Frances H. Arnold. Directed Evolution: Bringing New Chemistry to Life. Angewandte Chemie International Edition, 57(16):4143-4148, 2018. ISSN 1521-3773. doi: 10.1002/anie.201708408. URL https://onlinelibrary.wiley.com/doi/abs/10.1002/anie.201708408. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/anie.201708408.
Po-Ssu Huang, Scott E. Boyken, and David Baker. The coming of age of de novo protein design. Nature, 537(7620):320-327, September 2016. ISSN 1476-4687. doi: 10.1038/nature19946. URL https://www.nature.com/articles/nature19946. Number: 7620 Publisher: Nature Publishing Group.
Philip A. Romero, Andreas Krause, and Frances H. Arnold. Navigating the protein fitness landscape with Gaussian processes. Proceedings of the National Academy of Sciences, 110(3):E193-E201, January 2013. doi: 10.1073/pnas.1215251110. URL https://www.pnas.org/doi/10.1073/pnas.1215251110. Publisher: Proceedings of the National Academy of Sciences.
Surojit Biswas, Grigory Khimulya, Ethan C. Alley, Kevin M. Esvelt, and George M. Church. Low-N protein engineering with data-efficient deep learning. Nature Methods, 18(4):389-396, April 2021a. ISSN 1548-7091, 1548-7105. doi: 10.1038/s41592-021-01100-y. URL http://www.nature.com/articles/s41592-021-01100-y.
Michael J Dougherty and Frances H Arnold. Directed evolution: new parts and optimized function. Current Opinion in Biotechnology, 20(4):486-491, August 2009. ISSN 0958-1669. doi: 10.1016/j.copbio.2009.08.005. URL https://www.sciencedirect.com/science/article/pii/S0958166909000986.
Kevin K. Yang, Zachary Wu, and Frances H. Arnold. Machine-learning-guided directed evolution for protein engineering. Nature Methods, 16(8):687-694, August 2019. ISSN 1548-7105. doi: 10.1038/s41592-019-0496-6. URL https://www.nature.com/articles/s41592-019-0496-6. Number: 8 Publisher: Nature Publishing Group.
Bruce J. Wittmann, Yisong Yue, and Frances H. Arnold. Machine Learning-Assisted Directed Evolution Navigates a Combinatorial Epistatic Fitness Landscape with Minimal Screening Burden, December 2020. URL https://www.biorxiv.org/content/10.1101/2020.12.04.408955v1. Pages: 2020.12.04.408955 Section: New Results.
Michael Heinzinger, Ahmed Elnaggar, Yu Wang, Christian Dallago, Dmitrii Nechaev, Florian Matthes, and Burkhard Rost. Modeling aspects of the language of life through transfer-learning protein sequences. BMC Bioinformatics, 20(1):723, December 2019. ISSN 1471-2105. doi: 10.1186/s12859-019-3220-8. URL https://doi.org/10.1186/s12859-019-3220-8.
Christian Dallago, Konstantin Schütze, Michael Heinzinger, Tobias Olenyi, Maria Littmann, Amy X. Lu, Kevin K. Yang, Seonwoo Min, Sungroh Yoon, James T. Morton, and Burkhard Rost. Learned Embeddings from Deep Learning to Visualize and Predict Protein Sets. Current Protocols, 1(5):e113, 2021. ISSN 2691-1299. doi: 10.1002/cpz1.113. URL https://onlinelibrary.wiley.com/doi/abs/10.1002/cpz1.113. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpz1.113.
Christian Dallago, Jody Mou, Kadina E. Johnston, Bruce Wittmann, Nick Bhattacharya, Samuel Goldman, Ali Madani, and Kevin K. Yang. FLIP: Benchmark tasks in fitness landscape inference for proteins. January 2022. URL https://openreview.net/forum?id=p2dMLEwL8tF.
Hannes Stärk, Christian Dallago, Michael Heinzinger, and Burkhard Rost. Light attention predicts protein location from the language of life. Bioinformatics Advances, 1(1):vbab035, January 2021. ISSN 2635-0041. doi: 10.1093/bioadv/vbab035. URL https://doi.org/10.1093/bioadv/vbab035.
Thomas A. Hopf, John B. Ingraham, Frank J. Poelwijk, Charlotta P. I. Scharfe, Michael Springer, Chris Sander, and Debora S. Marks. Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35(2): 128–135, February 2017a. ISSN 1546-1696. doi: 10.1038/nbt.3769.