--- title: "Highly accurate protein structure prediction — Jumper 2021" description: "Experimentally determining a protein's 3D shape can take months or years." 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"] year: 2021 venue: "Nature" source: "https://doi.org/10.1038/s41586-021-03819-2" doi: "10.1038/s41586-021-03819-2" licence: "CC BY 4.0 (publisher, via OpenAlex)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/jumper-2021-highly-accurate-protein-structure-prediction/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/jumper-2021-highly-accurate-protein-structure-prediction/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2021](https://md.research.elamur.ai/free/en/papers/2021/page/3/) / Highly accurate protein structure prediction with AlphaFold # Highly accurate protein structure prediction with AlphaFold **Authors:** John Jumper, [Richard Evans](https://md.research.elamur.ai/free/en/authors/richard-evans/), Alexander Pritzel, Tim Green, Michael Figurnov, [Olaf Ronneberger](https://md.research.elamur.ai/free/en/authors/olaf-ronneberger/), Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, [Anna Potapenko](https://md.research.elamur.ai/free/en/authors/anna-potapenko/), Alex Bridgland, Clemens Meyer et al. (20 authors) · **Year:** 2021 · **Venue:** Nature · **Licence:** [CC BY 4.0 (publisher, via OpenAlex)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://doi.org/10.1038/s41586-021-03819-2) · [PDF](https://www.nature.com/articles/s41586-021-03819-2.pdf) · [DOI 10.1038/s41586-021-03819-2](https://doi.org/10.1038/s41586-021-03819-2) · [OpenAlex](https://openalex.org/W3177828909) **Citations:** 43269 citations (openalex-snapshot · 2026-10-14) · [201](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Experimentally determining a protein's 3D shape can take months or years. As a result, only about 100,000 unique proteins have solved structures, while billions of sequences are known. Predicting structure from the amino acid sequence alone has been an open problem for roughly half a century. Earlier computational methods fell well short of atomic accuracy whenever no homologous structure existed. The authors present a fully redesigned AlphaFold, a deep network that takes a sequence together with multiple sequence alignments (MSAs) and structural templates and directly outputs coordinates for all heavy atoms. Its core is the Evoformer, which jointly refines an MSA representation and a residue-pair representation before a structure module builds the 3D model. Training adds several ideas, including self-distillation on unlabelled sequences and a masked MSA loss. In the blind CASP14 assessment, AlphaFold matched experimental quality for most targets and far outperformed competing methods. This accuracy also held on recently released PDB chains. The model reports per-residue confidence, which makes its predictions usable for large-scale structural biology. ## Key points - **CASP14 results (entered as "AlphaFold2"):** median backbone error was 0.96 Å r.m.s.d.95, compared with 2.8 Å for the next-best method. All-atom error was 1.5 Å versus 3.5 Å. For scale, a carbon atom is about 1.4 Å wide. - **Architecture:** a 48-block Evoformer feeds an 8-block structure module, and the whole network is recycled three times. Inputs come from genetic database searches (UniRef90, BFD, Uniclust30, MGnify) and template search (PDB70). - **Generalisation:** on 3,144 recent PDB chains with low similarity to the training data, the median full-chain Cα r.m.s.d.95 was 1.46 Å. Training used the PDB snapshot of 28/08/2019. - **Confidence estimates:** pLDDT tracks true lDDT-Cα (Pearson's r = 0.76), and pTM tracks TM-score (r = 0.85), both measured on 10,795 chains. - **Scalability:** the model correctly predicted domain packing for T1044, a 2,180-residue single chain. It also produced accurate side chains, for example at a zinc-binding site, even though it does not model the ion explicitly. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/) ## Cited by Cited by 201 papers of our library cite it (of the 140673 whose bibliographies we read); 9 of them have a page here, 192 not yet. - [Automated Cognate Detection as a Supervised Link Prediction Task with Cognate Transformer](https://md.research.elamur.ai/free/en/papers/akavarapu-2024-cognate-link-prediction/) - [Reinforced Self-Training (ReST) for Language Modeling](https://md.research.elamur.ai/free/en/papers/gulcehre-2023-reinforced-self-training-rest-language/) - [MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation](https://md.research.elamur.ai/free/en/papers/huang-2023-mlagentbench-evaluating-language-agents/) - [Challenges and Applications of Large Language Models](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/) - [Sequence Parallelism: Long Sequence Training from System Perspective](https://md.research.elamur.ai/free/en/papers/li-2023-sequence-parallelism-long-sequence-training/) - [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://md.research.elamur.ai/free/en/papers/lu-2024-ai-scientist-fully-automated/) - [AlphaEvolve: A coding agent for scientific and algorithmic discovery](https://md.research.elamur.ai/free/en/papers/novikov-2025-alphaevolve-coding-agent-scientific-algorithmic/) - [Tool Learning with Foundation Models](https://md.research.elamur.ai/free/en/papers/qin-2023-tool-learning-foundation-models/) - [Agent Laboratory: Using LLM Agents as Research Assistants](https://md.research.elamur.ai/free/en/papers/schmidgall-2025-agent-laboratory-using-llm/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{jumper2021highly, title = {{Highly accurate protein structure prediction with AlphaFold}}, author = {Jumper, John and Evans, Richard and Pritzel, Alexander and Green, Tim and Figurnov, Michael and Ronneberger, Olaf and Tunyasuvunakool, Kathryn and Bates, Russ and Žídek, Augustin and Potapenko, Anna and Bridgland, Alex and Meyer, Clemens and Köhl, Simon and Ballard, Andrew J. and Cowie, Andrew and Romera‐Paredes, Bernardino and Nikolov, Stanislav and Jain, Rishub and Adler, Jonas and Back, Trevor}, booktitle = {Nature}, year = {2021}, doi = {10.1038/s41586-021-03819-2}, url = {https://doi.org/10.1038/s41586-021-03819-2} } ``` - [← previous Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger](https://md.research.elamur.ai/free/en/papers/qi-2021-hidden-killer-invisible-textual-backdoor/) - [next → HONEST: Measuring Hurtful Sentence Completion in Language Models](https://md.research.elamur.ai/free/en/papers/nozza-2021-honest-measuring-hurtful-sentence-completion/) - [↑ back to papers of 2021](https://md.research.elamur.ai/free/en/papers/2021/page/3/)