[1] A. Rivera-Calzada et al. Structure of a bacterial type IV secretion core complex at subnanometre resolution. The EMBO Journal (2013), 1195-1204. DOI: 10.1038/emboj.2013.58.
[2] F. Madeira, M. Pearce, A. R. N. Tivey, P. Basutkar, J. Lee, O. Edbali, N. Madhusoodanan, A. Kolesnikov, and R. Lopez. Search and sequence analysis tools services from EMBL-EBI in 2022. Nucleic Acids Research 50.W1 (2022), W276-W279. DOI: 10.1093/nar/gkac240.
[3] S. de Oliveira and C. Deane. Co-evolution techniques are reshaping the way we do structural bioinformatics. F1000Research 6 (2017), 1224. DOI: 10.12688/f1000research.11543.1.
[4] A. J. Riesselman, J. B. Ingraham, and D. S. Marks. Deep generative models of genetic variation capture the effects of mutations. Nature Methods 15 (10 2018), 816-822. DOI: 10.1038/s41592-018-0138-4.
[5] J. Meier, R. Rao, R. Verkuil, J. Liu, T. Sercu, and A. Rives. Language models enable zero-shot prediction of the effects of mutations on protein function. In: Advances in Neural Information Processing Systems. Ed. by M. Ranzato, A. Beygelzimer, Y. Dauphin, P. Liang, and J. W. Vaughan. Vol. 34. 2021, 29287-29303. https://proceedings.neurips.cc/paper_files/paper/2021/file/f51338d736f95dd42427296047067694-Paper.pdf.
[6] R. Rao, J. Meier, T. Sercu, S. Ovchinnikov, and A. Rives. Transformer protein language models are unsupervised structure learners. bioRxiv (2020). DOI: 10.1101/2020.12.15.422761.
[7] R. M. Rao, J. Liu, R. Verkuil, J. Meier, J. Canny, P. Abbeel, T. Sercu, and A. Rives. MSA Transformer. In: Proceedings of the 38th International Conference on Machine Learning. Ed. by M. Meila and T. Zhang. Vol. 139. Proceedings of Machine Learning Research. July 2021, 8844-8856. https://proceedings.mlr.press/v139/rao21a.html.
[8] A. Rives et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences 118.15 (2021), e2016239118. DOI: 10.1073/pnas.2016239118.
[9] Z. Lin et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379.6637 (2023), 1123-1130. DOI: 10.1126/science.ade2574.
[10] E. C. Alley, G. Khimulya, S. Biswas, M. AlQuraishi, and G. M. Church. Unified rational protein engineering with sequence-based deep representation learning. Nature Methods 16 (12 2019), 1315-1322. DOI: 10.1038/s41592-019-0598-1.
[11] A. J. Riesselman, J. B. Ingraham, and D. S. Marks. Deep generative models of genetic variation capture the effects of mutations. Nature Methods 15 (10 Nov. 2018), 816-822. DOI: 10.1038/s41592-018-0138-4.
[12] J. Frazer, P. Notin, M. Dias, A. Gomez, J. K. Min, K. Brock, Y. Gal, and D. S. Marks. Disease variant prediction with deep generative models of evolutionary data. Nature 599 (7883 Nov. 2021), 91–95. DOI: 10.1038/s41586-021-04043-8.
[13] K. Nguyen, X. Guo, and Y. Pan. Phylogeny in Multiple Sequence Alignments. In: Multiple Biological Sequence Alignment. 2016. Chap. 6, 103-112. DOI: https://doi.org/10.1002/9781119273769.ch6.
[14] H. Ashkenazy, I. Sela, E. Levy Karin, G. Landan, and T. Pupko. Multiple Sequence Alignment Averaging Improves Phylogeny Reconstruction. Systematic Biology 68.1 (June 2018), 117-130. DOI: 10.1093/sysbio/yy036.