{"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/variational-auto-encoding-of-protein","title":"Variational auto-encoding of protein sequences","arxiv_id":"1712.03346","date":"2017-12-09","proceeding":null,"authors":["Sam Sinai","Eric Kelsic","George M. Church","Martin A. Nowak"],"abstract":"Proteins are responsible for the most diverse set of functions in biology.\nThe ability to extract information from protein sequences and to predict the\neffects of mutations is extremely valuable in many domains of biology and\nmedicine. However the mapping between protein sequence and function is complex\nand poorly understood. Here we present an embedding of natural protein\nsequences using a Variational Auto-Encoder and use it to predict how mutations\naffect protein function. We use this unsupervised approach to cluster natural\nvariants and learn interactions between sets of positions within a protein.\nThis approach generally performs better than baseline methods that consider no\ninteractions within sequences, and in some cases better than the\nstate-of-the-art approaches that use the inverse-Potts model. This generative\nmodel can be used to computationally guide exploration of protein sequence\nspace and to better inform rational and automatic protein design.","url_abs":"http://arxiv.org/abs/1712.03346v3","url_pdf":"http://arxiv.org/pdf/1712.03346v3.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":"variational-auto-encoding-of-protein","repo_url":"https://github.com/samsinai/VAE_protein_function","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"variational-auto-encoding-of-protein","repo_url":"https://github.com/ahaldane/MSA_VAE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"protein-design","task_name":"Protein Design"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1712.03346","atlas_url":"https://app.syntology.ai/?focus=1712.03346","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}