{"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/deeper-profiles-and-cascaded-recurrent-and","title":"Deeper Profiles and Cascaded Recurrent and Convolutional Neural Networks for state-of-the-art Protein Secondary Structure Prediction","arxiv_id":null,"date":"2019-10-12","proceeding":"Scientific Reports 2019 10","authors":["Mirko Torrisi","Manaz Kaleel","Gianluca Pollastri"],"abstract":"Protein Secondary Structure prediction has been a central topic of research in Bioinformatics for decades. In spite of this, even the most sophisticated ab initio SS predictors are not able to reach the theoretical limit of three-state prediction accuracy (88–90%), while only a few predict more than the 3 traditional Helix, Strand and Coil classes. In this study we present tests on different models trained both on single sequence and evolutionary profile-based inputs and develop a new state-of-the-art system with Porter 5. Porter 5 is composed of ensembles of cascaded Bidirectional Recurrent Neural Networks and Convolutional Neural Networks, incorporates new input encoding techniques and is trained on a large set of protein structures. Porter 5 achieves 84% accuracy (81% SOV) when tested on 3 classes and 73% accuracy (70% SOV) on 8 classes on a large independent set. In our tests Porter 5 is 2% more accurate than its previous version and outperforms or matches the most recent predictors of secondary structure we tested. When Porter 5 is retrained on SCOPe based sets that eliminate homology between training/testing samples we obtain similar results. Porter is available as a web server and standalone program at http://distilldeep.ucd.ie/porter/ alongside all the datasets and alignments.","url_abs":"https://doi.org/10.1038/s41598-019-48786-x","url_pdf":"https://www.nature.com/articles/s41598-019-48786-x.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":"deeper-profiles-and-cascaded-recurrent-and","repo_url":"https://github.com/mircare/Porter5","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"protein-secondary-structure-prediction","task_name":"Protein Secondary Structure Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/protein-secondary-structure-prediction-on-4","task":"Protein Secondary Structure Prediction","dataset":"2017_test set","model":"Porter5","rank_in_archive_order":1,"of":1,"metrics":{"Q3":"84.19","Q8":"73.02"},"uses_additional_data":false},{"leaderboard":"/sota/protein-secondary-structure-prediction-on-3","task":"Protein Secondary Structure Prediction","dataset":"2019_test set","model":"Porter5","rank_in_archive_order":1,"of":1,"metrics":{"Q3":"81.74"},"uses_additional_data":false},{"leaderboard":"/sota/protein-secondary-structure-prediction-on-1","task":"Protein Secondary Structure Prediction","dataset":"CB513","model":"Porter5","rank_in_archive_order":4,"of":10,"metrics":{"Q8":"0.74"},"uses_additional_data":false},{"leaderboard":"/sota/protein-secondary-structure-prediction-on-2","task":"Protein Secondary Structure Prediction","dataset":"Jpred4 blind set","model":"Porter5","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"84.62"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}