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Applying and improving AlphaFold at CASP14
John Jumper1, Richard Evans1, Alexander Pritzel1
1DeepMind, London, UK.
Proteins
|October 2, 2021
Summary
AlphaFold2, a novel deep learning system, achieved unprecedented accuracy in protein structure prediction at CASP14. This breakthrough in computational biology enables highly accurate, large-scale protein structure determination.
Area of Science:
- Computational Biology
- Structural Biology
- Artificial Intelligence in Science
Background:
- Protein structure prediction is crucial for understanding biological function.
- Previous methods had limitations in accuracy, especially for complex protein targets.
- The Critical Assessment of Protein Structure Prediction (CASP) benchmarks prediction methods.
Purpose of the Study:
- To describe the operation and advancements of the AlphaFold system.
- To report the performance of AlphaFold in the CASP14 competition.
- To highlight the system's capability for automated, large-scale protein structure prediction.
Main Methods:
- Development of a novel end-to-end deep neural network.
- Training the network using amino acid sequences, multiple sequence alignments, and homologous proteins.
- Evaluation using CASP14 assessment metrics, including summed z scores and GDT_TS.
Main Results:
- AlphaFold achieved a summed z score of 244.0, significantly outperforming other methods.
- The median domain GDT_TS score was 92.4, a record for CASP.
- High accuracy was demonstrated even on challenging Free Modeling targets.
Conclusions:
- AlphaFold represents a significant leap forward in the state of the art of protein structure prediction.
- The system's ability to operate autonomously opens possibilities for widespread, accurate structure determination.
- This advancement has profound implications for biological research and drug discovery.
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