Intrinsically Disordered Region (IDR) Demethylase Target Predictor with BioEmbeddings
Intrinsically disordered regions (IDRs) restrain genomic targeting of histone and RNA demethylases across species. You will build a sequence feature extraction and protein targeting model using BioEmbeddings and PyTorch to predict IDR-mediated targeting inhibition with 88% cross-validation accuracy.
What you are building
Train a protein sequence classification model using BioEmbeddings representations to identify IDR-driven demethylase genomic target restraints.
How work is evaluated
Evaluates protein IDR target restraint classification accuracy against experimentally validated demethylase mutants.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
idr_detection_validity
Ensure sequence with high disorder yields idr_present=True
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
accuracy
Cross-validation classification accuracy • target: 0.88 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Learning goals
Extract per-residue protein embeddings using ProtT5 and ESM-2 models via BioEmbeddings
Identify intrinsically disordered region boundaries and biophysical property metrics
Predict demethylase genomic localization restraint changes upon IDR deletion
Resources
Protein sequence dataset containing 1,200 plant and mammalian demethylase variants with annotated IDR boundaries.
Challenge setup
Evaluates protein IDR target restraint classification accuracy against experimentally validated demethylase mutants.
Challenge input
JSON object with protein_id and fasta_sequence string
BioEmbeddings
Utility for extracting embeddings from protein transformers.
PyTorch
Neural network modeling engine.
Evaluated output
JSON object with idr_present boolean and restraint_score float
- Ensure sequence with high disorder yields idr_present=True
- Cross-validation classification accuracy • target: 0.88 • range: 0-1
- Benchmark: ProteinLM-Bench
- Accuracy target: 0.88
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Shows the first task, up to three tools, and up to three evidence items.
View technical recipe
Configured tools
- Langbase · Required
- Gemma 2 · Optional
- AWS · Optional
Evaluation contract
- idr_detection_validity · Weight 1
- accuracy · Weight 1
Metrics
- accuracy · Target 0.88
Benchmarks
- ProteinLM-Bench
- biotech
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON object with protein_id and fasta_sequence string
Output: JSON object with idr_present boolean and restraint_score float
Execution harness: Python
Evaluation tools
- BioEmbeddings · Protein Representation Toolkit
- PyTorch · Deep Learning Engine
Recipe freeze marker
No recipe freeze time is recorded.