Improved Fitness Optimization Landscapes for Sequence Design

Related tags

Deep LearningReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers
Overview

ReLSO

Improved Fitness Optimization Landscapes for Sequence Design

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

You might also like...
An implementation of a sequence to sequence neural network using an encoder-decoder
An implementation of a sequence to sequence neural network using an encoder-decoder

Keras implementation of a sequence to sequence model for time series prediction using an encoder-decoder architecture. I created this post to share a

Luke Tonin 195 Dec 17, 2022
Sequence lineage information extracted from RKI sequence data repo
Sequence lineage information extracted from RKI sequence data repo

Pango lineage information for German SARS-CoV-2 sequences This repository contains a join of the metadata and pango lineage tables of all German SARS-

Cornelius Roemer 24 Oct 26, 2022
Official repository of OFA. Paper: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
Official repository of OFA. Paper: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

Paper | Blog OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image gene

OFA Sys 1.4k Jan 8, 2023
Aircraft design optimization made fast through modern automatic differentiation
Aircraft design optimization made fast through modern automatic differentiation

Aircraft design optimization made fast through modern automatic differentiation. Plug-and-play analysis tools for aerodynamics, propulsion, structures, trajectory design, and much more.

Peter Sharpe 394 Dec 23, 2022
Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)
Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

scikit-opt Swarm Intelligence in Python (Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm,A

郭飞 3.7k Jan 3, 2023
library for nonlinear optimization, wrapping many algorithms for global and local, constrained or unconstrained, optimization

NLopt is a library for nonlinear local and global optimization, for functions with and without gradient information. It is designed as a simple, unifi

Steven G. Johnson 1.4k Dec 25, 2022
Racing line optimization algorithm in python that uses Particle Swarm Optimization.
Racing line optimization algorithm in python that uses Particle Swarm Optimization.

Racing Line Optimization with PSO This repository contains a racing line optimization algorithm in python that uses Particle Swarm Optimization. Requi

Parsa Dahesh 6 Dec 14, 2022
Puzzle-CAM: Improved localization via matching partial and full features.
Puzzle-CAM: Improved localization via matching partial and full features.

Puzzle-CAM The official implementation of "Puzzle-CAM: Improved localization via matching partial and full features".

Sanghyun Jo 150 Nov 14, 2022
[ECCVW2020] Robust Long-Term Object Tracking via Improved Discriminative Model Prediction (RLT-DiMP)
[ECCVW2020] Robust Long-Term Object Tracking via Improved Discriminative Model Prediction (RLT-DiMP)

Feel free to visit my homepage Robust Long-Term Object Tracking via Improved Discriminative Model Prediction (RLT-DIMP) [ECCVW2020 paper] Presentation

Seokeon Choi 35 Oct 26, 2022
Comments
  • Conda env create not working

    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?

    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)
Owner
Krishnaswamy Lab
Krishnaswamy Lab
GitHub Repository
Repository for the paper "Exploring the Sensory Spaces of English Perceptual Verbs in Natural Language Data"

Sensory Spaces of English Perceptual Verbs This repository contains the code and collocational data described in the paper "Exploring the Sensory Spac

David Peng 0 Sep 07, 2021
EsViT: Efficient self-supervised Vision Transformers

Efficient Self-Supervised Vision Transformers (EsViT) PyTorch implementation for EsViT, built with two techniques: A multi-stage Transformer architect

Microsoft 352 Dec 25, 2022
TransReID: Transformer-based Object Re-Identification

TransReID: Transformer-based Object Re-Identification [arxiv] The official repository for TransReID: Transformer-based Object Re-Identification achiev

569 Dec 30, 2022
Learning Facial Representations from the Cycle-consistency of Face (ICCV 2021)

Learning Facial Representations from the Cycle-consistency of Face (ICCV 2021) This repository contains the code for our ICCV2021 paper by Jia-Ren Cha

Jia-Ren Chang 40 Dec 27, 2022
Cweqgen - The CW Equation Generator

The CW Equation Generator The cweqgen (pronouced like "Queck-Jen") package provi

2 Jan 15, 2022
Cereal box identification in store shelves using computer vision and a single train image per model.

Product Recognition on Store Shelves Description You can read the task description here. Report You can read and download our report here. Step A - Mu

Nicholas Baraghini 1 Jan 21, 2022
The implementation of DeBERTa

DeBERTa: Decoding-enhanced BERT with Disentangled Attention This repository is the official implementation of DeBERTa: Decoding-enhanced BERT with Dis

Microsoft 1.2k Jan 06, 2023
Plenoxels: Radiance Fields without Neural Networks, Code release WIP

Plenoxels: Radiance Fields without Neural Networks Alex Yu*, Sara Fridovich-Keil*, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa UC Be

Alex Yu 2.3k Dec 30, 2022
How to Train a GAN? Tips and tricks to make GANs work

(this list is no longer maintained, and I am not sure how relevant it is in 2020) How to Train a GAN? Tips and tricks to make GANs work While research

Soumith Chintala 10.8k Dec 31, 2022
MMdet2-based reposity about lightweight detection model: Nanodet, PicoDet.

Lightweight-Detection-and-KD MMdet2-based reposity about lightweight detection model: Nanodet, PicoDet. This repo also includes detection knowledge di

Egqawkq 12 Jan 05, 2023
Landmarks Recogntion Web application using Streamlit.

Landmark Recognition Web-App using Streamlit Watch Tutorial for this project Source Trained model landmarks_classifier_asia_V1/1 is taken from the Ten

Kushal Bhavsar 5 Dec 12, 2022
PyTorch Implementation of NCSOFT's FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis

FastPitchFormant - PyTorch Implementation PyTorch Implementation of FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis. Qu

Keon Lee 63 Jan 02, 2023
SingleVC performs any-to-one VC, which is an important component of MediumVC project.

SingleVC performs any-to-one VC, which is an important component of MediumVC project. Here is the official implementation of the paper, MediumVC.

谷下雨 26 Dec 28, 2022
Exploring Classification Equilibrium in Long-Tailed Object Detection, ICCV2021

Exploring Classification Equilibrium in Long-Tailed Object Detection (LOCE, ICCV 2021) Paper Introduction The conventional detectors tend to make imba

52 Nov 21, 2022
FedGS: A Federated Group Synchronization Framework Implemented by LEAF-MX.

FedGS: Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT Preparation For instructions on generating data, plea

Lizonghang 9 Dec 22, 2022
AntiFuzz: Impeding Fuzzing Audits of Binary Executables

AntiFuzz: Impeding Fuzzing Audits of Binary Executables Get the paper here: https://www.usenix.org/system/files/sec19-guler.pdf Usage: The python scri

Chair for Sys­tems Se­cu­ri­ty 88 Dec 21, 2022
Ego4d dataset repository. Download the dataset, visualize, extract features & example usage of the dataset

Ego4D EGO4D is the world's largest egocentric (first person) video ML dataset and benchmark suite, with 3,600 hrs (and counting) of densely narrated v

Meta Research 118 Jan 07, 2023
This is a collection of our NAS and Vision Transformer work.

This is a collection of our NAS and Vision Transformer work.

Microsoft 828 Dec 28, 2022
duralava is a neural network which can simulate a lava lamp in an infinite loop.

duralava duralava is a neural network which can simulate a lava lamp in an infinite loop. Example This is not a real lava lamp but a "fake" one genera

Maximilian Bachl 87 Dec 20, 2022
Implementation for HFGI: High-Fidelity GAN Inversion for Image Attribute Editing

HFGI: High-Fidelity GAN Inversion for Image Attribute Editing High-Fidelity GAN Inversion for Image Attribute Editing Update: We released the inferenc

Tengfei Wang 371 Dec 30, 2022