Datasets:

DrewLab
/

DirectContacts2

---
license: cc-by-4.0
tags:
- PPIs
- mass_spectrometry
- biology
pretty_name: >-
  DirectContacts2: A network of direct physical protein interactions derived
  from high throughput mass spectrometry experiments
repo: https://github.com/KDrewLab/DirectContacts2_analysis.git
---
# DirectContacts2: A network of direct physical protein interactions derived from high throughput mass spectrometry experiments
Proteins carry out cellular functions by self-assembling into functional complexes, a process that depends on direct physical interactions 
between components. While tools like AlphaFold and RoseTTAFold have advanced structure prediction, they remain limited in scaling to the full 
human proteome. DirectContacts2 addresses this challenge by integrating diverse large-scale protrin interaction datasets, including AP/MS (BioPlex1–3, Boldt et al., Hein et al.), 
biochemical fractionation (Wan et al.), proximity labeling (Gupta et al., Youn et al.), and RNA pulldown (Treiber et al.), to predict whether ~26 million 
human protein pairs interact directly or indirectly.

## Funding

  NIH R00, NSF/BBSRC

## Citation

    Erin R. Claussen, Miles D Woodcock-Girard, Samantha N Fischer, Kevin Drew
## References
    Kevin Drew, Christian L. Müller , Richard Bonneau, Edward M. Marcotte (2017) Identifying direct contacts between protein complex subunits from their conditional dependence in proteomics datasets. PLOS Computational Biology 13(10): e1005625. https://doi.org/10.1371/journal.pcbi.1005625
    Samantha N. Fischer, Erin R Claussen, Savvas Kourtis, Sara Sdelci, Sandra Orchard, Henning Hermjakob, Georg Kustatscher, Kevin Drew hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000 proteomic experiments. Molecular Systems Biology 1–33 (2025) doi:10.1038/s44320-025-00121-5.
    Huttlin et al. Dual proteome-scale networks reveal cell-specific remodeling of the human interactome Cell. 2021 May 27;184(11):3022-3040.e28. doi: 10.1016/j.cell.2021.04.011.
    Huttlin et al. Architecture of the human interactome defines protein communities and disease networks. Nature. 2017 May 25;545(7655):505-509. DOI: 10.1038/nature22366.
    Treiber et al. A Compendium of RNA-Binding Proteins that Regulate MicroRNA Biogenesis.. Mol Cell. 2017 Apr 20;66(2):270-284.e13. doi: 10.1016/j.molcel.2017.03.014.
    Boldt et al. An organelle-specific protein landscape identifies novel diseases and molecular mechanisms. Nat Commun. 2016 May 13;7:11491. doi: 10.1038/ncomms11491.
    Youn et al. High-Density Proximity Mapping Reveals the Subcellular Organization of mRNA-Associated Granules and Bodies. Mol Cell. 2018 Feb 1;69(3):517-532.e11. doi: 10.1016/j.molcel.2017.12.020.
    Gupta et al. A Dynamic Protein Interaction Landscape of the Human Centrosome-Cilium Interface. Cell. 2015 Dec 3;163(6):1484-99. doi: 10.1016/j.cell.2015.10.065.
    Wan, Borgeson et al. Panorama of ancient metazoan macromolecular complexes. Nature. 2015 Sep 17;525(7569):339-44. doi: 10.1038/nature14877. Epub 2015 Sep 7.
    Hein et al. A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell. 2015 Oct 22;163(3):712-23. doi: 10.1016/j.cell.2015.09.053. Epub 2015 Oct 22.
    Huttlin et al. The BioPlex Network: A Systematic Exploration of the Human Interactome. Cell. 2015 Jul 16;162(2):425-40. doi: 10.1016/j.cell.2015.06.043.
    Reimand et al. g:Profiler-a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res. 2016 Jul 8;44(W1):W83-9. doi: 10.1093/nar/gkw199.
## Description of dataset files
  |-- **full**
  
    |-- humap3_full_feature_matrix_20220625.csv.gz
    
      This is the full feature matrix that contains all 26 million proteins pairs that DirectContacts2 makes 
      predictions on and the 324 features from >25,000 proteomic mass spectrometry experiments. The feature 
      matrix was originally developed in hu.MAP3.0.
      
  |-- **reference_interactions**
  
    |-- benchmark_test_INTRAandINTER_complex_NegativePairs_20250619.csv
    
      These are the test set negatives that contains both intra- and inter-complex negative pairs. The file
      contains a pair of proteins (defined by UniProt acession) per line. The set of structures (PDBs) these
      interactions come from all have at least 5 subunits.
      
    |-- benchmark_test_IntraComplex_NegativePairs_pdbsize5_20240326.txt
    
      These are the test set negatives that contain only intra-complex negative pairs and are the negatives used
      in the test feature matrix provided when reading in the test feature matrix using the *datasets* package.
      The other feature matrix is provided under this same directory (*reference_interactions). All PDBs that
      these interactions come from have at least 5 protein subunits. The file is structured with one pair of 
      proteins (defined by UniProt acession) per line.
      
    |-- benchmark_test_IntraComplex_PositivePairs_pdbsize5_20240326.txt
    
      These are the test set positives, which are only intra-complex pairs. The PDB structures from which the 
      interactions are derived all have at least 5 subunits. The file is structured with one pair of 
      proteins (defined by UniProt acession) per line.
      
    |-- benchmark_train_INTERandINTRA_complex_NegativePairs_pdbsize3_20240326.txt
    
      These are the training set negatives, which contain both inter- and intra-complex pairs. The set of 
      structures (PDBs) these interactions come from all have at least 3 subunits. The file is structured with 
      one pair of proteins (defined by UniProt acession) per line.
      
    |-- benchmark_train_IntraComplex_PositivePairs_pdbsize3_20240326.txt
    
      These are the test set positives, which are only intra-complex pairs. The PDB structures from which the 
      interactions are derived all have at least 3 subunits. The file is structured with one pair of 
      proteins (defined by UniProt acession) per line.
      
  |-- **test**
  
    |-- test_FeatureMatrix_pdbsize5_only_INTRA_complex_NegativePairs_20240326.csv.gz
    
      This is the feature matrix for the test set of interactions. This feature matrix only contains
      intra-complex negative protein pairs.
      
  |-- **train**
  
    |-- train_FeatureMatrix_pdbsize3_20240326.csv.gz
    
      This is the feature matrix for the training set of interactions.
  |-- **alternative_test_feature_matrix**
  
    |-- DirectContacts2_test_feature_matrix_inter_and_intra_negatives_20260624.csv.gz
      This is the feature matrix for the test set that contains intra- and inter-complex negative protein pairs.
      
## Associated code 
  Additional code examples can be found on our [GitHub](https://github.com/KDrewLab/DirectContacts2_analysis.git), including:
  importing the [DirectContacts2 model](https://huggingface.co/DrewLab/DirectContacts2_AutoGluon) to make predictions, importing the 
  training and testing data, or using the full feature matrix.
  
# Usage
## Accessing and using the model
DirectContacts2 was constructed using [AutoGluon](https://auto.gluon.ai/stable/index.html) an auto-ML tool. The module [TabularPredictor](https://auto.gluon.ai/stable/api/autogluon.tabular.TabularPredictor.html)
is used to is used train, test, and make predictions with the model.
This can be downloaded using the following:
      $ pip install autogluon==0.8.2
Then it can be imported as:
    >>> from autogluon.tabular import TabularPredictor
Note that to perform operations with our model the **0.8.2 version** must be used. Alternatively, if disk space is a concern
the user can just install *autogluon.tabular*
The [DirectContacts2 model](https://huggingface.co/DrewLab/DirectContacts2_AutoGluon) can be accessed through HuggingFace with [huggingface_hub](https://huggingface.co/docs/hub/index)
     >>> from huggingface_hub import snapshot_download
    >>> model_dir = snapshot_download(repo_id="DrewLab/DirectContacts2_AutoGluon")
    >>> predictor = TabularPredictor.load(f"{model_dir}/DirectContacts2_Autogluon_Model")
## Using the training and testing data
Both the train and test feature matrices can be loaded using the Huggingface [datasets](https://huggingface.co/docs/datasets/index) library.
This can be done from the command-line using:
    
      $ pip install datasets
When loading into Python use the following:
    >>> from datasets import load_dataset
    >>> dataset = load_dataset('DrewLab/DirectContacts2')
Training and test feature matrices can then be accessed as separate objects:
    >>> train = dataset["train"].to_pandas()
    >>> test = dataset["test"].to_pandas()
Jupyter notebooks containing more in-depth examples of model training, testing, and generating predictions can be found on our [GitHub](https://github.com/KDrewLab/DirectContacts2_analysis/tree/main)
## Accessing full feature matrix, all test/train interaction/complex files, and the alternative test feature matrix
All other files, such as the full feature matrix, can be accessed via Huggingface_hub.
    >>> from huggingface_hub import hf_hub_download
    >>> full_file = hf_hub_download(repo_id="DrewLab/DirectContacts2", filename='full/humap3_full_feature_matrix_20220625.csv.gz', repo_type='dataset')
This just provides the file for download. Depending on your workflow, if you wish to use as a pandas dataframe for example:
    >>> import pandas as pd
    >>> full_featmat = pd.read_csv(full_file, compression="gzip")
## Dataset card authors
Samantha Fischer (sfisch6@uic.edu)

	---
	license: cc-by-4.0
	tags:
	- PPIs
	- mass_spectrometry
	- biology
	pretty_name: >-
	DirectContacts2: A network of direct physical protein interactions derived
	from high throughput mass spectrometry experiments
	repo: https://github.com/KDrewLab/DirectContacts2_analysis.git
	---
	# DirectContacts2: A network of direct physical protein interactions derived from high throughput mass spectrometry experiments
	Proteins carry out cellular functions by self-assembling into functional complexes, a process that depends on direct physical interactions
	between components. While tools like AlphaFold and RoseTTAFold have advanced structure prediction, they remain limited in scaling to the full
	human proteome. DirectContacts2 addresses this challenge by integrating diverse large-scale protrin interaction datasets, including AP/MS (BioPlex1–3, Boldt et al., Hein et al.),
	biochemical fractionation (Wan et al.), proximity labeling (Gupta et al., Youn et al.), and RNA pulldown (Treiber et al.), to predict whether ~26 million
	human protein pairs interact directly or indirectly.

	## Funding

	NIH R00, NSF/BBSRC

	## Citation

	Erin R. Claussen, Miles D Woodcock-Girard, Samantha N Fischer, Kevin Drew

	## References
	Kevin Drew, Christian L. Müller , Richard Bonneau, Edward M. Marcotte (2017) Identifying direct contacts between protein complex subunits from their conditional dependence in proteomics datasets. PLOS Computational Biology 13(10): e1005625. https://doi.org/10.1371/journal.pcbi.1005625
	Samantha N. Fischer, Erin R Claussen, Savvas Kourtis, Sara Sdelci, Sandra Orchard, Henning Hermjakob, Georg Kustatscher, Kevin Drew hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000 proteomic experiments. Molecular Systems Biology 1–33 (2025) doi:10.1038/s44320-025-00121-5.
	Huttlin et al. Dual proteome-scale networks reveal cell-specific remodeling of the human interactome Cell. 2021 May 27;184(11):3022-3040.e28. doi: 10.1016/j.cell.2021.04.011.
	Huttlin et al. Architecture of the human interactome defines protein communities and disease networks. Nature. 2017 May 25;545(7655):505-509. DOI: 10.1038/nature22366.
	Treiber et al. A Compendium of RNA-Binding Proteins that Regulate MicroRNA Biogenesis.. Mol Cell. 2017 Apr 20;66(2):270-284.e13. doi: 10.1016/j.molcel.2017.03.014.
	Boldt et al. An organelle-specific protein landscape identifies novel diseases and molecular mechanisms. Nat Commun. 2016 May 13;7:11491. doi: 10.1038/ncomms11491.
	Youn et al. High-Density Proximity Mapping Reveals the Subcellular Organization of mRNA-Associated Granules and Bodies. Mol Cell. 2018 Feb 1;69(3):517-532.e11. doi: 10.1016/j.molcel.2017.12.020.
	Gupta et al. A Dynamic Protein Interaction Landscape of the Human Centrosome-Cilium Interface. Cell. 2015 Dec 3;163(6):1484-99. doi: 10.1016/j.cell.2015.10.065.
	Wan, Borgeson et al. Panorama of ancient metazoan macromolecular complexes. Nature. 2015 Sep 17;525(7569):339-44. doi: 10.1038/nature14877. Epub 2015 Sep 7.
	Hein et al. A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell. 2015 Oct 22;163(3):712-23. doi: 10.1016/j.cell.2015.09.053. Epub 2015 Oct 22.
	Huttlin et al. The BioPlex Network: A Systematic Exploration of the Human Interactome. Cell. 2015 Jul 16;162(2):425-40. doi: 10.1016/j.cell.2015.06.043.
	Reimand et al. g:Profiler-a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res. 2016 Jul 8;44(W1):W83-9. doi: 10.1093/nar/gkw199.

	## Description of dataset files
	\|-- full

	\|-- humap3_full_feature_matrix_20220625.csv.gz

	This is the full feature matrix that contains all 26 million proteins pairs that DirectContacts2 makes
	predictions on and the 324 features from >25,000 proteomic mass spectrometry experiments. The feature
	matrix was originally developed in hu.MAP3.0.

	\|-- reference_interactions

	\|-- benchmark_test_INTRAandINTER_complex_NegativePairs_20250619.csv

	These are the test set negatives that contains both intra- and inter-complex negative pairs. The file
	contains a pair of proteins (defined by UniProt acession) per line. The set of structures (PDBs) these
	interactions come from all have at least 5 subunits.

	\|-- benchmark_test_IntraComplex_NegativePairs_pdbsize5_20240326.txt

	These are the test set negatives that contain only intra-complex negative pairs and are the negatives used
	in the test feature matrix provided when reading in the test feature matrix using the datasets package.
	The other feature matrix is provided under this same directory (*reference_interactions). All PDBs that
	these interactions come from have at least 5 protein subunits. The file is structured with one pair of
	proteins (defined by UniProt acession) per line.

	\|-- benchmark_test_IntraComplex_PositivePairs_pdbsize5_20240326.txt

	These are the test set positives, which are only intra-complex pairs. The PDB structures from which the
	interactions are derived all have at least 5 subunits. The file is structured with one pair of
	proteins (defined by UniProt acession) per line.

	\|-- benchmark_train_INTERandINTRA_complex_NegativePairs_pdbsize3_20240326.txt

	These are the training set negatives, which contain both inter- and intra-complex pairs. The set of
	structures (PDBs) these interactions come from all have at least 3 subunits. The file is structured with
	one pair of proteins (defined by UniProt acession) per line.

	\|-- benchmark_train_IntraComplex_PositivePairs_pdbsize3_20240326.txt

	These are the test set positives, which are only intra-complex pairs. The PDB structures from which the
	interactions are derived all have at least 3 subunits. The file is structured with one pair of
	proteins (defined by UniProt acession) per line.

	\|-- test

	\|-- test_FeatureMatrix_pdbsize5_only_INTRA_complex_NegativePairs_20240326.csv.gz

	This is the feature matrix for the test set of interactions. This feature matrix only contains
	intra-complex negative protein pairs.

	\|-- train

	\|-- train_FeatureMatrix_pdbsize3_20240326.csv.gz

	This is the feature matrix for the training set of interactions.

	\|-- alternative_test_feature_matrix

	\|-- DirectContacts2_test_feature_matrix_inter_and_intra_negatives_20260624.csv.gz

	This is the feature matrix for the test set that contains intra- and inter-complex negative protein pairs.

	## Associated code
	Additional code examples can be found on our [GitHub](https://github.com/KDrewLab/DirectContacts2_analysis.git), including:
	importing the [DirectContacts2 model](https://huggingface.co/DrewLab/DirectContacts2_AutoGluon) to make predictions, importing the
	training and testing data, or using the full feature matrix.

	# Usage

	## Accessing and using the model
	DirectContacts2 was constructed using [AutoGluon](https://auto.gluon.ai/stable/index.html) an auto-ML tool. The module [TabularPredictor](https://auto.gluon.ai/stable/api/autogluon.tabular.TabularPredictor.html)
	is used to is used train, test, and make predictions with the model.

	This can be downloaded using the following:

	$ pip install autogluon==0.8.2

	Then it can be imported as:

	>>> from autogluon.tabular import TabularPredictor

	Note that to perform operations with our model the 0.8.2 version must be used. Alternatively, if disk space is a concern
	the user can just install autogluon.tabular

	The [DirectContacts2 model](https://huggingface.co/DrewLab/DirectContacts2_AutoGluon) can be accessed through HuggingFace with [huggingface_hub](https://huggingface.co/docs/hub/index)

	>>> from huggingface_hub import snapshot_download

	>>> model_dir = snapshot_download(repo_id="DrewLab/DirectContacts2_AutoGluon")

	>>> predictor = TabularPredictor.load(f"{model_dir}/DirectContacts2_Autogluon_Model")

	## Using the training and testing data

	Both the train and test feature matrices can be loaded using the Huggingface [datasets](https://huggingface.co/docs/datasets/index) library.

	This can be done from the command-line using:

	$ pip install datasets

	When loading into Python use the following:

	>>> from datasets import load_dataset
	>>> dataset = load_dataset('DrewLab/DirectContacts2')

	Training and test feature matrices can then be accessed as separate objects:

	>>> train = dataset["train"].to_pandas()
	>>> test = dataset["test"].to_pandas()

	Jupyter notebooks containing more in-depth examples of model training, testing, and generating predictions can be found on our [GitHub](https://github.com/KDrewLab/DirectContacts2_analysis/tree/main)

	## Accessing full feature matrix, all test/train interaction/complex files, and the alternative test feature matrix
	All other files, such as the full feature matrix, can be accessed via Huggingface_hub.

	>>> from huggingface_hub import hf_hub_download
	>>> full_file = hf_hub_download(repo_id="DrewLab/DirectContacts2", filename='full/humap3_full_feature_matrix_20220625.csv.gz', repo_type='dataset')

	This just provides the file for download. Depending on your workflow, if you wish to use as a pandas dataframe for example:

	>>> import pandas as pd
	>>> full_featmat = pd.read_csv(full_file, compression="gzip")

	## Dataset card authors
	Samantha Fischer (sfisch6@uic.edu)