Espresso

Use generative AI to create highly-expressed synthetic genes

User guide

Installation

For now, install Espresso by cloning the repository and using your favorite Python package management system. For example, using a regular virtual environment, first create and activate your environment (here I am using the Fish shell)

python -m venv .venv 


```shell 
git clone https://github.com/dacarlin/espresso.git
cd espresso
python -m pip install -e .  

Quickstart

First, make sure you can import Espresso!

import espresso 

Next, let's try generating a coding sequence for E. coli using a protein sequence that we provide

my_protein = "MENFHHRPFKGGFGVGRVPTSLYYSLSDFSLSAISIFPTHYDQPYLNEAPSWYKYSLESGLVCLYLYLIYRWITRSF"
my_gene = espresso.design_coding_sequence(my_protein, model="ec")

The resulting sequence will be optimized for expression in E. coli. Read on to learn more about Espresso and how to perform more complicated tasks.

Data for the independent model

For the independent model, learn the codon frequency from a set of genes using the provided learn.py script. For example

python learn.py espresso/data/cds/Saccharomyces_cerevisiae.R64-1-1.cds.all.fa.gz	

This will output a JSON file that can be used with the IndependentEncoder class

Data for the transformer models

For the transformer models, learn whole sequence design from native sequences stored in a FASTA file