{"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/deepiep-a-peptide-sequence-model-of","title":"DeepIEP: a Peptide Sequence Model of Isoelectric Point (IEP/pI) using Recurrent Neural Networks (RNNs)","arxiv_id":"1712.09553","date":"2017-12-27","proceeding":null,"authors":["Esben Jannik Bjerrum"],"abstract":"The isoelectric point (IEP or pI) is the pH where the net charge on the\nmolecular ensemble of peptides and proteins is zero. This physical-chemical\nproperty is dependent on protonable/deprotonable sidechains and their pKa\nvalues. Here an pI prediction model is trained from a database of peptide\nsequences and pIs using a recurrent neural network (RNN) with long short-term\nmemory (LSTM) cells. The trained model obtains an RMSE and R$^2$ of 0.28 and\n0.95 for the external test set. The model is not based on pKa values, but\nprediction of constructed test sequences show similar rankings as already known\npKa values. The prediction depends mostly on the existence of known acidic and\nbasic amino acids with fine-adjusted based on the neighboring sequence and\nposition of the charged amino acids in the peptide chain.","url_abs":"http://arxiv.org/abs/1712.09553v1","url_pdf":"http://arxiv.org/pdf/1712.09553v1.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":"deepiep-a-peptide-sequence-model-of","repo_url":"https://github.com/EBjerrum/DeepIEP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":null,"task_name":"Position"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}