{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/proteinnet-a-standardized-data-set-for","title":"ProteinNet: a standardized data set for machine learning of protein structure","arxiv_id":"1902.00249","date":"2019-02-01","proceeding":null,"authors":["Mohammed AlQuraishi"],"abstract":"Rapid progress in deep learning has spurred its application to bioinformatics\nproblems including protein structure prediction and design. In classic machine\nlearning problems like computer vision, progress has been driven by\nstandardized data sets that facilitate fair assessment of new methods and lower\nthe barrier to entry for non-domain experts. While data sets of protein\nsequence and structure exist, they lack certain components critical for machine\nlearning, including high-quality multiple sequence alignments and insulated\ntraining / validation splits that account for deep but only weakly detectable\nhomology across protein space. We have created the ProteinNet series of data\nsets to provide a standardized mechanism for training and assessing data-driven\nmodels of protein sequence-structure relationships. ProteinNet integrates\nsequence, structure, and evolutionary information in programmatically\naccessible file formats tailored for machine learning frameworks. Multiple\nsequence alignments of all structurally characterized proteins were created\nusing substantial high-performance computing resources. Standardized data\nsplits were also generated to emulate the difficulty of past CASP (Critical\nAssessment of protein Structure Prediction) experiments by resetting protein\nsequence and structure space to the historical states that preceded six prior\nCASPs. Utilizing sensitive evolution-based distance metrics to segregate\ndistantly related proteins, we have additionally created validation sets\ndistinct from the official CASP sets that faithfully mimic their difficulty.\nProteinNet thus represents a comprehensive and accessible resource for training\nand assessing machine-learned models of protein structure.","url_abs":"http://arxiv.org/abs/1902.00249v1","url_pdf":"http://arxiv.org/pdf/1902.00249v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"proteinnet-a-standardized-data-set-for","repo_url":"https://github.com/EricAlcaide/MiniFold","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"proteinnet-a-standardized-data-set-for","repo_url":"https://github.com/victor369basu/ProteinStructurePrediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"protein-secondary-structure-prediction","task_name":"Protein Secondary Structure Prediction"},{"task_slug":"protein-structure-prediction","task_name":"Protein Structure Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.00249","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.00249"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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