Hello world! We’re @MelodyFRO. Most wearables don’t go far beyond heart rate and activity. Lab diagnostics paint a deeper picture but don’t read continuously. Melody is building biosensors, devices, and standards to bridge this gap. Hear our story at melodyfro.org

We introduce ConforNets, a mechanism for conformational control in AlphaFold3 models - SoTA at producing diverse conformations on every multistate benchmark (N=104) - Novel capability: transfer state from one protein to another Outperforms BioEmu, ConforMix and AFsample3 🧵1/8

The Human Bindome: A Proteome-scale Atlas of Designed Binder Candidates 1. The paper introduces the Human Bindome: 306,146 designed, genetically encodable protein binder candidates (fixed length 90 aa) spanning 8,296 human proteins, each with an explicit sequence, a predicted binder–target complex structure, and in silico confidence metrics. 2. Core motivation: conventional antibody reagents are often expensive, variably characterized, and frequently lack disclosed sequences, contributing to reproducibility issues; the Bindome aims to provide openly specified alternatives at proteome scale. 3. Methodologically, they scale the experimentally benchmarked BindCraft pipeline (reported prior success rates 10–100% depending on target) by adding automated domain-level target selection and an accelerated parallel implementation suitable for proteome-scale execution. 4. Target selection is driven by AlphaFold DB full-length models plus PAE matrices: proteins are segmented into domains via PAE-based clustering, and “targetable domains” are filtered for confident folding, compactness, contact density, acceptable length, and exclusion of membrane-embedded regions. 5. A key engineering detail: designing against cropped domains makes computation cheaper and enables multiple distinct sites per protein, but can create clashes with other parts of the full-length protein; they address this by superimposing each complex onto the full-length model and rejecting sterically clashing designs unless the clash is in low-confidence regions (high PAE / low pLDDT). 6. All candidates pass unchanged BindCraft stringency thresholds, including binder monomer fold confidence (pLDDT > 0.8) and interaction placement confidence (normalized iPAE < 0.35); distributions are not piled up at cutoffs (medians pLDDT 0.92, iPAE 0.23), enabling within-target prioritization. 7. Coverage numbers: the “targetable proteome” is 10,664 of 20,279 proteins; Bindome covers 77.8% of that targetable set and 40.9% of the full human proteome, with ~37 candidates per target protein on average. 8. Structural breadth: mapping domains to CATH superfamilies suggests 78.2% of superfamilies represented in the human target-domain set have at least one binder candidate; uncovered regions are largely outside the current scope (notably integral membrane proteins). 9. Epitope biology: binder epitopes show convergence within domain families (e.g., grooves/pockets in WD40 and bromodomains; broad kinase-domain surfaces), and epitope residues are significantly more evolutionarily conserved than matched surface controls—more consensus across binders corresponds to higher conservation. 10. Functional relevance and perturbation potential: epitopes often overlap annotated functional sites (PTMs, protein–protein interfaces, ligand-binding sites, DNA-binding regions), and 26% of ASD-annotated allosteric targets have at least one binder overlapping an allosteric site; examples include candidates positioned to occlude TP53 DNA-binding or disrupt SOD1 interaction interfaces. 11. Interface properties vs native PPIs: binder interfaces are native-like in mean area and slightly more polar, but show a strong bias toward helix-mediated contacts on the designed binder side; binder folds are often not globally novel, yet interfaces appear largely novel by interface similarity comparisons. 12. Accessibility is treated as a first-class output: a free web interface supports ranking/inspection and bulk downloads; a standardized 3D-Beacons API enables programmatic retrieval; and an MCP server allows natural-language “agentic” querying via LLM tools. 13. ML utility: the dataset is released with leakage-controlled train/val/test splits based on interface clustering (205,553 train; 21,205 val; 21,955 test), designed to support development/evaluation of next-generation binder design models. 💻Code: github.com/wejulius/Bindome 📜Paper: biorxiv.org/content/10.64898… #ProteinDesign #ComputationalBiology #StructuralBioinformatics #AlphaFold #ProteinBinders #Reproducibility #FunctionalGenomics #DrugDiscovery #MachineLearning

gradients are ridiculous powerful tools we for some reason only use during training. it’s kinda crazy we don’t have any explicit gradients at test-time, that seems totally wrong

I never met my grandfather. He died of pancreatic cancer when my father was just 19. Today, Yash Bindal, 33, father to 18-month-old Maya, faces the same fate. @PopVaxIndia is using AI to make him a personalized generative medicine to extend his life. chronicles.popvax.com/p/one-…

In the essay I never thought I would write, I talk about my experience peaking at the "bad side" of medicine and present a defense for blood based early detection (3/3) Link: mundane.beauty/p/the-bad-sid…