README.md

metadata

license: mit
task_categories:
  - tabular-regression
  - reinforcement-learning
tags:
  - protein-language-model
  - biology
  - enzymes
  - gh114
  - saprot
  - dpo
pretty_name: GH114 Enzyme Activity (FLOP Benchmark)
size_categories:
  - n<1k
base_model: NorseDrunkenSailor/Qwen_smol_GH114

GH114 Enzyme Activity Dataset (from FLOP Benchmark)

Dataset Description

This dataset contains protein sequences, SaProt structure-aware sequence, and activity labels for 55 Glycoside Hydrolase Family 114 (GH114) enzymes.

It is specifically formatted for aligning the NorseDrunkenSailor/Qwen_smol_GH114 base model. This model is a generative Causal Language Model (CLM) adapted from Qwen2 and trained on SaProt 3Di-aware sequences. The goal is to use Direct Preference Optimization (DPO) or other Reinforcement Learning techniques to align the model towards correctly ranking highly active GH114 variants across Cross-Validation (CV) folds. The CV folds are specifically curated in FLOP (https://www.biorxiv.org/content/10.1101/2023.06.21.545880v2.full.pdf) to prevent train/test leakage. This is considered a archetypal wildtype-discovery task. The median sequence identity is 0.46. The three CV folds contain 20, 18, and 17 sequences respectively.

• Curated: NorseDrunkenSailor
• Original Source (Paper): FLOP: Tasks for Fitness Landscapes Of Protein wildtypes and (https://www.biorxiv.org/content/10.1101/2023.06.21.545880v2.full.pdf) (Groth et al., 2024)
• Original Source (Data): Patent WO/2019/228448, NOVOZYMES A/S

Biological Context & Assay

Family GH114 (endo-alpha-1,4-polygalactosaminidases) are enzymes capable of degrading PEL, an exopolysaccharide that forms a critical structural component of biofilms in bacteria like Pseudomonas aeruginosa. The 'target_reg' column represents the activity of the enzyme in concentration normalised units.

Note: While seemingly small in size (N=55), this dataset represents high-quality ground truth derived from purified enzymes, intended for testing few-shot learning and low-data regime alignment capabilities. This is in fact a luxury and relatively large wild-type discovery dataset to start with. These are rare.

The Assay

The target_reg column represents the hydrolytic activity of the enzyme against the PEL polysaccharide.

• Method: The data comes from assays performed on purified, concentration-normalized natural enzymes (as opposed to crude lysates), ensuring the signal reflects true catalytic efficiency rather than expression levels.
• Goal: Disruption of PEL compromises biofilm integrity. High-activity variants are sought for industrial applications to remove biofilms (cleaning and detergent compositions).

Data Fields

Column	Type	Description
name	string	Sequence ID (SEQ ID from Patent/Paper).
target_reg	float	Hydrolytic Activity. Higher values indicate better degradation of PEL.
seq	string	SaProt-formatted Sequence. Input for the Qwen_smol_GH114 tokenizer. Contains both amino acid and 3Di structural tokens.
part_0	int	Indicator (0/1) for FLOP Cross-Validation Fold 0.
part_1	int	Indicator (0/1) for FLOP Cross-Validation Fold 1.
part_2	int	Indicator (0/1) for FLOP Cross-Validation Fold 2.

Usage for Alignment (DPO/RL)

This dataset is designed to train a reward model or directly align the generator. The seq column allows the model to "read" the structure-aware sequence, while target_reg provides the ground truth signal for fitness.

from datasets import load_dataset
dataset = load_dataset("NorseDrunkenSailor/GH114_activity_DPO")

# Example: Select high-activity variants for DPO positive pairs
high_activity = dataset.filter(lambda x: x['target_reg'] > 0.8)

Cross-Validation Strategy This dataset uses the homology-stratified splitting defined in the FLOP paper. Do NOT random split. Use part_0, part_1, part_2 columns.

Splits were generated using GraphPart to ensure that sequences in the test set share <55% identity with the training set, testing the model's ability to generalize to new subfamilies rather than memorizing homologs.

Citation

@article{groth2024flop, title={FLOP: Tasks for Fitness Landscapes Of Protein wildtypes}, author={Groth, Peter M{\o}rch and Michael, Richard and Salomon, Jesper and Tian, Pengfei and Boomsma, Wouter}, journal={bioRxiv}, year={2024}, doi={2023.06.21.545880v2} }

@patent{WO2019228448, title={Polypeptides}, author={Novozymes A/S}, year={2019}, number={WO/2019/228448} }