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MIP 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 return a live, inline result card in chat, store the full numerical output as a downloadable dataset, and bill on actual compute time. Example of an embeddings result card in MIP

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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 the card returns a compact summary of how many were encoded and the vector dimensionality. The full numerical embeddings are saved as a downloadable JSON dataset 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 result card shows the top mutable positions with their highest-likelihood substitutions in a compact list. The full per-position × 20-amino-acid likelihood matrix is saved as a downloadable dataset 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.”
MIP validates the input, submits the job, and shows a live result card in the chat. You do not need to wait or refresh — the card updates itself as the job moves from queued to running to completed.

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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. MIP uppercases sequences and strips whitespace automatically.

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The result card

When you submit, an inline card appears in the chat. It updates in place without a page refresh. While running: a compact status card shows the model, the action, and a spinner. You can continue the chat — the card tracks its own job. When complete:
  • 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.
On failure: the card shows the failure reason (invalid input, provider error, out of credits) and the run is not billed for the failed attempt.

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Downloading the full output

Both capabilities save their full numerical output as a downloadable dataset, accessible from:
  • The result card’s Download action
  • The Files view, under your datasets
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 never inlined into the chat — they live in the dataset, which keeps the chat fast and your 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 MIP 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.