Designing high temperature protein sequences via learned language processing

TODO conda installs rosetta install seperate

Accelerate config

nomelt models are all designed to produce amino acid sequences of proteins stable at high temperature, conditioned on an input

• nomelt-s2s: (seq -> seq) translate from moderate to high temperature variants of proteins
  • Traditional architectures (eg seq2seq T5, autoregressive Decoder only) and tokenizers for protein LM usable out of the box
  • TODO
• nomelt-hmm: (hmm -> seq) develop high temperature variants of protein from a representative HMM
  • Traditional architectures (eg seq2seq T5, autoregressive Decoder only) LM usable out of the box
  • Novel tokenizer required to prepare HMM inputs
  • TODO
• nomelt-hmm+: (hmm, T -> seq) develop variants of a protein stable at a specific temperature from a representative HMM
  • Novel architecure and tokenizer required
  • TODO