Lasso Workbench

A GUI application for semantic lasso peptide precursor discovery and dataset curation.

Overview

The Lasso Workbench implements a semantic discovery pipeline combining 6-frame translation with ESM-2 embeddings to identify lasso peptide precursors in genomic data.

Key Features:

• Semantic Discovery: Encodes candidate ORFs using ESM-2 (t6/t12/t30/t33) and ranks them by cosine similarity to validated precursors.
• Exhaustive Search: Extracts all potential ORFs (limited by min/max AA length) from 6-frame translation, ensuring no candidates are missed due to annotation errors.
• Rule-Based Ranking: Filters candidates using customizable heuristic constraints (leader motifs, core length) on top of semantic scores.
• Dataset Management: Utilities for curating of validated precursors.

Gaps:

• No Tail handling: The current pipeline does not handle tail pruning in core prediction.

---

Quick Start

Installation

# Install dependencies
python -m venv .venv
source .venv/bin/activate
pip install -e .

# Launch Application
lasso-workbench

Usage

1. Pipeline: Upload GBK files (MiBIG, antiSMASH, or GenBank).
2. Configuration: Select an ESM-2 model (default: esm2_t6_8M_UR50D) and a validated precursor reference set.
3. Execution: The pipeline performs:
   • Extraction: 6-frame translation of input sequences.
   • Embedding: Batch inference via ESM-2 (masked padding).
   • Scoring: Compute pairwise cosine similarity against references.
   • Ranking: Sort by Top-N Mean similarity and apply rule filters.
4. Analysis: Review ranked candidates in the tabular view, html visualisation and export to JSON/CSV/FASTA.

---

Architecture

• Core Logic: lasso_workbench/core - Embedding scoring (NumPy/Scikit-learn), Rule Engine.
• Pipeline: lasso_workbench/pipeline - ORF extraction (BioPython), ESM-2 Embedding (HuggingFace Transformers).
• UI: lasso_workbench/ui - Gradio-based interface.
• Data: JSON-based persistence for easy portability.

Precursor Datasets

Validated precursor reference sets live in data/precursors/.

Generated artifacts (do not edit by hand):

• precursor_proteins_multi_strict.faa — multi‑candidate validated set generated by scripts/generate_precursor_dataset_multi.py
• lab_core_candidates_multi_strict.tsv
• lab_core_loci_multi_strict.tsv
• lab_core_cases_summary.tsv
• lab_core_dataset_summary.json

Persistent curated additions (safe to edit via script):

• precursor_proteins_curated.faa
• precursor_proteins_curated.tsv

Default validated set used by the UI:

• precursor_proteins_verified.faa — merged, de‑duplicated set combining multi_strict + curated
• precursor_proteins_verified.tsv — merged metadata table (read‑only; used by the Dataset tab)

Reference FASTA format (Pipeline + Bench UI)

Any reference FASTA you drop into the Pipeline or Bench UI must include a locus= token in the header. This is required for grouped top‑N mean scoring.

Minimal example:

>name=foo|locus=foo_001
MSEQ...

Lasso benchmark (peptidase holdout) expects:

>pep=WP_012345678|locus=lab_0001_locus_01|case=lab_0001|orf=0001
MSEQ...

Beta‑lactamase benchmark expects:

>prot=AAC09015|locus=AAC09015|name=AER-1|class=A|source=ambler_table
MSEQ...

If locus= is missing, scoring will error with: Missing locus= token in reference id.

Add new curated precursors

Use the helper script to append curated sequences without touching generated files:

.venv/bin/python scripts/add_validated_precursors.py \
  --input data/lab_dataset/newlassos.txt \
  --format newlassos \
  --output-dir data/precursors \
  --merge

The script also accepts TSV/CSV files with these columns (case-insensitive):

• sequence (or precursor / full_precursor)
• optional core (or core_aa)
• optional name
• optional orientation (leader_core / core_leader)

Example:

.venv/bin/python scripts/add_validated_precursors.py \
  --input data/lab_dataset/curated_pairs.tsv \
  --format tsv \
  --output-dir data/precursors \
  --merge

This updates:

• precursor_proteins_curated.faa
• precursor_proteins_curated.tsv
• precursor_proteins_verified.faa (merged set used by default)
• precursor_proteins_verified.tsv (merged metadata table)

LICENSE

MIT License

Made by Magnus Ohle

NOTICE

See NOTICE for attribution and reuse guidance.