You want ESM-3 embeddings for a protein — the feature vectors that power alignment-free similarity, clustering, and ML models — and this runs it end to end: enter the sequence (or fetch it by UniProt ID), keep the default model, submit, and download the embedding vectors, usually within 5–30 seconds per sequence.

Quick protein embedding generation with .bloom* platform

ESM-3 is a multimodal protein language model that produces high-dimensional embeddings encoding functional and evolutionary information. This guide walks through generating your first embeddings, from sequence input to similarity analysis.

Step-by-step embedding workflow

Access the ESM-3 tool

Navigate to ESM-3 in the .bloom* tool selector, or ask Ivy directly:

"Generate embeddings for this protein using ESM-3"
"Run ESM-3 on my sequence"

Prepare your protein sequence

Option A: Direct sequence entry (fastest)

Click Sequence in the input section
Paste your amino acid sequence (e.g., MKTAYIAKQRQISFVKSHFSRQDILDLWIYHTQGYFPDWQNYTPGPGIRYPLTFGWCYKLVPVEPEK)
.bloom* validates the sequence in real time
For FASTA format, include a header line starting with >

Option B: UniProt lookup

Click UniProt ID in the input section
Enter a UniProt accession (e.g., P68871 for hemoglobin subunit beta)
.bloom* fetches the canonical sequence automatically

Option C: From previous jobs

Select sequences from BLAST searches, ESMFold predictions, or other protein analysis tools
.bloom* handles multi-sequence outputs and format conversion

Configure embedding parameters

Basic settings (recommended for first run):

Keep default model size and parameters
Job name: Enter a descriptive name or leave blank

Advanced settings (via gear icon):

Model size: Select from available ESM-3 model variants (esm3_sm, esm3_md, esm3_lg — larger = better embeddings, slower)
Pooling: mean, cls, or none (none returns unpooled per-residue embeddings)
Return attention: Enable to extract attention maps for contact prediction

Submit and monitor the job

Click Generate Embeddings to start the job
.bloom* shows real-time progress
Typical completion: 5-30 seconds per sequence depending on length and model size
Batch submissions process sequences in parallel

Review and analyze results

Embedding summary:

Embedding dimensions (sequence length x hidden dimension)
Model variant used
Per-sequence pooled embedding vector

Visualization options:

Attention heatmap showing residue-residue interaction strengths
Per-residue embedding magnitude plot highlighting important positions
t-SNE or UMAP projection for batch embedding comparison

Download options:

NPY/PT: Raw embedding tensors for custom ML pipelines
JSON: Metadata with pooled embeddings and attention maps
CSV: Per-residue embedding values in tabular format

Common embedding scenarios

Single protein analysis

Enter a protein sequence via direct input or UniProt ID
Generate embeddings with default settings
Review attention maps for predicted contacts
Download embeddings for downstream ML tasks

Protein similarity comparison

Submit two or more sequences in a single batch
Generate per-sequence embeddings for each
Compute cosine similarity between embedding vectors
Identify functionally related proteins even with low sequence identity

Batch embedding for clustering

Prepare a multi-sequence FASTA file with your protein dataset
Submit as a batch job
Download per-sequence embeddings for all sequences
Run clustering (k-means, HDBSCAN) on the embedding vectors
Visualize clusters with t-SNE or UMAP projections

Feature extraction for ML models

Generate per-residue embeddings for your training set
Download NPY/PT tensors
Use embeddings as input features for activity prediction, localization, or stability models
Per-sequence pooled embeddings work well for whole-protein classification tasks

Quality control checklist

Before submission

Only standard amino acids (ACDEFGHIKLMNPQRSTVWY) in the sequence
Sequence length is within supported range (10-2,048 residues)
No ambiguous characters (B, J, O, U, X, Z)
FASTA headers are unique if submitting multiple sequences

After generation

Embedding dimensions match expected shape (length x hidden_dim)
No NaN or infinity values in the embedding vectors
Attention maps show reasonable patterns (not all zeros or uniform)
Batch embeddings have expected count matching input sequences

Next steps after embedding generation

Similarity analysis:

Compute pairwise cosine similarity for protein comparison
Build a similarity network for functional annotation transfer
Compare embeddings from wild-type and mutant sequences to estimate variant effects

Machine learning:

Use per-sequence embeddings as features in scikit-learn, PyTorch, or XGBoost models
Fine-tune downstream classifiers for specific prediction tasks
Combine ESM-3 embeddings with molecular descriptors from DeepChem

Structure prediction:

Use ESMFold for 3D structure prediction of the same sequences
Compare ESM-3 attention-based contact predictions with ESMFold-predicted structures
Feed structural insight back into function prediction pipelines

ESM-3 quickstart