New paper! How do RNAs "know" where to go inside a cell? We dug into the sequence elements that route RNAs to the right place. It turns out that, in mammals, they're surprisingly massive (>200 nt), multipartite, and wonderfully complicated. 🧵

Calling all early-career plant-microbe researchers! 🌱🦠📢👋 Rising Stars of IS-MPMI: your chance to present your work to a global audience. PhD students & postdocs - self-nominate for the July 29 session. ⏳ Deadline: June 30 Apply now: docs.google.com/forms/d/e/1F…

Counting down the days until #ESN2026! Don’t miss out on our YNN workshop where we will explore unexpected and negative results to extract meaningful messages! 🪱

AF Cache: Efficient Pipeline for Running AlphaFold for High-Throughput Protein-Protein Interaction Prediction 1 AF Cache is a Nextflow pipeline designed to make large-scale protein–protein interaction (PPI) screening practical with AlphaFold2 and AlphaFold3 by cutting the dominant runtime bottlenecks: repeated MSA generation and (for AF2) repeated JAX recompilations. 2 The key idea is to generate MSAs once per unique monomer for the whole dataset, then reuse (“cache”) them across all pairs. For an all-against-all screen of N proteins, default multimer workflows can trigger ~N(N 1) chain-level MSA generations, while AF Cache reduces this to N by construction. 3 AF Cache accelerates MSA generation by using GPU-accelerated MMseqs2 and batching all proteins in a single dataset-level step (rather than per-target or small batches). It also overlaps CPU and GPU steps across UniRef and environmental database alignments to reduce GPU idle time. 4 For AlphaFold2 specifically, AF Cache adds sequence-length bucketing for multimers (bucketed by total chain length) and pads within each bucket so JAX compilation happens once per bucket instead of once per pair—saving ~1–2 minutes of GPU time for every additional multimer in that bucket. 5 Benchmark: 100 human mitochondrial proteins (40–1000 aa) were screened all-against-all including homodimers (5,050 pairs). Settings were streamlined for throughput (AF2: single model, 3 recycles, templates disabled; AF3: single seed, one diffusion sample). 6 Pre-prediction speedups (MSA stage): when comparing to a realistic 128-CPU-core baseline, AF Cache achieved ~13x faster MSA generation for AF2 settings using full BFD, and ~5x faster for AF3 settings using small BFD. The paper also reports very large raw GPU-vs-CPU core-hour reductions, emphasizing the benefit of GPU-based MSA search in high-throughput regimes. 7 End-to-end impact for AF2: caching bucketing reduced AF2 prediction compilation time from 253 to 125 GPU hours (>50% reduction), and made inference ~90 seconds faster per protein pair on average while preserving the same runtime scaling with pair length. 8 Output similarity was assessed via ipTM comparisons. Correlations between default vs AF Cache runs were moderate across all pairs (Pearson r ~0.70 for AF2; ~0.64 for AF3), but much higher for pairs where both proteins map to a shared PDB entry (r ~0.98 for AF2; ~0.94 for AF3), suggesting structurally supported cases are highly consistent across pipelines. 9 Practical deployment details: AF Cache provides a ready-to-use workflow for local and HPC environments, supports all-against-all or user-specified pair lists, automates AF3 JSON preparation, and can download/install dependencies (including databases; AF2 network parameters) as needed. 💻Code: github.com/clami66/AF_cache 📜Paper: arxiv.org/abs/2606.04566 #AlphaFold #ProteinInteractions #PPI #Bioinformatics #ComputationalBiology #StructuralBioinformatics #Nextflow #HPC #MMseqs2 #Proteomics

Another major problem, this time in additive combinatorics, has fallen, this time to humans rather than AI, but using methods related to the AI solution to the unit distance conjecture.

How do predatory wasps find their caterpillar prey? In our new preprint on Biorxiv we show that the host plant's immune system detects herbivory to sound the alarm. 🌱🐛 See thread from @nataliagupa -- a surprising role for a plant PRR in indirect defense against herbivores.