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
PyTorch code for DriveGAN: Towards a Controllable High-Quality Neural Simulation

PyTorch code for DriveGAN: Towards a Controllable High-Quality Neural Simulation

76 Dec 24, 2022
Pytorch implementation for "Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets" (ECCV 2020 Spotlight)

Distribution-Balanced Loss [Paper] The implementation of our paper Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets (

Tong WU 304 Dec 22, 2022
PyTorch implementation for ACL 2021 paper "Maria: A Visual Experience Powered Conversational Agent".

Maria: A Visual Experience Powered Conversational Agent This repository is the Pytorch implementation of our paper "Maria: A Visual Experience Powered

Jokie 22 Dec 12, 2022
TensorFlow Implementation of Unsupervised Cross-Domain Image Generation

Domain Transfer Network (DTN) TensorFlow implementation of Unsupervised Cross-Domain Image Generation. Requirements Python 2.7 TensorFlow 0.12 Pickle

Yunjey Choi 864 Dec 30, 2022
An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning

Mammoth - An Extendible (General) Continual Learning Framework for Pytorch NEWS STAY TUNED: We are working on an update of this repository to include

AImageLab 277 Dec 28, 2022
Official repository of the AAAI'2022 paper "Contrast and Generation Make BART a Good Dialogue Emotion Recognizer"

CoG-BART Contrast and Generation Make BART a Good Dialogue Emotion Recognizer Quick Start: To run the model on test sets of four datasets, Download th

39 Dec 24, 2022
Code for "Learning Graph Cellular Automata"

Learning Graph Cellular Automata This code implements the experiments from the NeurIPS 2021 paper: "Learning Graph Cellular Automata" Daniele Grattaro

Daniele Grattarola 37 Oct 26, 2022
StyleGAN2-ADA - Official PyTorch implementation

Abstract: Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. We propose an adaptive discriminator augmenta

NVIDIA Research Projects 3.2k Dec 30, 2022
Code & Data for Enhancing Photorealism Enhancement

Enhancing Photorealism Enhancement Stephan R. Richter, Hassan Abu AlHaija, Vladlen Koltun Paper | Website (with side-by-side comparisons) | Video (Pap

Intelligent Systems Lab Org 1.1k Dec 31, 2022
Some pvbatch (paraview) scripts for postprocessing OpenFOAM data

pvbatchForFoam Some pvbatch (paraview) scripts for postprocessing OpenFOAM data For every script there is a help message available: pvbatch pv_state_s

Morev Ilya 2 Oct 26, 2022
The offcial repository for 'CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos', SIGIR2022

CharacterBERT-DR The offcial repository for CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos, Sh

ielab 11 Nov 15, 2022
Implementation of PyTorch-based multi-task pre-trained models

mtdp Library containing implementation related to the research paper "Multi-task pre-training of deep neural networks for digital pathology" (Mormont

Romain Mormont 27 Oct 14, 2022
Pytorch Performace Tuning, WandB, AMP, Multi-GPU, TensorRT, Triton

Plant Pathology 2020 FGVC7 Introduction A deep learning model pipeline for training, experimentaiton and deployment for the Kaggle Competition, Plant

Bharat Giddwani 0 Feb 25, 2022
MGFN: Multi-Graph Fusion Networks for Urban Region Embedding was accepted by IJCAI-2022.

Multi-Graph Fusion Networks for Urban Region Embedding (IJCAI-22) This is the implementation of Multi-Graph Fusion Networks for Urban Region Embedding

202 Nov 18, 2022
Code repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" (NeurIPS'20)

IGNN Code repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" [paper] [supp] Prepare datasets 1 Download training dataset

Shangchen Zhou 278 Jan 03, 2023
A robotic arm that mimics hand movement through MediaPipe tracking.

La-Z-Arm A robotic arm that mimics hand movement through MediaPipe tracking. Hardware NVidia Jetson Nano Sparkfun Pi Servo Shield Micro Servos Webcam

Alfred 1 Jun 05, 2022
Pyramid Scene Parsing Network, CVPR2017.

Pyramid Scene Parsing Network by Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, details are in project page. Introduction This

Hengshuang Zhao 1.5k Jan 05, 2023
Torchserve server using a YoloV5 model running on docker with GPU and static batch inference to perform production ready inference.

Yolov5 running on TorchServe (GPU compatible) ! This is a dockerfile to run TorchServe for Yolo v5 object detection model. (TorchServe (PyTorch librar

82 Nov 29, 2022
Deep Crop Rotation

Deep Crop Rotation Paper (to come very soon!) We propose a deep learning approach to modelling both inter- and intra-annual patterns for parcel classi

Félix Quinton 5 Sep 23, 2022
A PaddlePaddle version image model zoo.

Paddle-Image-Models English | 简体中文 A PaddlePaddle version image model zoo. Install Package Install by pip： $ pip install ppim Install by wheel package

AgentMaker 131 Dec 07, 2022