Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

updated haddock3 tutorials #669

Merged
merged 4 commits into from
Jul 22, 2024
Merged

updated haddock3 tutorials #669

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
ea5a721
updated haddock3 tutorials
mgiulini Feb 29, 2024
bf60d8b
Merge branch 'master' into change_parameter_names
mgiulini Apr 24, 2024
f658a6f
implemented suggestions
mgiulini Apr 24, 2024
3bee078
typo
mgiulini Apr 24, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
12 changes: 2 additions & 10 deletions education/HADDOCK3/HADDOCK3-antibody-antigen-bioexcel2023/index.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -612,7 +612,7 @@ The difference between `active` and `passive` residues in HADDOCK is:
*__passive__* residues should be at the interface, if they are not no energetic penalty is paid.

In general it is better to be too generous rather than too strict in the definition of passive residues. An important aspect is to filter both the active (the residues identified from
your mapping experiment) and passive residues by their solvent accessibility. This is done automatically when using the `haddock3-restraints passive_from_active` command: residues with less that 15% relative solvent accessibility (same cutoff as the default in the HADDOCK server) are discared.
your mapping experiment) and passive residues by their solvent accessibility. This is done automatically when using the `haddock3-restraints passive_from_active` command: residues with less that 15% relative solvent accessibility (same cutoff as the default in the HADDOCK server) are discarded.
This is however not a hard limit and you might consider including even more buried residues if some
important chemical group seems solvent accessible from a visual inspection.

Expand Down Expand Up @@ -857,10 +857,6 @@ ncores=50
# Self contained rundir (to avoid problems with long filename paths)
self_contained = true

# Post-processing to generate statistics and plots
postprocess = true
clean = true

molecules = [
"pdbs/4G6K_clean.pdb",
"pdbs/4I1B_clean.pdb"
Expand Down Expand Up @@ -1717,7 +1713,7 @@ All three antibody structures used in input give good to reasonable results. The

<hr>
<hr>
## BONUS 3: Ensemble-docking using a combination of exprimental and AI-predicted antibody structures
## BONUS 3: Ensemble-docking using a combination of experimental and AI-predicted antibody structures


Instead of running haddock3 using a specific input structure of the antibody we can also use an ensemble of all available models.
Expand Down Expand Up @@ -1748,10 +1744,6 @@ ncores=50
# Self contained rundir (to avoid problems with long filename paths)
self_contained = true

# Post-processing to generate statistics and plots
postprocess = true
clean = true

molecules = [
"pdbs/4G6K-ensemble.pdb",
"pdbs/4I1B_clean.pdb"
Expand Down
69 changes: 26 additions & 43 deletions education/HADDOCK3/HADDOCK3-antibody-antigen/index.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -261,10 +261,13 @@ all categories and modules. Below is a summary of the available modules:
* **Analysis modules**
* `caprieval`: *Calculates CAPRI metrics (i-RMSD, l-RMSD, Fnat, DockQ) with respect to the top scoring model or reference structure if provided.*
* `clustfcc`: *Clusters models based on the fraction of common contacts (FCC)*
* `clustrmsd`: *Clusters models based on pairwise RMSD matrix calculated with the `rmsdmatrix` module.*
* `rmsdmatrix`: *Calculates the pairwise RMSD matrix between all the models generated in the previous step.*
* `ilrmsdmatrix`: *Calculates the pairwise interface-ligand RMSD matrix between all the models generated in the previous step.*
* `clustrmsd`: *Clusters models based on pairwise RMSD matrix calculated with the `rmsdmatrix`/`ilrmsdmatrix` module.*
* `seletop`: *Selects the top N models from the previous step.*
* `seletopclusts`: *Selects top N clusters from the previous step.*
* `contactmap`: *Generates a contact map for the models generated in the previous step.*
* `alascan`: *Performs an alanine scanning on the models generated in the previous step.*

The HADDOCK3 workflows are defined in simple configuration text files, similar to the TOML format but with extra features.
Contrarily to HADDOCK2.X which follows a rigid (yet highly parameterisable)
Expand Down Expand Up @@ -517,12 +520,10 @@ If you want to generate the same file, first create an empty line and then use t
awk \'{if (NF==13 && ($7>15 || $9>15)) printf \"\%d \",$3; if (NF==14 && ($8>15 || $10>15)) printf \"\%d \",$4}\' 4I1B_clean.rsa \>\> antigen-surface.pass<br>
</a>


**_Note_**: If the command line version of freesasa is not available, provided the freesasa python libraries have been installed
(can simply be done with: `pip install freesasa`), the same can be done with the _calc-accessibility.py_ script provided in the `scripts` directory:
The same can be achieved using the `haddock3-restraints` command line tool:

<a class="prompt prompt-cmd">
python ./scripts/calc-accessibility.py \-\-cutoff 0.15 pdbs/4I1B_clean.pdb
haddock3-restraints calc_accessibility --cutoff 0.15 pdbs/4I1B_clean.pdb
</a>

The simple output directly reports the list of residues:
Expand Down Expand Up @@ -629,11 +630,10 @@ binding site in the final models, while the residues defined as `active` (typica
site residues) will. When using the HADDOCK server, `passive` residues will be automatically defined. Here since we are
using a local version, we need to define those manually.

This can easily be done using a script from our [haddock-tools][haddock-tools] repository, which is also provided for convenience
in the `scripts` directly of the archive you downloaded for this tutorial:
This can easily be done using the following HADDOCK3 command line interface:

<a class="prompt prompt-cmd">
python ./scripts/passive_from_active.py 4I1B_clean.pdb 72,73,74,75,81,83,84,89,90,92,94,96,97,98,115,116,117
haddock3-restraints passive_from_active 4I1B_clean.pdb 72,73,74,75,81,83,84,89,90,92,94,96,97,98,115,116,117
</a>

The NMR-identified residues and their surface neighbors generated with the above command can be used to define ambiguous interactions restraints, either using the NMR identified residues as active in HADDOCK, or combining those with the surface neighbors and use this combination as passive only.
Expand Down Expand Up @@ -661,7 +661,7 @@ For this you can either make use of our online [GenTBL][gentbl] web service, ent
list of active and passive residues for each molecule, and saving the resulting
restraint list to a text file, or use the relevant `haddock-tools` script.

To use our `haddock-tools` `active-passive-to-ambig.py` script you need to
To use our `haddock3-restraints` `active_passive_to_ambig` script you need to
create for each molecule a file containing two lines:

* The first line corresponds to the list of active residues (numbers separated by spaces)
Expand All @@ -683,7 +683,7 @@ Using those two files, we can generate the CNS-formatted AIR restraint files
with the following command:

<a class="prompt prompt-cmd">
python ./scripts/active-passive-to-ambig.py ./restraints/antibody-paratope.act-pass ./restraints/antigen-surface.pass > ambig-paratope-surface.tbl
haddock3-restraints active_passive_to_ambig ./restraints/antibody-paratope.act-pass ./restraints/antigen-surface.pass > ambig-paratope-surface.tbl
</a>

This generates a file called `ambig-paratope-surface.tbl` that contains the AIR
Expand All @@ -697,12 +697,11 @@ distance combinations between an active residue and all the active+passive on
the other molecule: SUM[1/r^6]^(-1/6).

If you modify manually this file, it is possible to quickly check if the format is valid.
To do so, you can find in our [haddock-tools][haddock-tools] repository a folder named
`haddock_tbl_validation` that contains a script called `validate_tbl.py` (also provided here in the `scripts` directory).
To do so, you can use the `haddock3-restraints validate_tbl` utility.
To use it, type:

<a class="prompt prompt-cmd">
python ./scripts/validate_tbl.py \-\-silent ambig-paratope-surface.tbl
haddock3-restraints validate_tbl \-\-silent ambig-paratope-surface.tbl
</a>

No output means that your TBL file is valid.
Expand All @@ -716,7 +715,7 @@ In this scenario the NMR epitope combined with the surface neighbors are used as
The creation of the AIR tbl file for scenario 2a is similar to scenario 1, but instead using the `antigen-NMR-epitope.pass` file for the antigen:

<a class="prompt prompt-cmd">
./scripts/active-passive-to-ambig.py ./restraints/antibody-paratope.act-pass ./restraints/antigen-NMR-epitope.pass > ambig-paratope-NMR-epitope-pass.tbl
haddock3-restraints active_passive_to_ambig ./restraints/antibody-paratope.act-pass ./restraints/antigen-NMR-epitope.pass > ambig-paratope-NMR-epitope-pass.tbl
</a>

<hr>
Expand All @@ -728,7 +727,7 @@ In this scenario the NMR epitope is defined as active (meaning ambiguous distanc
The creation of the AIR tbl file for scenario 2b is similar to scenario 1, but instead using the `antigen-NMR-epitope.act-pass` file for the antigen:

<a class="prompt prompt-cmd">
./scripts/active-passive-to-ambig.py ./restraints/antibody-paratope.act-pass ./restraints/antigen-NMR-epitope.act-pass > ambig-paratope-NMR-epitope.tbl
haddock3-restraints active_passive_to_ambig ./restraints/antibody-paratope.act-pass ./restraints/antigen-NMR-epitope.act-pass > ambig-paratope-NMR-epitope.tbl
</a>

<hr>
Expand All @@ -737,11 +736,10 @@ The creation of the AIR tbl file for scenario 2b is similar to scenario 1, but i

As an antibody consists of two separate chains, it is important to define a few distance restraints
to keep them together during the high temperature flexible refinement stage of HADDOCK. This can easily be
done using a script from [haddock-tools][haddock-tools] repository, which is also provided for convenience
in the `scripts` directly of the archive you downloaded for this tutorial.
done using another `haddock3-restraints` subcommand.

<a class="prompt prompt-cmd">
python ./scripts/restrain_bodies.py 4G6K_clean.pdb >antibody-unambig.tbl
haddock3-restraints restrain_bodies 4G6K_clean.pdb > antibody-unambig.tbl
</a>

The result file contains two CA-CA distance restraints with the exact distance measured between the picked CA atoms:
Expand Down Expand Up @@ -969,12 +967,6 @@ ncores = 96
# Self contained rundir (to avoid problems with long filename paths)
self_contained = true

# Post-processing to generate statistics and plots
postprocess = true

# Cleaning to compress files and save space
clean = true

# molecules to be docked
molecules = [
"pdbs/4G6K_clean.pdb",
Expand All @@ -997,7 +989,8 @@ randremoval = false
sampling = 10000

[clustfcc]
threshold = 10
min_population = 4
top_models = 10

[seletopclusts]
## select all the clusters
mgiulini marked this conversation as resolved.
Show resolved Hide resolved
Expand Down Expand Up @@ -1077,12 +1070,6 @@ ncores = 96
# Self contained rundir (to avoid problems with long filename paths)
self_contained = true

# Post-processing to generate statistics and plots
postprocess = true

# Cleaning to compress files and save space
clean = true

# molecules to be docked
molecules = [
"pdbs/4G6K_clean.pdb",
Expand All @@ -1103,7 +1090,8 @@ unambig_fname = "restraints/antibody-unambig.tbl"
randremoval = false

[clustfcc]
threshold = 10
min_population = 4
top_models = 10

[seletopclusts]
## select all the clusters
mgiulini marked this conversation as resolved.
Show resolved Hide resolved
Expand Down Expand Up @@ -1177,12 +1165,6 @@ ncores = 96
# Self contained rundir (to avoid problems with long filename paths)
self_contained = true

# Post-processing to generate statistics and plots
postprocess = true

# Cleaning to compress files and save space
clean = true

# molecules to be docked
molecules = [
"pdbs/4G6K_clean.pdb",
Expand All @@ -1201,7 +1183,8 @@ ambig_fname = "restraints/ambig-paratope-NMR-epitope-act.tbl"
unambig_fname = "restraints/antibody-unambig.tbl"

[clustfcc]
threshold = 10
min_population = 4
top_models = 10

[seletopclusts]
## select all the clusters
mgiulini marked this conversation as resolved.
Show resolved Hide resolved
Expand Down Expand Up @@ -1771,7 +1754,7 @@ _**Note**_ that this kind of analysis only makes sense when we know the referenc

<br>

<a class="prompt prompt-question">Is the best model always rank as first?</a>
<a class="prompt prompt-question">Is the best model always ranked as first?</a>

<details style="background-color:#DAE4E7">
<summary style="bold">
Expand Down Expand Up @@ -1989,7 +1972,7 @@ _**Note**_ that this kind of analysis only makes sense when we know the referenc
</details>
<br>

<a class="prompt prompt-question">Is the best model always rank as first?</a>
<a class="prompt prompt-question">Is the best model always ranked as first?</a>

<details style="background-color:#DAE4E7">
<summary style="bold">
Expand All @@ -2004,7 +1987,7 @@ _**Note**_ that this kind of analysis only makes sense when we know the referenc

#### Analysis scenario 2a: visualizing the scores and their components

By setting `postprocess=true` in the config files, interactive plots have been automatically generated in the _analysis_ directory of the run.
Using the default settings (option `postprocess=true`) in the config files, interactive plots have been automatically generated in the _analysis_ directory of the run.
These are useful to visualise the scores and their components versus ranks and model quality.

<a class="prompt prompt-info">
Expand Down Expand Up @@ -2203,7 +2186,7 @@ _**Note**_ that this kind of analysis only makes sense when we know the referenc

<br>

<a class="prompt prompt-question">Is the best model always rank as first?</a>
<a class="prompt prompt-question">Is the best model always ranked as first?</a>

<details style="background-color:#DAE4E7">
<summary style="bold">
Expand Down