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
Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction

Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction. arxiv This repository contains python scripts for tr

12 Dec 12, 2022
🐦 Quickly annotate data from the comfort of your Jupyter notebook

🐦 pigeon - Quickly annotate data on Jupyter Pigeon is a simple widget that lets you quickly annotate a dataset of unlabeled examples from the comfort

Anastasis Germanidis 647 Jan 05, 2023
PointPillars inference with TensorRT

A project demonstrating how to use CUDA-PointPillars to deal with cloud points data from lidar.

NVIDIA AI IOT 315 Dec 31, 2022
A scikit-learn compatible neural network library that wraps PyTorch

A scikit-learn compatible neural network library that wraps PyTorch. Resources Documentation Source Code Examples To see more elaborate examples, look

4.9k Jan 03, 2023
Revisiting Weakly Supervised Pre-Training of Visual Perception Models

SWAG: Supervised Weakly from hashtAGs This repository contains SWAG models from the paper Revisiting Weakly Supervised Pre-Training of Visual Percepti

Meta Research 134 Jan 05, 2023
🏖 Keras Implementation of Painting outside the box

Keras implementation of Image OutPainting This is an implementation of Painting Outside the Box: Image Outpainting paper from Standford University. So

Bendang 1.1k Dec 10, 2022
Attentive Implicit Representation Networks (AIR-Nets)

Attentive Implicit Representation Networks (AIR-Nets) Preprint | Supplementary | Accepted at the International Conference on 3D Vision (3DV) teaser.mo

29 Dec 07, 2022
Label Studio is a multi-type data labeling and annotation tool with standardized output format

Website • Docs • Twitter • Join Slack Community What is Label Studio? Label Studio is an open source data labeling tool. It lets you label data types

Heartex 11.7k Jan 09, 2023
House-GAN++: Generative Adversarial Layout Refinement Network towards Intelligent Computational Agent for Professional Architects

House-GAN++ Code and instructions for our paper: House-GAN++: Generative Adversarial Layout Refinement Network towards Intelligent Computational Agent

122 Dec 28, 2022
Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception, IROS 2021

For academic use only. Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception Ziwei Wang, Liyuan Pan, Yonhon Ng, Zheyu Zhuang and Robert Mahony Th

Ziwei Wang 11 Jan 04, 2023
AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation

AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation AniGAN: Style-Guided Generative Adversarial Networks for U

Bing Li 81 Dec 14, 2022
Gym environments used in the paper: "Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors"

gym_multirotor Gym to train reinforcement learning agents on UAV platforms Quadrotor Tiltrotor Requirements This package has been tested on Ubuntu 18.

Aditya M. Deshpande 19 Dec 29, 2022
Official implementation for CVPR 2021 paper: Adaptive Class Suppression Loss for Long-Tail Object Detection

Adaptive Class Suppression Loss for Long-Tail Object Detection This repo is the official implementation for CVPR 2021 paper: Adaptive Class Suppressio

CASIA-IVA-Lab 67 Dec 04, 2022
Imagededup - 😎 Finding duplicate images made easy

imagededup is a python package that simplifies the task of finding exact and near duplicates in an image collection.

idealo 4.3k Jan 07, 2023
This is the PyTorch implementation of GANs N’ Roses: Stable, Controllable, Diverse Image to Image Translation

Official PyTorch repo for GAN's N' Roses. Diverse im2im and vid2vid selfie to anime translation.

1.1k Jan 01, 2023
RNN Predict Street Commercial Vitality

RNN-for-Predicting-Street-Vitality Code and dataset for Predicting the Vitality of Stores along the Street based on Business Type Sequence via Recurre

Zidong LIU 1 Dec 15, 2021
OcclusionFusion: realtime dynamic 3D reconstruction based on single-view RGB-D

OcclusionFusion (CVPR'2022) Project Page | Paper | Video Overview This repository contains the code for the CVPR 2022 paper OcclusionFusion, where we

Wenbin Lin 193 Dec 15, 2022
FrankMocap: A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator

FrankMocap pursues an easy-to-use single view 3D motion capture system developed by Facebook AI Research (FAIR). FrankMocap provides state-of-the-art 3D pose estimation outputs for body, hand, and bo

Facebook Research 1.9k Jan 07, 2023
Dense Passage Retriever - is a set of tools and models for open domain Q&A task.

Dense Passage Retrieval Dense Passage Retrieval (DPR) - is a set of tools and models for state-of-the-art open-domain Q&A research. It is based on the

Meta Research 1.1k Jan 03, 2023
Code for training and evaluation of the model from "Language Generation with Recurrent Generative Adversarial Networks without Pre-training"

Language Generation with Recurrent Generative Adversarial Networks without Pre-training Code for training and evaluation of the model from "Language G

Amir Bar 253 Sep 14, 2022