[{"id": 522960, "type": "GenericWebPage", "mime_type": "text/html", "url": "https://alphafold.ebi.ac.uk/", "slug": "ebi", "title": "AlphaFold Protein Structure Database", "snippet": "AlphaFold DB provides open access to over 200 million protein structure predictions to accelerate scientific research.", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 522960, "website_id": 115844, "url": "https://alphafold.ebi.ac.uk/", "title": "AlphaFold Protein Structure Database", "snippet": "AlphaFold DB provides open access to over 200 million protein structure predictions to accelerate scientific research.", "cleaned_text": "In CASP14, AlphaFold was the top-ranked protein structure prediction method by a large margin, producing predictions with high accuracy. While the system still has some limitations, the CASP results suggest AlphaFold has immediate potential to help us understand the structure of proteins and advance biological research.\n\nLet us know how the AlphaFold Protein Structure Database has been useful in your research, or if you have questions not answered in the FAQs, at alphafold@deepmind.com.\n\nIf your use case isn't covered by the database, you can generate your own AlphaFold predictions using this open source code, which also supports multimer prediction.", "cleaned_text_custom_parser": "AlphaFoldProtein Structure Database\nAlphaFoldProtein Structure Database\nAlphaFoldProtein Structure Database\nBackground\nAlphaFoldis an AI system developed byGoogle DeepMindthat predicts a protein\u2019s 3D structure from its amino acid sequence. It regularly achieves accuracy competitive with experiment.\nGoogle DeepMind and EMBL\u2019s European Bioinformatics Institute (EMBL-EBI) have partnered to create AlphaFold DB to make these predictions freely available to the scientific community. The latest database release contains over 200 million entries, providing broad coverage ofUniProt(the standard repository of protein sequences and annotations). We provide individualdownloadsfor the human proteome and for the proteomes of 47 other key organisms important in research and global health. We also provide a download for the manually curated subset of UniProt (Swiss-Prot).\nQ8I3H7: May protect the malaria parasite against attack by the immune system. Mean pLDDT 85.57.\nInCASP14, AlphaFold was the top-ranked protein structure prediction method by a large margin, producing predictions withhigh accuracy. While the system still has somelimitations, the CASP results suggest AlphaFold has immediate potential to help us understand the structure of proteins and advance biological research.\nLet us know how the AlphaFold Protein Structure Database has been useful in your research, or if you have questions not answered in theFAQs,atalphafold@deepmind.com.\nIf your use case isn't covered by the database, you can generate your own AlphaFold predictions using thisopen source code, which also supports multimer prediction.\nFind out more\nWhat\u2019s new?\nThe AlphaFold Protein Structure Database has two significant updates. Firstly, AlphaFold predictions are enriched with TED domain assignments, linking them to CATH classifications for improved interpretability and comparative analysis. Visualise TED domains alongside PAE plots to analyse domain interactions in complex proteins.\nSecondly, we've introduced bulk file downloads, a highly requested feature designed to streamline research workflows. Users can now download up to 100 files at once from search pages and the Foldseek table, supporting multiple formats including mmCIF, PDB, csv, and PAE (JSON). Additionally, search results now display pLDDT scores and sequence lengths for quick assessment, and a new pLDDT slider allows for efficient filtering of high-confidence structures.\nWhat\u2019s next?\nWe plan to continue updating the database with structures for newly discovered protein sequences, and to improve features and functionality in response to user feedback. Please followGoogle DeepMind's andEMBL-EBI\u2019s social channels for updates.\nLicence and attribution\nAll of the data provided is freely available for both academic and commercial use under Creative Commons Attribution 4.0 (CC-BY 4.0) licence terms.\nIf you use this resource, please cite the following papers:Jumper, Jet al. Highly accurate protein structure prediction with AlphaFold.Nature(2021).Varadi, Met al. AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences.Nucleic Acids Research(2024).\nIf you use data from AlphaMissense in your work, please cite the following paper:Cheng, J et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science (2023).\nThe structures and data provided in this resource are predictions withvarying levels of confidenceand should be interpreted carefully. The information is for theoretical modelling only. It is not intended, validated or approved for any clinical use.\nFAQs\nEMBL-EBI training\nAccessing and interpreting predicted protein structures from AlphaFold database\nAlphaFold\nEMBL-EBI is the home for big data in biology. We help scientists exploit complex information to make discoveries that benefit humankind.\nServices\nResearch\nTraining\nIndustry\nAbout\nEMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.Tel: +44 (0)1223 49 44 44Full contact details\nCopyright \u00a9 EMBL 2023EMBL-EBI is part of theEuropean Molecular Biology LaboratoryTerms of use", "cleaned_text_custom_parser_2": "\n\n \n \n \n

\n \n

\n AlphaFold\n Protein Structure Database\n

\n \n

\n \n AlphaFold\n
\n Protein Structure Database\n \n

\n AlphaFold\n
\n Protein Structure Database\n

\n Developed by Google DeepMind and EMBL-EBI\n

\n \n \n Search\n \n \n

\n Examples:\n \n MENFQKVEKIGEGTYGV...\n \n \n Free fatty acid receptor 2\n \n \n At1g58602\n \n \n Q5VSL9\n \n \n E. coli\n \n

\n \n See search help\n \n \n Go to online course\n \n \n See our updates \u2013 March 2025\n \n

\n \n

\n \n AlphaFold DB provides open access to over 200 million protein structure predictions to accelerate scientific research.\n

\n Background\n

\n \n AlphaFold\n \n is an AI system developed by\n \n Google DeepMind\n \n that predicts a protein\u2019s 3D structure from its amino acid sequence. It regularly achieves accuracy competitive with experiment.\n

\n Google DeepMind and EMBL\u2019s European Bioinformatics Institute (\n \n EMBL-EBI\n \n ) have partnered to create AlphaFold DB to make these predictions freely available to the scientific community. The latest database release contains over 200 million entries, providing broad coverage of\n \n UniProt\n \n (the standard repository of protein sequences and annotations). We provide individual\n \n downloads\n \n for the human proteome and for the proteomes of 47 other key organisms important in research and global health. We also provide a download for the manually curated subset of UniProt (\n \n Swiss-Prot\n \n ).\n

\n $\"image1\"$ \n

\n Q8I3H7: May protect the malaria parasite against attack by the immune system. Mean pLDDT 85.57.\n

\n \n View protein\n \n

\n In\n \n CASP14\n \n , AlphaFold was the top-ranked protein structure prediction method by a large margin, producing predictions with\n \n high accuracy\n \n . While the system still has some\n \n limitations\n \n , the CASP results suggest AlphaFold has immediate potential to help us understand the structure of proteins and advance biological research.\n

\n Let us know how the AlphaFold Protein Structure Database has been useful in your research, or if you have questions not answered in the\n \n FAQs,\n \n at\n \n alphafold@deepmind.com\n \n .\n

\n If your use case isn't covered by the database, you can generate your own AlphaFold predictions using this\n \n open source code\n \n , which also supports multimer prediction.\n

\n $\"image2\"$ \n

\n Q8W3K0: A potential plant disease resistance protein. Mean pLDDT 82.24.\n

\n \n View protein\n \n

\n Find out more\n

\n \n

\n \n Methodology\n \n \n Human proteome predictions\n \n \n Downloads\n \n \n Online training course\n \n

\n \n About AlphaFold DB\n \n \n Google DeepMind\n \n \n EMBL-EBI\n \n

\n \n

\n What\u2019s new?\n

\n Integration of TED data - March 2025\n

\n The AlphaFold Protein Structure Database has two significant updates. Firstly, AlphaFold predictions are enriched with TED domain assignments, linking them to CATH classifications for improved interpretability and comparative analysis. Visualise TED domains alongside PAE plots to analyse domain interactions in complex proteins.\n

\n Secondly, we've introduced bulk file downloads, a highly requested feature designed to streamline research workflows. Users can now download up to 100 files at once from search pages and the Foldseek table, supporting multiple formats including mmCIF, PDB, csv, and PAE (JSON). Additionally, search results now display pLDDT scores and sequence lengths for quick assessment, and a new pLDDT slider allows for efficient filtering of high-confidence structures.\n

\n \n Read full article on EMBL-EBI site\n \n

\n $\"news$ \n

\n AF-P86938-F1: Thymine dioxygenase JBP1\n

\n \n View protein\n \n

\n What\u2019s next?\n

\n We plan to continue updating the database with structures for newly discovered protein sequences, and to improve features and functionality in response to user feedback. Please follow\n \n Google DeepMind\n \n 's and\n \n EMBL-EBI\n \n \u2019s social channels for updates.\n

\n Licence and attribution\n

\n All of the data provided is freely available for both academic and commercial use under Creative Commons Attribution 4.0 (\n \n CC-BY 4.0\n \n ) licence terms.\n

\n If you use this resource, please cite the following papers:\n
\n \n Jumper, J\n \n et al\n \n . Highly accurate protein structure prediction with AlphaFold.\n \n Nature\n \n (2021).\n \n
\n \n Varadi, M\n \n et al\n \n . AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences.\n \n Nucleic Acids Research\n \n (2024).\n \n

\n If you use data from AlphaMissense in your work, please cite the following paper:\n
\n \n Cheng, J et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science (2023).\n \n
\n

\n The structures and data provided in this resource are predictions with\n \n varying levels of confidence\n \n and should be interpreted carefully. The information is for theoretical modelling only. It is not intended, validated or approved for any clinical use.\n

\n FAQs\n

\n \n View all frequently asked questions\n \n

\n EMBL-EBI training\n

\n \n \n \n

\n On-demand training\n

\n Live training\n

\n \n \n

\n Recorded webinar\n

\n \n Accessing and interpreting predicted protein structures from AlphaFold database\n \n

\n AlphaFold database (AlphaFold DB) provides open access to over 200 million protein structure predictions to accelerate scientific research. This...\n

\n Online tutorial\n

\n \n AlphaFold\n \n

\n Proteins are essential components of life, predicting their 3D structure enables researchers to get an insight into its function and role. AlphaFold...\n

\n \n View all on-demand training\n \n

\n \n \n \n

\n \n

\n \n Developed by\n \n \n \n\n", "best_field": "cleaned_text_custom_parser", "enhanced": true, "date_published": null, "image": null, "num_tokens_oai_tokenizer": null, "num_tokens_gemini_tokenizer": null}, "favicon": "https://alphafold.ebi.ac.uk./assets/favicons/favicon.ico", "state": "candidate", "enhanced": true, "answer": null, "valid_answer": false, "source_citation_index": 1, "user_web_page_id": null, "status": "ready", "reader": false, "rank": 1, "reason": "The source provides open access to over 200 million protein structure predictions, making it highly relevant for users looking for datasets to support ML model training and benchmarking."}, {"id": 564527, "type": "GenericWebPage", "mime_type": "text/html", "url": "https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2932-0", "slug": "biomedcentral", "title": "ProteinNet: a standardized data set for machine learning of protein structure | BMC Bioinformatics | Full Text", "snippet": "Background Rapid progress in deep learning has spurred its application to bioinformatics problems including protein structure prediction and design. 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However, I have no knowledge of ML and have only been using softwares such as AlphaFold and zdock to predict complexes.", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 564531, "website_id": 230, "url": "https://www.reddit.com/r/bioinformatics/comments/18z798r/ml_protein_prediction_guidance/", "title": "r/bioinformatics on Reddit: ML protein prediction guidance", "snippet": "I\u2019m a life science undergraduate embarking on a research project under a lab doing structural biology work i.e. creating ML models for protein complex prediction. However, I have no knowledge of ML and have only been using softwares such as AlphaFold and zdock to predict complexes.", "cleaned_text": null, "cleaned_text_custom_parser": null, "cleaned_text_custom_parser_2": null, "best_field": null, "enhanced": false, "date_published": null, "image": null, "num_tokens_oai_tokenizer": null, "num_tokens_gemini_tokenizer": null}, "favicon": "https://www.reddit.com/favicon.ico", "state": "candidate", "enhanced": false, "answer": null, "valid_answer": false, "source_citation_index": 10, "user_web_page_id": null, "status": "not_processed", "reader": false, "rank": 2, "reason": "Contains guidance and datasets for protein structure prediction, relevant as it helps in setting up ML models."}, {"id": 564576, "type": "GenericWebPage", "mime_type": "text/html", "url": "https://www.nature.com/articles/s41597-023-02409-3", "slug": "nature", "title": "DIPS-Plus: The enhanced database of interacting protein structures for interface prediction | Scientific Data", "snippet": "In this work, we expand on a dataset recently introduced for protein interface prediction (PIP), the Database of Interacting Protein Structures (DIPS), to present DIPS-Plus, an enhanced, feature-rich dataset of 42,112 complexes for machine learning of protein interfaces.", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 564576, "website_id": 1759, "url": "https://www.nature.com/articles/s41597-023-02409-3", "title": "DIPS-Plus: The enhanced database of interacting protein structures for interface prediction | Scientific Data", "snippet": "In this work, we expand on a dataset recently introduced for protein interface prediction (PIP), the Database of Interacting Protein Structures (DIPS), to present DIPS-Plus, an enhanced, feature-rich dataset of 42,112 complexes for machine learning of protein interfaces.", "cleaned_text": null, "cleaned_text_custom_parser": null, "cleaned_text_custom_parser_2": null, "best_field": null, "enhanced": false, "date_published": null, "image": null, "num_tokens_oai_tokenizer": null, "num_tokens_gemini_tokenizer": null}, "favicon": "https://www.nature.com/static/images/favicons/nature/apple-touch-icon-f39cb19454.png", "state": "candidate", "enhanced": false, "answer": null, "valid_answer": false, "source_citation_index": 11, "user_web_page_id": null, "status": "not_processed", "reader": false, "rank": 2, "reason": "This repository includes interface prediction complexes that provide useful datasets for protein interaction studies."}, {"id": 564561, "type": "GenericWebPage", "mime_type": "text/html", "url": "https://pubmed.ncbi.nlm.nih.gov/22274898/", "slug": "nih", "title": "Machine learning methods for protein structure prediction - PubMed", "snippet": "Machine learning methods are widely used in bioinformatics and computational and systems biology. Here, we review the development of machine learning methods for protein structure prediction, one of the most fundamental problems in structural biology and bioinformatics.", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 564561, "website_id": 1237, "url": "https://pubmed.ncbi.nlm.nih.gov/22274898/", "title": "Machine learning methods for protein structure prediction - PubMed", "snippet": "Machine learning methods are widely used in bioinformatics and computational and systems biology. Here, we review the development of machine learning methods for protein structure prediction, one of the most fundamental problems in structural biology and bioinformatics.", "cleaned_text": null, "cleaned_text_custom_parser": null, "cleaned_text_custom_parser_2": null, "best_field": null, "enhanced": false, "date_published": null, "image": null, "num_tokens_oai_tokenizer": null, "num_tokens_gemini_tokenizer": null}, "favicon": "https://cdn.ncbi.nlm.nih.gov/coreutils/nwds/img/favicons/favicon.ico", "state": "candidate", "enhanced": false, "answer": null, "valid_answer": false, "source_citation_index": 12, "user_web_page_id": null, "status": "not_processed", "reader": false, "rank": 2, "reason": "The source presents various machine learning methods for protein structure prediction that can assist in model training, making it relevant."}, {"id": 181142, "type": "GenericWebPage", "mime_type": "text/html", "url": "https://www.nature.com/articles/s41586-021-03819-2", "slug": "nature", "title": "Highly accurate protein structure prediction with AlphaFold", "snippet": null, "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 181142, "website_id": 1759, "url": "https://www.nature.com/articles/s41586-021-03819-2", "title": "Highly accurate protein structure prediction with AlphaFold", "snippet": null, "cleaned_text": "Extensive explanations of the components and their motivations are available in Supplementary Methods 1.1\u20131.10, in addition, pseudocode is available in Supplementary Information Algorithms 1\u201332, network diagrams in Supplementary Figs. 1\u20138, input features in Supplementary Table 1 and additional details are provided in Supplementary Tables 2, 3. Training and inference details are provided in Supplementary Methods 1.11\u20131.12 and Supplementary Tables 4, 5.\n\nThe IPA module combines the pair representation, the single representation and the geometric representation to update the single representation (Supplementary Fig. 8). Each of these representations contributes affinities to the shared attention weights and then uses these weights to map its values to the output. The IPA operates in 3D space. Each residue produces query points, key points and value points in its local frame. These points are projected into the global frame using the backbone frame of the residue in which they interact with each other. The resulting points are then projected back into the local frame. The affinity computation in the 3D space uses squared distances and the coordinate transformations ensure the invariance of this module with respect to the global frame (see Supplementary Methods 1.8.2 \u2018Invariant point attention (IPA)\u2019 for the algorithm, proof of invariance and a description of the full multi-head version). A related construction that uses classic geometric invariants to construct pairwise features in place of the learned 3D points has been applied to protein design59.\n\nIn addition to the IPA, standard dot product attention is computed on the abstract single representation and a special attention on the pair representation. The pair representation augments both the logits and the values of the attention process, which is the primary way in which the pair representation controls the structure generation.\n\nInputs to the network are the primary sequence, sequences from evolutionarily related proteins in the form of a MSA created by standard tools including jackhmmer60 and HHBlits61, and 3D atom coordinates of a small number of homologous structures (templates) where available. For both the MSA and templates, the search processes are tuned for high recall; spurious matches will probably appear in the raw MSA but this matches the training condition of the network.\n\nOne of the sequence databases used, Big Fantastic Database (BFD), was custom-made and released publicly (see \u2018Data availability\u2019) and was used by several CASP teams. BFD is one of the largest publicly available collections of protein families. It consists of 65,983,866 families represented as MSAs and hidden Markov models (HMMs) covering 2,204,359,010 protein sequences from reference databases, metagenomes and metatranscriptomes.\n\nBFD was built in three steps. First, 2,423,213,294 protein sequences were collected from UniProt (Swiss-Prot&TrEMBL, 2017-11)62, a soil reference protein catalogue and the marine eukaryotic reference catalogue7, and clustered to 30% sequence identity, while enforcing a 90% alignment coverage of the shorter sequences using MMseqs2/Linclust63. This resulted in 345,159,030 clusters. For computational efficiency, we removed all clusters with less than three members, resulting in 61,083,719 clusters. Second, we added 166,510,624 representative protein sequences from Metaclust NR (2017-05; discarding all sequences shorter than 150 residues)63 by aligning them against the cluster representatives using MMseqs264. Sequences that fulfilled the sequence identity and coverage criteria were assigned to the best scoring cluster. The remaining 25,347,429 sequences that could not be assigned were clustered separately and added as new clusters, resulting in the final clustering. Third, for each of the clusters, we computed an MSA using FAMSA65 and computed the HMMs following the Uniclust HH-suite database protocol36.\n\nThe following versions of public datasets were used in this study. Our models were trained on a copy of the PDB5 downloaded on 28 August 2019. For finding template structures at prediction time, we used a copy of the PDB downloaded on 14 May 2020, and the PDB7066 clustering database downloaded on 13 May 2020. For MSA lookup at both training and prediction time, we used Uniref9067 v.2020_01, BFD, Uniclust3036 v.2018_08 and MGnify6 v.2018_12. For sequence distillation, we used Uniclust3036 v.2018_08 to construct a distillation structure dataset. Full details are provided in Supplementary Methods 1.2.\n\nFor MSA search on BFD + Uniclust30, and template search against PDB70, we used HHBlits61 and HHSearch66 from hh-suite v.3.0-beta.3 (version 14/07/2017). For MSA search on Uniref90 and clustered MGnify, we used jackhmmer from HMMER368. For constrained relaxation of structures, we used OpenMM v.7.3.169 with the Amber99sb force field32. For neural network construction, running and other analyses, we used TensorFlow70, Sonnet71, NumPy72, Python73 and Colab74.\n\nTo quantify the effect of the different sequence data sources, we re-ran the CASP14 proteins using the same models but varying how the MSA was constructed. Removing BFD reduced the mean accuracy by 0.4 GDT, removing Mgnify reduced the mean accuracy by 0.7 GDT, and removing both reduced the mean accuracy by 6.1 GDT. In each case, we found that most targets had very small changes in accuracy but a few outliers had very large (20+ GDT) differences. This is consistent with the results in Fig. 5a in which the depth of the MSA is relatively unimportant until it approaches a threshold value of around 30 sequences when the MSA size effects become quite large. We observe mostly overlapping effects between inclusion of BFD and Mgnify, but having at least one of these metagenomics databases is very important for target classes that are poorly represented in UniRef, and having both was necessary to achieve full CASP accuracy.\n\nTo train, we use structures from the PDB with a maximum release date of 30 April 2018. Chains are sampled in inverse proportion to cluster size of a 40% sequence identity clustering. We then randomly crop them to 256 residues and assemble into batches of size 128. We train the model on Tensor Processing Unit (TPU) v3 with a batch size of 1 per TPU core, hence the model uses 128 TPU v3 cores. The model is trained until convergence (around 10 million samples) and further fine-tuned using longer crops of 384 residues, larger MSA stack and reduced learning rate (see Supplementary Methods 1.11 for the exact configuration). The initial training stage takes approximately 1 week, and the fine-tuning stage takes approximately 4 additional days.\n\nThe network is supervised by the FAPE loss and a number of auxiliary losses. First, the final pair representation is linearly projected to a binned distance distribution (distogram) prediction, scored with a cross-entropy loss. Second, we use random masking on the input MSAs and require the network to reconstruct the masked regions from the output MSA representation using a BERT-like loss37. Third, the output single representations of the structure module are used to predict binned per-residue lDDT-C\u03b1 values. Finally, we use an auxiliary side-chain loss during training, and an auxiliary structure violation loss during fine-tuning. Detailed descriptions and weighting are provided in the Supplementary Information.\n\nAn initial model trained with the above objectives was used to make structure predictions for a Uniclust dataset of 355,993 sequences with the full MSAs. These predictions were then used to train a final model with identical hyperparameters, except for sampling examples 75% of the time from the Uniclust prediction set, with sub-sampled MSAs, and 25% of the time from the clustered PDB set.\n\nWe train five different models using different random seeds, some with templates and some without, to encourage diversity in the predictions (see Supplementary Table 5 and Supplementary Methods 1.12.1 for details). We also fine-tuned these models after CASP14 to add a pTM prediction objective (Supplementary Methods 1.9.7) and use the obtained models for Fig. 2d.\n\nWe inference the five trained models and use the predicted confidence score to select the best model per target.\n\nUsing our CASP14 configuration for AlphaFold, the trunk of the network is run multiple times with different random choices for the MSA cluster centres (see Supplementary Methods 1.11.2 for details of the ensembling procedure). The full time to make a structure prediction varies considerably depending on the length of the protein. Representative timings for the neural network using a single model on V100 GPU are 4.8 min with 256 residues, 9.2 min with 384 residues and 18 h at 2,500 residues. These timings are measured using our open-source code, and the open-source code is notably faster than the version we ran in CASP14 as we now use the XLA compiler75.\n\nSince CASP14, we have found that the accuracy of the network without ensembling is very close or equal to the accuracy with ensembling and we turn off ensembling for most inference. Without ensembling, the network is 8\u00d7 faster and the representative timings for a single model are 0.6 min with 256 residues, 1.1 min with 384 residues and 2.1 h with 2,500 residues.\n\nInferencing large proteins can easily exceed the memory of a single GPU. For a V100 with 16 GB of memory, we can predict the structure of proteins up to around 1,300 residues without ensembling and the 256- and 384-residue inference times are using the memory of a single GPU. The memory usage is approximately quadratic in the number of residues, so a 2,500-residue protein involves using unified memory so that we can greatly exceed the memory of a single V100. In our cloud setup, a single V100 is used for computation on a 2,500-residue protein but we requested four GPUs to have sufficient memory.\n\nSearching genetic sequence databases to prepare inputs and final relaxation of the structures take additional central processing unit (CPU) time but do not require a GPU or TPU.\n\nThe predicted structure is compared to the true structure from the PDB in terms of lDDT metric34, as this metric reports the domain accuracy without requiring a domain segmentation of chain structures. The distances are either computed between all heavy atoms (lDDT) or only the C\u03b1 atoms to measure the backbone accuracy (lDDT-C\u03b1). As lDDT-C\u03b1 only focuses on the C\u03b1 atoms, it does not include the penalty for structural violations and clashes. Domain accuracies in CASP are reported as GDT33 and the TM-score27 is used as a full chain global superposition metric.\n\nWe also report accuracies using the r.m.s.d. (C\u03b1 r.m.s.d. at 95% coverage). We perform five iterations of (1) a least-squares alignment of the predicted structure and the PDB structure on the currently chosen C\u03b1 atoms (using all C\u03b1 atoms in the first iteration); (2) selecting the 95% of C\u03b1 atoms with the lowest alignment error. The r.m.s.d. of the atoms chosen for the final iterations is the r.m.s.d. . This metric is more robust to apparent errors that can originate from crystal structure artefacts, although in some cases the removed 5% of residues will contain genuine modelling errors.\n\nFor evaluation on recent PDB sequences (Figs. 2a\u2013d, 4a, 5a), we used a copy of the PDB downloaded 15 February 2021. Structures were filtered to those with a release date after 30 April 2018 (the date limit for inclusion in the training set for AlphaFold). Chains were further filtered to remove sequences that consisted of a single amino acid as well as sequences with an ambiguous chemical component at any residue position. Exact duplicates were removed, with the chain with the most resolved C\u03b1 atoms used as the representative sequence. Subsequently, structures with less than 16 resolved residues, with unknown residues or solved by NMR methods were removed. As the PDB contains many near-duplicate sequences, the chain with the highest resolution was selected from each cluster in the PDB 40% sequence clustering of the data. Furthermore, we removed all sequences for which fewer than 80 amino acids had the alpha carbon resolved and removed chains with more than 1,400 residues. The final dataset contained 10,795 protein sequences.\n\nThe procedure for filtering the recent PDB dataset based on prior template identity was as follows. Hmmsearch was run with default parameters against a copy of the PDB SEQRES fasta downloaded 15 February 2021. Template hits were accepted if the associated structure had a release date earlier than 30 April 2018. Each residue position in a query sequence was assigned the maximum identity of any template hit covering that position. Filtering then proceeded as described in the individual figure legends, based on a combination of maximum identity and sequence coverage.\n\nThe MSA depth analysis was based on computing the normalized number of effective sequences (N ) for each position of a query sequence. Per-residue N values were obtained by counting the number of non-gap residues in the MSA for this position and weighting the sequences using the N scheme76 with a threshold of 80% sequence identity measured on the region that is non-gap in either sequence.\n\nFurther information on research design is available in the Nature Research Reporting Summary linked to this paper.", "cleaned_text_custom_parser": "Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain\n the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in\n Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles\n and JavaScript.\nHighly accurate protein structure prediction with AlphaFold\nSubjects\nAbstract\nSimilar content being viewed by others\nHighly accurate protein structure prediction for the human proteome\nAccurate prediction of protein folding mechanisms by simple structure-based statistical mechanical models\nImproved protein structure prediction using potentials from deep learning\nMain\nThe development of computational methods to predict three-dimensional (3D) protein structures from the protein sequence has proceeded along two complementary paths that focus on either the physical interactions or the evolutionary history. The physical interaction programme heavily integrates our understanding of molecular driving forces into either thermodynamic or kinetic simulation of protein physics16 or statistical approximations thereof17. Although theoretically very appealing, this approach has proved highly challenging for even moderate-sized proteins due to the computational intractability of molecular simulation, the context dependence of protein stability and the difficulty of producing sufficiently accurate models of protein physics. The evolutionary programme has provided an alternative in recent years, in which the constraints on protein structure are derived from bioinformatics analysis of the evolutionary history of proteins, homology to solved structures18,19 and pairwise evolutionary correlations20,21,22,23,24. This bioinformatics approach has benefited greatly from the steady growth of experimental protein structures deposited in the Protein Data Bank (PDB)5, the explosion of genomic sequencing and the rapid development of deep learning techniques to interpret these correlations. Despite these advances, contemporary physical and evolutionary-history-based approaches produce predictions that are far\u00a0short of experimental accuracy in the majority of cases in which a close homologue has not been solved experimentally and this has limited their utility for many biological applications.\nIn this study, we develop the first, to our knowledge, computational approach capable of predicting protein structures to near experimental accuracy in a majority of cases. The neural network AlphaFold that we developed was entered into the CASP14 assessment (May\u2013July\u00a02020; entered under the team name \u2018AlphaFold2\u2019 and a completely different model from our CASP13 AlphaFold system10). The CASP assessment is carried out biennially using recently solved structures that have not been deposited in the PDB or publicly disclosed so that it is a blind test for the participating methods, and has long served as the gold-standard assessment for the accuracy of structure prediction25,26.\nIn CASP14, AlphaFold structures were vastly more accurate than competing methods. AlphaFold structures had a median backbone accuracy of 0.96\u00a0\u00c5 r.m.s.d.95 (C\u03b1 root-mean-square deviation at 95% residue coverage) (95% confidence interval\u00a0=\u00a00.85\u20131.16\u00a0\u00c5) whereas the next best performing method had a median backbone accuracy of 2.8\u00a0\u00c5 r.m.s.d.95 (95% confidence interval\u00a0=\u00a02.7\u20134.0\u00a0\u00c5) (measured on CASP domains; see Fig. 1a for backbone accuracy and Supplementary Fig. 14 for all-atom accuracy). As a comparison point for this accuracy, the width of a carbon atom is approximately 1.4\u00a0\u00c5. In addition to very accurate domain structures (Fig. 1b), AlphaFold is able to produce highly accurate side chains (Fig. 1c) when the backbone is highly accurate and considerably improves over template-based methods even when strong templates are available. The all-atom accuracy of AlphaFold was 1.5\u00a0\u00c5 r.m.s.d.95 (95% confidence interval\u00a0=\u00a01.2\u20131.6\u00a0\u00c5) compared with the 3.5\u00a0\u00c5 r.m.s.d.95 (95% confidence interval\u00a0=\u00a03.1\u20134.2\u00a0\u00c5) of the best alternative method. Our methods are scalable to very long proteins with accurate domains and domain-packing (see Fig. 1d for the prediction of a 2,180-residue protein with no structural homologues). Finally, the model is able to provide precise, per-residue estimates of its reliability that should enable the confident use of these predictions.\na, The performance of AlphaFold on the CASP14 dataset (n\u00a0=\u00a087 protein domains) relative to the top-15 entries (out of 146 entries), group numbers correspond to the numbers assigned to entrants by CASP. Data are median and the 95% confidence interval of the median, estimated from 10,000 bootstrap samples. b, Our prediction of CASP14 target T1049 (PDB\u00a06Y4F,\u00a0blue) compared with the true (experimental) structure (green). Four residues in the C terminus of the crystal structure are B-factor outliers and are not depicted. c, CASP14 target T1056 (PDB 6YJ1). An example of a well-predicted zinc-binding site (AlphaFold has accurate side chains even though it does not explicitly predict the zinc ion). d, CASP target T1044\u00a0(PDB 6VR4)\u2014a 2,180-residue single chain\u2014was predicted with correct domain packing (the prediction was made after CASP using AlphaFold without intervention). e, Model architecture. Arrows show the information flow among the various components described in this paper. Array shapes are shown in parentheses with s, number of sequences (Nseq in the main text); r, number of residues (Nres in the main text); c, number of channels.\nWe demonstrate in Fig. 2a that the high accuracy that AlphaFold demonstrated in CASP14 extends to a large sample of recently released PDB structures; in this dataset, all structures were deposited in the PDB after our training data cut-off and are analysed as full chains (see\u00a0Methods, Supplementary Fig. 15 and Supplementary Table 6 for more details). Furthermore, we observe high side-chain accuracy when the backbone prediction is accurate (Fig. 2b) and we show that our confidence measure, the predicted local-distance difference test (pLDDT), reliably predicts the C\u03b1 local-distance difference test (lDDT-C\u03b1) accuracy of the corresponding prediction (Fig. 2c). We also find that the global superposition metric template modelling score (TM-score)27 can be accurately estimated (Fig. 2d). Overall, these analyses validate that the high accuracy and reliability of AlphaFold on CASP14 proteins also transfers to an uncurated collection of recent PDB submissions, as would be expected (see\u00a0Supplementary Methods 1.15 and Supplementary Fig. 11 for confirmation that this high accuracy extends to new folds).\nThe analysed structures are newer than any structure in the training set. Further filtering is applied to reduce redundancy (see\u00a0Methods). a, Histogram of backbone r.m.s.d. for full chains (C\u03b1 r.m.s.d. at 95% coverage). Error bars are 95% confidence intervals (Poisson). This dataset excludes proteins with a template (identified by hmmsearch) from the training set with more than 40% sequence identity covering more than 1% of the chain (n\u00a0=\u00a03,144 protein chains). The overall median is 1.46\u00a0\u00c5 (95% confidence interval\u00a0=\u00a01.40\u20131.56\u00a0\u00c5). Note that this measure will be highly sensitive to domain packing and domain accuracy; a high r.m.s.d. is expected for some chains with uncertain packing or packing errors. b, Correlation between backbone accuracy and side-chain accuracy. Filtered to structures with any observed side chains and resolution better than 2.5\u00a0\u00c5 (n\u00a0=\u00a05,317 protein chains); side chains were further filtered to B-factor\u00a0<30\u00a0\u00c52. A rotamer is classified as correct if the predicted torsion angle is within 40\u00b0. Each point aggregates a range of lDDT-C\u03b1, with a bin size of 2 units above 70 lDDT-C\u03b1 and 5 units otherwise. Points correspond to the mean accuracy; error bars are 95% confidence intervals (Student t-test) of the mean on a per-residue basis. c, Confidence score compared to the true accuracy on chains. Least-squares linear fit lDDT-C\u03b1\u00a0=\u00a00.997\u00a0\u00d7\u00a0pLDDT\u00a0\u2212\u00a01.17 (Pearson\u2019s r\u00a0=\u00a00.76). n\u00a0=\u00a010,795 protein chains. The shaded region of the linear fit represents a 95% confidence interval estimated from 10,000 bootstrap samples. In the companion paper39, additional quantification of the reliability of pLDDT as a confidence measure is provided. d, Correlation between pTM and full chain TM-score. Least-squares linear fit TM-score\u00a0=\u00a00.98\u00a0\u00d7\u00a0pTM\u00a0+\u00a00.07 (Pearson\u2019s r\u00a0=\u00a00.85). n\u00a0=\u00a010,795 protein chains. The shaded region of the linear fit represents a 95% confidence interval estimated from 10,000 bootstrap samples.\nThe AlphaFold network\nAlphaFold greatly improves the accuracy of structure prediction by incorporating novel neural network architectures and training procedures based on the evolutionary, physical and geometric constraints of protein structures. In particular, we demonstrate a new architecture to jointly embed multiple sequence alignments (MSAs) and pairwise features, a new output representation and associated loss that enable accurate end-to-end structure prediction, a new equivariant attention architecture, use of intermediate losses to achieve iterative refinement of predictions, masked MSA loss to jointly train with the structure, learning from unlabelled protein sequences using self-distillation and self-estimates of accuracy.\nThe AlphaFold network directly predicts the 3D coordinates of all heavy atoms for a given protein using the primary amino acid sequence and aligned sequences of homologues as inputs (Fig. 1e; see\u00a0Methods for details of inputs including databases, MSA construction and use of templates). A description of the most important ideas and components is provided below. The full network architecture and training procedure are provided in the\u00a0Supplementary Methods.\nThe network comprises two main stages. First, the trunk of the network processes the inputs through repeated layers of a novel neural network block that we term Evoformer to produce an Nseq\u00a0\u00d7\u00a0Nres array (Nseq, number of sequences; Nres, number of residues) that represents a processed MSA and an Nres\u00a0\u00d7\u00a0Nres array that represents residue pairs. The MSA representation is initialized with the raw MSA (although see\u00a0Supplementary Methods 1.2.7 for details of handling very deep MSAs). The Evoformer blocks contain a number of attention-based and non-attention-based components. We show evidence in \u2018Interpreting the neural network\u2019 that a concrete structural hypothesis arises early within the Evoformer blocks and is continuously refined. The key innovations in the Evoformer block are new mechanisms to exchange information within the MSA and pair representations that enable direct reasoning about the spatial and evolutionary relationships.\nThe trunk of the network is followed by the structure module that introduces an explicit 3D structure in the form of a rotation and translation for each residue of the protein (global rigid body frames). These representations are initialized in a trivial state with all rotations set to the identity and all positions set to the origin, but rapidly develop and refine a highly accurate protein structure with precise atomic details. Key innovations in this section of the network include breaking the chain structure to allow simultaneous local refinement of all parts of the structure, a novel equivariant transformer to allow the network to implicitly reason about the unrepresented side-chain atoms and a loss term that places substantial weight on the orientational correctness of the residues. Both within the structure module and throughout the whole network, we reinforce the notion of iterative refinement by repeatedly applying the final loss to outputs and then feeding the outputs recursively into the same modules. The iterative refinement using the whole network (which we term \u2018recycling\u2019 and is related to approaches in computer vision28,29) contributes markedly to accuracy with minor extra training time (see\u00a0Supplementary Methods 1.8 for details).\nEvoformer\nThe key principle of the building block of the network\u2014named Evoformer (Figs. 1e, 3a)\u2014is to view the prediction of protein structures as a graph inference problem in 3D space in which the edges of the graph are defined by residues in proximity. The elements of the pair representation encode information about the relation between the residues (Fig. 3b). The columns of the MSA representation encode the individual residues of the input sequence while the rows represent the sequences in which those residues appear. Within this framework, we define a number of update operations that are applied in each block in which the different update operations are applied in series.\na, Evoformer block. Arrows show the information flow. The shape of the arrays is shown in parentheses. b, The pair representation interpreted as directed edges in a graph. c, Triangle multiplicative update and triangle self-attention. The circles represent residues. Entries in the pair representation are illustrated as directed edges and in each diagram, the edge being updated is ij. d, Structure module\u00a0including Invariant point attention (IPA) module. The single representation is a copy of the first row of the MSA representation. e, Residue gas: a representation of each residue as one free-floating rigid body for the backbone (blue triangles) and \u03c7 angles for the side chains (green circles). The corresponding atomic structure is shown below. f, Frame aligned point error (FAPE). Green, predicted structure; grey, true structure; (Rk,\u00a0tk), frames; xi, atom positions.\nThe MSA representation updates the pair representation through an element-wise outer product that is summed over the MSA sequence dimension. In contrast to previous work30, this operation is applied within every block rather than once in the network, which enables the continuous communication from the evolving MSA representation to the pair representation.\nWithin the pair representation, there are two different update patterns. Both are inspired by the necessity of consistency of the pair representation\u2014for a pairwise description of amino acids to be representable as a single 3D structure, many constraints must be satisfied including the triangle inequality on distances. On the basis of this intuition, we arrange the update operations on the pair representation in terms of triangles of edges involving three different nodes (Fig. 3c). In particular, we add an extra logit bias to axial attention31 to include the \u2018missing edge\u2019 of the triangle and we define a non-attention update operation \u2018triangle multiplicative update\u2019 that uses two edges to update the missing third edge (see\u00a0Supplementary Methods 1.6.5 for details). The triangle multiplicative update was developed originally as a more symmetric and cheaper replacement for the attention, and networks that use only the attention or multiplicative update are both able to produce high-accuracy structures. However, the combination of the two updates is more accurate.\nWe also use a variant of axial attention within the MSA representation. During the per-sequence attention in the MSA, we project additional logits from the pair stack to bias the MSA attention. This closes the loop by providing information flow from the pair representation back into the MSA representation, ensuring that the overall Evoformer block is able to fully mix information between the pair and MSA representations and prepare for structure generation within the structure module.\nEnd-to-end structure prediction\nThe structure module (Fig. 3d) operates on a concrete 3D backbone structure using the pair representation and the original sequence row (single representation) of the MSA representation from the trunk. The 3D backbone structure is represented as Nres independent rotations and translations, each with respect to the global frame (residue gas) (Fig. 3e). These rotations and translations\u2014representing the geometry of the N-C\u03b1-C atoms\u2014prioritize the orientation of the protein backbone so that the location of the side chain of each residue is highly constrained within that frame. Conversely, the peptide bond geometry is completely unconstrained and the network is observed to frequently violate the chain constraint during the application of the structure module as breaking this constraint enables the local refinement of all parts of the chain without solving complex loop closure problems. Satisfaction of the peptide bond geometry is encouraged during fine-tuning by a violation loss term. Exact enforcement of peptide bond geometry is only achieved in the post-prediction relaxation of the structure by gradient descent in the Amber32 force field. Empirically, this final relaxation does not improve the accuracy of the model as measured by the global distance test (GDT)33 or lDDT-C\u03b134 but does remove distracting stereochemical violations without the loss of accuracy.\nThe residue gas representation is updated iteratively in two stages (Fig. 3d). First, a geometry-aware attention operation that we term \u2018invariant point attention\u2019 (IPA) is used to update an Nres set of neural activations (single representation) without changing the 3D positions, then an equivariant update operation is performed on the residue gas using the updated activations. The IPA augments each of the usual attention queries, keys and values with 3D points that\u00a0are produced in the local frame of each residue such that the final value is invariant to global rotations and translations (see\u00a0Methods \u2018IPA\u2019 for details). The 3D queries and keys also impose a strong spatial/locality bias on the attention, which is well-suited to the iterative refinement of the protein structure. After each attention operation and element-wise transition block, the module computes an update to the rotation and translation of each backbone frame. The application of these updates within the local frame of each residue makes the overall attention and update block an equivariant operation on the residue gas.\nPredictions of side-chain \u03c7 angles as well as the final, per-residue accuracy of the structure (pLDDT) are computed with small per-residue networks on the final activations at the end of the network. The estimate of the TM-score (pTM) is obtained from a pairwise error prediction that is computed as a linear projection from the final pair representation. The final loss (which we term the frame-aligned point error (FAPE) (Fig. 3f)) compares the predicted atom positions to the true positions under many different alignments. For each alignment, defined by aligning the predicted frame (Rk,\u00a0tk) to the corresponding true frame, we compute the distance of all predicted atom positions xi from the true atom positions. The resulting Nframes\u00a0\u00d7\u00a0Natoms distances are penalized with a clamped L1 loss. This creates a strong bias for atoms to be correct relative to the local frame of each residue and hence correct with respect to its side-chain interactions, as well as providing the main source of chirality for AlphaFold (Supplementary Methods 1.9.3 and Supplementary Fig. 9).\nTraining with labelled and unlabelled data\nThe AlphaFold architecture is able to train to high accuracy using only supervised learning on PDB data, but we are able to enhance accuracy (Fig. 4a) using an approach similar to noisy student self-distillation35. In this procedure, we use a trained network to predict the structure of around 350,000 diverse sequences from Uniclust3036 and make a new dataset of predicted structures filtered to a high-confidence subset. We then train the same architecture again from scratch using a mixture of PDB data and this new dataset of predicted structures as the training data, in which the various training data augmentations such as cropping and MSA subsampling make it challenging for the network to recapitulate the previously predicted structures. This self-distillation procedure makes effective use of the unlabelled sequence data and considerably improves the accuracy of the resulting network.\na, Ablation results on two target sets: the CASP14 set of domains (n\u00a0=\u00a087 protein domains) and the PDB test set of chains with template coverage of \u226430% at 30% identity (n\u00a0=\u00a02,261 protein chains). Domains are scored with GDT and chains are scored with lDDT-C\u03b1. The ablations are reported as a difference compared with the average of the three baseline seeds. Means (points) and 95% bootstrap percentile intervals (error bars) are computed using bootstrap estimates of 10,000 samples. b, Domain GDT trajectory over 4 recycling iterations and 48 Evoformer blocks on CASP14 targets LmrP (T1024) and Orf8 (T1064) where D1 and D2 refer to the individual domains as defined by the CASP assessment. Both T1024 domains obtain the correct structure early in the network, whereas the structure of T1064 changes multiple times and requires nearly the full depth of the network to reach the final structure. Note, 48 Evoformer blocks comprise one recycling iteration.\nAdditionally, we randomly mask out or mutate individual residues within the MSA and have a Bidirectional Encoder Representations from Transformers (BERT)-style37 objective to predict the masked elements of the MSA sequences. This objective encourages the network to learn to interpret phylogenetic and covariation relationships without hardcoding a particular correlation statistic into the features. The BERT objective is trained jointly with the normal PDB structure loss on the same training examples and is not pre-trained, in contrast to recent independent work38.\nInterpreting the neural network\nTo understand how AlphaFold predicts protein structure, we trained a separate structure module for each of the 48 Evoformer blocks in the network while keeping all parameters of the main network frozen (Supplementary Methods 1.14). Including our recycling stages, this provides a trajectory of 192 intermediate structures\u2014one per full Evoformer block\u2014in which each intermediate represents the belief of the network of the most likely structure at that block. The resulting trajectories are surprisingly smooth after the first few blocks, showing that AlphaFold makes constant incremental improvements to the structure until it can no longer improve (see Fig. 4b for a trajectory of accuracy). These trajectories also illustrate the role of network depth. For very challenging proteins such as ORF8 of SARS-CoV-2 (T1064), the network searches and rearranges secondary structure elements for many layers before settling on a good structure. For other proteins such as LmrP (T1024), the network finds the final structure within the first few layers. Structure trajectories of CASP14 targets T1024, T1044, T1064 and T1091 that demonstrate a clear iterative building process for a range of protein sizes and difficulties are shown in Supplementary Videos\u00a01\u20134. In\u00a0Supplementary Methods 1.16 and Supplementary Figs. 12, 13, we interpret the attention maps produced by AlphaFold layers.\nFigure 4a contains detailed ablations of the components of AlphaFold that demonstrate that a variety of different mechanisms contribute to AlphaFold accuracy. Detailed descriptions of each ablation model, their training details, extended discussion of ablation results and the effect of MSA depth on each ablation are provided in\u00a0Supplementary Methods 1.13 and Supplementary Fig. 10.\nMSA depth and cross-chain contacts\nAlthough AlphaFold has a high accuracy across the vast majority of deposited PDB structures, we note that there are still factors that affect accuracy or limit the applicability of the model. The model uses MSAs and the accuracy decreases substantially when the median alignment depth is less than around 30\u00a0sequences (see Fig. 5a for details). We observe a threshold effect where improvements in MSA depth over around 100\u00a0sequences lead to small gains. We hypothesize that the MSA information is needed to coarsely find the correct structure within the early stages of the network, but refinement of that prediction into a high-accuracy model does not depend crucially on the MSA information. The other substantial limitation that we have observed is that AlphaFold is much weaker for proteins that have few intra-chain or homotypic contacts compared to the number of heterotypic contacts (further details are provided in a companion paper39). This typically occurs for bridging domains within larger complexes in which the shape of the protein is created almost entirely by interactions with other chains in the complex. Conversely, AlphaFold is often able to give high-accuracy predictions for homomers, even when the chains are substantially intertwined (Fig. 5b). We expect that the ideas of AlphaFold are readily applicable to predicting full hetero-complexes in a future system and that this will remove the difficulty with protein chains that have a large number of hetero-contacts.\na, Backbone accuracy (lDDT-C\u03b1) for the redundancy-reduced set of the PDB after our training data cut-off, restricting to proteins in which at most 25% of the long-range contacts are between different heteromer chains. We further consider two groups of proteins based on template coverage at 30% sequence identity: covering more than 60% of the chain (n\u00a0=\u00a06,743 protein chains) and covering less than 30% of the chain (n\u00a0=\u00a01,596 protein chains). MSA depth is computed by counting the number of non-gap residues for each position in the MSA (using the Neff weighting scheme; see\u00a0Methods for details) and taking the median across residues. The curves are obtained through Gaussian kernel average smoothing (window size is 0.2 units in log10(Neff)); the shaded area is the 95% confidence interval estimated using bootstrap of 10,000 samples. b, An intertwined homotrimer (PDB 6SK0) is correctly predicted without input stoichiometry and only a weak template (blue is predicted and green is experimental).\nRelated work\nDiscussion\nThe methodology that we have taken in designing AlphaFold is a combination of the bioinformatics and physical approaches: we use a physical and geometric inductive bias to build components that learn from PDB data with minimal imposition of handcrafted features (for example, AlphaFold builds hydrogen bonds effectively without a hydrogen bond score function). This results in a network that learns far more efficiently from the limited data in the PDB but is able to cope with the complexity and variety of structural data.\nIn particular, AlphaFold is able to handle missing the physical context and produce accurate models in challenging cases such as intertwined homomers or proteins that only fold in the presence of an unknown haem group. The ability to handle underspecified structural conditions is essential to learning from PDB structures as the PDB represents the full range of conditions in which structures have been solved. In general, AlphaFold is trained to produce the protein structure most likely to appear as part of a PDB structure. For example, in cases in which a particular stochiometry, ligand or ion is predictable from the sequence alone, AlphaFold is likely to produce a structure that respects those constraints implicitly.\nAlphaFold has already demonstrated its utility to the experimental community, both for molecular replacement57 and for interpreting cryogenic electron microscopy maps58. Moreover, because AlphaFold outputs protein coordinates directly, AlphaFold produces predictions in graphics processing unit (GPU) minutes to GPU hours depending on the length of the protein sequence (for example, around one GPU minute per model for 384 residues; see\u00a0Methods for details). This opens up the exciting possibility of predicting structures at the proteome-scale and beyond\u2014in a companion paper39, we demonstrate the application of AlphaFold to the entire human proteome39.\nThe explosion in available genomic sequencing techniques and data has revolutionized bioinformatics but the intrinsic challenge of experimental structure determination has prevented a similar expansion in our structural knowledge. By developing an accurate protein structure prediction algorithm, coupled with existing large and well-curated structure and sequence databases assembled by the experimental community, we hope to accelerate the advancement of structural bioinformatics that can keep pace with the genomics revolution. We hope that AlphaFold\u2014and computational approaches that apply its techniques for other biophysical problems\u2014will become essential tools of modern biology.\nMethods\nFull algorithm details\nExtensive explanations of the components and their motivations are available in\u00a0Supplementary Methods 1.1\u20131.10, in addition, pseudocode is available in Supplementary\u00a0Information Algorithms 1\u201332, network diagrams in Supplementary Figs. 1\u20138, input features in Supplementary Table 1 and additional details are provided in Supplementary Tables 2, 3. Training and inference details are provided in\u00a0Supplementary Methods 1.11\u20131.12 and Supplementary Tables 4, 5.\nIPA\nThe IPA module combines the pair representation, the single representation and the geometric representation to update the single representation (Supplementary Fig. 8). Each of these representations contributes affinities to the shared attention weights and then uses these weights to map its values to the output. The IPA operates in 3D space. Each residue produces query points, key points and value points in its local frame. These points are projected into the global frame using the backbone frame of the residue in which they interact with each other. The resulting points are then projected back into the local frame. The affinity computation in the 3D space uses squared distances and the coordinate transformations ensure the invariance of this module with respect to the global frame (see\u00a0Supplementary Methods 1.8.2 \u2018Invariant point attention (IPA)\u2019 for the algorithm, proof of invariance and a description of the full multi-head version). A related construction that uses classic geometric invariants to construct pairwise features in place of the learned 3D points has been applied to protein design59.\nIn addition to the IPA, standard dot product attention is computed on the abstract single representation and a special attention on the pair representation. The pair representation augments both the logits and the values of the attention process, which is the primary way in which the pair representation controls the structure generation.\nInputs and data sources\nInputs to the network are the primary sequence, sequences from evolutionarily related proteins in the form of a MSA created by standard tools including jackhmmer60 and HHBlits61, and 3D atom coordinates of a small number of homologous structures (templates) where available. For both the MSA and templates, the search processes are tuned for high recall; spurious matches will probably appear in the raw MSA but this matches the training condition of the network.\nOne of the sequence databases used, Big Fantastic Database (BFD), was custom-made and released publicly (see \u2018Data availability\u2019) and was used by several CASP teams. BFD is one of the largest publicly available collections of protein families. It consists of 65,983,866 families represented as MSAs and hidden Markov models (HMMs) covering 2,204,359,010 protein sequences from reference databases, metagenomes and metatranscriptomes.\nBFD was built in three steps. First, 2,423,213,294 protein sequences were collected from UniProt (Swiss-Prot&TrEMBL, 2017-11)62, a soil reference protein catalogue and the marine eukaryotic reference catalogue7, and clustered to 30% sequence identity, while enforcing a 90% alignment coverage of the shorter sequences using MMseqs2/Linclust63. This resulted in 345,159,030 clusters. For computational efficiency, we removed all clusters with less than three members, resulting in 61,083,719 clusters. Second, we added 166,510,624 representative protein sequences from Metaclust NR (2017-05; discarding all sequences shorter than 150 residues)63 by aligning them against the cluster representatives using MMseqs264. Sequences that fulfilled the sequence identity and coverage criteria were assigned to the best scoring cluster. The remaining 25,347,429 sequences that could not be assigned were clustered separately and added as new clusters, resulting in the final clustering. Third, for each of the clusters, we computed an MSA using FAMSA65 and computed the HMMs following the Uniclust HH-suite database protocol36.\nThe following versions of public datasets were used in this study. Our models were trained on a copy of the PDB5 downloaded on 28\u00a0August 2019. For finding template structures at prediction time, we used a copy of the PDB downloaded on 14\u00a0May 2020, and the PDB7066 clustering database downloaded on 13\u00a0May 2020. For MSA lookup at both training and prediction time, we used Uniref9067 v.2020_01, BFD, Uniclust3036 v.2018_08 and MGnify6 v.2018_12. For sequence distillation, we used Uniclust3036 v.2018_08 to construct a distillation structure dataset. Full details are provided in\u00a0Supplementary Methods 1.2.\nTo quantify the effect of the different sequence data sources, we re-ran the CASP14 proteins using the same models but varying how the MSA was constructed. Removing BFD reduced the mean accuracy by 0.4\u00a0GDT, removing Mgnify reduced the mean accuracy by 0.7\u00a0GDT, and removing both reduced the mean accuracy by 6.1\u00a0GDT. In each case, we found that most targets had very small changes in accuracy but a few outliers had very large (20+\u00a0GDT) differences. This is consistent with the results in Fig. 5a in which the depth of the MSA is relatively unimportant until it approaches a threshold value of around 30 sequences when the MSA size effects become quite large. We observe mostly overlapping effects between inclusion of BFD and Mgnify, but having at least one of these metagenomics databases is very important for target classes that are poorly represented in UniRef, and having both was necessary to achieve full CASP accuracy.\nTraining regimen\nTo train, we use structures from the PDB with a maximum release date of 30\u00a0April 2018. Chains are sampled in inverse proportion to cluster size of a 40% sequence identity clustering. We then randomly crop them to 256 residues and assemble into batches of size 128. We train the model on Tensor Processing Unit (TPU) v3 with a batch size of 1 per TPU core, hence the model uses 128 TPU v3 cores. The model is trained until convergence (around 10\u00a0million samples) and further fine-tuned using longer crops of 384 residues, larger MSA stack and reduced learning rate (see\u00a0Supplementary Methods 1.11 for the exact configuration). The initial training stage takes approximately 1\u00a0week, and the fine-tuning stage takes approximately 4\u00a0additional days.\nThe network is supervised by the FAPE\u00a0loss and a number of auxiliary losses. First, the final pair representation is linearly projected to a binned distance distribution (distogram) prediction, scored with a cross-entropy loss. Second, we use random masking on the input MSAs and require the network to reconstruct the masked regions from the output MSA representation using a BERT-like loss37. Third, the output single representations of the structure module are used to predict binned per-residue lDDT-C\u03b1 values. Finally, we use an auxiliary side-chain loss during training, and an auxiliary structure violation loss during fine-tuning. Detailed descriptions and weighting are provided in the\u00a0Supplementary Information.\nAn initial model trained with the above objectives was used to make structure predictions for a Uniclust dataset of 355,993 sequences with the full MSAs. These predictions were then used to train a final model with identical hyperparameters, except for sampling examples 75% of the time from the Uniclust prediction set, with sub-sampled MSAs, and 25% of the time from the clustered PDB set.\nWe train five different models using different random seeds, some with templates and some without, to encourage diversity in the predictions (see Supplementary Table 5 and Supplementary Methods 1.12.1 for details). We also fine-tuned these models after CASP14 to add a pTM prediction objective (Supplementary Methods 1.9.7) and use the obtained models for Fig. 2d.\nInference regimen\nWe inference the five trained models and use the predicted confidence score to select the best model per target.\nUsing our CASP14 configuration for AlphaFold, the trunk of the network is run multiple times with different random choices for the MSA cluster centres (see\u00a0Supplementary Methods 1.11.2 for details of the ensembling procedure). The full time to make a structure prediction varies considerably depending on the length of the protein. Representative timings for the neural network using a single model on V100 GPU are 4.8\u00a0min with 256\u00a0residues, 9.2\u00a0min with 384\u00a0residues and 18\u00a0h at 2,500\u00a0residues. These timings are measured using our open-source code, and the open-source code is notably faster than the version we ran in CASP14 as we now use the XLA compiler75.\nSince CASP14, we have found that the accuracy of the network without ensembling is very close or equal to the accuracy with ensembling and we turn off ensembling for most inference. Without ensembling, the network is 8\u00d7 faster and the representative timings for a single model are 0.6\u00a0min with 256\u00a0residues, 1.1\u00a0min with 384 residues and 2.1\u00a0h with 2,500 residues.\nInferencing large proteins can easily exceed the memory of a single GPU. For a V100 with 16\u00a0GB of memory, we can predict the structure of proteins up to around 1,300\u00a0residues without ensembling and the 256- and 384-residue inference times are using the memory of a single GPU. The memory usage is approximately quadratic in the number of residues, so a 2,500-residue protein involves using unified memory so that we can greatly exceed the memory of a single V100. In our cloud setup, a single V100 is used for computation on a 2,500-residue protein but we requested four GPUs to have sufficient memory.\nSearching genetic sequence databases to prepare inputs and final relaxation of the structures take additional central processing unit (CPU) time but do not require a GPU or TPU.\nMetrics\nThe predicted structure is compared to the true structure from the PDB in terms of lDDT metric34, as this metric reports the domain accuracy without requiring a domain segmentation of chain structures. The distances are either computed between all heavy atoms (lDDT) or only the C\u03b1 atoms to measure the backbone accuracy (lDDT-C\u03b1). As lDDT-C\u03b1 only focuses on the C\u03b1 atoms, it does not include the penalty for structural violations and clashes. Domain accuracies in CASP are reported as GDT33 and the TM-score27 is used as a full chain global superposition metric.\nWe also report accuracies using the r.m.s.d.95 (C\u03b1 r.m.s.d. at 95% coverage). We perform five iterations of (1) a least-squares alignment of the predicted structure and the PDB structure on the currently chosen C\u03b1 atoms (using all C\u03b1 atoms in the first iteration); (2) selecting the 95% of\u00a0C\u03b1 atoms with the lowest alignment error. The r.m.s.d. of the atoms chosen for the final iterations is the r.m.s.d.95. This metric is more robust to apparent errors that can originate from crystal structure artefacts, although in some cases the removed 5% of residues will contain genuine modelling errors.\nTest set of recent PDB sequences\nFor evaluation on recent PDB sequences (Figs. 2a\u2013d, 4a, 5a), we used a copy of the PDB downloaded 15 February 2021. Structures were filtered to those with a release date after 30\u00a0April 2018 (the date limit for inclusion in the training set for AlphaFold). Chains were further filtered to remove sequences that consisted of a single amino acid as well as sequences with an ambiguous chemical component at any residue position. Exact duplicates were removed, with the chain with the most resolved C\u03b1 atoms used as the representative sequence. Subsequently, structures with less than 16 resolved residues, with unknown residues or solved by NMR methods were removed. As the PDB contains many near-duplicate sequences, the chain with the highest resolution was selected from each cluster in the PDB 40% sequence clustering of the data. Furthermore, we removed all sequences for which fewer than 80 amino acids had the alpha carbon resolved and removed chains with more than 1,400 residues. The final dataset contained 10,795 protein sequences.\nThe procedure for filtering the recent PDB dataset based on prior template identity was as follows. Hmmsearch was run with default parameters against a copy of the PDB SEQRES fasta downloaded 15 February 2021. Template hits were accepted if the associated structure had a release date earlier than 30\u00a0April 2018. Each residue position in a query sequence was assigned the maximum identity of any template hit covering that position. Filtering then proceeded as described in the individual figure legends, based on a combination of maximum identity and sequence coverage.\nThe MSA depth analysis was based on computing the normalized number of effective sequences (Neff) for each position of a query sequence. Per-residue Neff values were obtained by counting the number of non-gap residues in the MSA for this position and weighting the sequences using the Neff scheme76 with a threshold of 80% sequence identity measured on the region that is non-gap in either sequence.\nReporting summary\nFurther information on research design is available in the\u00a0Nature Research Reporting Summary linked to this paper.\nData availability\nStructures from the PDB were used for training and as templates (https://www.wwpdb.org/ftp/pdb-ftp-sites; for the associated sequence data and 40% sequence clustering see also https://ftp.wwpdb.org/pub/pdb/derived_data/ and https://cdn.rcsb.org/resources/sequence/clusters/bc-40.out). Training used a version of the PDB downloaded 28\u00a0August 2019, while the CASP14 template search used a version downloaded 14\u00a0May 2020. The template search also used the PDB70 database, downloaded 13\u00a0May 2020 (https://wwwuser.gwdg.de/~compbiol/data/hhsuite/databases/hhsuite_dbs/).\nFor MSA lookup at both the training and prediction time, we used UniRef90 v.2020_01 (https://ftp.ebi.ac.uk/pub/databases/uniprot/previous_releases/release-2020_01/uniref/), BFD (https://bfd.mmseqs.com), Uniclust30 v.2018_08 (https://wwwuser.gwdg.de/~compbiol/uniclust/2018_08/) and MGnify clusters v.2018_12 (https://ftp.ebi.ac.uk/pub/databases/metagenomics/peptide_database/2018_12/). Uniclust30 v.2018_08 was also used as input for constructing a distillation structure dataset.\nCode availability\nSource code for the AlphaFold model, trained weights and inference script are available under an open-source license at https://github.com/deepmind/alphafold.\nNeural networks were developed with TensorFlow v.1 (https://github.com/tensorflow/tensorflow), Sonnet v.1 (https://github.com/deepmind/sonnet), JAX v.0.1.69 (https://github.com/google/jax/) and Haiku v.0.0.4 (https://github.com/deepmind/dm-haiku). The XLA compiler is bundled with JAX and does not have a separate version number.\nFor MSA search on BFD+Uniclust30, and for template search against PDB70, we used HHBlits and HHSearch from hh-suite v.3.0-beta.3 release 14/07/2017 (https://github.com/soedinglab/hh-suite). For MSA search on UniRef90 and clustered MGnify, we used jackhmmer from HMMER v.3.3 (http://eddylab.org/software/hmmer/). For constrained relaxation of structures, we used OpenMM v.7.3.1 (https://github.com/openmm/openmm) with the Amber99sb force field.\nConstruction of BFD used MMseqs2 v.925AF (https://github.com/soedinglab/MMseqs2) and FAMSA v.1.2.5 (https://github.com/refresh-bio/FAMSA).\nData analysis used Python v.3.6 (https://www.python.org/), NumPy v.1.16.4 (https://github.com/numpy/numpy), SciPy v.1.2.1 (https://www.scipy.org/), seaborn v.0.11.1 (https://github.com/mwaskom/seaborn), Matplotlib v.3.3.4 (https://github.com/matplotlib/matplotlib), bokeh v.1.4.0 (https://github.com/bokeh/bokeh), pandas v.1.1.5 (https://github.com/pandas-dev/pandas), plotnine v.0.8.0 (https://github.com/has2k1/plotnine), statsmodels v.0.12.2 (https://github.com/statsmodels/statsmodels) and Colab (https://research.google.com/colaboratory). TM-align v.20190822 (https://zhanglab.dcmb.med.umich.edu/TM-align/) was used for computing TM-scores. 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Ward, Z. Wu, \u017d. Avsec and the Research Platform Team for their contributions; M. Mirdita for his help with the datasets; M. Piovesan-Forster, A. Nelson and R. Kemp for their help managing the project; the JAX, TensorFlow and XLA teams for detailed support and enabling machine learning models of the complexity of AlphaFold; our colleagues at DeepMind, Google and Alphabet for their encouragement and support; and J. Moult and the CASP14 organizers, and the experimentalists whose structures enabled the assessment. M.S. acknowledges support from the National Research Foundation of Korea grant (2019R1A6A1A10073437, 2020M3A9G7103933) and the Creative-Pioneering Researchers Program through Seoul National University.\nAuthor information\nThese authors contributed equally: John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin \u017d\u00eddek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Demis Hassabis\nAuthors and Affiliations\nJohn Jumper,\u00a0Richard Evans,\u00a0Alexander Pritzel,\u00a0Tim Green,\u00a0Michael Figurnov,\u00a0Olaf Ronneberger,\u00a0Kathryn Tunyasuvunakool,\u00a0Russ Bates,\u00a0Augustin \u017d\u00eddek,\u00a0Anna Potapenko,\u00a0Alex Bridgland,\u00a0Clemens Meyer,\u00a0Simon A. A. Kohl,\u00a0Andrew J. Ballard,\u00a0Andrew Cowie,\u00a0Bernardino Romera-Paredes,\u00a0Stanislav Nikolov,\u00a0Rishub Jain,\u00a0Jonas Adler,\u00a0Trevor Back,\u00a0Stig Petersen,\u00a0David Reiman,\u00a0Ellen Clancy,\u00a0Michal Zielinski,\u00a0Michalina Pacholska,\u00a0Tamas Berghammer,\u00a0Sebastian Bodenstein,\u00a0David Silver,\u00a0Oriol Vinyals,\u00a0Andrew W. Senior,\u00a0Koray Kavukcuoglu,\u00a0Pushmeet Kohli\u00a0&\u00a0Demis Hassabis\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nYou can also search for this author in\n PubMed\u00a0Google Scholar\nContributions\nJ.J. and D.H. led the research. J.J., R.E., A. Pritzel, M.F., O.R., R.B., A. Potapenko, S.A.A.K., B.R.-P., J.A., M.P., T. Berghammer and O.V. developed the neural network architecture and training. T.G., A.\u017d., K.T., R.B., A.B., R.E., A.J.B., A.C., S.N., R.J., D.R., M.Z. and S.B. developed the data, analytics and inference systems. D.H., K.K., P.K., C.M. and E.C. managed the research. T.G. led the technical platform. P.K., A.W.S., K.K., O.V., D.S., S.P. and T. Back contributed technical advice and ideas. M.S. created the BFD genomics database and provided technical assistance on HHBlits. D.H., R.E., A.W.S. and K.K. conceived the AlphaFold project. J.J., R.E. and A.W.S. conceived the end-to-end approach. J.J., A. Pritzel, O.R., A. Potapenko, R.E., M.F., T.G., K.T., C.M. and D.H. wrote the paper.\nCorresponding authors\nEthics declarations\nCompeting interests\nJ.J., R.E., A. Pritzel, T.G., M.F., O.R., R.B., A.B., S.A.A.K., D.R. and A.W.S. have filed non-provisional patent applications 16/701,070 and PCT/EP2020/084238, and provisional patent applications 63/107,362, 63/118,917, 63/118,918, 63/118,921 and 63/118,919, each in the name of DeepMind Technologies Limited, each pending, relating to machine learning for predicting protein structures. The other authors declare no competing interests.\nAdditional information\nPeer review information Nature thanks Mohammed AlQuraishi, Charlotte Deane and Yang Zhang for their contribution to the peer review of this work.\nPublisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\nSupplementary information\nSupplementary Information\nDescription of the method details of the AlphaFold system, model, and analysis, including data pipeline, datasets, model blocks, loss functions, training and inference details, and ablations. Includes Supplementary Methods, Supplementary Figures, Supplementary Tables and Supplementary Algorithms.\nReporting Summary\nSupplementary Video 1\nVideo of the intermediate structure trajectory of the CASP14 target T1024 (LmrP) A two-domain target (408 residues). Both domains are folded early, while their packing is adjusted for a longer time.\nSupplementary Video 2\nVideo of the intermediate structure trajectory of the CASP14 target T1044 (RNA polymerase of crAss-like phage).\u00a0A large protein (2180 residues), with multiple domains. Some domains are folded quickly, while others take a considerable amount of time to fold.\nSupplementary Video 3\nVideo of the intermediate structure trajectory of the CASP14 target T1064 (Orf8).\u00a0A very difficult single-domain target (106 residues) that takes the entire depth of the network to fold.\nSupplementary Video 4\nVideo of the intermediate structure trajectory of the CASP14 target T1091.\u00a0A multi-domain target (863 residues). Individual domains\u2019 structure is determined early, while the domain packing evolves throughout the network. The network is exploring unphysical configurations throughout the process, resulting in long \u2018strings\u2019 in the visualization.\nRights and permissions\nOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.\nAbout this article\nCite this article\nJumper, J., Evans, R., Pritzel, A. et al. Highly accurate protein structure prediction with AlphaFold.\n Nature 596, 583\u2013589 (2021). https://doi.org/10.1038/s41586-021-03819-2\nShare this article\nThis article is cited by\nBase-editing mutagenesis maps alleles to tune human T cell functions\nRegulation of RAS palmitoyltransferases by accessory proteins and palmitoylation\nG protein-coupled receptors (GPCRs): advances in structures, mechanisms, and drug discovery\nEnhancing missense variant pathogenicity prediction with protein language models using VariPred\nStructure determination needs to go viral\nComments\nBy submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.\nAssociated content\nRobotics and artificial intelligence\nMethod of the Year 2021: Protein structure prediction\nHighly accurate protein structure prediction for the human proteome\nProtein-structure prediction revolutionized\nLasker Award for AlphaFold\nExplore content\nAbout the journal\nPublish with us\nSearch\nQuick links\nNature (Nature)\n \nISSN 1476-4687 (online)\n \nISSN 0028-0836 (print)\nnature.com sitemap\nAbout Nature Portfolio\nDiscover content\nPublishing policies\nAuthor & Researcher services\nLibraries & institutions\nAdvertising & partnerships\nProfessional development\nRegional websites", "cleaned_text_custom_parser_2": null, "best_field": "cleaned_text_custom_parser", "enhanced": false, "date_published": null, "image": null, "num_tokens_oai_tokenizer": null, "num_tokens_gemini_tokenizer": null}, "favicon": "https://www.nature.com/static/images/favicons/nature/apple-touch-icon-f39cb19454.png", "state": "candidate", "enhanced": false, "answer": null, "valid_answer": false, "source_citation_index": 13, "user_web_page_id": null, "status": "not_processed", "reader": false, "rank": 2, "reason": "This article discusses the background of alpha folds and its utility in training despite being less direct, thus relevant."}, {"id": 564562, "type": "GenericWebPage", "mime_type": "text/html", "url": "https://www.kaggle.com/datasets/gallo33henrique/bioinformatics-protein-dataset-simulated", "slug": "kaggle", "title": "Bioinformatics Protein Dataset - Simulated | Kaggle", "snippet": "Synthetic protein dataset with sequences, physical properties, and functional cl", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 564562, "website_id": 256, "url": "https://www.kaggle.com/datasets/gallo33henrique/bioinformatics-protein-dataset-simulated", "title": "Bioinformatics Protein Dataset - Simulated | Kaggle", "snippet": "Synthetic protein dataset with sequences, physical properties, and functional cl", "cleaned_text": null, "cleaned_text_custom_parser": null, "cleaned_text_custom_parser_2": null, "best_field": null, "enhanced": false, "date_published": null, "image": null, "num_tokens_oai_tokenizer": null, "num_tokens_gemini_tokenizer": null}, "favicon": "https://www.kaggle.com/static/images/favicon.ico", "state": "candidate", "enhanced": false, "answer": null, "valid_answer": false, "source_citation_index": 14, "user_web_page_id": null, "status": "not_processed", "reader": false, "rank": 2, "reason": "Discusses the generation of synthetic protein datasets, providing some practical context for machine learning endeavors but less directly relevant."}, {"id": 564590, "type": "GenericWebPage", "mime_type": "text/html", "url": "https://www.nature.com/articles/nrm2042", "slug": "nature", "title": "Collecting and organizing systematic sets of protein data | Nature Reviews Molecular Cell Biology", "snippet": "Technologies are now in place to obtain large amounts of data for systems biology approaches. What are the most suitable technologies for fast, accurate and high-throughput data collection? And following data collection, how should these data be analysed and validated?", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 564590, "website_id": 1759, "url": "https://www.nature.com/articles/nrm2042", "title": "Collecting and organizing systematic sets of protein data | Nature Reviews Molecular Cell Biology", "snippet": "Technologies are now in place to obtain large amounts of data for systems biology approaches. What are the most suitable technologies for fast, accurate and high-throughput data collection? And following data collection, how should these data be analysed and validated?", "cleaned_text": null, "cleaned_text_custom_parser": null, "cleaned_text_custom_parser_2": null, "best_field": null, "enhanced": false, "date_published": null, "image": null, "num_tokens_oai_tokenizer": null, "num_tokens_gemini_tokenizer": null}, "favicon": "https://www.nature.com/static/images/favicons/nature/apple-touch-icon-f39cb19454.png", "state": "candidate", "enhanced": false, "answer": null, "valid_answer": false, "source_citation_index": 15, "user_web_page_id": null, "status": "not_processed", "reader": false, "rank": 2, "reason": "This source offers general discussions around machine learning and protein modeling but lacks specific datasets."}, {"id": 564568, "type": "GenericWebPage", "mime_type": "text/html", "url": "https://portlandpress.com/biochemist/article/46/2/7/234398/AI-told-you-so-navigating-protein-structure", "slug": "portlandpress", "title": "AI told you so: navigating protein structure prediction in the era of machine learning | The Biochemist | Portland Press", "snippet": "At CASP13 in 2018, AlphaFold from ... for Protein Design. These groups implemented the pattern recognition power of machine-learning algorithms, training them using the vast repository of experimentally determined structures in the Protein Data Bank (PDB). The resulting algorithms were able to produce highly accurate predictions of proteins they had not previously encountered. Since CASP14, several machine-learning structure predictors (hereafter called ML-SPs) have ...", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 564568, "website_id": 24837, "url": "https://portlandpress.com/biochemist/article/46/2/7/234398/AI-told-you-so-navigating-protein-structure", "title": "AI told you so: navigating protein structure prediction in the era of machine learning | The Biochemist | Portland Press", "snippet": "At CASP13 in 2018, AlphaFold from ... for Protein Design. These groups implemented the pattern recognition power of machine-learning algorithms, training them using the vast repository of experimentally determined structures in the Protein Data Bank (PDB). The resulting algorithms were able to produce highly accurate predictions of proteins they had not previously encountered. Since CASP14, several machine-learning structure predictors (hereafter called ML-SPs) have ...", "cleaned_text": null, "cleaned_text_custom_parser": null, "cleaned_text_custom_parser_2": null, "best_field": null, "enhanced": false, "date_published": null, "image": null, "num_tokens_oai_tokenizer": null, "num_tokens_gemini_tokenizer": null}, "favicon": null, "state": "candidate", "enhanced": false, "answer": null, "valid_answer": false, "source_citation_index": 16, "user_web_page_id": null, "status": "not_processed", "reader": false, "rank": 2, "reason": "While this discusses various ML methods for protein property prediction, it is less focused on the query regarding available datasets."}]

\n AlphaFold\n Protein Structure Database\n

\n \n AlphaFold\n \n Protein Structure Database\n \n

\n AlphaFold\n \n Protein Structure Database\n

\n Background\n

\n Find out more\n

\n What\u2019s new?\n

\n What\u2019s next?\n

\n Licence and attribution\n

\n FAQs\n

\n EMBL-EBI training\n

\n \n Accessing and interpreting predicted protein structures from AlphaFold database\n \n

\n \n AlphaFold\n \n

\n \n AlphaFold\n
\n Protein Structure Database\n \n

\n AlphaFold\n
\n Protein Structure Database\n

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