[1] Douglas M. Fowler and Stanley Fields. Deep mutational scanning: a new style of protein science. Nature Methods, 11(8):801-807, Aug 2014. ISSN 1548-7105. doi: 10.1038/nmeth.3027. URL https://doi.org/10.1038/nmeth.3027.
[2] Ryan E Cobb, Ran Chao, and Huimin Zhao. Directed evolution: Past, present and future. AChE J., 59(5):1432-1440, May 2013.
[3] Louis Tiefenauer and Sophie Demarche. Challenges in the development of functional assays of membrane proteins. Materials (Basel), 5(11):2205-2242, November 2012.
[4] Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu, and Alex Rives. Language models enable zero-shot prediction of the effects of mutations on protein function. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, volume 34, pages 29287-29303. Curran Associates, Inc., 2021. URL https://proceedings.neurips.cc/paper/2021/file/f51338d736f95dd42427296047067694-Paper.pdf.
[5] Erik Nijkamp, Jeffrey Ruffolo, Eli N. Weinstein, Nikhil Naik, and Ali Madani. Progen2: Exploring the boundaries of protein language models, 2022. URL https://arxiv.org/abs/2206.13517.
[6] Tristan Bepler and Bonnie Berger. Learning the protein language: Evolution, structure, and function. Cell Systems, 12(6):654-669.e3, 2021. ISSN 2405-4712. doi: https://doi.org/10.1016/j.cels.2021.05.017. URL https://www.sciencedirect.com/science/article/pii/S2405471221002039.
[7] J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan, B. Koepnick, H. Nguyen, A. Kang, B. Sankaran, A. K. Bera, N. P. King, and D. Baker. Robust deep learning&#x2013;based protein sequence design using proteinmpnn. Science, 378(6615): 49-56, 2022. doi: 10.1126/science.add2187. URL https://www.science.org/doi/abs/ 10.1126/science.add2187.
[8] Ali Madani, Ben Krause, Eric R Greene, Subu Subramanian, Benjamin P Mohr, James M Holton, Jose Luis Olmos, Jr, Caiming Xiong, Zachary Z Sun, Richard Socher, James S Fraser, and Nikhil Naik. Large language models generate functional protein sequences across diverse families. Nat. Biotechnol., January 2023.
[9] William P. Russ, Matteo Figliuzzi, Christian Stocker, Pierre Barrat-Charlaix, Michael Socolich, Peter Kast, Donald Hilvert, Remi Monasson, Simona Cocco, Martin Weigt, and Rama Ranganathan. An evolution-based model for designing chorismate mutase enzymes. Science, 369 (6502):440–445, 2020. doi: 10.1126/science.aba3304. URL https://www.science.org/doi/abs/10.1126/science.aba3304.
[10] Daniel Hesslow, Niccoló Zanichelli, Pascal Notin, Iacopo Poli, and Debora Marks. Rita: a study on scaling up generative protein sequence models. arXiv preprint arXiv:2205.05789, 2022.
[11] Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena Hurtado, Aidan N Gomez, Debora Marks, and Yarin Gal. Trancection: Protein fitness prediction with autoregressive transformers and inference-time retrieval. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 16990-17017. PMLR, 17-23 Jul 2022. URL https://proceedings.mlr.press/v162/notin22a.html.
[12] Elodie Laine, Yasaman Karami, and Alessandra Carbone. GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects. Molecular Biology and Evolution, 36 (11):2604-2619, 08 2019. ISSN 0737-4038. doi: 10.1093/molbev/msz179. URL https://doi.org/10.1093/molbev/msz179.