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In this tutorial, we perform protein folding with PLEX.
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In this tutorial we perform protein folding with **plex**.
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There are multiple reasons we believe PLEX is a new standard for computational biology 🧫:
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1.With a simple python interface, running containerised tools with your data is only a few commands away
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2.The infrastructure of the compute network is fully open source - use the public network or work with us to set up your own node
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3.Every event on the compute network is tracked - no more results are lost in an interactive compute session. You can base your decisions and publications on fully reproducible results.
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4.We made adding new tools to the network as easy as possible - moving your favorite tool to PLEX is one JSON document away.
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There are multiple reasons we believe plex is a new standard for computational biology 🧫:
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1.with a simple python interface, running containerised tools with your data is only a few commands away
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2.the infrastructure of the compute network is fully open source - use the public network or work with us to set up your own node
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3.every event on the compute network is tracked - no more results are lost in an interactive compute session. You can base your decisions and publications on fully reproducible results.
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4.we made adding new tools to the network as easy as possible - moving your favorite tool to plex is one JSON document away.
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We'll walk through an example of how to use PLEX to predict a protein's 3D structure using [ColabFold](https://www.nature.com/articles/s41592-022-01488-1). We will use the sequence of the Streptavidin protein for this demo.
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In this tutorial, we'll walk through an example of how to use plex to predict a protein's 3D structure using [ColabFold](https://www.nature.com/articles/s41592-022-01488-1). We will use the sequence of the Streptavidin protein for this demo.
We will also walk through the process of minting a ProofOfScience NFT. These tokens represent on-chain, verifiable records of the compute job and its input/output data. This enables reproducible scientific results.
2023/08/08 18:51:17 failed to sufficiently increase receive buffer size (was: 208 kiB, wanted: 2048 kiB, got: 416 kiB). See https://github.com/quic-go/quic-go/wiki/UDP-Receive-Buffer-Size for details.
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After the job is complete, we can retrieve and view the results. The state of each object is written in a JSON object. Every file has a unique content-address.
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## Viewing the results
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After the job is complete, we can retrieve and view the results. The state of each object is written in a JSON object. Every file has a unique content address.
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```python
@@ -181,6 +196,21 @@ with open(completed_io_filepath, 'r') as f:
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}
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The output is a JSON file with information about the folded protein structures. This can be used for further analysis, visualization, and more.
The results can also be viewed using an IPFS gateway. Below, the state of the IO JSON is read using the ipfs.io gateway.
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**Note:** Depending on how long it takes for the results to propagate to the ipfs.io nodes, the data may not be available immediately. The results can also be viewed on IPFS Desktop or by accessing IPFS through the Brave browser (ipfs://completed_io_cid)
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```python
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print(f"View this result on IPFS: https://ipfs.io/ipfs/{completed_io_cid}")
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```
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View this result on IPFS: https://ipfs.io/ipfs/QmdnjMsUar6nTqGwgjCwN1Fyjaan4i3zyht9SE9L235YRm
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## Visualization and NFT minting
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For visualization and NFT minting steps, please visit the Colab notebook below.
Small molecule binding is a fundamental aspect of drug discovery, facilitating the interaction of potential drugs with target proteins. With PLEX, this intricate process is simplified and made efficient.
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In this tutorial we perform small molecule docking with **plex**.
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In the following tutorial, we illustrate how PLEX can be used to conduct small molecule binding studies to explore potential drug interactions with proteins. We demonstrate this with [Equibind](https://hannes-stark.com/assets/EquiBind.pdf).
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There are multiple reasons we believe plex is a new standard for computational biology 🧫:
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1. with a simple python interface, running containerised tools with your data is only a few commands away
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2. the infrastructure of the compute network is fully open source - use the public network or work with us to set up your own node
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3. every event on the compute network is tracked - no more results are lost in an interactive compute session. You can base your decisions and publications on fully reproducible results.
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4. we made adding new tools to the network as easy as possible - moving your favorite tool to PLEX is one JSON document away.
In the following tutorial, we illustrate how plex can be used to conduct smallmoleculebinding studies to explore potential drug interactions with proteins. We demonstrate this with [Equibind](https://hannes-stark.com/assets/EquiBind.pdf).
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## Install PLEX
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We will also walk through the process of minting a ProofOfScience NFT. These tokens represent on-chain, verifiable records of the compute job and its input/output data. This enables reproducible scientific results.
Then, create a directory where we can save our project files.
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Next, we need to load the data about the small molecule and the protein that we're studying. This data, which is available on IPFS, will be used to initialize an IO JSON. This JSON file will serve as the job instructions for our binding study.
## Dock the small molecule and protein using Equibind
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This code initiates the docking process. We'll need to run it to complete the operation.
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Now that we've prepared our job instructions, we're ready to dock the small molecule and protein using Equibind. With the IO JSON created and pinned to IPFS, we submit the job to the LabDAO Bacalhau cluster for computation.
2023/08/08 18:56:21 failed to sufficiently increase receive buffer size (was: 208 kiB, wanted: 2048 kiB, got: 416 kiB). See https://github.com/quic-go/quic-go/wiki/UDP-Receive-Buffer-Size for details.
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## Viewing the results
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The final step is to view our results. We read in the IO JSON file that contains the output from our job and print it. This data includes the best docked small molecule and the protein used, each with their own IPFS CIDs.
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After the job is complete, we can retrieve and view the results. The state of each object is written in a JSON object. Every file has a unique content-address.
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```python
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}
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This output provides us with key information about the small molecule-protein interaction. The "best_docked_small_molecule" represents the most likely interaction between the protein and the small molecule, which can inform subsequent analysis and experiments.
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The results can also be viewed using an IPFS gateway. Below, the state of the IO JSON is read using the ipfs.io gateway.
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**Note:** Depending on how long it takes for the results to propagate to the ipfs.io nodes, the data may not be available immediately. The results can also be viewed on IPFS Desktop or by accessing IPFS through the Brave browser (ipfs://completed_io_cid)
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```python
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print(f"View this result on IPFS: https://ipfs.io/ipfs/{completed_io_cid}")
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```
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View this result on IPFS: https://ipfs.io/ipfs/QmVG4mT2kkPSb6wzT5QxYZndB5VbKLU8nH2dErZW2zxae6
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## Visualization and NFT minting
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For visualization and NFT minting steps, please visit the Colab notebook below.
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