kurtshowmaker/effectorome_h_schachtii

Intro

Protein Folding

Programs used

ColabFold v1.5.2 (AlphaFold v2.3.1)

FoldSeek (8-ef4e960)

ESMfold v1.0.3

Python 3.11

SignalP - 4.1

OS

CentOS Linux 7

Prediction pipeline

SignalP

signalp -fasta /path/to/genome_or_effector.fasta -mature

ColabFold

1: MMSeqs2 alignment to ColabFold database

colabfold_search genome_or_effector.fasata /path/to/ColabFold/DataBase /path/to/out/dir/msas -s 6 --threads 64 --db-load-mode 1

2: Protein fold prediction

colabfold_batch --pair-mode 'unpaired_paired' \
--num-recycle 3 \
--num-models 3 \
--stop-at-score 100 \
--zip \
path/to/msas/1 \
path/to/out

pLDDT score and pTM analyses

Adjust settings like work directory and score strictness in ProteinFolding/AnalyseMTEandpLDDT.py. Then run:

python3 ProteinFolding/AnalyseMTEandpLDDT.py

ESMFold

Use: https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/ESMFold.ipynb with default settings

Foldseek

1: Make database

foldseek createdb /path/to/pdb/files /path/to/db/out/folder

2: all vs all search

db=/path/to/db/out/folder
cd /path/to/desired/result/folder
foldseek easy-search $db $db aln tmp --format-output query,target,fident,alnlen,mismatch,gapopen,qstart,qend,tstart,tend,evalue,bits,lddt,prob,pident,alntmscore,qtmscore,ttmscore,u

2-Class network creation and analysis

Programs used

R 4.2.2

Gephi 0.10

OS

Windows 11

Workflow

Network creation

Requires 2 files, containing the expression data across all time points of the genes to be considered in both gene classes

Load these files into 2_class_network_creator.R, then run 2_class_gexf_creator on the output

This will create a gexf file containing a visualisation of the network that can be opened in Gephi. To create the visualisations seen in the paper, the layout applied was first Fruchterman Reingold with an area of 100,000, followed by Network Splitter 3d (https://gephi.org/plugins/#/plugin/network-splitter-3d).

For the effector-TF network, [z] was set equal to the degree of the node for TFs, and to 0 for effectors. The paramaters applied were then:

Z-Maximum Level: 50 (equal to the largest [z] in the network)

Z-Distance Factor: 10

Z-Scale: 100

Alfa: 80

For all effector-plant network visualisations, [z] was set to 80 for all effectors, and to 0 for all plant genes. The parameters applied were then:

Z-Maximum Level: 80

Z-Distance Factor: 10

Z-Scale: 100

Alfa: 80

Adding GO Slim attributes

The list of GO terms and GO slim terms assosciated with plant genes in the network was downloaded from TAIR, and the subset of genes with assosciated terms of interest identified by GO_Slim_attribute_applier_1.R. Presence or absence in this subset was then applied to the data as a binary attribute by GO_Slim_attribute_applier_2.R.

Analysis of stress or defence genes

Bootstrapping to analyse an over or underabundance of edges between effector superclusters and genes involved in immunity or defense was done using Attribute_analysis.R.