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AlphaRED

AlphaFold-initiated replica exchange protein docking is a pipeline to predict protein complex structures from sequences. This pipeline follows a sequence to structure to complex paradigm while employing AlphaFold for structure prediction followed by ReplicaDock2.0 for protein-protein docking.

For more details, please check out our paper here: Harmalkar A, Lyskov S, Gray JJ, "Reliable protein-protein docking with AlphaFold, Rosetta and replica-exchange", bioRxiv, July 2023.

Pipeline

Currently, this approach is distributed as a step-wise pipeline involving sequence-to-structure prediction, analysis of prediction accuracy, and docking. For structure prediction, we have equipped AlphaFold, however, ESMFold and OmegaFold structures could also be used as all predictive methods deposit structure confidence values (e.g., pLDDT) in the B-factor column of the generated models. For docking, we use ReplicaDock 2.0 with in-built upgrades to select flexible ("mobile") residues based on pLDDT values.

How to use AlphaRED?

  1. Go to AlphaRED/pipeline/1-structure_prediction/ to set-up local installation of ColabFold. This would be used for structure prediction. Alternative structure prediction tools such as ESMFold, OmegaFold, etc could also be used in place of AlphaFold based on the preference of the user.
  2. Next, go to AlphaRED/pipeline/2-prediction_analysis/ to analyze the state of the prediction, i.e. is the binding site correctly identified or not? If not, perform global docking. Otherwise, perform local docking and refinement.
  3. Finally, in AlphaRED/pipeline/3-docking/, based on the binding mode identified, perform the respective docking analysis. If you perform global docking first, the top decoys from global docking are down-selected for local docking. If you perform local docking directly, select the top-scoring decoys (based on Interface scores) for side-chian refinement and relaxation.

Installation

RosettaCommons AlphaRed container

Licensing

The AlphaRed protocol requires a comprehensive set of tools. To streamline this process, we provide a Docker container that includes all necessary applications pre-installed, offering a straightforward command-line interface for running the AlphaRed protocol.

Please note that the provided Docker image includes Rosetta which require a commercial license for non-academic use. For more details, please refer to: RosettaCommons

Running AlphaRed Using the RosettaCommons Docker Container

To run the application, use the alpha-red script, which executes all required steps sequentially. The script requires a docking partner string and a sequence as input. Additionally, you may specify the -jN flag to utilize multiple CPU cores for improved performance.

Example Usage:

mkdir ./alpha-red
docker run -it -v ./alpha-red:/alpha-red rosettacommons/alphared alpha-red -j32 A_B AACD:BBCCC

For a full list of available options, run: docker run -it rosettacommons/alphared alpha-red --help

Local Install

The installation instructions for each stage of the pipeline are detailed in its respective directory in pipeline folder. To navigate, the folder hierarchy is as follows:

  • benchmark
    • difficult_targets
    • medium_targets
    • rigid_targets
  • pipeline
    • 1-structure_prediction
    • 2-prediction_analysis
    • 3-docking
  • utilities

Web Server

We are working to set up a web-server for structure prediction and docking. This service would be shortly available on ROSIE. Updates coming soon!

Bug reports

If you run into any problems while using AlphaRED, please create a Github issue with a description of the problem and the steps to reproduce it.

References

Please use the following references to cite our work and corresponding literature:

  1. Structure prediction
  1. Docking

About

AlphaFold-initiated replica exchange protein docking

Resources

Readme
MIT license
Activity
Custom properties

Stars

94 stars

Watchers

6 watching

Forks

19 forks
Report repository

Releases

Packages

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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AlphaRED
AlphaFold-initiated replica exchange protein docking is a pipeline to predict protein complex structures from sequences. This pipeline follows a sequence to structure to complex paradigm while employing AlphaFold for structure prediction followed by ReplicaDock2.0 for protein-protein docking.
For more details, please check out our paper here:
Harmalkar A, Lyskov S, Gray JJ, "Reliable protein-protein docking with AlphaFold, Rosetta and replica-exchange", bioRxiv, July 2023.
Pipeline
Currently, this approach is distributed as a step-wise pipeline involving sequence-to-structure prediction, analysis of prediction accuracy, and docking.
For structure prediction, we have equipped AlphaFold, however, ESMFold and OmegaFold structures could also be used as all predictive methods deposit structure confidence values (e.g., pLDDT) in the B-factor column of the generated models.
For docking, we use ReplicaDock 2.0 with in-built upgrades to select flexible ("mobile") residues based on pLDDT values.
How to use AlphaRED?
  1. Go to AlphaRED/pipeline/1-structure_prediction/ to set-up local installation of ColabFold. This would be used for structure prediction. Alternative structure prediction tools such as ESMFold, OmegaFold, etc could also be used in place of AlphaFold based on the preference of the user.
  2. Next, go to AlphaRED/pipeline/2-prediction_analysis/ to analyze the state of the prediction, i.e. is the binding site correctly identified or not? If not, perform global docking. Otherwise, perform local docking and refinement.
  3. Finally, in AlphaRED/pipeline/3-docking/, based on the binding mode identified, perform the respective docking analysis. If you perform global docking first, the top decoys from global docking are down-selected for local docking. If you perform local docking directly, select the top-scoring decoys (based on Interface scores) for side-chian refinement and relaxation.
Installation
RosettaCommons AlphaRed container
Licensing
The AlphaRed protocol requires a comprehensive set of tools. To streamline this process, we provide a Docker container that includes all necessary applications pre-installed, offering a straightforward command-line interface for running the AlphaRed protocol.
Please note that the provided Docker image includes Rosetta which require a commercial license for non-academic use. For more details, please refer to: RosettaCommons
Running AlphaRed Using the RosettaCommons Docker Container
To run the application, use the alpha-red script, which executes all required steps sequentially. The script requires a docking partner string and a sequence as input. Additionally, you may specify the -jN flag to utilize multiple CPU cores for improved performance.
Example Usage:
mkdir ./alpha-red
docker run -it -v ./alpha-red:/alpha-red rosettacommons/alphared alpha-red -j32 A_B AACD:BBCCC
For a full list of available options, run: docker run -it rosettacommons/alphared alpha-red --help
Local Install
The installation instructions for each stage of the pipeline are detailed in its respective directory in pipeline folder.
To navigate, the folder hierarchy is as follows:
  • benchmark

    • difficult_targets
    • medium_targets
    • rigid_targets
  • pipeline

    • 1-structure_prediction
    • 2-prediction_analysis
    • 3-docking
  • utilities
Web Server
We are working to set up a web-server for structure prediction and docking. This service would be shortly available on ROSIE. Updates coming soon!
Bug reports
If you run into any problems while using AlphaRED, please create a Github issue with a description of the problem and the steps to reproduce it.
References
Please use the following references to cite our work and corresponding literature:
  1. Structure prediction
  1. Docking
About
AlphaFold-initiated replica exchange protein docking
Resources
Readme
MIT license
Activity
Custom properties
Stars
94 stars
Watchers
6 watching
Forks
19 forks
Report repository
Releases
Packages
Contributors
Languages

, '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");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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AlphaRED
AlphaFold-initiated replica exchange protein docking is a pipeline to predict protein complex structures from sequences. This pipeline follows a sequence to structure to complex paradigm while employing AlphaFold for structure prediction followed by ReplicaDock2.0 for protein-protein docking.
For more details, please check out our paper here:
Harmalkar A, Lyskov S, Gray JJ, "Reliable protein-protein docking with AlphaFold, Rosetta and replica-exchange", bioRxiv, July 2023.
Pipeline
Currently, this approach is distributed as a step-wise pipeline involving sequence-to-structure prediction, analysis of prediction accuracy, and docking.
For structure prediction, we have equipped AlphaFold, however, ESMFold and OmegaFold structures could also be used as all predictive methods deposit structure confidence values (e.g., pLDDT) in the B-factor column of the generated models.
For docking, we use ReplicaDock 2.0 with in-built upgrades to select flexible ("mobile") residues based on pLDDT values.
How to use AlphaRED?
  1. Go to AlphaRED/pipeline/1-structure_prediction/ to set-up local installation of ColabFold. This would be used for structure prediction. Alternative structure prediction tools such as ESMFold, OmegaFold, etc could also be used in place of AlphaFold based on the preference of the user.
  2. Next, go to AlphaRED/pipeline/2-prediction_analysis/ to analyze the state of the prediction, i.e. is the binding site correctly identified or not? If not, perform global docking. Otherwise, perform local docking and refinement.
  3. Finally, in AlphaRED/pipeline/3-docking/, based on the binding mode identified, perform the respective docking analysis. If you perform global docking first, the top decoys from global docking are down-selected for local docking. If you perform local docking directly, select the top-scoring decoys (based on Interface scores) for side-chian refinement and relaxation.
Installation
RosettaCommons AlphaRed container
Licensing
The AlphaRed protocol requires a comprehensive set of tools. To streamline this process, we provide a Docker container that includes all necessary applications pre-installed, offering a straightforward command-line interface for running the AlphaRed protocol.
Please note that the provided Docker image includes Rosetta which require a commercial license for non-academic use. For more details, please refer to: RosettaCommons
Running AlphaRed Using the RosettaCommons Docker Container
To run the application, use the alpha-red script, which executes all required steps sequentially. The script requires a docking partner string and a sequence as input. Additionally, you may specify the -jN flag to utilize multiple CPU cores for improved performance.
Example Usage:
mkdir ./alpha-red
docker run -it -v ./alpha-red:/alpha-red rosettacommons/alphared alpha-red -j32 A_B AACD:BBCCC
For a full list of available options, run: docker run -it rosettacommons/alphared alpha-red --help
Local Install
The installation instructions for each stage of the pipeline are detailed in its respective directory in pipeline folder.
To navigate, the folder hierarchy is as follows:
  • benchmark

    • difficult_targets
    • medium_targets
    • rigid_targets
  • pipeline

    • 1-structure_prediction
    • 2-prediction_analysis
    • 3-docking
  • utilities
Web Server
We are working to set up a web-server for structure prediction and docking. This service would be shortly available on ROSIE. Updates coming soon!
Bug reports
If you run into any problems while using AlphaRED, please create a Github issue with a description of the problem and the steps to reproduce it.
References
Please use the following references to cite our work and corresponding literature:
  1. Structure prediction
  1. Docking
About
AlphaFold-initiated replica exchange protein docking
Resources
Readme
MIT license
Activity
Custom properties
Stars
94 stars
Watchers
6 watching
Forks
19 forks
Report repository
Releases
Packages
Contributors
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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AlphaRED
AlphaFold-initiated replica exchange protein docking is a pipeline to predict protein complex structures from sequences. This pipeline follows a sequence to structure to complex paradigm while employing AlphaFold for structure prediction followed by ReplicaDock2.0 for protein-protein docking.
For more details, please check out our paper here:
Harmalkar A, Lyskov S, Gray JJ, "Reliable protein-protein docking with AlphaFold, Rosetta and replica-exchange", bioRxiv, July 2023.
Pipeline
Currently, this approach is distributed as a step-wise pipeline involving sequence-to-structure prediction, analysis of prediction accuracy, and docking.
For structure prediction, we have equipped AlphaFold, however, ESMFold and OmegaFold structures could also be used as all predictive methods deposit structure confidence values (e.g., pLDDT) in the B-factor column of the generated models.
For docking, we use ReplicaDock 2.0 with in-built upgrades to select flexible ("mobile") residues based on pLDDT values.
How to use AlphaRED?
  1. Go to AlphaRED/pipeline/1-structure_prediction/ to set-up local installation of ColabFold. This would be used for structure prediction. Alternative structure prediction tools such as ESMFold, OmegaFold, etc could also be used in place of AlphaFold based on the preference of the user.
  2. Next, go to AlphaRED/pipeline/2-prediction_analysis/ to analyze the state of the prediction, i.e. is the binding site correctly identified or not? If not, perform global docking. Otherwise, perform local docking and refinement.
  3. Finally, in AlphaRED/pipeline/3-docking/, based on the binding mode identified, perform the respective docking analysis. If you perform global docking first, the top decoys from global docking are down-selected for local docking. If you perform local docking directly, select the top-scoring decoys (based on Interface scores) for side-chian refinement and relaxation.
Installation
RosettaCommons AlphaRed container
Licensing
The AlphaRed protocol requires a comprehensive set of tools. To streamline this process, we provide a Docker container that includes all necessary applications pre-installed, offering a straightforward command-line interface for running the AlphaRed protocol.
Please note that the provided Docker image includes Rosetta which require a commercial license for non-academic use. For more details, please refer to: RosettaCommons
Running AlphaRed Using the RosettaCommons Docker Container
To run the application, use the alpha-red script, which executes all required steps sequentially. The script requires a docking partner string and a sequence as input. Additionally, you may specify the -jN flag to utilize multiple CPU cores for improved performance.
Example Usage:
mkdir ./alpha-red
docker run -it -v ./alpha-red:/alpha-red rosettacommons/alphared alpha-red -j32 A_B AACD:BBCCC
For a full list of available options, run: docker run -it rosettacommons/alphared alpha-red --help
Local Install
The installation instructions for each stage of the pipeline are detailed in its respective directory in pipeline folder.
To navigate, the folder hierarchy is as follows:
  • benchmark

    • difficult_targets
    • medium_targets
    • rigid_targets
  • pipeline

    • 1-structure_prediction
    • 2-prediction_analysis
    • 3-docking
  • utilities
Web Server
We are working to set up a web-server for structure prediction and docking. This service would be shortly available on ROSIE. Updates coming soon!
Bug reports
If you run into any problems while using AlphaRED, please create a Github issue with a description of the problem and the steps to reproduce it.
References
Please use the following references to cite our work and corresponding literature:
  1. Structure prediction
  1. Docking
About
AlphaFold-initiated replica exchange protein docking
Resources
Readme
MIT license
Activity
Custom properties
Stars
94 stars
Watchers
6 watching
Forks
19 forks
Report repository
Releases
Packages
Contributors
Languages

, '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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AlphaRED
AlphaFold-initiated replica exchange protein docking is a pipeline to predict protein complex structures from sequences. This pipeline follows a sequence to structure to complex paradigm while employing AlphaFold for structure prediction followed by ReplicaDock2.0 for protein-protein docking.
For more details, please check out our paper here:
Harmalkar A, Lyskov S, Gray JJ, "Reliable protein-protein docking with AlphaFold, Rosetta and replica-exchange", bioRxiv, July 2023.
Pipeline
Currently, this approach is distributed as a step-wise pipeline involving sequence-to-structure prediction, analysis of prediction accuracy, and docking.
For structure prediction, we have equipped AlphaFold, however, ESMFold and OmegaFold structures could also be used as all predictive methods deposit structure confidence values (e.g., pLDDT) in the B-factor column of the generated models.
For docking, we use ReplicaDock 2.0 with in-built upgrades to select flexible ("mobile") residues based on pLDDT values.
How to use AlphaRED?
  1. Go to AlphaRED/pipeline/1-structure_prediction/ to set-up local installation of ColabFold. This would be used for structure prediction. Alternative structure prediction tools such as ESMFold, OmegaFold, etc could also be used in place of AlphaFold based on the preference of the user.
  2. Next, go to AlphaRED/pipeline/2-prediction_analysis/ to analyze the state of the prediction, i.e. is the binding site correctly identified or not? If not, perform global docking. Otherwise, perform local docking and refinement.
  3. Finally, in AlphaRED/pipeline/3-docking/, based on the binding mode identified, perform the respective docking analysis. If you perform global docking first, the top decoys from global docking are down-selected for local docking. If you perform local docking directly, select the top-scoring decoys (based on Interface scores) for side-chian refinement and relaxation.
Installation
RosettaCommons AlphaRed container
Licensing
The AlphaRed protocol requires a comprehensive set of tools. To streamline this process, we provide a Docker container that includes all necessary applications pre-installed, offering a straightforward command-line interface for running the AlphaRed protocol.
Please note that the provided Docker image includes Rosetta which require a commercial license for non-academic use. For more details, please refer to: RosettaCommons
Running AlphaRed Using the RosettaCommons Docker Container
To run the application, use the alpha-red script, which executes all required steps sequentially. The script requires a docking partner string and a sequence as input. Additionally, you may specify the -jN flag to utilize multiple CPU cores for improved performance.
Example Usage:
mkdir ./alpha-red
docker run -it -v ./alpha-red:/alpha-red rosettacommons/alphared alpha-red -j32 A_B AACD:BBCCC
For a full list of available options, run: docker run -it rosettacommons/alphared alpha-red --help
Local Install
The installation instructions for each stage of the pipeline are detailed in its respective directory in pipeline folder.
To navigate, the folder hierarchy is as follows:
  • benchmark

    • difficult_targets
    • medium_targets
    • rigid_targets
  • pipeline

    • 1-structure_prediction
    • 2-prediction_analysis
    • 3-docking
  • utilities
Web Server
We are working to set up a web-server for structure prediction and docking. This service would be shortly available on ROSIE. Updates coming soon!
Bug reports
If you run into any problems while using AlphaRED, please create a Github issue with a description of the problem and the steps to reproduce it.
References
Please use the following references to cite our work and corresponding literature:
  1. Structure prediction
  1. Docking
About
AlphaFold-initiated replica exchange protein docking
Resources
Readme
MIT license
Activity
Custom properties
Stars
94 stars
Watchers
6 watching
Forks
19 forks
Report repository
Releases
Packages
Contributors
Languages

, '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); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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AlphaRED
AlphaFold-initiated replica exchange protein docking is a pipeline to predict protein complex structures from sequences. This pipeline follows a sequence to structure to complex paradigm while employing AlphaFold for structure prediction followed by ReplicaDock2.0 for protein-protein docking.
For more details, please check out our paper here:
Harmalkar A, Lyskov S, Gray JJ, "Reliable protein-protein docking with AlphaFold, Rosetta and replica-exchange", bioRxiv, July 2023.
Pipeline
Currently, this approach is distributed as a step-wise pipeline involving sequence-to-structure prediction, analysis of prediction accuracy, and docking.
For structure prediction, we have equipped AlphaFold, however, ESMFold and OmegaFold structures could also be used as all predictive methods deposit structure confidence values (e.g., pLDDT) in the B-factor column of the generated models.
For docking, we use ReplicaDock 2.0 with in-built upgrades to select flexible ("mobile") residues based on pLDDT values.
How to use AlphaRED?
  1. Go to AlphaRED/pipeline/1-structure_prediction/ to set-up local installation of ColabFold. This would be used for structure prediction. Alternative structure prediction tools such as ESMFold, OmegaFold, etc could also be used in place of AlphaFold based on the preference of the user.
  2. Next, go to AlphaRED/pipeline/2-prediction_analysis/ to analyze the state of the prediction, i.e. is the binding site correctly identified or not? If not, perform global docking. Otherwise, perform local docking and refinement.
  3. Finally, in AlphaRED/pipeline/3-docking/, based on the binding mode identified, perform the respective docking analysis. If you perform global docking first, the top decoys from global docking are down-selected for local docking. If you perform local docking directly, select the top-scoring decoys (based on Interface scores) for side-chian refinement and relaxation.
Installation
RosettaCommons AlphaRed container
Licensing
The AlphaRed protocol requires a comprehensive set of tools. To streamline this process, we provide a Docker container that includes all necessary applications pre-installed, offering a straightforward command-line interface for running the AlphaRed protocol.
Please note that the provided Docker image includes Rosetta which require a commercial license for non-academic use. For more details, please refer to: RosettaCommons
Running AlphaRed Using the RosettaCommons Docker Container
To run the application, use the alpha-red script, which executes all required steps sequentially. The script requires a docking partner string and a sequence as input. Additionally, you may specify the -jN flag to utilize multiple CPU cores for improved performance.
Example Usage:
mkdir ./alpha-red
docker run -it -v ./alpha-red:/alpha-red rosettacommons/alphared alpha-red -j32 A_B AACD:BBCCC
For a full list of available options, run: docker run -it rosettacommons/alphared alpha-red --help
Local Install
The installation instructions for each stage of the pipeline are detailed in its respective directory in pipeline folder.
To navigate, the folder hierarchy is as follows:
  • benchmark

    • difficult_targets
    • medium_targets
    • rigid_targets
  • pipeline

    • 1-structure_prediction
    • 2-prediction_analysis
    • 3-docking
  • utilities
Web Server
We are working to set up a web-server for structure prediction and docking. This service would be shortly available on ROSIE. Updates coming soon!
Bug reports
If you run into any problems while using AlphaRED, please create a Github issue with a description of the problem and the steps to reproduce it.
References
Please use the following references to cite our work and corresponding literature:
  1. Structure prediction
  1. Docking
About
AlphaFold-initiated replica exchange protein docking
Resources
Readme
MIT license
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94 stars
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6 watching
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19 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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AlphaRED
AlphaFold-initiated replica exchange protein docking is a pipeline to predict protein complex structures from sequences. This pipeline follows a sequence to structure to complex paradigm while employing AlphaFold for structure prediction followed by ReplicaDock2.0 for protein-protein docking.
For more details, please check out our paper here:
Harmalkar A, Lyskov S, Gray JJ, "Reliable protein-protein docking with AlphaFold, Rosetta and replica-exchange", bioRxiv, July 2023.
Pipeline
Currently, this approach is distributed as a step-wise pipeline involving sequence-to-structure prediction, analysis of prediction accuracy, and docking.
For structure prediction, we have equipped AlphaFold, however, ESMFold and OmegaFold structures could also be used as all predictive methods deposit structure confidence values (e.g., pLDDT) in the B-factor column of the generated models.
For docking, we use ReplicaDock 2.0 with in-built upgrades to select flexible ("mobile") residues based on pLDDT values.
How to use AlphaRED?
  1. Go to AlphaRED/pipeline/1-structure_prediction/ to set-up local installation of ColabFold. This would be used for structure prediction. Alternative structure prediction tools such as ESMFold, OmegaFold, etc could also be used in place of AlphaFold based on the preference of the user.
  2. Next, go to AlphaRED/pipeline/2-prediction_analysis/ to analyze the state of the prediction, i.e. is the binding site correctly identified or not? If not, perform global docking. Otherwise, perform local docking and refinement.
  3. Finally, in AlphaRED/pipeline/3-docking/, based on the binding mode identified, perform the respective docking analysis. If you perform global docking first, the top decoys from global docking are down-selected for local docking. If you perform local docking directly, select the top-scoring decoys (based on Interface scores) for side-chian refinement and relaxation.
Installation
RosettaCommons AlphaRed container
Licensing
The AlphaRed protocol requires a comprehensive set of tools. To streamline this process, we provide a Docker container that includes all necessary applications pre-installed, offering a straightforward command-line interface for running the AlphaRed protocol.
Please note that the provided Docker image includes Rosetta which require a commercial license for non-academic use. For more details, please refer to: RosettaCommons
Running AlphaRed Using the RosettaCommons Docker Container
To run the application, use the alpha-red script, which executes all required steps sequentially. The script requires a docking partner string and a sequence as input. Additionally, you may specify the -jN flag to utilize multiple CPU cores for improved performance.
Example Usage:
mkdir ./alpha-red
docker run -it -v ./alpha-red:/alpha-red rosettacommons/alphared alpha-red -j32 A_B AACD:BBCCC
For a full list of available options, run: docker run -it rosettacommons/alphared alpha-red --help
Local Install
The installation instructions for each stage of the pipeline are detailed in its respective directory in pipeline folder.
To navigate, the folder hierarchy is as follows:
  • benchmark

    • difficult_targets
    • medium_targets
    • rigid_targets
  • pipeline

    • 1-structure_prediction
    • 2-prediction_analysis
    • 3-docking
  • utilities
Web Server
We are working to set up a web-server for structure prediction and docking. This service would be shortly available on ROSIE. Updates coming soon!
Bug reports
If you run into any problems while using AlphaRED, please create a Github issue with a description of the problem and the steps to reproduce it.
References
Please use the following references to cite our work and corresponding literature:
  1. Structure prediction
  1. Docking
About
AlphaFold-initiated replica exchange protein docking
Resources
Readme
MIT license
Activity
Custom properties
Stars
94 stars
Watchers
6 watching
Forks
19 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
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Repository files navigation
AlphaRED
AlphaFold-initiated replica exchange protein docking is a pipeline to predict protein complex structures from sequences. This pipeline follows a sequence to structure to complex paradigm while employing AlphaFold for structure prediction followed by ReplicaDock2.0 for protein-protein docking.
For more details, please check out our paper here:
Harmalkar A, Lyskov S, Gray JJ, "Reliable protein-protein docking with AlphaFold, Rosetta and replica-exchange", bioRxiv, July 2023.
Pipeline
Currently, this approach is distributed as a step-wise pipeline involving sequence-to-structure prediction, analysis of prediction accuracy, and docking.
For structure prediction, we have equipped AlphaFold, however, ESMFold and OmegaFold structures could also be used as all predictive methods deposit structure confidence values (e.g., pLDDT) in the B-factor column of the generated models.
For docking, we use ReplicaDock 2.0 with in-built upgrades to select flexible ("mobile") residues based on pLDDT values.
How to use AlphaRED?
  1. Go to AlphaRED/pipeline/1-structure_prediction/ to set-up local installation of ColabFold. This would be used for structure prediction. Alternative structure prediction tools such as ESMFold, OmegaFold, etc could also be used in place of AlphaFold based on the preference of the user.
  2. Next, go to AlphaRED/pipeline/2-prediction_analysis/ to analyze the state of the prediction, i.e. is the binding site correctly identified or not? If not, perform global docking. Otherwise, perform local docking and refinement.
  3. Finally, in AlphaRED/pipeline/3-docking/, based on the binding mode identified, perform the respective docking analysis. If you perform global docking first, the top decoys from global docking are down-selected for local docking. If you perform local docking directly, select the top-scoring decoys (based on Interface scores) for side-chian refinement and relaxation.
Installation
RosettaCommons AlphaRed container
Licensing
The AlphaRed protocol requires a comprehensive set of tools. To streamline this process, we provide a Docker container that includes all necessary applications pre-installed, offering a straightforward command-line interface for running the AlphaRed protocol.
Please note that the provided Docker image includes Rosetta which require a commercial license for non-academic use. For more details, please refer to: RosettaCommons
Running AlphaRed Using the RosettaCommons Docker Container
To run the application, use the alpha-red script, which executes all required steps sequentially. The script requires a docking partner string and a sequence as input. Additionally, you may specify the -jN flag to utilize multiple CPU cores for improved performance.
Example Usage:
mkdir ./alpha-red
docker run -it -v ./alpha-red:/alpha-red rosettacommons/alphared alpha-red -j32 A_B AACD:BBCCC
For a full list of available options, run: docker run -it rosettacommons/alphared alpha-red --help
Local Install
The installation instructions for each stage of the pipeline are detailed in its respective directory in pipeline folder.
To navigate, the folder hierarchy is as follows:
  • benchmark

    • difficult_targets
    • medium_targets
    • rigid_targets
  • pipeline

    • 1-structure_prediction
    • 2-prediction_analysis
    • 3-docking
  • utilities
Web Server
We are working to set up a web-server for structure prediction and docking. This service would be shortly available on ROSIE. Updates coming soon!
Bug reports
If you run into any problems while using AlphaRED, please create a Github issue with a description of the problem and the steps to reproduce it.
References
Please use the following references to cite our work and corresponding literature:
  1. Structure prediction
  1. Docking
About
AlphaFold-initiated replica exchange protein docking
Resources
Readme
MIT license
Activity
Custom properties
Stars
94 stars
Watchers
6 watching
Forks
19 forks
Report repository
Releases
Packages
Contributors
Languages