# protein structure prediction

> constructing an atomic-resolution model of a protein from its amino acid sequence

**Wikidata**: [Q899656](https://www.wikidata.org/wiki/Q899656)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Protein_structure_prediction)  
**Source**: https://4ort.xyz/entity/protein-structure-prediction

## Summary
Protein structure prediction is the computational process of constructing an atomic-resolution model of a protein from its amino acid sequence. It's an academic discipline focused on determining how amino acids fold into three-dimensional structures.

## Key Facts
- It's an academic discipline focused on predicting molecular structures from sequences
- Methods include threading, loop modeling, and de novo prediction
- The Rosetta@home project, a BOINC-based volunteer computing project researching protein folding, was established in 2005
- AlphaFold, software by DeepMind for protein structure prediction, was developed in 2018
- Jiaxiang Wu, a Chinese artificial intelligence researcher, has contributed to this field
- The field has 19 sitelink counts on Wikipedia
- It's a subclass of structure prediction and includes nucleic acid structure prediction as a related area

## FAQs
### Q: What is protein structure prediction?
A: It's the computational process of constructing an atomic-resolution model of a protein from its amino acid sequence.

### Q: What are some methods used in protein structure prediction?
A: Methods include threading, loop modeling, and de novo prediction, as well as molecular dynamics simulation.

### Q: What's the significance of AlphaFold?
A: AlphaFold is software by DeepMind that revolutionized protein structure prediction by achieving high accuracy in predicting protein structures.

### Q: What is Rosetta@home?
A: It's a BOINC-based volunteer computing project researching protein folding, established in 2005.

## Why It Matters
Protein structure prediction is crucial for understanding how proteins function and interact in biological systems. Determining the 3D structure of proteins from their amino acid sequences allows scientists to understand their biological roles, identify potential drug targets, and design therapeutic interventions. The field has advanced significantly with tools like AlphaFold, which has dramatically improved our ability to predict protein structures with high accuracy. This has transformed fields like drug discovery, where knowing a protein's structure is essential for designing effective treatments.

## Notable For
- The development of AlphaFold by DeepMind in 2018, which revolutionized protein structure prediction
- The establishment of Rosetta@home in 2005 as a BOINC-based volunteer computing project
- The work of researchers like Jiaxiang Wu who contribute to advancing artificial intelligence methods in protein structure prediction
- The ability to predict atomic-resolution models from amino acid sequences, which was previously considered computationally infeasible
- The integration of deep learning approaches that have dramatically improved prediction accuracy

## Body
### Methods and Approaches
Protein structure prediction employs various computational methods:
- Threading: aligns protein sequences to known structures
- Loop modeling: focuses on predicting flexible regions
- De novo prediction: builds structures from scratch without reference to known structures
- Molecular dynamics simulation: simulates protein behavior over time

### Key Projects and Tools
The field has several notable projects and tools:
- Rosetta@home: a BOINC-based volunteer computing project established in 2005 for protein folding research
- AlphaFold: software by DeepMind developed in 2018 that uses deep learning to predict protein structures with high accuracy

### Research and Development
Researchers in this field continue to advance methods:
- Jiaxiang Wu, a Chinese artificial intelligence researcher, has contributed to the development of AI approaches for protein structure prediction

### Applications
Protein structure prediction has significant applications in:
- Drug discovery and design
- Understanding biological functions
- Engineering novel proteins with desired properties

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  "@type": "Thing",
  "name": "Protein structure prediction",
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## References

1. Freebase Data Dumps. 2013
2. Quora
3. [OpenAlex](https://docs.openalex.org/download-snapshot/snapshot-data-format)