Toward machine-guided design of proteins

By Surojit Biswas, Gleb Kuznetsov, Pierce J. Ogden, Nicholas J Conway, Ryan P. Adams, George M. Church. 2018

35 citations recorded by the source corpus.

Proteins—molecular machines that underpin all biological life—are of significant therapeutic and industrial value. Directed evolution is a high-throughput experimental approach for improving protein function, but has difficulty escaping local maxima in the fitness landscape. Here, we investigate how supervised learning in a closed loop with DNA synthesis and high-throughput screening can be used to improve protein design. Using the green fluorescent protein (GFP) as an illustrative example, we demonstrate the opportunities and challenges of generating training datasets conducive to selecting strongly generalizing models. With prospectively designed wet lab experiments, we then validate that these models can generalize to unseen regions of the fitness landscape, even when constrained to explore combinations of non-trivial mutations. Taken together, this suggests a hybrid optimization strategy for protein design in which a predictive model is used to explore difficult-to-access but promising regions of the fitness landscape that directed evolution can then exploit at scale.

Subfields: Artificial Intelligence, Genetics, Molecular Biology

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Citations in the corpus

Unified rational protein engineering with sequence-based deep representation learning (2019, background): “…representation learning for proteins via RNNs. Unlike other approaches to representing proteins, namely as one-hot-encoded matrices as in Biswas et al. 3 , RNNs produce fixed-length representations for arbitrary-length proteins by extracting the hidden state passed forward along a sequence.”
Unified rational protein engineering with sequence-only deep representation learning (2019, methodology): “To confirm our cluster assignments, we picked a small set of 3 Cytochrome Oxidase (1,2,3) families from OXBench, 8 proteins total.”
Low-N protein engineering with data-efficient deep learning (2020, methodology): “This data was generated from variants obtained from Biswas et al. (2018) 29 , and will be made publicly available upon peer-reviewed publication.”
AlphaDesign: A de novo protein design framework based on AlphaFold (2021, methodology): “A number of works explore re-using previously trained predictors of protein structure or function as parts of an in silico screening framework [69–76].”
Leveraging implicit knowledge in neural networks for functional dissection and engineering of proteins (2019, unknown): “9 and 10).”
Deep generative models for T cell receptor protein sequences (2019, background): “This vector is mapped to a latent embedding via a linear transformation that is learned during training (Biswas et al., 2018).”
AdaLead: A simple and robust adaptive greedy search algorithm for sequence design (2020, methodology): “…φ (cid:48) on already published data (Otwinowski et al., 2018; Alipanahi et al., 2015; Riesselman et al., 2017; Sinai et al., 2017), the more recent work is being done on optimization in this setting (Biswas et al., 2018; Angermueller et al., 2020; Brookes & Listgarten, 2018; Brookes et al., 2019).”