@article{discovery10172239,
           title = {Machine learning methods for predicting protein structure from single sequences},
            year = {2023},
          volume = {81},
           month = {August},
         journal = {Current Opinion in Structural Biology},
            note = {{\copyright} 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/).},
             url = {https://doi.org/10.1016/j.sbi.2023.102627},
        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.},
          author = {Kandathil, Shaun M and Lau, Andy M and Jones, David T},
            issn = {0959-440X}
}