Guided Generative Protein Design using Regularized Transformers. (arXiv:2201.09948v1 [cs.LG])

Web: http://arxiv.org/abs/2201.09948

The development of powerful natural language models have increased the
ability to learn meaningful representations of protein sequences. In addition,
advances in high-throughput mutagenesis, directed evolution, and
next-generation sequencing have allowed for the accumulation of large amounts
of labeled fitness data. Leveraging these two trends, we introduce Regularized
Latent Space Optimization (ReLSO), a deep transformer-based autoencoder which
is trained to jointly generate sequences as well as predict fitness. Using
ReLSO, we explicitly model the underlying sequence-function landscape of large
labeled …

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