# Threading

> method for computational protein structure prediction

**Wikidata**: [Q7797175](https://www.wikidata.org/wiki/Q7797175)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Threading_(protein_sequence))  
**Source**: https://4ort.xyz/entity/threading-q7797175

## Summary
Threading is a computational method used in protein structure prediction to model a protein's 3D structure by aligning its amino acid sequence with known structures. It identifies structural templates from databases, enabling predictions even when sequence similarity is low. This approach aids in understanding protein function and designing drugs.

## Key Facts
- **Parent Class**: Protein structure prediction (constructing atomic models from sequences).
- **Aliases**: タンパク質スレッディング (Japanese).
- **Freebase ID**: `/m/08tqfy`.
- **Microsoft Academic ID (Discontinued)**: `200307862`.
- **Wikipedia Coverage**: Titles in Czech, English, French, and Japanese (4 sitelinks).
- **Method Type**: Subclass of protein structure prediction.

## FAQs
### Q: How does Threading work?
A: Threading aligns a protein sequence with known structures from databases, using scoring functions to identify compatible templates and predict 3D conformations.

### Q: What is Threading used for?
A: It predicts protein structures when homology modeling (which requires high sequence similarity) is ineffective, aiding in functional studies and drug design.

### Q: How does Threading differ from homology modeling?
A: Unlike homology modeling, Threading does not rely on high sequence similarity, instead detecting remote evolutionary relationships through structural compatibility.

## Why It Matters
Threading addresses a critical challenge in structural biology: predicting protein structures when evolutionary relationships are unclear. By leveraging known structures as templates, it bridges gaps where traditional homology modeling fails, particularly for proteins with low sequence similarity. This method accelerates discoveries in drug development, enzyme design, and understanding genetic disorders. Its ability to model structures computationally reduces reliance on labor-intensive experimental techniques like X-ray crystallography, making it a cost-effective tool for large-scale genomic studies. As protein structures dictate function, Threading’s predictions enable insights into molecular mechanisms and therapeutic targets.

## Notable For
- **Template-Based Approach**: Relies on structural databases (e.g., PDB) to identify folding patterns.
- **Remote Homology Detection**: Identifies distant evolutionary relationships missed by sequence alignment alone.
- **Computational Efficiency**: Streamlines structure prediction compared to experimental methods.
- **Role in Structural Genomics**: Supports initiatives to map the protein universe.

## Body
### Definition and Purpose
Threading, or fold recognition, is a computational technique to predict protein structures by mapping sequences onto known folds. It solves the "folding problem" for proteins lacking clear homologs, providing structural insights critical for biology and medicine.

### Methodology
- **Template Selection**: Queries structural databases (e.g., Protein Data Bank) for potential templates.
- **Alignment Scoring**: Uses profiles (e.g., PSI-BLAST) or machine learning to rank template compatibility.
- **Model Building**: Constructs 3D models based on top-scoring alignments, refining structures via energy minimization.

### Relation to Other Techniques
- **Homology Modeling**: Requires >30% sequence identity; Threading operates at lower thresholds.
- **Ab Initio Methods**: Predicts structures from physical principles alone, whereas Threading leverages empirical data.
- **AlphaFold**: Modern AI-driven approaches (e.g., DeepMind’s AlphaFold) integrate Threading principles with deep learning.

### Applications
- **Drug Discovery**: Guides rational design by identifying binding sites.
- **Functional Annotation**: Infers protein roles from structural similarities.
- **Mutational Analysis**: Predicts pathogenicity of genetic variants.

### Challenges
- **Template Dependence**: Accuracy limited by database coverage.
- **False Positives**: Risk of incorrect alignments without experimental validation.
- **Computational Cost**: Complex scoring functions require significant resources.

## References

1. [OpenAlex](https://docs.openalex.org/download-snapshot/snapshot-data-format)