---
title: Protein AI stops predicting structures and starts designing them
description: Four preprints cover annotation, antibody screening, molecule design and a cancer target map. The shared move: judge proteins by what they do.
canonical: https://hangingcontext.com/stories/2026-11-06/protein-ai-moves-from-prediction-to-design/
last-updated: 2026-11-06
url: https://hangingcontext.com/stories/2026-11-06/protein-ai-moves-from-prediction-to-design/
published: 2026-11-06T00:36:59Z
edition: 2026-11-06T00
newsroom: research-science
beat: None
trust: confirmed
source_count: 4
cited_manifest_ids: ['1793890278325506047', '1793890278325393910', '1793882732857552163', '1793880415971562804']
---

# Protein AI stops predicting structures and starts designing them

*Four preprints cover annotation, antibody screening, molecule design and a cancer target map. The shared move: judge proteins by what they do.*

Confirmed · 4 Manifests · Research & Science · Published 2026-11-06 00:36 UTC

## Key facts

- PALM, a protein language model trained on 15 million InterPro-annotated sequences, scored domain and site F1s of 0.941 and 0.877 on 2.3 million held-out proteins, and reached a ClinVar AUROC of 0.835 against 0.710 for ESM2. (self-reported) [cites: 1793890278325506047]
- FoldAbS, which repurposes frozen protein folding models as encoders for antibody screening, improved specificity by 13.1%, affinity by 9.2% and developability by 6.2% over baseline methods, scoring all three in a single model. (self-reported) [cites: 1793890278325393910]
- PartiDesign, a unified structure-sequence co-design framework for proteins, nucleic acids and small molecules, achieved the highest estimated successful-design throughput among the methods evaluated. (self-reported) [cites: 1793882732857552163]
- An experimental map of Ras binding its effector Nore1A, built by screening about 33,000 Nore1A variants, found the cancer-linked RasG12D mutation reshapes the binding landscape while GTP hydrolysis barely remodels it. (self-reported) [cites: 1793880415971562804]
- None of the four has clinical or cellular validation yet: FoldAbS has no clinical validation, and the Ras paper's engineered inhibitors have not been validated in cells. [cites: 1793890278325393910, 1793880415971562804]

## What changed

The four preprints posted this week share a shift in emphasis: from predicting a protein's shape to deciding what it does, which antibody is worth making, and which molecule to design. A language model called PALM annotates protein function and scores variant effects; a method called FoldAbS repurposes frozen folding models to screen antibodies; a framework called PartiDesign designs proteins, nucleic acids and small molecules together; and an experimental map of the cancer protein Ras shows what the design tools are aiming at. [cites: 1793890278325506047, 1793890278325393910, 1793882732857552163, 1793880415971562804]

PALM, described in a preprint posted this week, was trained on 15 million InterPro-annotated sequences and scored domain and functional-site F1s of 0.941 and 0.877 on 2.3 million held-out proteins. On ClinVar missense variants it reached an AUROC of 0.835, against 0.710 for ESM2, and 0.877 when combined with ESM2 likelihood-ratio signals. Its embeddings also beat AlphaMissense on a benchmark of 128,161 literature-derived missense variants, and the authors present the embeddings as a stand-in for deep mutational scanning, a way to read variant effects without running the experiment for every variant. [cites: 1793890278325506047]

## Screening and design, in one pass

FoldAbS takes a different shortcut: instead of training a new model, it freezes a protein folding model and uses it as an encoder for antibody screening. In a preprint posted this week, the method, built on the Protenix folding model, improved specificity by 13.1%, affinity by 9.2% and developability by 6.2% over baseline methods, and it scores all three in a single model rather than in separate pipeline stages. The point is practical: antibody discovery currently judges candidates on separate criteria in separate steps, and a unified screen changes which candidates survive. [cites: 1793890278325393910]

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PartiDesign goes further toward generation. The framework designs structure and sequence together across proteins, nucleic acids and small molecules, with fine-grained control over secondary structure, chemical bonding and amino-acid preferences. In the preprint, it achieved the highest estimated successful-design throughput among the methods evaluated, and its configurable constraints cover specialised targets such as cyclic peptides and antibodies. The authors describe it as extending the partially latent framework of La-Proteina. [cites: 1793882732857552163]

## The experimental check

The Ras study is the experimental anchor for all of this. Researchers mapped how the small GTPase Ras binds its effector Nore1A by screening about 33,000 Nore1A variants with yeast surface display and deep mutational scanning. They found that GTP hydrolysis barely remodels the binding landscape, while the cancer-linked RasG12D mutation reshapes it substantially, and that some Nore1A variants bind the mutant more strongly while staying selective against the normal protein. The authors trace that to the mutant's greater intrinsic flexibility, and present the map as a starting point for designing inhibitors that hit only the cancer variant. [cites: 1793880415971562804]

None of this is proven where it counts yet. The Ras paper's engineered inhibitors have not been validated in cells, and the map covers only the Nore1A effector. FoldAbS has no clinical validation, and its performance on targets other than antibodies is uncharacterised. The PALM preprint makes no mention of releasing public code or model weights. These are preprints, and the gains are measured on benchmarks and in screening assays, not in patients or in production pipelines. [cites: 1793880415971562804, 1793890278325393910, 1793890278325506047]

## What it means for builders

The implication for anyone building with protein models is a change in where the work sits. Structure prediction is now a component, frozen, reused and built on, rather than the product. The product is the judgement layered on top: which variant is pathogenic, which antibody is developable, which designed molecule is worth synthesising. The papers this week each claim a piece of that judgement, and each backs the claim with a number. What none of them yet shows is the piece that matters most: a designed or screened candidate surviving contact with a real biological system. That is the test to watch for next. [cites: 1793890278325506047, 1793890278325393910, 1793882732857552163, 1793880415971562804]

## What is still unresolved

- All four are preprints, not peer-reviewed work.
- No clinical validation for FoldAbS; no cellular validation of the Ras inhibitors.
- The PALM preprint makes no mention of public code or model weight release.
- FoldAbS performance on non-antibody protein targets is uncharacterised.
- The Ras map covers only the Nore1A effector; broader applicability to other Ras effectors is untested.

## Decision card

- **Useful for:** AI developers building protein annotation, screening or design tools; Biotech teams prioritising antibody or variant candidates
- **Not for:** Clinical decisions; Production drug pipelines
- **Compare against:** Benchmark gains against wet-lab and cellular validation

## Provenance ledger

| Manifest id | Title | Source | Claims used | Published |
|---|---|---|---|---|
| `1793890278325506047` | A protein language model for domain, functional site, and variant effect prediction | [biorxiv-computational-biology-ai](https://biorxiv.org/content/10.64898/2026.11.03.764008v1.full) | 1793890662598108448, 1793890662600691628, 1793890662601848754, 1793890662603442390, 1793890662604692039 | 11-05 00:00 |
| `1793890278325393910` | FoldAbS: Repurposing the Protein Folding Model as a Foundation Encoder for Antibody Screening | [biorxiv-computational-biology-ai](https://biorxiv.org/content/10.64898/2026.11.03.763899v1.full) | 1793890661516115984, 1793890661523235155, 1793890661513956665 | 11-05 00:00 |
| `1793882732857552163` | PartiDesign: A structure-sequence co-design paradigm for practical all-to-all biomolecules design | [biorxiv-research-methods-reproducibility](https://biorxiv.org/content/10.64898/2026.11.03.763860v1.full) | 1793883117104595959, 1793883117106938789, 1793883117107347680 | 11-05 00:00 |
| `1793880415971562804` | The binding landscape of Ras/effector interactions provides insights on Ras switching and oncogenesis | [biorxiv-protein-structure-drug-discovery](https://biorxiv.org/content/10.64898/2026.11.04.764156v1.full) | 1793880792532844490, 1793880792534156408, 1793880792536642703 | 11-05 00:00 |

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Cited Manifest ids:

- `1793890278325506047`
- `1793890278325393910`
- `1793882732857552163`
- `1793880415971562804`

```sh
for id in 1793890278325506047 1793890278325393910 1793882732857552163 1793880415971562804; do
  curl -s -H "Authorization: Bearer $SYNORB_KEY" "https://api.synorb.com/manifests/by-id/$id"
done
```

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- [For molecule prediction, the representation is the decision](https://hangingcontext.com/stories/2026-11-04/molecular-prediction-representation-choice/)
- [Protein science now runs on AI pipelines, and their choices decide the result](https://hangingcontext.com/stories/2026-10-31/ai-pipelines-run-protein-science/)

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