A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein Sequences

By Johannes Linder, Nicholas Bogard, Alexander B. Rosenberg, Georg Seelig. 2020

99 citations recorded by the source corpus.

SUMMARY Engineering gene and protein sequences with defined functional properties is a major goal of synthetic biology. Deep neural network models, together with gradient ascent-style optimization, show promise for sequence design. The generated sequences can however get stuck in local minima and often have low diversity. Here, we develop deep exploration networks (DENs), a class of activation-maximizing generative models, which minimize the cost of a neural network fitness predictor by gradient descent. By penalizing any two generated patterns on the basis of a similarity metric, DENs explicitly maximize sequence diversity. To avoid drifting into low-confidence regions of the predictor, we incorporate variational autoencoders to maintain the likelihood ratio of generated sequences. Using DENs, we engineered polyadenylation signals with more than 10-fold higher selection odds than the best gradient ascent-generated patterns, identified splice regulatory sequences predicted to result in highly differential splicing between cell lines, and improved on state-of-the-art results for protein design tasks.

Subfields: Molecular Biology

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Citations in the corpus

Guided Generative Protein Design using Regularized Transformers (2022, methodology): “The latter group of methods in model-based design operates directly on sequences, where a model seeks to optimize the sequence itself to maximize a target property Linder et al. [2020].”
Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design (2021, methodology): “…architectures have been used including variational autoencoders (Greener et al., 2018; Hawkins-Hooker et al., 2021), deep exploration networks (Linder et al., 2020), graph neural networks (Strokach et al., 2020), recurrent neural networks (Alley et al., 2019) and autoregressive models (Shin…”
A guide to machine learning for biologists (2021, methodology): “Fixed or variable input size depending on architecture Protein and gene engineering [82]”
Protein sequence design with deep generative models (2021, background): “staying within each model’s trusted regions of sequence space [44, 46] or other desired protein properties will be necessary to broaden our understanding of protein sequence space.”