Skip to content

Commit

Permalink
Cleaning/updating the software page
Browse files Browse the repository at this point in the history
  • Loading branch information
@amjjbonvin
amjjbonvin committed Nov 23, 2024
1 parent ac032e8 commit 8538a6e
Showing 1 changed file with 56 additions and 45 deletions.
101 changes: 56 additions & 45 deletions software/index.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -7,100 +7,111 @@ image:
feature: pages/banner_software.jpg
---

This page provide you links to software and software manuals of the computational structural biology group.
This page provide you links to software, web services and datasets of the computational structural biology group.

* table of contents
{:toc}

<HR>
### WEB PORTAL
All software offered as web service can be access from our web portal at:

* [**wenmr.science.uu.nl**](https://wenmr.science.uu.nl){:target="_blank"}

<HR>
### HADDOCK
Software package for integrative modelling of biomolecular complexes

* [**HADDOCK best practice guide**](/software/bpg) - A must read when starting to use our software!

* [**HADDOCK2.4/5 software**](/software/haddock2.4/) - Official 2.4/5 production version
* [**HADDOCK2.5 software**](/software/haddock2.5/) - Official 2.5 production version

* [**HADDOCK2.4 web server**](https://wenmr.science.uu.nl/haddock2.4/) - production version
* [**HADDOCK2.4 web server**](https://wenmr.science.uu.nl/haddock2.4/){:target="_blank"} - production version (running the 2.5 version of HADDOCK in background)

* [**HADDOCK3 software**](/software/haddock3) - A new, very experimental [BioExcel](https://www.bioexcel.eu) redesign of HADDOCK in a modular code. Use it at your own risk!
* [**HADDOCK3 software**](/software/haddock3) - A [BioExcel](https://www.bioexcel.eu){:target="_blank"} redesign of HADDOCK in a modular code. Still in beta release.

<HR>
### [HADDOCKING GitHub repository](https://github.com/haddocking)
The GitHub repository for HADDOCK and its associated tools

* [**Arctic-3D**](https://github.com/haddocking/arctic3d):
ARCTIC-3D is a software for data-mining and clustering of protein interface information. It allows you to retrieve all the existing interface information for your desired protein from the [PDBE graph database](https://www.ebi.ac.uk/pdbe/pdbe-kb/), grouping similar interfaces in interacting surfaces.
* [**Arctic-3D**](https://github.com/haddocking/arctic3d){:target="_blank"}:
ARCTIC-3D is a software for data-mining and clustering of protein interface information.
It allows you to retrieve all the existing interface information for your desired protein from
the [PDBE graph database](https://www.ebi.ac.uk/pdbe/pdbe-kb/){:target="_blank"}, grouping similar interfaces in interacting surfaces.<br>
Also available as **[web service](https://wenmr.science.uu.nl/arctic3d){:target="_blank"}**

* [**Binding_affinity: PRODIGY**](https://github.com/haddocking/binding_affinity){:target="_blank"}:
A collection of Python scripts to predict the binding affinity in protein-protein complexes.<br>
Also available as **[web service](https://wenmr.science.uu.nl/prodigy){:target="_blank"}**

* [**Binding_affinity: PRODIGY**](https://github.com/haddocking/binding_affinity):
A collection of Python scripts to predict the binding affinity in protein-protein complexes.
* [**DisVis**](https://github.com/haddocking/disvis){:target="_blank"}:
A Python package and command-line tool to quantify and visualize the accessible interaction space of distance-restrained biomolecular complexes.<br>
Also available as **[web service](https://wenmr.science.uu.nl/disvis){:target="_blank"}**

* [**DisVis**](https://github.com/haddocking/disvis):
A Python package and command-line tool to quantify and visualize the accessible interaction space of distance-restrained biomolecular complexes.
* [**Fraction of common contact clustering**](https://github.com/haddocking/fcc){:target="_blank"}:
Clustering of biomolecular complexes based on the fraction of common contacts

* [**Fraction of common contact clustering**](https://github.com/haddocking/fcc):
Clustering of biomolecular complexes based on the fraction of common contacts
* [**HADDOCK-tools**](https://github.com/haddocking/haddock-tools){:target="_blank"}:
A collection of useful scripts related to HADDOCK

* [**HADDOCK-tools**](https://github.com/haddocking/haddock-tools):
A collection of useful scripts related to HADDOCK
* [**PDB-tools**](https://github.com/haddocking/pdb-tools){:target="_blank"}:
A collection of Python scripts for the manipulation (renumbering, changing chain and segIDs...) of PDB files.
For documentation refer to [https://www.bonvinlab.org/pdb-tools/](https://www.bonvinlab.org/pdb-tools/){:target="_blank"}.<br>
Also available as **[web service](https://wenmr.science.uu.nl/pdbtools){:target="_blank"}**

* [**PDB-tools**](https://github.com/haddocking/pdb-tools):
A collection of Python scripts for the manipulation (renumbering, changing chain and segIDs...) of PDB files.
For documentation refer to [https://www.bonvinlab.org/pdb-tools/](https://www.bonvinlab.org/pdb-tools/).
And now also available as [web portal](https://wenmr.science.uu.nl/pdbtools)!
* [**PowerFit**](https://github.com/haddocking/powerfit){:target="_blank"}:
PowerFit is a Python package and simple command-line program to automatically fit high-resolution atomic structures in cryo-EM densities.<br>
Also available as **[web service](https://alcazar.science.uu.nl/services/POWERFIT){:target="_blank"}**

* [**PowerFit**](https://github.com/haddocking/powerfit):
PowerFit is a Python package and simple command-line program to automatically fit high-resolution atomic structures in cryo-EM densities.
* [**Samplex**](https://github.com/haddocking/samplex){:target="_blank"}:
Samplex is an automatic and unbiased method to distinguish perturbed and unperturbed regions in a protein existing
in two distinct states (folded/partially unfolded, bound/unbound). Samplex takes as input a set of data and the corresponding
3D structure and returns the confidence for each residue to be in a perturbed or unperturbed state.

* [**Samplex**](https://github.com/haddocking/samplex):
Samplex is an automatic and unbiased method to distinguish perturbed and unperturbed regions in a protein existing in two distinct states (folded/partially unfolded, bound/unbound). Samplex takes as input a set of data and the corresponding three-dimensional structure and returns the confidence for each residue to be in a perturbed or unperturbed state.
* [**WHISCY**](https://github.com/haddocking/whiscy){:target="_blank"}:
WHISCY is a program to predict protein-protein interfaces. It is primarily based on conservation,
but it also takes into account structural information.<br>
Also available as **[web service](https://wenmr.science.uu.nl/whiscy){:target="_blank"}**

<HR>
### 3D-DART DNA modelling
3D-DART provides a convenient means of generating custom structural models of DNA. Our server is no longer in operation because of security issues, but you can run it yourself from a docker container. Visit for this our GitHub repo below.

* [**3D-DART**](https://github.com/haddocking/3D-DART-server/)

<HR>
### Bioinformatics interface predictors

* [**WHISCY**](https://nmr.chem.uu.nl/Software/whiscy/index.html)
WHISCY is a program to predict protein-protein interfaces. It is primarily based on conservation, but it also takes into account structural information.

* [**CPORT**](https://alcazar.science.uu.nl/services/CPORT)
CPORT is an algorithm for the prediction of protein-protein interface residues. It combines six interface prediction methods into a consensus predictor
* [**3D-DART**](https://github.com/haddocking/3D-DART-server/){:target="_blank"}

<HR>
### Deep learning protein interactions

* [**DeepRank**](https://github.com/DeepRank/deeprank)
* [**DeepRank**](https://github.com/DeepRank/deeprank){:target="_blank"}
DeepRank is a general, configurable deep learning framework for data mining protein-protein interactions (PPIs) using 3D convolutional neural networks (CNNs).

* [**DeepRank-GNN**](https://github.com/DeepRank/Deeprank-GNN)
* [**DeepRank-GNN**](https://github.com/DeepRank/Deeprank-GNN){:target="_blank"}
DeepRank-GNN is a general, configurable deep learning framework for data mining protein-protein interactions (PPIs) using graph convolutional neural networks (CNNs).

* [**DeepRank-GNN-esm**](https://github.com/haddocking/DeepRank-GNN-esm)
* [**DeepRank-GNN-esm**](https://github.com/haddocking/DeepRank-GNN-esm){:target="_blank"}
DeepRank-GNN-esm is a general, configurable deep learning framework for data mining protein-protein interactions (PPIs) using graph convolutional neural networks (CNNs) and including language model features.

<HR>
### Benchmarks and datasets

* Docking benchmark of membrane protein complexes ([GitHub](https://github.com/haddocking/MemCplxDB)) and associated decoy dataset [ https://doi.org/10.15785/SBGRID/618]( https://doi.org/10.15785/SBGRID/618)
* Docking benchmark of membrane protein complexes ([GitHub](https://github.com/haddocking/MemCplxDB){:target="_blank"}) and associated decoy dataset [ https://doi.org/10.15785/SBGRID/618]( https://doi.org/10.15785/SBGRID/618){:target="_blank"}

* Cleaned Docking Benchmark 5 dataset, HADDOCK-ready, with unbound and bound structures matched: [https://github.com/haddocking/BM5-clean](https://github.com/haddocking/BM5-clean){:target="_blank"}

* Cleaned Docking Benchmark 5 dataset, HADDOCK-ready, with unbound and bound structures matched: [https://github.com/haddocking/BM5-clean](https://github.com/haddocking/BM5-clean)
* HADDOCK docking decoys for the new entries (55) of the protein-protein Docking Benchmark5: [https://data.sbgrid.org/dataset/131/](https://data.sbgrid.org/dataset/131/){:target="_blank"}

* HADDOCK docking decoys for the new entries (55) of the protein-protein Docking Benchmark5: [https://data.sbgrid.org/dataset/131/](https://data.sbgrid.org/dataset/131/)
* Docking models for Docking Benchmark 4, 5 and CAPRI score_set: [https://doi.org/10.15785/SBGRID/684](https://doi.org/10.15785/SBGRID/684){:target="_blank"}

* Docking models for Docking Benchmark 4, 5 and CAPRI score_set: [https://doi.org/10.15785/SBGRID/684](https://doi.org/10.15785/SBGRID/684)
* HADDOCK refined models for the biological/crystallographic interfaces collected in the DC and MANY datasets: [https://doi.org/10.15785/SBGRID/566](https://doi.org/10.15785/SBGRID/566){:target="_blank"}

* HADDOCK refined models for the biological/crystallographic interfaces collected in the DC and MANY datasets: [https://doi.org/10.15785/SBGRID/566](https://doi.org/10.15785/SBGRID/566)
* HADDOCK models of mutant protein complexes: [https://doi.org/10.15785/SBGRID/651](https://doi.org/10.15785/SBGRID/651){:target="_blank"}

* HADDOCK models of mutant protein complexes: [https://doi.org/10.15785/SBGRID/651](https://doi.org/10.15785/SBGRID/651)
* [Protein-DNA docking benchmark](https://github.com/haddocking/Prot-DNABenchmark){:target="_blank"}

* [Protein-DNA docking benchmark](https://github.com/haddocking/Prot-DNABenchmark)
* [Protein-small molecule benchmark](https://github.com/haddocking/shape-restrained-haddocking){:target="_blank"} containing all data related to our shape-restrained protein-ligand docking protocol (based on the DUDe small molecule benchmark. See the related tutorial [here](https://www.bonvinlab.org/education/HADDOCK24/shape-small-molecule/){:target="_blank"}

* [Protein-cyclic peptide docking benchmark](https://github.com/haddocking/cyclic-peptides) and associated models dataset [https://data.sbgrid.org/dataset/912](https://data.sbgrid.org/dataset/912)
* [Protein-cyclic peptide docking benchmark](https://github.com/haddocking/cyclic-peptides){:target="_blank"} and associated models dataset [https://data.sbgrid.org/dataset/912](https://data.sbgrid.org/dataset/912){:target="_blank"}.

* All-atom and Coarse-grained HADDOCK docking models for Protein-DNA complexes: [https://zenodo.org/record/3941636](https://zenodo.org/record/3941636)
* All-atom and Coarse-grained HADDOCK docking models for Protein-DNA complexes: [https://zenodo.org/record/3941636](https://zenodo.org/record/3941636){:target="_blank"}

* [Dataset of modelled protein-cyclic peptide complexes](https://data.sbgrid.org/dataset/912/) obtained with HADDOCK corresponding to the optimal protocol described in [Charitou et al. JCTC 2022](https://doi.org/10.1021/acs.jctc.2c00075)
* [Dataset of modelled protein-cyclic peptide complexes](https://data.sbgrid.org/dataset/912/){:target="_blank"} obtained with HADDOCK corresponding to the optimal protocol described in [Charitou et al. JCTC 2022](https://doi.org/10.1021/acs.jctc.2c00075){:target="_blank"}

0 comments on commit 8538a6e

Please sign in to comment.