diff --git a/README.md b/README.md
index 62a7752645b29c719837aa0699034f425a370134..a1964f558dc5046e1ea54eb69f8ca860ec3ee0c8 100644
--- a/README.md
+++ b/README.md
@@ -1,32 +1,63 @@
 # spongfold
 
-This repository documents the idea of Fabi and Niko to use predicted protein structures and structure similarity to annotate proteomes.
+This repository documents the idea of Fabi and Niko to use predicted protein structures and
+structure similarity to annotate proteomes.
 
 ## Background
-[AlphaFold](https://www.nature.com/articles/s41586-021-03819-2) changed how we think about protein structures. By leveraging deep learning, multiple sequence alignments, and the ever-expanding library of solved protein structures, AlphaFold is able to predict three-dimensional protein structures at resolutions that rival solved crystal structures, and has immediately found use in large parts of biological research.
+[AlphaFold](https://www.nature.com/articles/s41586-021-03819-2) changed how we think about protein
+structures. By leveraging deep learning, multiple sequence alignments, and the ever-expanding
+library of solved protein structures, AlphaFold is able to predict three-dimensional protein
+structures at resolutions that rival solved crystal structures, and has immediately found use in
+large parts of biological research.
 
 One of the older dogmata in molecular biology concerns proteins, and holds that
 
 > Sequence defines structure defines function.
 
-In the past this was taken to mean that sequence similarity, above a certain threshold, correlated with sequence homology or even orthology. Sufficient sequence similarity was then inferred to mean high structural similarity (this was mostly proven by the success of homology modelling in CASP) as well as functional equivalence. In fact, "bidirectional best BLAST hits" is the _de facto_ baseline for functional annotation.
+In the past this was taken to mean that sequence similarity, above a certain threshold, correlated
+with sequence homology or even orthology. Sufficient sequence similarity was then inferred to mean
+high structural similarity (this was mostly proven by the success of homology modelling in CASP) as
+well as functional equivalence. In fact, "bidirectional best BLAST hits" is the _de facto_ baseline
+for functional annotation.
 
-The question of the threshold turns out to be an important one. Burkhardt Rost famously outlined it as the ["twilight zone"](https://pubmed.ncbi.nlm.nih.gov/10195279/) of protein similarity; briefly, while we can confidently assert structural similarity (at least to the fold level) for sequences with >30% sequence identity, this deteriorates impressively fast at 25% sequence identity. In the years since this pronouncement, sequence search algorithms with increased sensitivity exploited the mountains of new sequencing data to dive into the twilight zone and detect remote homology; however, at low sequence identity, the sequence information alone is still not enough to guarantee homology.
+The question of the threshold turns out to be an important one. Burkhardt Rost famously outlined it
+as the ["twilight zone"](https://pubmed.ncbi.nlm.nih.gov/10195279/) of protein similarity; briefly,
+while we can confidently assert structural similarity (at least to the fold level) for sequences
+with >30% sequence identity, this deteriorates impressively fast at 25% sequence identity. In the
+years since this pronouncement, sequence search algorithms with increased sensitivity exploited the
+mountains of new sequencing data to dive into the twilight zone and detect remote homology; however,
+at low sequence identity, the sequence information alone is still not enough to guarantee homology.
 
-However, structure is more conserved than sequence. In theory, predicted structures can be compared against known structures that are otherwise annotated, allowing for the transfer of functional annotations (albeit less specific than sequence-based ones, since we will be detecting very remote homology at best). This is of particular interest for non-model organisms, especially ones outside the well-studied taxonomic groups (e.g. vertebrates or ecdysozoans).
+However, structure is more conserved than sequence. In theory, predicted structures can be compared
+against known structures that are otherwise annotated, allowing for the transfer of functional
+annotations (albeit less specific than sequence-based ones, since we will be detecting very remote
+homology at best). This is of particular interest for non-model organisms, especially ones outside
+the well-studied taxonomic groups (e.g. vertebrates or ecdysozoans).
 
 ## Follow-up ideas
-Having a phylome, scRNAseq/cell type annotation, functional proteomic data and the prediction of protein structures for a non-bilaterian Metazoan presents a unique combination that would allow to ask many fundamental questions. Potential follow-up analysis include:
+Having a phylome, scRNAseq/cell type annotation, functional proteomic data and the prediction of
+protein structures for a non-bilaterian Metazoan presents a unique combination that would allow to
+ask many fundamental questions. Potential follow-up analysis include:
 
-- Correltation between AF prediction accuracy (overall, domain specific, etc.) and sequence identity/similarity or bitscore of best FoldSeek hit. I.e.: "Does higher sequence identity mean better prediction accuracy?"
-- Relationship between identified homologs through sequence search (orthofinder, eggnog-mapper, blast, phylome) and best hits in FoldSeek for single sponge proteins. I.e.: "Do the best AF hits also include proteins identified as homologs in the phylome? Is there a sequence identity threshold to that?"
-- Is there biological meaning to best FoldSeek hits of un-annotated, highly expressed genes in the scRNAseq dataset or differentially regulated hits in the functional proteomic datasets?. I.e.: "Can we transfer function / functionally annotate previously un-annotated hits in scRNAseq and protomics data and most importantly, do these hits make sense when taking prior knowledge into account?"
+- Correlation between AF prediction accuracy (overall, domain specific, etc.) and sequence
+  identity/similarity or bitscore of best FoldSeek hit. I.e.: "Does higher sequence identity mean
+  better prediction accuracy?"
+- Relationship between identified homologs through sequence search (orthofinder, eggnog-mapper,
+  blast, phylome) and best hits in FoldSeek for single sponge proteins. I.e.: "Do the best AF hits
+  also include proteins identified as homologs in the phylome? Is there a sequence identity
+  threshold to that?"
+- Is there biological meaning to best FoldSeek hits of un-annotated, highly expressed genes in the
+  scRNAseq dataset or differentially regulated hits in the functional proteomic datasets?. I.e.:
+  "Can we transfer function / functionally annotate previously un-annotated hits in scRNAseq and
+  protomics data and most importantly, do these hits make sense when taking prior knowledge into
+  account?"
 
 ## Usage
 (eventually a tutorial on what order to use the scripts in, if we don't have a master script).
 
 ## Roadmap
-We will write this in as general a way as possible so that it can be reused by others/for other species. Platynereis is an obvious candidate after we proof _Spongilla_.
+We will write this in as general a way as possible so that it can be reused by others/for other
+species. Platynereis is an obvious candidate after we proof _Spongilla_.
 
 ## Authors and contributions
 
diff --git a/scripts/README.md b/scripts/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..c388a3ccc885ce35faff215427e9dbecf0cb88a4
--- /dev/null
+++ b/scripts/README.md
@@ -0,0 +1,26 @@
+# scripts usage
+
+Here we list the scripts in their logical use order:
+
+### Get sequence databases and obtain multiple sequence alignments for _Spongilla_.
+
+* `databases.sh`: download and build indices for the sequence databases UniRef30 and ColabFold
+  EnvDB. Modified from ColabFold.
+* `databases_pdb.sh`: download (sequence) PDB and build index
+* `align.sh`: build multiple sequence alignments for the _Spongilla_ proteome. A wrapper around
+  `colabfold_search`.
+
+### Predict structures for _Spongilla_
+
+* `fasta-splitter.pl`: written by Kirill Kryukov. A utility to partition FASTA files into pieces.
+  Used with `--n-parts 32` to split the _Spongilla_ proteome in batches.
+* `predict_structures.sh`: a wrapper around `colabfold_batch` to submit jobs to the EMBL cluster.
+* `submit_colab.sh`: a simple for loop to handle all 32 batches.
+
+### Use FoldSeek to search against available structures
+
+* `fs_query.sh`: build structural database for _Spongilla_ predicted structures.
+* `fs_afdb.sh, fs_pdb.sh, fs_sp.sh`: download the precomputed FoldSeek databases for AFDB, PDB, and
+  SwissProt, respectively. Split in three so we could run them in parallel.
+* `fs_search_afdb.sh, fs_search_pdb.sh, fs_search_swissprot.sh`: search with the _Spongilla_
+  structure database against the three target databases, AFDB, PDB, and SwissProt. 
\ No newline at end of file
diff --git a/scripts/batch_predict.sh b/scripts/batch_predict.sh
deleted file mode 100755
index 63d6a7654f3b85ce1848c0f676c8bd68d792b687..0000000000000000000000000000000000000000
--- a/scripts/batch_predict.sh
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/bin/bash -ex
-#SBATCH -t 03-00
-#SBATCH -o /g/arendt/npapadop/cluster/alphafold-%j.out
-#SBATCH -e /g/arendt/npapadop/cluster/alphafold-%j.err
-#SBATCH -p gpu
-#SBATCH -C gpu=A100
-#SBATCH --gres=gpu:1
-#SBATCH -N 1
-#SBATCH --ntasks=32
-#SBATCH --mem=512000
-
-module load Anaconda3
-module load GCC
-module load bzip2
-module load CUDA
-source ~/.bash_profile
-conda activate /g/arendt/npapadop/repos/condas/maf
-
-BASE="/scratch/npapadop/"
-BATCH=$1
-
-cd "${BASE}"
-
-echo "$BATCH"
-
-colabfold_batch msas/"$BATCH"/ spongilla_structures/ --stop-at-score 85
-
-module unload
-
diff --git a/scripts/create_index.sh b/scripts/create_index.sh
deleted file mode 100755
index 166150a1a60b2be3036bb8328b3b34047dd2138f..0000000000000000000000000000000000000000
--- a/scripts/create_index.sh
+++ /dev/null
@@ -1,18 +0,0 @@
-#!/bin/bash -ex
-#SBATCH -J create_index
-#SBATCH -t 08:00:00
-#SBATCH -c 64
-#SBATCH --mem=200G
-#SBATCH -o /g/arendt/npapadop/cluster/create_index.out
-#SBATCH -e /g/arendt/npapadop/cluster/create_index.err
-
-cd /scratch/npapadop/database
-
-module load MMseqs2
-
-mmseqs tsv2exprofiledb "uniref30_2103" "uniref30_2103_db"
-mmseqs createindex "uniref30_2103_db" tmp1 --remove-tmp-files 1 --threads 64
-mmseqs databases PDB PDB tmp
-mmseqs createindex PDB tmp3 --remove-tmp-files 1 --threads 64
-
-module unload MMseqs2
diff --git a/scripts/gpu_test.sh b/scripts/gpu_test.sh
deleted file mode 100755
index c1f7b356875124b430c7e8f9257fdaba0049432d..0000000000000000000000000000000000000000
--- a/scripts/gpu_test.sh
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/bin/bash -ex
-#SBATCH -J predict
-#SBATCH -t 30:00
-#SBATCH -o /g/arendt/npapadop/cluster/2080alphafold.out
-#SBATCH -e /g/arendt/npapadop/cluster/2080alphafold.err
-#SBATCH -p gpu
-#SBATCH -C gpu=2080Ti
-#SBATCH --gres=gpu:1
-#SBATCH -N 1
-#SBATCH --ntasks=32
-#SBATCH --mem=256000
-
-module load Anaconda3
-module load GCC
-module load bzip2
-module load CUDA
-source ~/.bash_profile
-conda activate /g/arendt/npapadop/repos/condas/maf
-
-BASE="/scratch/npapadop/results/"
-
-cd "${BASE}"
-colabfold_batch test/ predictions/ --stop-at-score 85
-
-module unload
\ No newline at end of file
diff --git a/scripts/mkdb.sh b/scripts/mkdb.sh
deleted file mode 100755
index 568d741d7374ac503348ec12e84e911c1a95004a..0000000000000000000000000000000000000000
--- a/scripts/mkdb.sh
+++ /dev/null
@@ -1,20 +0,0 @@
-#!/bin/bash -ex
-#SBATCH -J mkdb
-#SBATCH -t 10:00
-#SBATCH -c 1
-#SBATCH --mem=1G
-#SBATCH -o /g/arendt/npapadop/cluster/mkdb.out
-#SBATCH -e /g/arendt/npapadop/cluster/mkdb.err
-
-INPUT_PEP="$1"
-OUTPUT_DIR="$2"
-NAME="$3"
-
-mkdir ${OUTPUT_DIR}
-cd ${OUTPUT_DIR}
-
-module load MMseqs2
-
-mmseqs createdb ${INPUT_PEP} ${NAME}
-
-module unload MMseqs2
\ No newline at end of file
diff --git a/scripts/predict_protein.sh b/scripts/predict_protein.sh
deleted file mode 100755
index 123c1eebef8db04848a06334b6bfa2236ef19961..0000000000000000000000000000000000000000
--- a/scripts/predict_protein.sh
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/bin/bash
-
-#SBATCH --time=2-00:00:00
-#SBATCH -e AF_%x_err.txt
-#SBATCH -o AF_%x_out.txt
-#SBATCH --qos=normal
-#SBATCH -p gpu
-#SBATCH -N 1
-#SBATCH --ntasks=32
-#SBATCH --mem=512000
-
-module load AlphaFold
-module load GCC/10.2.0
-module load tqdm
-module load matplotlib
-
-SOFTWARE_DIR=<your dir>
-export PYTHONPATH=$SOFTWARE_DIR/ColabFold:$PYTHONPATH
-
-# If you use --cpus-per-task=X and --ntasks=1 your script should contain:
-# export ALPHAFOLD_JACKHMMER_N_CPU=$SLURM_CPUS_PER_TASK
-# export ALPHAFOLD_HHBLITS_N_CPU=$SLURM_CPUS_PER_TASK
-
-# TF_FORCE_UNIFIED_MEMORY='1' XLA_PYTHON_CLIENT_MEM_FRACTION='4.0' are optional but may be necessary for bigger sequences.
-# If you read this after 2050-01-01, probably you want to adjust the date
-# Add "--model-type AlphaFold2-ptm" option to run the old ColabFold for complexes,
-# equivalent to the original https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb
-TF_FORCE_UNIFIED_MEMORY='1' XLA_PYTHON_CLIENT_MEM_FRACTION='4.0' time $SOFTWARE_DIR/ColabFold/bin/colabfold_batch dimer.fasta ./ --templates
\ No newline at end of file
diff --git a/scripts/predict_structure.sh b/scripts/predict_structure.sh
deleted file mode 100755
index 193de402e3f2c55f1364cf1211463af96662b6a9..0000000000000000000000000000000000000000
--- a/scripts/predict_structure.sh
+++ /dev/null
@@ -1,24 +0,0 @@
-#!/bin/bash -ex
-#SBATCH -J test_colab
-#SBATCH -t 30:00
-#SBATCH -o /g/arendt/npapadop/cluster/alphafold_test.out
-#SBATCH -e /g/arendt/npapadop/cluster/alphafold_test.err
-#SBATCH -p gpu
-#SBATCH -C gpu=A100
-#SBATCH --gres=gpu:1
-#SBATCH -N 1
-#SBATCH --ntasks=32
-#SBATCH --mem=512000
-
-module load Anaconda3
-module load GCC
-module load bzip2
-module load CUDA
-source ~/.bash_profile
-conda activate /g/arendt/npapadop/repos/condas/colabfold
-
-BASE="/scratch/npapadop/"
-
-colabfold_batch "$BASE"/msas/test/ "$BASE"/test_res/ --stop-at-score 85
-
-module unload
diff --git a/scripts/milot.sh b/scripts/predict_structures.sh
old mode 100644
new mode 100755
similarity index 100%
rename from scripts/milot.sh
rename to scripts/predict_structures.sh
diff --git a/scripts/spongilla_create_index.sh b/scripts/spongilla_create_index.sh
deleted file mode 100755
index e0e60615c3880eac942df4dd2bc3d204024251ab..0000000000000000000000000000000000000000
--- a/scripts/spongilla_create_index.sh
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/bin/bash -ex
-
-SCRIPT="/g/arendt/npapadop/repos/spongfold/create_index.sh"
-OUTPUT_DIR="/scratch/npapadop/Spongilla_lacustris_70AAcutoffTransDecoder/"
-DB_NAME="spongilla"
-
-sbatch ${SCRIPT} ${OUTPUT_DIR} ${DB_NAME}
\ No newline at end of file
diff --git a/scripts/spongilla_mkdb.sh b/scripts/spongilla_mkdb.sh
deleted file mode 100755
index f441ea3aa93a2cc5f38cba93f874cdab2a8ceb07..0000000000000000000000000000000000000000
--- a/scripts/spongilla_mkdb.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-#!/bin/bash -ex
-
-SCRIPT="/g/arendt/npapadop/repos/spongfold/mkdb.sh"
-INPUT="/g/arendt/data/spongilla_singlecell_dataset/spongilla_lacustris_Trinity.fasta.transdecoder_70AA_mediumheader.pep"
-OUTPUT_DIR="/scratch/npapadop/Spongilla_lacustris_70AAcutoffTransDecoder/"
-DB_NAME="spongilla"
-
-sbatch ${SCRIPT} ${INPUT} ${OUTPUT_DIR} ${DB_NAME}
\ No newline at end of file
diff --git a/scripts/spongilla_pdb70.sh b/scripts/spongilla_pdb70.sh
deleted file mode 100755
index 754ec40fdd3d2d3bdfa31ed6dea7ec43bba2ee0d..0000000000000000000000000000000000000000
--- a/scripts/spongilla_pdb70.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash -ex
-
-SCRIPT="/g/arendt/npapadop/repos/spongfold/align.sh"
-QUERYDB="/scratch/npapadop/Spongilla_lacustris_70AAcutoffTransDecoder/spongilla"
-TARGETDB="/scratch/npapadop/PDB70/PDB70"
-RESULTDB="/scratch/npapadop/spongilla_pdb70/result"
-TMP="/scratch/npapadop/spongilla_pdb70/tmp"
-
-sbatch ${SCRIPT} ${QUERYDB} ${TARGETDB} ${RESULTDB} ${TMP}
\ No newline at end of file
diff --git a/scripts/spongilla_uniref100.sh b/scripts/spongilla_uniref100.sh
deleted file mode 100755
index b18cd4ad20808674288cd9fd57d3f90993a42edb..0000000000000000000000000000000000000000
--- a/scripts/spongilla_uniref100.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/bin/bash -ex
-
-SCRIPT="/g/arendt/npapadop/repos/spongfold/align.sh"
-QUERY="/g/arendt/data/spongilla_singlecell_dataset/spongilla_lacustris_Trinity.fasta.transdecoder_70AA_mediumheader.pep"
-DBBASE="/scratch/npapadop"
-BASE="/scratch/npapadop"
-DB1="Uniref100/Uniref100"
-FILTER="1"
-
-sbatch ${SCRIPT} ${QUERY} ${DBBASE} ${BASE} ${DB1} ${FILTER}
\ No newline at end of file