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.
Preview |
Text
1-s2.0-S0959440X2300101X-main.pdf - Published Version Download (1MB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | Machine learning methods for predicting protein structure from single sequences |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.sbi.2023.102627 |
| Publisher version: | https://doi.org/10.1016/j.sbi.2023.102627 |
| Language: | English |
| Additional information: | © 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/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10172239 |
Archive Staff Only
 |
View Item |
