pytorch implementation of trDesign

Related tags

Deep Learningtrdesign-pytorch
Overview

trdesign-pytorch

This repository is a PyTorch implementation of the trDesign paper based on the official TensorFlow implementation. The initial port of the trRosetta network was done by @lucidrains.

Figure 1: trDesign Architecture

Figure 1 of De novo protein design by deep network hallucination (p. 12, Anishchenko et al., CC-BY-ND)

Requirements

Requires python 3.6+

pip install matplotlib numpy torch

Usage (protein design):

  1. Edit src/config.py to set the experiment configuration.
  2. Run python design.py
  3. All results will be saved under results/

Design Configuration Options

  • Sequence length (int)
  • AA_weight (float): how strongly we want the amino acid type composition to be 'natural'
  • RM_AA (str): disable specific amino acid types
  • n_models (int): how many trRosetta model ensembles we want to use during the MCMC loop
  • sequence constraint (str): fix a subset of the sequence residues to specific amino acids
  • target_motif (path): optimize a sequence with a target motif provided as an .npz file
  • MCMC options

Usage (protein structure prediction):

python predict.py example.a3m
# or
python predict.py example.fasta

To get a .pdb from the resulting .npz you need to request the trRosetta package from the original authors.

Then you can run:

python trRosetta.py example.npz example.fasta output.pdb -w /tmp

References

@article {Yang1496,
  author = {Yang, Jianyi and Anishchenko, Ivan and Park, Hahnbeom and Peng, Zhenling and Ovchinnikov, Sergey and Baker, David},
  title = {Improved protein structure prediction using predicted interresidue orientations},
  year = {2020},
  doi = {10.1073/pnas.1914677117},
  URL = {https://www.pnas.org/content/117/3/1496},
  eprint = {https://www.pnas.org/content/117/3/1496.full.pdf},
  journal = {Proceedings of the National Academy of Sciences}
}
@article {Anishchenko2020.07.22.211482,
  author = {Anishchenko, Ivan and Chidyausiku, Tamuka M. and Ovchinnikov, Sergey and Pellock, Samuel J. and Baker, David},
  title = {De novo protein design by deep network hallucination},
  year = {2020},
  doi = {10.1101/2020.07.22.211482},
  URL = {https://www.biorxiv.org/content/early/2020/07/23/2020.07.22.211482},
  eprint = {https://www.biorxiv.org/content/early/2020/07/23/2020.07.22.211482.full.pdf},
  journal = {bioRxiv}
}
@article {Tischer2020.11.29.402743,
  author = {Tischer, Doug and Lisanza, Sidney and Wang, Jue and Dong, Runze and Anishchenko, Ivan and Milles, Lukas F. and Ovchinnikov, Sergey and Baker, David},
  title = {Design of proteins presenting discontinuous functional sites using deep learning},
  year = {2020},
  doi = {10.1101/2020.11.29.402743},
  URL = {https://www.biorxiv.org/content/early/2020/11/29/2020.11.29.402743},
  eprint = {https://www.biorxiv.org/content/early/2020/11/29/2020.11.29.402743.full.pdf},
  journal = {bioRxiv}
}
Owner
Learn Ventures Inc.
Learn Ventures Inc.
GitHub Repository
Github Traffic Insights as Prometheus metrics.

github-traffic Github Traffic collects your repository's traffic data and exposes it as Prometheus metrics. Grafana dashboard that displays the metric

Grafana Labs 34 Oct 27, 2022
This repository contains pre-trained models and some evaluation code for our paper Towards Unsupervised Dense Information Retrieval with Contrastive Learning

Contriever: Towards Unsupervised Dense Information Retrieval with Contrastive Learning This repository contains pre-trained models and some evaluation

Meta Research 207 Jan 08, 2023
Animate molecular orbital transitions using Psi4 and Blender

Molecular Orbital Transitions (MOT) Animate molecular orbital transitions using Psi4 and Blender Author: Maximilian Paradiz Dominguez, University of A

3 Feb 01, 2022
Implementation of a Transformer using ReLA (Rectified Linear Attention)

ReLA (Rectified Linear Attention) Transformer Implementation of a Transformer using ReLA (Rectified Linear Attention). It will also contain an attempt

Phil Wang 49 Oct 14, 2022
Everything you need to know about NumPy( Creating Arrays, Indexing, Math,Statistics,Reshaping).

Everything you need to know about NumPy( Creating Arrays, Indexing, Math,Statistics,Reshaping).

1 Feb 14, 2022
learning and feeling SLAM together with hands-on-experiments

modern-slam-tutorial-python Learning and feeling SLAM together with hands-on-experiments 😀 😃 😆 Dependencies Most of the examples are based on GTSAM

Giseop Kim 59 Dec 22, 2022
MixRNet(Using mixup as regularization and tuning hyper-parameters for ResNets)

MixRNet(Using mixup as regularization and tuning hyper-parameters for ResNets) Using mixup data augmentation as reguliraztion and tuning the hyper par

Bhanu 2 Jan 16, 2022
A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning", IJCAI-21

MERIT A PyTorch implementation of our IJCAI-21 paper Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning. Depen

Graph Analysis & Deep Learning Laboratory, GRAND 32 Jan 02, 2023
Convert dog pictures into various painting styles. Try LimnPet

LimnPet Cartoon stylization service project Try our service » Home page · Team notion · Members 목차 프로젝트 소개 프로젝트 목표 사용한 기술스택과 수행도구 팀원 구현 기능 주요 기능 추가 기능

LiJell 7 Jul 14, 2022
Official implementation of Monocular Quasi-Dense 3D Object Tracking

Monocular Quasi-Dense 3D Object Tracking Monocular Quasi-Dense 3D Object Tracking (QD-3DT) is an online framework detects and tracks objects in 3D usi

Visual Intelligence and Systems Group 441 Dec 20, 2022
[ICRA 2022] An opensource framework for cooperative detection. Official implementation for OPV2V.

OpenCOOD OpenCOOD is an Open COOperative Detection framework for autonomous driving. It is also the official implementation of the ICRA 2022 paper OPV

Runsheng Xu 322 Dec 23, 2022
ReSSL: Relational Self-Supervised Learning with Weak Augmentation

ReSSL: Relational Self-Supervised Learning with Weak Augmentation This repository contains PyTorch evaluation code, training code and pretrained model

mingkai 45 Oct 25, 2022
object recognition with machine learning on Respberry pi

Respberrypi_object-recognition object recognition with machine learning on Respberry pi line.py 建立一支與樹梅派連線的 linebot 使用此 linebot 遠端控制樹梅派拍照 config.ini l

1 Dec 11, 2021
ROS Basics and TurtleSim

Waypoint Follower Anna Garverick This package draws given waypoints, then waits for a service call with a start position to send the turtle to each wa

Anna Garverick 1 Dec 13, 2021
Surrogate- and Invariance-Boosted Contrastive Learning (SIB-CL)

Surrogate- and Invariance-Boosted Contrastive Learning (SIB-CL) This repository contains all source code used to generate the results in the article "

Charlotte Loh 3 Jul 23, 2022
Kaggle | 9th place single model solution for TGS Salt Identification Challenge

UNet for segmenting salt deposits from seismic images with PyTorch. General We, tugstugi and xuyuan, have participated in the Kaggle competition TGS S

Erdene-Ochir Tuguldur 276 Dec 20, 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 to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)

Learning to Adapt Structured Output Space for Semantic Segmentation Pytorch implementation of our method for adapting semantic segmentation from the s

Yi-Hsuan Tsai 782 Dec 30, 2022
codebase for "A Theory of the Inductive Bias and Generalization of Kernel Regression and Wide Neural Networks"

Eigenlearning This repo contains code for replicating the experiments of the paper A Theory of the Inductive Bias and Generalization of Kernel Regress

Jamie Simon 45 Dec 02, 2022
Keras attention models including botnet,CoaT,CoAtNet,CMT,cotnet,halonet,resnest,resnext,resnetd,volo,mlp-mixer,resmlp,gmlp,levit

Keras_cv_attention_models Keras_cv_attention_models Usage Basic Usage Layers Model surgery AotNet ResNetD ResNeXt ResNetQ BotNet VOLO ResNeSt HaloNet

319 Dec 28, 2022