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RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design

GitHub starsGitHub forksOpen In Colab

[2024-08-21] News: We provided a comprehensive evaluation system for RNA sequence design and prediction named R3Design. APIs and Colab demos are also provided. Feel free to check out our new repo!

[2024-08-15] Update: Thank you all for the interests and inquries about our paper, we are sorry that we haven't provided detailed documentation and demo of the paper for such a long time. Now, it has been solved. Feel free to check out our updated documentation and colab! :)

Introduction

While artificial intelligence has made remarkable strides in revealing the relationship between biological macromolecules' primary sequence and tertiary structure, designing RNA sequences based on specified tertiary structures remains challenging. Though existing approaches in protein design have thoroughly explored structure-to-sequence dependencies in proteins, RNA design still confronts difficulties due to structural complexity and data scarcity.

In this study, we aim to systematically construct a data-driven RNA design pipeline. We crafted a large, well-curated benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure. More importantly, we proposed a hierarchical data-efficient representation learning framework that learns structural representations through contrastive learning at both cluster-level and sample-level to fully leverage the limited data. Extensive experiments demonstrate the effectiveness of our proposed method, providing a reliable baseline for future RNA design tasks.

Dataset

We carefully collected representative RNA tertiary structure data from two sources, RNAsolo and the Protein Data Bank (PDB). The refined data has been released here. Please download the datasets and organize them as follows.

RDesign
├── API
├── assets
├── checkpoints
├── methods
├── model
└── data
├── RNAsolo
│ ├── train_data.pt
│ ├── val_data.pt
│ ├── test_data.pt

Main Environment

cd RDesign
conda env create -f environment.yml
conda activate RDesign

Load Data

# If you want to see the details inside our dataset, you could use Pickle package from Python
import _pickle as cPickle
train_data = cPickle.load(open('data/train_data.pt', 'rb'))
print(train_data[0].keys())
#For external datasets, loading data could be in this way:
from API.rpuzzles_dataset import RPuzzlesDataset
rfam_dataset = RPuzzlesDataset('./data/rfam_data.pt')
rpuz_dataset = RPuzzlesDataset('./data/rpuz_data.pt')

Test the model

# For more details, please refer to the colab# We provided detailed functions and pipeline to show how our model operates

Colab Link:

Open In Colab

Citation

If you are interested in our repository and our paper, please cite the following paper:

@inproceedings{tan2024rdesign,
title={RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design},
author={Tan, Cheng and Zhang, Yijie and Gao, Zhangyang and Hu, Bozhen and Li, Siyuan and Liu, Zicheng and Li, Stan Z},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024}
}

Feedback

If you have any issue about this work, please feel free to contact me by email:

License

This project is released under the Apache 2.0 license. See LICENSE for more information.

About

The official implementation of the ICLR'24 paper RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design
GitHub starsGitHub forksOpen In Colab
[2024-08-21] News: We provided a comprehensive evaluation system for RNA sequence design and prediction named R3Design. APIs and Colab demos are also provided. Feel free to check out our new repo!
[2024-08-15] Update: Thank you all for the interests and inquries about our paper, we are sorry that we haven't provided detailed documentation and demo of the paper for such a long time. Now, it has been solved. Feel free to check out our updated documentation and colab! :)
Introduction
While artificial intelligence has made remarkable strides in revealing the relationship between biological macromolecules' primary sequence and tertiary structure, designing RNA sequences based on specified tertiary structures remains challenging. Though existing approaches in protein design have thoroughly explored structure-to-sequence dependencies in proteins, RNA design still confronts difficulties due to structural complexity and data scarcity.
In this study, we aim to systematically construct a data-driven RNA design pipeline. We crafted a large, well-curated benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure. More importantly, we proposed a hierarchical data-efficient representation learning framework that learns structural representations through contrastive learning at both cluster-level and sample-level to fully leverage the limited data. Extensive experiments demonstrate the effectiveness of our proposed method, providing a reliable baseline for future RNA design tasks.
Dataset
We carefully collected representative RNA tertiary structure data from two sources, RNAsolo and the Protein Data Bank (PDB). The refined data has been released here. Please download the datasets and organize them as follows.
RDesign
├── API
├── assets
├── checkpoints
├── methods
├── model
└── data
├── RNAsolo
│ ├── train_data.pt
│ ├── val_data.pt
│ ├── test_data.pt

Main Environment
cd RDesign
conda env create -f environment.yml
conda activate RDesign
Load Data
# If you want to see the details inside our dataset, you could use Pickle package from Python
import _pickle as cPickle
train_data = cPickle.load(open('data/train_data.pt', 'rb'))
print(train_data[0].keys())
#For external datasets, loading data could be in this way:
from API.rpuzzles_dataset import RPuzzlesDataset
rfam_dataset = RPuzzlesDataset('./data/rfam_data.pt')
rpuz_dataset = RPuzzlesDataset('./data/rpuz_data.pt')
Test the model
# For more details, please refer to the colab# We provided detailed functions and pipeline to show how our model operates
Colab Link:
Open In Colab
Citation
If you are interested in our repository and our paper, please cite the following paper:
@inproceedings{tan2024rdesign,
title={RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design},
author={Tan, Cheng and Zhang, Yijie and Gao, Zhangyang and Hu, Bozhen and Li, Siyuan and Liu, Zicheng and Li, Stan Z},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024}
}

Feedback
If you have any issue about this work, please feel free to contact me by email:
License
This project is released under the Apache 2.0 license. See LICENSE for more information.
About
The official implementation of the ICLR'24 paper RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design.
Resources
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design
GitHub starsGitHub forksOpen In Colab
[2024-08-21] News: We provided a comprehensive evaluation system for RNA sequence design and prediction named R3Design. APIs and Colab demos are also provided. Feel free to check out our new repo!
[2024-08-15] Update: Thank you all for the interests and inquries about our paper, we are sorry that we haven't provided detailed documentation and demo of the paper for such a long time. Now, it has been solved. Feel free to check out our updated documentation and colab! :)
Introduction
While artificial intelligence has made remarkable strides in revealing the relationship between biological macromolecules' primary sequence and tertiary structure, designing RNA sequences based on specified tertiary structures remains challenging. Though existing approaches in protein design have thoroughly explored structure-to-sequence dependencies in proteins, RNA design still confronts difficulties due to structural complexity and data scarcity.
In this study, we aim to systematically construct a data-driven RNA design pipeline. We crafted a large, well-curated benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure. More importantly, we proposed a hierarchical data-efficient representation learning framework that learns structural representations through contrastive learning at both cluster-level and sample-level to fully leverage the limited data. Extensive experiments demonstrate the effectiveness of our proposed method, providing a reliable baseline for future RNA design tasks.
Dataset
We carefully collected representative RNA tertiary structure data from two sources, RNAsolo and the Protein Data Bank (PDB). The refined data has been released here. Please download the datasets and organize them as follows.
RDesign
├── API
├── assets
├── checkpoints
├── methods
├── model
└── data
├── RNAsolo
│ ├── train_data.pt
│ ├── val_data.pt
│ ├── test_data.pt

Main Environment
cd RDesign
conda env create -f environment.yml
conda activate RDesign
Load Data
# If you want to see the details inside our dataset, you could use Pickle package from Python
import _pickle as cPickle
train_data = cPickle.load(open('data/train_data.pt', 'rb'))
print(train_data[0].keys())
#For external datasets, loading data could be in this way:
from API.rpuzzles_dataset import RPuzzlesDataset
rfam_dataset = RPuzzlesDataset('./data/rfam_data.pt')
rpuz_dataset = RPuzzlesDataset('./data/rpuz_data.pt')
Test the model
# For more details, please refer to the colab# We provided detailed functions and pipeline to show how our model operates
Colab Link:
Open In Colab
Citation
If you are interested in our repository and our paper, please cite the following paper:
@inproceedings{tan2024rdesign,
title={RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design},
author={Tan, Cheng and Zhang, Yijie and Gao, Zhangyang and Hu, Bozhen and Li, Siyuan and Liu, Zicheng and Li, Stan Z},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024}
}

Feedback
If you have any issue about this work, please feel free to contact me by email:
License
This project is released under the Apache 2.0 license. See LICENSE for more information.
About
The official implementation of the ICLR'24 paper RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design.
Resources
Readme
Activity
Custom properties
Stars
50 stars
Watchers
1 watching
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2 forks
Report repository
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Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design
GitHub starsGitHub forksOpen In Colab
[2024-08-21] News: We provided a comprehensive evaluation system for RNA sequence design and prediction named R3Design. APIs and Colab demos are also provided. Feel free to check out our new repo!
[2024-08-15] Update: Thank you all for the interests and inquries about our paper, we are sorry that we haven't provided detailed documentation and demo of the paper for such a long time. Now, it has been solved. Feel free to check out our updated documentation and colab! :)
Introduction
While artificial intelligence has made remarkable strides in revealing the relationship between biological macromolecules' primary sequence and tertiary structure, designing RNA sequences based on specified tertiary structures remains challenging. Though existing approaches in protein design have thoroughly explored structure-to-sequence dependencies in proteins, RNA design still confronts difficulties due to structural complexity and data scarcity.
In this study, we aim to systematically construct a data-driven RNA design pipeline. We crafted a large, well-curated benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure. More importantly, we proposed a hierarchical data-efficient representation learning framework that learns structural representations through contrastive learning at both cluster-level and sample-level to fully leverage the limited data. Extensive experiments demonstrate the effectiveness of our proposed method, providing a reliable baseline for future RNA design tasks.
Dataset
We carefully collected representative RNA tertiary structure data from two sources, RNAsolo and the Protein Data Bank (PDB). The refined data has been released here. Please download the datasets and organize them as follows.
RDesign
├── API
├── assets
├── checkpoints
├── methods
├── model
└── data
├── RNAsolo
│ ├── train_data.pt
│ ├── val_data.pt
│ ├── test_data.pt

Main Environment
cd RDesign
conda env create -f environment.yml
conda activate RDesign
Load Data
# If you want to see the details inside our dataset, you could use Pickle package from Python
import _pickle as cPickle
train_data = cPickle.load(open('data/train_data.pt', 'rb'))
print(train_data[0].keys())
#For external datasets, loading data could be in this way:
from API.rpuzzles_dataset import RPuzzlesDataset
rfam_dataset = RPuzzlesDataset('./data/rfam_data.pt')
rpuz_dataset = RPuzzlesDataset('./data/rpuz_data.pt')
Test the model
# For more details, please refer to the colab# We provided detailed functions and pipeline to show how our model operates
Colab Link:
Open In Colab
Citation
If you are interested in our repository and our paper, please cite the following paper:
@inproceedings{tan2024rdesign,
title={RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design},
author={Tan, Cheng and Zhang, Yijie and Gao, Zhangyang and Hu, Bozhen and Li, Siyuan and Liu, Zicheng and Li, Stan Z},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024}
}

Feedback
If you have any issue about this work, please feel free to contact me by email:
License
This project is released under the Apache 2.0 license. See LICENSE for more information.
About
The official implementation of the ICLR'24 paper RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design.
Resources
Readme
Activity
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Stars
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1 watching
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Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design
GitHub starsGitHub forksOpen In Colab
[2024-08-21] News: We provided a comprehensive evaluation system for RNA sequence design and prediction named R3Design. APIs and Colab demos are also provided. Feel free to check out our new repo!
[2024-08-15] Update: Thank you all for the interests and inquries about our paper, we are sorry that we haven't provided detailed documentation and demo of the paper for such a long time. Now, it has been solved. Feel free to check out our updated documentation and colab! :)
Introduction
While artificial intelligence has made remarkable strides in revealing the relationship between biological macromolecules' primary sequence and tertiary structure, designing RNA sequences based on specified tertiary structures remains challenging. Though existing approaches in protein design have thoroughly explored structure-to-sequence dependencies in proteins, RNA design still confronts difficulties due to structural complexity and data scarcity.
In this study, we aim to systematically construct a data-driven RNA design pipeline. We crafted a large, well-curated benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure. More importantly, we proposed a hierarchical data-efficient representation learning framework that learns structural representations through contrastive learning at both cluster-level and sample-level to fully leverage the limited data. Extensive experiments demonstrate the effectiveness of our proposed method, providing a reliable baseline for future RNA design tasks.
Dataset
We carefully collected representative RNA tertiary structure data from two sources, RNAsolo and the Protein Data Bank (PDB). The refined data has been released here. Please download the datasets and organize them as follows.
RDesign
├── API
├── assets
├── checkpoints
├── methods
├── model
└── data
├── RNAsolo
│ ├── train_data.pt
│ ├── val_data.pt
│ ├── test_data.pt

Main Environment
cd RDesign
conda env create -f environment.yml
conda activate RDesign
Load Data
# If you want to see the details inside our dataset, you could use Pickle package from Python
import _pickle as cPickle
train_data = cPickle.load(open('data/train_data.pt', 'rb'))
print(train_data[0].keys())
#For external datasets, loading data could be in this way:
from API.rpuzzles_dataset import RPuzzlesDataset
rfam_dataset = RPuzzlesDataset('./data/rfam_data.pt')
rpuz_dataset = RPuzzlesDataset('./data/rpuz_data.pt')
Test the model
# For more details, please refer to the colab# We provided detailed functions and pipeline to show how our model operates
Colab Link:
Open In Colab
Citation
If you are interested in our repository and our paper, please cite the following paper:
@inproceedings{tan2024rdesign,
title={RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design},
author={Tan, Cheng and Zhang, Yijie and Gao, Zhangyang and Hu, Bozhen and Li, Siyuan and Liu, Zicheng and Li, Stan Z},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024}
}

Feedback
If you have any issue about this work, please feel free to contact me by email:
License
This project is released under the Apache 2.0 license. See LICENSE for more information.
About
The official implementation of the ICLR'24 paper RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design.
Resources
Readme
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1 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design
GitHub starsGitHub forksOpen In Colab
[2024-08-21] News: We provided a comprehensive evaluation system for RNA sequence design and prediction named R3Design. APIs and Colab demos are also provided. Feel free to check out our new repo!
[2024-08-15] Update: Thank you all for the interests and inquries about our paper, we are sorry that we haven't provided detailed documentation and demo of the paper for such a long time. Now, it has been solved. Feel free to check out our updated documentation and colab! :)
Introduction
While artificial intelligence has made remarkable strides in revealing the relationship between biological macromolecules' primary sequence and tertiary structure, designing RNA sequences based on specified tertiary structures remains challenging. Though existing approaches in protein design have thoroughly explored structure-to-sequence dependencies in proteins, RNA design still confronts difficulties due to structural complexity and data scarcity.
In this study, we aim to systematically construct a data-driven RNA design pipeline. We crafted a large, well-curated benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure. More importantly, we proposed a hierarchical data-efficient representation learning framework that learns structural representations through contrastive learning at both cluster-level and sample-level to fully leverage the limited data. Extensive experiments demonstrate the effectiveness of our proposed method, providing a reliable baseline for future RNA design tasks.
Dataset
We carefully collected representative RNA tertiary structure data from two sources, RNAsolo and the Protein Data Bank (PDB). The refined data has been released here. Please download the datasets and organize them as follows.
RDesign
├── API
├── assets
├── checkpoints
├── methods
├── model
└── data
├── RNAsolo
│ ├── train_data.pt
│ ├── val_data.pt
│ ├── test_data.pt

Main Environment
cd RDesign
conda env create -f environment.yml
conda activate RDesign
Load Data
# If you want to see the details inside our dataset, you could use Pickle package from Python
import _pickle as cPickle
train_data = cPickle.load(open('data/train_data.pt', 'rb'))
print(train_data[0].keys())
#For external datasets, loading data could be in this way:
from API.rpuzzles_dataset import RPuzzlesDataset
rfam_dataset = RPuzzlesDataset('./data/rfam_data.pt')
rpuz_dataset = RPuzzlesDataset('./data/rpuz_data.pt')
Test the model
# For more details, please refer to the colab# We provided detailed functions and pipeline to show how our model operates
Colab Link:
Open In Colab
Citation
If you are interested in our repository and our paper, please cite the following paper:
@inproceedings{tan2024rdesign,
title={RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design},
author={Tan, Cheng and Zhang, Yijie and Gao, Zhangyang and Hu, Bozhen and Li, Siyuan and Liu, Zicheng and Li, Stan Z},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024}
}

Feedback
If you have any issue about this work, please feel free to contact me by email:
License
This project is released under the Apache 2.0 license. See LICENSE for more information.
About
The official implementation of the ICLR'24 paper RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design.
Resources
Readme
Activity
Custom properties
Stars
50 stars
Watchers
1 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
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RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design
GitHub starsGitHub forksOpen In Colab
[2024-08-21] News: We provided a comprehensive evaluation system for RNA sequence design and prediction named R3Design. APIs and Colab demos are also provided. Feel free to check out our new repo!
[2024-08-15] Update: Thank you all for the interests and inquries about our paper, we are sorry that we haven't provided detailed documentation and demo of the paper for such a long time. Now, it has been solved. Feel free to check out our updated documentation and colab! :)
Introduction
While artificial intelligence has made remarkable strides in revealing the relationship between biological macromolecules' primary sequence and tertiary structure, designing RNA sequences based on specified tertiary structures remains challenging. Though existing approaches in protein design have thoroughly explored structure-to-sequence dependencies in proteins, RNA design still confronts difficulties due to structural complexity and data scarcity.
In this study, we aim to systematically construct a data-driven RNA design pipeline. We crafted a large, well-curated benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure. More importantly, we proposed a hierarchical data-efficient representation learning framework that learns structural representations through contrastive learning at both cluster-level and sample-level to fully leverage the limited data. Extensive experiments demonstrate the effectiveness of our proposed method, providing a reliable baseline for future RNA design tasks.
Dataset
We carefully collected representative RNA tertiary structure data from two sources, RNAsolo and the Protein Data Bank (PDB). The refined data has been released here. Please download the datasets and organize them as follows.
RDesign
├── API
├── assets
├── checkpoints
├── methods
├── model
└── data
├── RNAsolo
│ ├── train_data.pt
│ ├── val_data.pt
│ ├── test_data.pt

Main Environment
cd RDesign
conda env create -f environment.yml
conda activate RDesign
Load Data
# If you want to see the details inside our dataset, you could use Pickle package from Python
import _pickle as cPickle
train_data = cPickle.load(open('data/train_data.pt', 'rb'))
print(train_data[0].keys())
#For external datasets, loading data could be in this way:
from API.rpuzzles_dataset import RPuzzlesDataset
rfam_dataset = RPuzzlesDataset('./data/rfam_data.pt')
rpuz_dataset = RPuzzlesDataset('./data/rpuz_data.pt')
Test the model
# For more details, please refer to the colab# We provided detailed functions and pipeline to show how our model operates
Colab Link:
Open In Colab
Citation
If you are interested in our repository and our paper, please cite the following paper:
@inproceedings{tan2024rdesign,
title={RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design},
author={Tan, Cheng and Zhang, Yijie and Gao, Zhangyang and Hu, Bozhen and Li, Siyuan and Liu, Zicheng and Li, Stan Z},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024}
}

Feedback
If you have any issue about this work, please feel free to contact me by email:
License
This project is released under the Apache 2.0 license. See LICENSE for more information.
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The official implementation of the ICLR'24 paper RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design.
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} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design
GitHub starsGitHub forksOpen In Colab
[2024-08-21] News: We provided a comprehensive evaluation system for RNA sequence design and prediction named R3Design. APIs and Colab demos are also provided. Feel free to check out our new repo!
[2024-08-15] Update: Thank you all for the interests and inquries about our paper, we are sorry that we haven't provided detailed documentation and demo of the paper for such a long time. Now, it has been solved. Feel free to check out our updated documentation and colab! :)
Introduction
While artificial intelligence has made remarkable strides in revealing the relationship between biological macromolecules' primary sequence and tertiary structure, designing RNA sequences based on specified tertiary structures remains challenging. Though existing approaches in protein design have thoroughly explored structure-to-sequence dependencies in proteins, RNA design still confronts difficulties due to structural complexity and data scarcity.
In this study, we aim to systematically construct a data-driven RNA design pipeline. We crafted a large, well-curated benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure. More importantly, we proposed a hierarchical data-efficient representation learning framework that learns structural representations through contrastive learning at both cluster-level and sample-level to fully leverage the limited data. Extensive experiments demonstrate the effectiveness of our proposed method, providing a reliable baseline for future RNA design tasks.
Dataset
We carefully collected representative RNA tertiary structure data from two sources, RNAsolo and the Protein Data Bank (PDB). The refined data has been released here. Please download the datasets and organize them as follows.
RDesign
├── API
├── assets
├── checkpoints
├── methods
├── model
└── data
├── RNAsolo
│ ├── train_data.pt
│ ├── val_data.pt
│ ├── test_data.pt

Main Environment
cd RDesign
conda env create -f environment.yml
conda activate RDesign
Load Data
# If you want to see the details inside our dataset, you could use Pickle package from Python
import _pickle as cPickle
train_data = cPickle.load(open('data/train_data.pt', 'rb'))
print(train_data[0].keys())
#For external datasets, loading data could be in this way:
from API.rpuzzles_dataset import RPuzzlesDataset
rfam_dataset = RPuzzlesDataset('./data/rfam_data.pt')
rpuz_dataset = RPuzzlesDataset('./data/rpuz_data.pt')
Test the model
# For more details, please refer to the colab# We provided detailed functions and pipeline to show how our model operates
Colab Link:
Open In Colab
Citation
If you are interested in our repository and our paper, please cite the following paper:
@inproceedings{tan2024rdesign,
title={RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design},
author={Tan, Cheng and Zhang, Yijie and Gao, Zhangyang and Hu, Bozhen and Li, Siyuan and Liu, Zicheng and Li, Stan Z},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024}
}

Feedback
If you have any issue about this work, please feel free to contact me by email:
License
This project is released under the Apache 2.0 license. See LICENSE for more information.
About
The official implementation of the ICLR'24 paper RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design.
Resources
Readme
Activity
Custom properties
Stars
50 stars
Watchers
1 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
Languages