
Aidan Tinafar • cell-free.com
712 posts

Aidan Tinafar • cell-free.com
@Tinafar
Making biology user-friendly #liberumbio #cell-free Ad vitam per naturam Opinions are my own


This organism has no organs, it has just one hole (for food to enter and exit) and scientists still argue where it belongs in the tree of life. It just slides on the deep sea floor, existing for no clear reason. It is called Xenoturbella.



NEW: DUTCH UNREALIZED GAINS TAX 🇳🇱 The Netherlands just voted to overhaul annual income tax filings with a new tax of up to 36% for unrealized capital gains, starting in 2028. Assets like Bitcoin on bitcoin, stocks, and bonds will trigger tax liabilities each year based on changes in value, even if nothing has been sold. I've been warning you: asset seizures are coming to Europe.



SeedFold: Scaling Biomolecular Structure Prediction 1. SeedFold introduces an innovative approach to scaling biomolecular structure prediction models, achieving state-of-the-art performance on FoldBench. The model outperforms AlphaFold3 in multiple tasks, highlighting its potential for advancing structural biology and drug discovery. 2. The study proposes a novel linear triangular attention mechanism that reduces computational complexity from cubic to quadratic, enabling more efficient scaling. This innovation addresses the computational bottleneck of traditional triangular operations in folding models. 3. SeedFold explores different scaling strategies, finding that increasing the width of the Pairformer module is more effective than deepening the model. This width-scaling strategy significantly enhances the model's capacity to encode complex pairwise interactions. 4. A large-scale distillation dataset was constructed to expand the training set to 26.5 million samples. This approach leverages knowledge from AlphaFold2 to improve the generalization of SeedFold, especially for tasks with limited experimental data. 5. Experiments show that SeedFold excels in predicting protein monomers, antibody-antigen interactions, and protein-RNA interfaces, while its linear variant performs exceptionally well in protein-ligand interactions. This indicates the versatility of the model across various biomolecular tasks. 6. The training process includes a two-stage strategy and stability optimizations to handle the challenges of scaling large models. Techniques like extended warm-up periods and reduced learning rates ensure stable convergence during training. 📜Paper: arxiv.org/abs/2512.24354






In the spirit of #BlueSoup and my friend Elinne, I am sequencing the Triton X-100 contaminant. Stay tuned! Big thanks to @johnowhitaker for bankrolling the plasmidsaurus run. All data will be made public and strain sharable. Yay, community science!












