Use ProGen2

Overview

ProGen2 is generative protein language model capable of generating de novo proteins and protein variants given an input sequence. Additionally, another version of ProGen2, progen2-oas can be used to generate antibody sequences.

Features

  • Allows you to generate novel proteins or extend existing proteins.
  • Includes options to control the number of output sequences.
  • Supports different sampling techniques to better explore the protein landscape and to diversify prompt outputs.
  • Allows you to specify max length.
  • Includes an output fasta of all the sequences.
  • Includes an output plot of most common residues per residue which can be further interepreted as residue probabilities per position.
  • Includes an output plot likelihoods by sequence.
  • Optimized to execute quickly and efficiently.
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Configuration & Options

Model Inputs

The input amino acid sequence to extend. Provide ProGen2 with an amino acid sequence and it will do it's best to expand the rest of the protein.

Core Settings

The number of output samples to generate. Must be a minimum of 5 and maximum of 10000

The maximum allowed sequence length. This value must be greater than 50 and greater than the length of the input sequence.

Select the model you want to use for your prediction. Larger models tend to perform better but take longer to finish predictions. The base model appears to have the best results while the progen2-oas model is trained to work with antibody sequences only.

Sampling Options

The temperature / entropy controls how likely it is to choose less probable amino acids. A temperature of 0 gives the same response every time because there's a 0% chance to choose any amino acid aside from the most likely. A temperature of 1 is the default and picks based on the model's base confidence. However, a temperature greater than 1 gives more weight to unlikely amino acids than to likely ones.

The Top-p goes for a minimal set of words, the probability of which exceeds p. Choosing a top p such as 0.9 means you would likely get a set of the most likely words for the model to choose.

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