Abstract: Systems, methods, and apparatus for generating a DNA/gene sequence output, given a protein sequence input. In one aspect, the protein-to-DNA mapping system includes a protein language model (embedder) that generates a high-dimensional protein embedding from a low-dimensional protein sequence input; and the system includes a protein-to-DNA translator that processes the protein embedding and generates a DNA sequence output, based on the protein embedding. The protein-to-DNA translator is a neural network (e.g., a Seq2Seq model) and is configured with translation coefficients that may be trained with a training dataset, comprising the training protein and DNA embeddings. Training the translation coefficients can include supervised learning that maps the protein embedding (input) to a DNA sequence (target).

Abstract: A computer-implemented method is provided. The computer implemented method includes generating high-yield training datasets by excluding those genetic sequences that do not satisfy at least one abundance condition, at least one stability condition, at least one expression condition, and/or at least one translation efficiency condition. The computer-implemented method further includes training at least one model on the high-yield training datasets to generate high-yield genetic sequences.

Abstract: Systems, methods, and apparatus for generating a DNA/gene sequence output, given a protein sequence input. In one aspect, the protein-to-DNA mapping system includes a protein language model (embedder) that generates a high-dimensional protein embedding from a low-dimensional protein sequence input; and the system includes a protein-to-DNA translator that processes the protein embedding and generates a DNA sequence output, based on the protein embedding. The protein-to-DNA translator is a neural network (e.g., a Seq2Seq model) and is configured with translation coefficients that may be trained with a training dataset, comprising the training protein and DNA embeddings. Training the translation coefficients can include supervised learning that maps the protein embedding (input) to a DNA sequence (target).