eprintid: 10172239 rev_number: 8 eprint_status: archive userid: 699 dir: disk0/10/17/22/39 datestamp: 2023-06-21 06:42:43 lastmod: 2023-06-21 06:42:43 status_changed: 2023-06-21 06:42:43 type: article metadata_visibility: show sword_depositor: 699 creators_name: Kandathil, Shaun M creators_name: Lau, Andy M creators_name: Jones, David T title: Machine learning methods for predicting protein structure from single sequences ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F48 note: © 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/). abstract: Recent breakthroughs in protein structure prediction have increasingly relied on the use of deep neural networks. These recent methods are notable in that they produce 3-D atomic coordinates as a direct output of the networks, a feature which presents many advantages. Although most techniques of this type make use of multiple sequence alignments as their primary input, a new wave of methods have attempted to use just single sequences as the input. We discuss the make-up and operating principles of these models, and highlight new developments in these areas, as well as areas for future development. date: 2023-08-01 date_type: published official_url: https://doi.org/10.1016/j.sbi.2023.102627 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2030323 doi: 10.1016/j.sbi.2023.102627 medium: Print-Electronic pii: S0959-440X(23)00101-X lyricists_name: Jones, David lyricists_id: DTJON81 actors_name: Flynn, Bernadette actors_id: BFFLY94 actors_role: owner full_text_status: public publication: Current Opinion in Structural Biology volume: 81 article_number: 102627 event_location: England issn: 0959-440X citation: Kandathil, Shaun M; Lau, Andy M; Jones, David T; (2023) Machine learning methods for predicting protein structure from single sequences. Current Opinion in Structural Biology , 81 , Article 102627. 10.1016/j.sbi.2023.102627 <https://doi.org/10.1016/j.sbi.2023.102627>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10172239/1/1-s2.0-S0959440X2300101X-main.pdf