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Purna can run two families of protein language models on your sequences, directly from chat:
  • ESM-2 — for generic proteins. Use it for embeddings or per-position mutation likelihoods on enzymes, receptors, scaffolds, or any non-antibody protein.
  • AbLang-2 — for paired antibodies. Use it on heavy + light chain pairs for antibody-aware embeddings or per-position mutation likelihoods that reflect natural antibody repertoires.
Both capabilities show a compact submission entry in chat, store the full numerical output as a downloadable file, and bill on actual compute time. Use Compute or Jobs to follow the run and Files to open its outputs.

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What each capability does

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Embeddings

An embedding is a fixed-length vector that represents a full protein sequence. Two sequences with similar embeddings are treated as similar by the underlying model, typically reflecting similar fold, family, or function. Use embeddings when you want to:
  • Cluster a set of sequences by similarity
  • Compare two or more proteins numerically
  • Search a library for sequences similar to a reference
  • Build features for downstream ML (activity prediction, property prediction)
You submit a batch of sequences, and Purna records how many were encoded and the vector dimensionality. The full numerical embeddings are saved as a downloadable JSON file so you can load them into a notebook, a clustering pipeline, or your own models.

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Mutation likelihoods

A mutation likelihood is a per-position score for every possible amino acid substitution at every residue. High scores at a position mean the model finds multiple alternatives plausible, which is often a signal of an evolvable, tolerant, or flexible site. Low scores mean the model strongly prefers the natural residue. Use mutation likelihoods when you want to:
  • Identify candidate sites for mutagenesis or affinity maturation
  • Flag unusual residues in a sequence
  • Rank substitutions at a known hotspot
  • Get a starting point for protein engineering, before committing to wet-lab rounds
The completed run summarizes the top mutable positions and their highest-likelihood substitutions. The full per-position × 20-amino-acid likelihood matrix is saved as a downloadable file for deeper analysis.

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When to use ESM-2 vs AbLang-2

AbLang-2 is trained on paired antibody repertoires, so its likelihoods and embeddings reflect the statistics of real antibody sequences — CDR variability, framework conservation, and the pairing between heavy and light chains. ESM-2 is a general-protein model: broader coverage, no antibody-specific bias. For antibodies, start with AbLang-2 for repertoire-aware signal. For everything else, start with ESM-2.

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Asking for a run

You can trigger either capability with a natural-language prompt. A few examples: ESM-2 embeddings
  • “Compute ESM-2 embeddings for this sequence: MKWV…”
  • “Encode these 5 protein sequences with ESM-2 so I can cluster them.”
ESM-2 mutation likelihoods
  • “Give me per-position mutation likelihoods for this enzyme using ESM-2.”
  • “Run ESM-2 mutation scoring on this protein and show me the top mutable sites.”
AbLang-2 embeddings
  • “Encode these heavy/light chain pairs with AbLang-2.”
  • “Give me AbLang-2 embeddings for this antibody pair.”
AbLang-2 mutation likelihoods
  • “Run AbLang-2 mutation likelihoods on this paired antibody.”
  • “Show me the top mutable CDR positions for this antibody pair.”
Purna validates the input and asks you to approve the model run before submission. After approval, the chat shows a compact submission entry. You can continue working while the run proceeds and follow its status from Compute or Jobs.

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Input formats

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ESM-2

Paste sequences directly in the message, or attach a FASTA file.

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AbLang-2

Provide the heavy and light chain for each antibody. Purna uppercases sequences and strips whitespace automatically.

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Track and inspect the run

After you approve the run:
  1. The chat records that the model run was submitted.
  2. Compute or Jobs shows the run status and execution details.
  3. When the run completes, Purna registers its downloadable outputs in Files.
  4. Open a generated file to inspect it in a notebook tab, or download it for downstream analysis.
The completed outputs depend on the requested action:
  • Embeddings runs show the number of sequences encoded and the vector dimensionality (for example, 3 × 1280 dims for ESM-2 or 2 × 480 dims for AbLang-2), plus a one-line hint on common uses.
  • Mutation-likelihood runs show a ranked list of the top mutable positions with their highest-likelihood substitutions and scores. Positions are labeled with the observed residue and a clear arrow to the predicted alternative (for example, Q127 → E (3.02)). The list starts with the top 5 and expands on request.
If a run fails, inspect its status for the failure reason, such as invalid input, a provider error, or insufficient credits.

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Downloading the full output

Both capabilities save their full numerical output as a downloadable file in the Files view. Embedding runs save the full vector matrix as JSON. Mutation-likelihood runs save the full per-position × 20-amino-acid logit matrix as JSON. Both are ready to load into a notebook, a clustering pipeline, or your own downstream scripts. Raw vectors and raw likelihood matrices are not inlined into the chat. They remain in the generated output file, which keeps the conversation compact and the results portable.

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Pricing and limits

Most runs finish in under a minute. A single billed minute is the typical cost for a small batch.

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Enabling models in your workspace

Protein embeddings and mutation likelihoods are managed per model in your capability settings, so you can turn on exactly the ones you need:
  • AbLang-2 — Embeddings
  • AbLang-2 — Mutation likelihoods
  • ESM-2 — Embeddings
  • ESM-2 — Mutation likelihoods
Enable the capabilities you need; leave off the ones you do not. Disabled capabilities are not offered to Purna as tools, which keeps your chat responses focused on the models you care about.

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When to use which in a single workflow

A common pattern is combining the two model families across a multi-step analysis:
  1. Embed a batch of candidates to cluster them and pick representatives.
  2. Score mutation likelihoods on the representatives to find tolerant positions.
  3. Pick a small set of mutations at the top-scoring positions for wet-lab testing.
For an antibody workflow, replace ESM-2 with AbLang-2 in both steps. For generic proteins, ESM-2 is the right choice for both.
Mutation likelihoods are a ranking signal, not a guarantee. Use them to prioritize which positions to test, not to predict functional outcomes directly. Pair them with structure prediction to sanity-check that high-likelihood mutations sit in plausible structural contexts.
Both ESM-2 and AbLang-2 are computational models. The outputs are ranking signals and feature representations, not experimental measurements. Treat them as hypothesis-generating inputs to your downstream workflow.