# Highly accurate protein structure prediction with AlphaFold

> Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort<sup>1-4</sup>, the structures of around 100,000 unique proteins have been determined<sup>5</sup>, but this represents a small fracti...

## Metadata
- Authors: John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon Köhl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera‐Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michał Zieliński, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian W. Bodenstein, David Silver, Oriol Vinyals, Andrew Senior, Koray Kavukcuoglu, Pushmeet Kohli, Demis Hassabis
- Journal: Nature
- Published: 2021-07-15
- DOI: https://doi.org/10.1038/s41586-021-03819-2
- Citations: 43,225
- Source: OpenAlex
- Access: Open Access

## Technology Hub
- Hub: Protein Structure Prediction
- Discipline: Biochemistry / AI
- Hub URL: https://science-database.com/technology/protein-structure
- Hub llms.txt: https://science-database.com/technology/protein-structure/llms.txt

## Abstract
Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort<sup>1-4</sup>, the structures of around 100,000 unique proteins have been determined<sup>5</sup>, but this represents a small fraction of the billions of known protein sequences<sup>6,7</sup>. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence-the structure prediction component of the 'protein folding problem'<sup>8</sup>-has been an important open research problem for more than 50 years<sup>9</sup>. Despite recent progress<sup>10-14</sup>, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)<sup>15</sup>, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.

## Links
- DOI: https://doi.org/10.1038/s41586-021-03819-2
- OpenAlex: https://openalex.org/W3177828909
- PDF: https://www.nature.com/articles/s41586-021-03819-2.pdf
- JSON API: https://science-database.com/api/v1/technology/protein-structure

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Paper ID: oa-W3177828909 | Hub: protein-structure