to get involved!

Joins us for Machine Learning in Computational Biology (MLCB) 2024 mlcb.github.io Submission deadline: June 15 Conference dates: Sept 5 & 6

Dr. John C. Mather from @NASA will give the next Frontiers of Physics Public Lecture at @UW Seattle on May 8th. Register for free at frontiers.uw.edu and come learn about @NASAWebb !

Holographic-(V)AE: An end-to-end SO(3)-equivariant (variational) autoencoder in Fourier space, Gian Marco Visani, Michael N. Pun, Arman Angaji, and Armita Nourmohammad @TheArmita @GMarcoVisani @UW @uwcse @physicsuw #NeuralNetworks go.aps.org/4cyPTMd

@FakhriLab @UW Thanks for the visit and for the wonderful talk.

Had a wonderful visit to Physics Department @UW. Thank you @TheArmita for hosting me. And got to see the peak bloom on campus…