merging current docs to main (#571)

Co-authored-by: thetechnocrat-dev <josh.mcmenemy@openzyme.bio>

docs/docs/tutorials/protein-folding-nft-minting.md

@@ -0,0 +1,11 @@
+---
+title: Minting ProofOfScience tokens
+sidebar_label: ProofOfScience NFTs
+sidebar_position: 3
+---
+
+import OpenInColab from '../../src/components/OpenInColab.js';
+
+The following interactive notebook demo has been prepared to demonstrate minting ProofOfScience tokens using plex. It is an extension of the Protein Folding tutorial with a few additional modules appended at the end. To use the notebook, please visit the Google Colab link below.
+
+<OpenInColab link="https://colab.research.google.com/drive/1312M2VOx_YpTFgy60ZYChgR9h3a7aorr?usp=sharing"></OpenInColab>

docs/docs/tutorials/protein-folding.md

@@ -1,18 +1,24 @@
 ---
 title: Folding proteins with ColabFold
 sidebar_label: Protein Folding
-sidebar_position: 3
+sidebar_position: 2
 ---
 
 import OpenInColab from '../../src/components/OpenInColab.js';
 
-<OpenInColab link="https://colab.research.google.com/drive/1AmxLoU5W2vYoi9KDw9IDoj3k4ijSCqoh?usp=sharing"></OpenInColab>
+<OpenInColab link="https://colab.research.google.com/drive/1AfnJ50Ei4_9KXdKgexwdmEiwwDDXsfWJ?usp=sharing"></OpenInColab>
 
 ## Protein folding in silico
 
-Protein folding is a crucial process in drug discovery. It helps research understand the 3D structure of experimental proteins and identify potential drug targets. With PLEX, predicting a protein's 3D structure from the amino acid sequence is streamlined and efficient.
+In this tutorial, we perform protein folding with PLEX.
 
-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).
+There are multiple reasons we believe PLEX is a new standard for computational biology 🧫:
+1. With a simple python interface, running containerised tools with your data is only a few commands away
+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
+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.
+4. We made adding new tools to the network as easy as possible - moving your favorite tool to PLEX is one JSON document away.
+
+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.
 
 ![img](../../static/img/protein-folding-graphic.png)
 
@@ -47,29 +53,19 @@ We'll download a `.fasta` file containing the sequence of the protein we want to
 
 
 ```python
-!pip install requests
-
-import requests
-
-def download_file(url, directory, filename=None):
-    local_filename = filename if filename else url.split('/')[-1]
-    with requests.get(url, stream=True) as r:
-        r.raise_for_status()
-        with open(os.path.join(directory, local_filename), 'wb') as f:
-            for chunk in r.iter_content(chunk_size=8192):
-                f.write(chunk)
-    return local_filename
+!wget https://rest.uniprot.org/uniprotkb/P22629.fasta -O {dir_path}/P22629.fasta # Streptavidin
+```
 
-url = 'https://rest.uniprot.org/uniprotkb/P22629.fasta' # Streptavidin
+    --2023-08-01 21:39:21--  https://rest.uniprot.org/uniprotkb/P22629.fasta
+    Resolving rest.uniprot.org (rest.uniprot.org)... 193.62.193.81
+    Connecting to rest.uniprot.org (rest.uniprot.org)|193.62.193.81|:443... connected.
+    HTTP request sent, awaiting response... 200 OK
+    Length: 264 [text/plain]
+    Saving to: ‘/content/project/P22629.fasta’
 
-fasta_filepath = download_file(url, dir_path)
-```
+    /content/project/P2 100%[===================>]     264  --.-KB/s    in 0s      
 
-    Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (2.27.1)
-    Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests) (1.26.16)
-    Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests) (2023.5.7)
-    Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests) (2.0.12)
-    Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests) (3.4)
+    2023-08-01 21:39:21 (144 MB/s) - ‘/content/project/P22629.fasta’ saved [264/264]
 
 
 ## Fold the protein
@@ -79,16 +75,14 @@ With the sequence downloaded, we can now use ColabFold to fold the protein.
 ```python
 from plex import CoreTools, plex_create
 
-sequences = [fasta_filepath]
-
 initial_io_cid = plex_create(CoreTools.COLABFOLD_MINI.value, dir_path)
 ```
 
-    Plex version (v0.8.3) up to date.
-    Temporary directory created: /tmp/2604ada3-04ec-4d58-9ecc-1e65134c15674117000244
+    Plex version (v0.8.4) up to date.
+    Temporary directory created: /tmp/9ed8c638-c1b0-43da-bf92-7f054517d45c2889128719
     Reading tool config:  QmcRH74qfqDBJFku3mEDGxkAf6CSpaHTpdbe1pMkHnbcZD
     Creating IO entries from input directory:  /content/project
-    Initialized IO file at:  /tmp/2604ada3-04ec-4d58-9ecc-1e65134c15674117000244/io.json
+    Initialized IO file at:  /tmp/9ed8c638-c1b0-43da-bf92-7f054517d45c2889128719/io.json
     Initial IO JSON file CID:  QmUhysTE4aLZNw2ePRMCxHWko868xmQoXnGP25fKM1aofb
 
 This code initiates the folding process. We'll need to run it to complete the operation.
@@ -99,29 +93,28 @@ from plex import plex_run
 completed_io_cid, completed_io_filepath = plex_run(initial_io_cid, dir_path)
 ```
 
-    Plex version (v0.8.3) up to date.
-    Created working directory:  /content/project/03ef6ae4-b2ff-424b-894c-05f8fbe48888
-    Initialized IO file at:  /content/project/03ef6ae4-b2ff-424b-894c-05f8fbe48888/io.json
+    Plex version (v0.8.4) up to date.
+    Created working directory:  /content/project/2ef79c16-6f59-4e44-aea7-c39db85280cb
+    Initialized IO file at:  /content/project/2ef79c16-6f59-4e44-aea7-c39db85280cb/io.json
     Processing IO Entries
     Starting to process IO entry 0 
     Job running...
-    Bacalhau job id: ac42f8de-1fea-4e09-9644-75c940bdbd5c 
-    
+    Bacalhau job id: 476d232b-e1c6-42d6-b1c0-2f4d237244b1 
+
     Computing default go-libp2p Resource Manager limits based on:
         - 'Swarm.ResourceMgr.MaxMemory': "6.8 GB"
         - 'Swarm.ResourceMgr.MaxFileDescriptors': 524288
-    
+
     Applying any user-supplied overrides on top.
     Run 'ipfs swarm limit all' to see the resulting limits.
-    
+
     Success processing IO entry 0 
-    Finished processing, results written to /content/project/03ef6ae4-b2ff-424b-894c-05f8fbe48888/io.json
+    Finished processing, results written to /content/project/2ef79c16-6f59-4e44-aea7-c39db85280cb/io.json
     Completed IO JSON CID: QmdnjMsUar6nTqGwgjCwN1Fyjaan4i3zyht9SE9L235YRm
-    2023/07/20 04:50:10 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.
 
 ## Viewing the results
 
-After the job is complete, we can retrieve and view the results.
+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.
 
 
 ```python
@@ -190,4 +183,4 @@ with open(completed_io_filepath, 'r') as f:
 
 The output is a JSON file with information about the folded protein structures. This can be used for further analysis, visualization, and more.
 
-<OpenInColab link="https://colab.research.google.com/drive/1AmxLoU5W2vYoi9KDw9IDoj3k4ijSCqoh?usp=sharing"></OpenInColab>
+<OpenInColab link="https://colab.research.google.com/drive/1AfnJ50Ei4_9KXdKgexwdmEiwwDDXsfWJ?usp=sharing"></OpenInColab>