Improved Fitness Optimization Landscapes for Sequence Design

Last update: Dec 20, 2022

Overview

ReLSO

Improved Fitness Optimization Landscapes for Sequence Design

Description
Citation
How to run
Training models
Original data source

Description

In recent years, deep learning approaches for determining protein sequence-fitness relationships have gained traction. Advances in high-throughput mutagenesis, directed evolution, and next-generation sequencing have allowed for the accumulation of large amounts of labelled fitness data and consequently, attracted the application of various deep learning methods. Although these methods learn an implicit fitness landscape, there is little work on using the latent encoding directly for protein sequence optimization. Here we show that this latent space representation of a fitness landscape can be made very amenable to latent space optimization through a joint-training process. We also show that this encoding strategy which also provides improvements to generalization over more traditional training strategies. We apply our approach to several biological contexts and show that latent space optimization in a smooth learned folding landscape allows for more accurate and efficient optimization of protein sequences.

Citation

This repo accompanies the following publication:

Egbert Castro, Abhinav Godavarthi, Julien Rubinfien, Smita Krishnaswamy. Guided Generative Protein Design using Regularized Transformers. Nature Machine Intelligence, in review (2021).

How to run

First, install dependencies

# clone project   
git clone https://github.com/KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers.git


# install project   
cd ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers 
pip install -e .   
pip install -r requirements.txt

Usage

Training models

# run training script
python train_relso.py  --data gifford

*note: if arg option is not relevant to current model selection, it will not be used. See init method of each model to see what's used.

available dataset args:

    gifford, GB1_WU, GFP, TAPE

available auxnetwork args:

    base_reg

Original data sources

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Comments

Conda env create not working
When I type in the command as instructed in how to run, I get this error:

Warning: you have pip-installed dependencies in your environment file, but you do not list pip itself as one of your conda dependencies. Conda may not use the correct pip to install your packages, and they may end up in the wrong place. Please add an explicit pip dependency. I'm adding one for you, but still nagging you. Collecting package metadata (repodata.json): done Solving environment: failed

ResolvePackageNotFound:

libcxx==12.0.0=h2f01273_0

python==3.10.4=hdfd78df_0

openssl==1.1.1q=hca72f7f_0

ncurses==6.3=hca72f7f_3

readline==8.1.2=hca72f7f_1

bzip2==1.0.8=h1de35cc_0

ca-certificates==2022.07.19=hecd8cb5_0

xz==5.2.5=hca72f7f_1

libffi==3.3=hb1e8313_2

zlib==1.2.12=h4dc903c_2

sqlite==3.38.5=h707629a_0

tk==8.6.12=h5d9f67b_0
opened by Pixelatory 1
May the internal information of gifford data leads to a bias results given by model?

I'm very intersted in your work and analysize the gifford data. Firstly, I use the CD-HIT( a Cluster tool) split into different clusters.Then, I chose the sequence (comes the Clsuter-1(a cluster subset contaiing similar sequences given by CD-HIT)) with highest enrich value as a baseline, and focus on the residue difference between it and others sequences. Very interstingly, i find those sequences that containg 2 or 3 different residues compared to baseline sequence, usually have high enrichments. In Top-100 high enrichments, it can at 65%. As i know， your work is a multitask that both focus on generation and prediction. **I wonder that whether the JT-VAE tends to produce the new sequences that different from the corresponding baseline sequence with highest enrichment about 2 or 3 different residues , and the prediction neural network may think such sequences are good results.**It means that the model only need to realize the fact that compared to high enrich sequnces,the new sequnces contain 2 or 3 different residues is good enough. Beacuse i not find your results, i hope you can give me some advices.

opened by chengyunzhang 0

Releases(v1.0)

v1.0(Jul 31, 2022)

Published version of code and dataset.
Source code(tar.gz)
Source code(zip)

Owner

Krishnaswamy Lab

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Improved Fitness Optimization Landscapes for Sequence Design

Related tags

Overview

ReLSO

Description

Citation

How to run

Usage

Training models

available dataset args:

available auxnetwork args:

Original data sources

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Comments

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v1.0(Jul 31, 2022)

Owner

Krishnaswamy Lab

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