Protein sequence design with deep generative models
By Zachary Wu, Kadina E. Johnston, Frances H. Arnold, Kevin Kaichuang Yang. 2021
165 citations recorded by the source corpus.
Protein engineering seeks to identify protein sequences with optimized properties. When guided by machine learning, protein sequence generation methods can draw on prior knowledge and experimental efforts to improve this process. In this review, we highlight recent applications of machine learning to generate protein sequences, focusing on the emerging field of deep generative methods.
Subfields: Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Michael Heinzinger
- Kevin Kaichuang Yang
- Burkhard Rost
- Brian L. Hie
- Alexander Rives
- Niccoló Zanichelli
- Robert Verkuil
- Hugh Yeh
Citations in the corpus
- Learning inverse folding from millions of predicted structures (2022, background): “Anishchenko et al. (2021) Structure-agnostic protein sequence design We point the reader to Wu et al. (2021) for a review of the many machine learning-based sequence design approaches that do not explicitly model protein structures.”
- Protein sequence design with a learned potential (2022, neutral): “Recent experimentally validated efforts for machine learning-based sequence generation have focused on sequence representation learning without structural information, requiring fi tting to data from experiments or from known protein families to produce functional designs 23,24 .”
- Masked Inverse Folding with Sequence Transfer for Protein Representation Learning (2023, background): “GB1: Wu et al. [2016] performed a 4-site combinatorial deep mutational scan on protein G domain B1, an immunoglobulin-binding protein expressed in Streptococcal bacteria.”
- Protein language model embeddings for fast, accurate, alignment-free protein structure prediction (2021, methodology): “…using no other annotation than the amino acid sequences of 10^7-10^9 proteins (Alley et al., 2019; Asgari & Mofrad, 2015; Bepler & Berger, 2019, 2021; Elnaggar et al., 2021; Heinzinger et al., 2019; Madani et al., 2020; Ofer et al., 2021; Rao et al., 2019; Rives et al., 2021; Wu et al., 2021).”