Learned protein embeddings for machine learning

By Kevin Kaichuang Yang, Zachary Wu, Claire N. Bedbrook, Frances H. Arnold. 2018

195 citations recorded by the source corpus.

The authors of the above paper wish to inform readers that the following article was incorrectly included as a reference: McIsaac, R.S. et al. (2014) Directed evolution of a far-red fluorescent rhodopsin. Proc. Natl. Acad. Sci. USA, 111, 13034–13039. The article which should have appeared in its place is: Engqvist, M.K.M. et al. (2015) Directed evolution of Gloeobacter violaceus rhodopsin spectral properties. Journal of Molecular Biology 427, 205-220.

Subfields: Molecular Biology, Representation & Self-Supervised Learning, Computational Biology & Bioinformatics

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Citations in the corpus

Learning protein sequence embeddings using information from structure (2019, methodology): “Current work in protein sequence embeddings has primarily been focused on unsupervised k-mer co-occurence approaches to learn fixed sized vector representations [7, 8] based on similar methods in NLP [9, 10, 11, 12].”
Machine learning in protein engineering (2018, background): “However, more sophisticated continuous vector encodings of sequences can be learned from patterns in unlabeled sequences (Asgari & Mofrad, 2015; Mazzaferro, 2017; Ng, 2017; Kimothi et al., 2016; Yang et al., 2018a; Schwartz et al., 2018).”
Truth from the machine: artificial intelligence and the materialization of identity (2021, background): “Rather than painstakingly (which is to say, manually ) exploring particle interactions or protein embeddings, a computational model can rapidly simulate all possibilities and relations (Carrasquilla and Melko 2017; Yang et al. 2018).”
Machine learning-assisted directed protein evolution with combinatorial libraries (2019, unknown): “This study and others focused on increasing the accuracy of protein modeling by developing other physical descriptors (42, 43) or embedded representations (44), suggesting that machine learning will assist directed evolution beyond the baseline implementation employed here.”
Deep Learning in Protein Structural Modeling and Design (2020, background): “Alternatively, Alley et al.87 directly used an RNN (unidirectional multiplicative long-short-term-memory or mLSTM)88 model, called UniRep, to summarize arbitrary length protein sequences into a fixed-length real representation by averaging over the representation of each residue.87 Their representat”
Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures (2021, background): “Early works on learning protein representations (Asgari & Mofrad, 2015; Yang et al., 2018) used word embedding algorithms (Mikolov et al., 2013), as employed in Natural Language Processing (NLP).”