DOI: 10.5061/dryad.15dv41p84
title: A dataset for predicting protein-protein interactions in humans

description: Protein-protein interactions (PPIs) are fundamental to biological
 function. While recent advances in coevolutionary analysis and deep
 learning (DL)-based structure prediction have enabled large-scale PPI
 identification in bacterial and yeast proteomes, their application to the
 more complex human proteome has remained limited. To address this
 challenge, we 1) enhanced coevolutionary signals by generating 7-fold
 deeper multiple sequence alignments (MSAs) from 30 petabytes of
 unassembled genomic data, and 2) developed a new DL model trained on
 augmented datasets of domain-domain interactions derived from 200 million
 predicted protein structures. These improvements led to a 4-fold increase
 in the performance of our de novo PPI prediction pipeline for human
 proteins. We systematically screened around 190 million human protein
 pairs and predicted 17,849 high-confidence PPIs at an estimated precision
 of 90%, including 3,631 interactions not previously detected by
 experimental methods. The resulting dataset includes omicsMSA alignments,
 training data (domain-domain and protein-protein interactions),
 high-confidence predicted pairs, oligomeric assemblies inferred from
 predicted and known interactions, novel components predicted for known
 complexes, structural models (PDB format), contact probabilities from
 AlphaFold and RoseTTAFold2-PPI, and DCA scores.
description: # A dataset for predicting protein-protein interactions in humans Dataset
 DOI: [10.5061/dryad.15dv41p84](10.5061/dryad.15dv41p84) ## Description of
 the data and file structure ### **protein_omicMSAs.tar.gz (17 GB)** These
 MSAs are in an A3M-like format. Compared to the standard A3M format, we
 inserted an additional sequence at the beginning, named “mask,” to
 indicate the alignment quality at each position. In this “mask,” an
 asterisk (*) indicates high-quality positions, and a dash (-) indicates
 low-quality positions (these are poorly conserved and thus cannot be
 reliably assembled from genomic data). We recommend using only the
 high-quality positions (marked with *), as we did in our work. Insertions
 relative to the human (query) sequence are represented by lowercase
 letters. Each sequence corresponds to one draft genome or genomic dataset,
 and the NCBI accession number of the genome or dataset is used to name the
 sequence in the header. We also include the taxonomic information of each
 sequence in the header, following the format:
 [genus]:[family]:[order]:[class]:[phylum]. Please note that because we
 assemble these sequences by aligning draft genomes or genomic reads to
 human proteins, insertions present in other species relative to the human
 sequence are often missed. Similarly, gaps in the MSAs may not represent
 deletions relative to the human sequence; they could result from alignment
 failures or incompleteness in the genomic dataset. --- ###
 **segment_omicMSAs.tar.gz (13 GB)** Building on the omicMSAs for
 full-length human proteins (see description above), these MSAs correspond
 to segments of human proteins used in our work. We split larger proteins
 into multiple segments and excluded the “low-quality” positions from these
 segments. The definition of each segment can be found segment_def. These
 MSAs are in an A3M-like format. Insertions relative to the human (query)
 sequence are represented as lowercase letters. Each sequence corresponds
 to one draft genome or genomic dataset, and the NCBI accession number of
 the genome or dataset is used to name the sequence in the header. We also
 provide the taxonomic information for each sequence in the header,
 following the format: [genus]:[family]:[order]:[class]:[phylum]. Please
 note that because we assemble these sequences by aligning draft genomes or
 genomic reads to human proteins, insertions present in other species
 relative to the human sequence will often be missed. Similarly, gaps in
 the MSAs may not represent deletions relative to the human sequence; they
 could result from alignment failures or incompleteness of the genomic
 dataset. --- ### **benchmarks.tar.gz (286 KB)** Two files are included.
 The file “positives_and_negatives.tsv” contains the positive and negative
 controls used to benchmark different methods. The file
 “pairs_partitioned_by_interface_sizes” contains additional positive
 controls derived from PDB complexes, partitioned into different categories
 based on interface size, which correlates with binding affinity. --- ###
 **final_predictions.tar.gz (14 MB)** Two final sets of predicted PPIs
 generated in our study are included, along with additional metadata.
 “final_predictions_80.tsv” includes all predictions obtained at an
 expected precision of 80%, while “final_predictions_90.tsv” includes
 predictions obtained at an expected precision of 90%. The latter is a
 subset of the former. --- ### **best_models.tar.gz (67 GB)**
 **Description:** The best 3D structural models for 29,246 out of 29,257
 predicted PPIs are provided here (selected based on the presence of
 consistently predicted inter-protein contacts across multiple models). A
 3D model is considered confident only if it contains inter-protein
 contacts (distance &lt; 6 Å) with AlphaFold2 interaction probabilities
 above 0.5. We were unable to obtain such 3D models for 11 predicted PPIs.
 Our PPI screening and structural modeling were performed using
 **segments** rather than full-length protein sequences. These segments
 exclude 70% of residues in intrinsically disordered regions (which make up
 25% of all residues) that have low-quality MSAs, and they are short enough
 to fit within GPU memory. Each segment contains one or more domains, and
 the relative orientation of different segments within a protein is largely
 flexible. The definition of each segment can be found here. Each protein
 pair may be modeled using multiple segment pairs, and the results are
 organized into folders named after the protein pair. The model files are
 named using the format: `segment1__segment2__model`, where the model can
 be: * `"AF"`: a model built using the AlphaFold2 model 3 *
 `"AF1-5"`: five models built by ColabFold using the AlphaFold2
 network * `"AFMM1-5"`: five models built by ColabFold using the
 AlphaFold-Multimer network We provide three files for each segment pair: *
 `*.pdb`: the predicted 3D structure * `*.npz`: a matrix of shape L1 × L2,
 where L1 and L2 are the lengths of the first and second segments,
 respectively --- ### **AF_scores.gz (32 MB)** Interaction probabilities
 between 3.4 million protein pairs were predicted by AlphaFold2 based on
 omicMSAs (excluding low-quality positions). We applied AlphaFold2 only to
 protein pairs that showed high RF2-PPI interaction probabilities or had
 prior experimental evidence supporting their interactions. Each protein
 pair has a single probability value, representing the maximum interaction
 probability among all residue pairs between the two proteins. We also
 indicate the source of each pair, defined as follows: * **DNS**: de novo
 screen of pairs with shared subcellular localization * **DNU**: de novo
 screen of pairs involving proteins with unknown subcellular localization *
 **PPI**: pairs from PPI databases—BioGRID, STRING (physical), and UniProt
 * **STR**: genetically interacting pairs from STRING * **NEG**: negative
 controls used for accuracy estimation --- ### **RF2-PPI_scores.gz (444
 MB)** Interaction probabilities between 47 million protein pairs were
 predicted by RF2-PPI based on omicMSAs (excluding low-quality positions).
 We applied RF2-PPI only to protein pairs that exhibited high DCA scores or
 had prior experimental evidence supporting their interactions. Each
 protein pair has one probability value, representing the maximum
 interaction probability (first column) among all residue pairs between the
 two proteins. We also indicate the source of each pair (second column),
 defined as follows: * **DNS**: de novo screen of pairs with shared
 subcellular localization * **DNU**: de novo screen of pairs involving
 proteins with unknown subcellular localization * **PPI**: pairs from PPI
 databases—BioGRID, STRING (physical), and UniProt * **STR**: genetically
 interacting pairs from STRING * **NEG**: negative controls used for
 accuracy estimation --- ### **DCA_scores.gz (778 MB)** Coevolution between
 189 million protein pairs was evaluated using direct coupling analysis
 (DCA) on omicMSAs (excluding low-quality positions), followed by Average
 Product Correction (APC). Each protein pair has a single score,
 representing the maximum score among all residue pairs between the two
 proteins. --- ### **screened_pairs.tar.gz (336** MB) Human protein pairs
 (among the 19,528 proteins included in our PPI screen) from various
 sources are provided in four files: (1) “PPI_database_pairs” contains
 candidate PPIs gathered from UniProt, BioGRID, and STRING physical
 interactions; (2) “STRING_genetic_pairs” includes genetically associated
 pairs from STRING genetic interactions; (3) “same_locality_pairs” lists
 protein pairs that share subcellular locality as annotated by UniProt
 keywords; and (4) “unknown_locality_pairs” includes pairs involving
 proteins without known subcellular locality. --- ### **RF2-PPI.pt (310
 MB)** Trained weights for RoseTTAFold2-PPI, which can be used with our
 code deposited at:
 [https://github.com/CongLabCode/RoseTTAFold2-PPI](https://github.com/CongLabCode/RoseTTAFold2-PPI) --- ## Access information Other publicly accessible locations of the data are provided below. Large data files (such as the PPI and DDI datasets used for training, which cannot be deposited in Dryad due to file size limitations) are available at the following websites.  * [Download Supplementary Data and Code for humanPPI](https://conglab.swmed.edu/humanPPI/humanPPI_download.html) * [human PPI](http://prodata.swmed.edu/humanPPI)