1. create conda env with python2.7
   conda create -y -n py27 python=2.7
2. install biopython on a python2.7 conda env
   python -m pip install biopython==1.76

Dssp installation via conda (conda install -c salilab dssp) leads to a lot of issues and the Notebook is apparently not working anymore. Install dssp via old approach from here locally (a python env is not necessary).
mkdssp --version #mkdssp version 3.1.4

1. Go inside the folder you want to git (after you have a repo on github)
2. Initialize the folder as a git repo: 'git init'
3. From repo to local : git pull [email protected]:Ch-rode/Chiara-LabBook.git
4. From local to repo:

git add .
git status
git commit -m '14/06/2023'
git remote add origin [email protected]:Ch-rode/Chiara-LabBook.git
git push -u origin master

Before doing this you should create an ssh key on your local laptop (see following links for instructions) and add it on your personal page on github: https://github.com/settings/keys

echo "# SS_AA_tokenizer" >> README.md
git init
git add README.md
git commit -m "first commit"
git branch -M main
git remote add origin https://github.com/ibmm-unibe-ch/SS_AA_tokenizer.git
git push -u origin main

git remote add origin https://github.com/ibmm-unibe-ch/SS_AA_tokenizer.git
git branch -M main
git push -u origin main

.a3m is an advanced fasta format that allows for inserts, but prody does not like that, the simplest fix is to reformat a3m to fasta with reformat.pl, which is really fast.
i.e. ./reformat.pl uniref90_hits.a3m test.fasta

1hl9 pdb
spectrum b, blue_red
bg white
cartoon putty
select prot, name CA
ray 4500, 4500
png /.png
ray [width,height [,renderer [,angle [,shift ]]]
set_color orangef=[255,165,0]
set_color greenf=[74,103,65 ]

https://pymolwiki.org/index.php/Spectrumbar

rsync -av --ignore-existing source-folder/* destination-folder/

scp -r [email protected]://storage/homefs/ciao ./

https://www.ncbi.nlm.nih.gov/books/NBK52640/

python svg_grid.py --rows 1 --cols 2  \
./img/30_clusters_size.svg \
./img/30barplot_avgsimilarities.svg \
> hamming_distance_clusters.png