The possible evolutionary significance of pyrophosphate (PPi) has been discussed since the early 1960s. Lipmann suggested that PPi could have been an ancient currency or a possible environmental source of metabolic energy at origins, while Kornberg proposed that PPi vectorializes metabolism because ubiquitous pyrophosphatases render PPi forming reactions kinetically irreversible. To test those ideas, we investigated the reactions that consume phosphoanhydride bonds among the 402 reactions of the universal biosynthetic core that generates amino acids, nucleotides, and cofactors from H2, CO2, and NH3. We find that 36% of the core’s phosphoanhydride hydrolyzing reactions generate PPi, while no reactions use PPi as an energy currency. The polymerization reactions that generate ~80% of cell mass – protein, RNA, and DNA synthesis – all generate PPi, while none use PPi as an energy source. In typical prokaryotic cells, aminoacyl tRNA synthetases (AARS) underlie ~80% of PPi production. We show that the irreversibility of the AARS reaction is a kinetic, not a thermodynamic effect. The data indicate that PPi is not an ancient energy currency and probably never was. Instead, PPi hydrolysis is an ancient mechanism that imparts irreversibility, as Kornberg suggested, functioning like a ratchet’s pawl to vectorialize the life process toward growth. The two anhydride bonds in nucleoside triphosphates offer ATP-cleaving enzymes an option to impart either thermodynamic control (Pi formation) or kinetic control (PPi formation) upon reactions. This dual capacity explains why nature chose the triphosphate moiety of ATP as biochemistry’s universal energy currency.

The possible evolutionary significance of pyrophosphate (PPi) has been discussed since the early 1960s. Lipmann suggested that PPi could have been an ancient currency or a possible environmental source of metabolic energy at origins, while Kornberg proposed that PPi vectorializes metabolism because ubiquitous pyrophosphatases render PPi forming reactions kinetically irreversible. To test those ideas, we investigated the reactions that consume phosphoanhydride bonds among the 402 reactions of the universal biosynthetic core that generates amino acids, nucleotides, and cofactors from H2, CO2, and NH3. We find that 36% of the core’s phosphoanhydride hydrolyzing reactions generate PPi, while no reactions use PPi as an energy currency. The polymerization reactions that generate ~80% of cell mass – protein, RNA, and DNA synthesis – all generate PPi, while none use PPi as an energy source. In typical prokaryotic cells, aminoacyl tRNA synthetases (AARS) underlie ~80% of PPi production. We show that the irreversibility of the AARS reaction is a kinetic, not a thermodynamic effect. The data indicate that PPi is not an ancient energy currency and probably never was. Instead, PPi hydrolysis is an ancient mechanism that imparts irreversibility, as Kornberg suggested, functioning like a ratchet’s pawl to vectorialize the life process toward growth. The two anhydride bonds in nucleoside triphosphates offer ATP-cleaving enzymes an option to impart either thermodynamic control (Pi formation) or kinetic control (PPi formation) upon reactions. This dual capacity explains why nature chose the triphosphate moiety of ATP as biochemistry’s universal energy currency.

Interest in the evolution of protein-protein and genetic interaction networks has been rising in recent years, but the lack of large-scale high quality comparative datasets has acted as a barrier. Here, we carried out a comparative analysis of computationally predicted protein-protein interaction (PPI) networks from five closely related yeast species. We used the Protein-protein Interaction Prediction Engine (PIPE), which uses a database of known interactions to make sequence-based PPI predictions, to generate high quality predicted interactomes. Simulated proteomes and corresponding PPI networks were used to provide null expectations for the extent and nature of PPI network evolution. We found strong evidence for conservation of PPIs, with lower than expected levels of change in PPIs for about a quarter of the proteome. Furthermore, we found that changes in predicted PPI networks are poorly predicted by sequence divergence. Our analyses identified a number of functional classes experiencing fewer PPI changes than expected, suggestive of purifying selection on PPIs. Our results demonstrate the added benefit of considering predicted PPI networks when studying the evolution of closely related organisms.

An extensive dataset of binary physical protein-protein interaction extracted from STRING 12.0 (>12,000 organisms) with artificially generated negatives. The dataset includes 72M positive pairs with STRING confidence scores> 0.9 and 720M negative pairs. The corresponding protein sequences are located in the .fasta files. The generation of the negatives was derived from https://doi.org/10.1016/j.isci.2024.110371

Nucleotide pyrophosphatase that generates diphosphate (PPi) and functions in bone mineralization and soft tissue calcification by regulating pyrophosphate levels (PubMed:10352096, PubMed:11004006, PubMed:12082181, PubMed:22510396, PubMed:25260930, PubMed:9662402). PPi inhibits bone mineralization and soft tissue calcification by binding to nascent hydroxyapatite crystals, thereby preventing further growth of these crystals (PubMed:10352096, PubMed:11004006, PubMed:12082181, PubMed:19419305, PubMed:22510396, PubMed:25260930, PubMed:25479107, PubMed:26910915, PubMed:30111653, PubMed:35147247, PubMed:9662402). Preferentially hydrolyzes ATP, but can also hydrolyze other nucleoside 5' triphosphates such as GTP, CTP and UTP to their corresponding monophosphates with release of pyrophosphate, as well as diadenosine polyphosphates, and also 3',5'-cAMP to AMP (PubMed:11027689, PubMed:1647027, PubMed:23027977, PubMed:8223581). May also be involved in the regulation of the availability of nucleotide sugars in the endoplasmic reticulum and Golgi, and the regulation of purinergic signaling (PubMed:1647027). Inhibits ectopic joint calcification and maintains articular chondrocytes by repressing hedgehog signaling; it is however unclear whether hedgehog inhibition is direct or indirect (PubMed:30111653). Appears to modulate insulin sensitivity (By similarity). Also involved in melanogenesis (By similarity). Also able to hydrolyze 2',3'-cGAMP (cyclic GMP-AMP), a second messenger that activates TMEM173/STING and triggers type-I interferon production (PubMed:25344812). 2',3'-cGAMP degradation takes place in the lumen or extracellular space, and not in the cytosol where it is produced; the role of 2',3'-cGAMP hydrolysis is therefore unclear (By similarity). Not able to hydrolyze the 2',3'-cGAMP linkage isomer 3',3'-cGAMP (By similarity)

This dataset accompanies the NeurIPS 2025 submission titled "Face to Face with Proteins: Contrastive Surface Learning for Protein–Protein Interaction Prediction."

It contains homology-aware and random train/val/test splits.

All files were generated using the pipeline included in the supplementary code repository.

Virtual screening of protein–protein and protein–peptide interactions is a challenging task that directly impacts the processes of hit identification and hit-to-lead optimization in drug design projects involving peptide-based pharmaceuticals. Although several screening tools designed to predict the binding affinity of protein–protein complexes have been proposed, methods specifically developed to predict protein–peptide binding affinity are comparatively scarce. Frequently, predictors trained to score the affinity of small molecules are used for peptides indistinctively, despite the larger complexity and heterogeneity of interactions rendered by peptide binders. To address this issue, we introduce PPI-Affinity, a tool that leverages support vector machine (SVM) predictors of binding affinity to screen datasets of protein–protein and protein–peptide complexes, as well as to generate and rank mutants of a given structure. The performance of the SVM models was assessed on four benchmark datasets, which include protein–protein and protein–peptide binding affinity data. In addition, we evaluated our model on a set of mutants of EPI-X4, an endogenous peptide inhibitor of the chemokine receptor CXCR4, and on complexes of the serine proteases HTRA1 and HTRA3 with peptides. PPI-Affinity is freely accessible at https://protdcal.zmb.uni-due.de/PPIAffinity.

The dataset contains 5,604 proteins and 23,322 interactions generated by multiple orthogonal high-throughput yeast two-hybrid screens.

This family includes proteins predominantly found in Bacteroidetes, such as [swissprot:Q9WYP6] from Thermotoga maritima. This enzyme, member of the ribokinase family, uses inorganic pyrophosphate (PPi) but neither ATP nor ADP to generate myo-inositol monophosphate. It contains two large hydrophobic residues partially obstructing the ATP-binding pocket and three basic residues involved in PPi recognition . Many members of this family show the domain [interpro:IPR011611].

Nucleotide pyrophosphatase that generates diphosphate (PPi) and functions in bone mineralization and soft tissue calcification by regulating pyrophosphate levels. PPi inhibits bone mineralization and soft tissue calcification by binding to nascent hydroxyapatite crystals, thereby preventing further growth of these crystals. Preferentially hydrolyzes ATP, but can also hydrolyze other nucleoside 5' triphosphates such as GTP, CTP and UTP to their corresponding monophosphates with release of pyrophosphate, as well as diadenosine polyphosphates, and also 3',5'-cAMP to AMP. May also be involved in the regulation of the availability of nucleotide sugars in the endoplasmic reticulum and Golgi, and the regulation of purinergic signaling. Inhibits ectopic joint calcification and maintains articular chondrocytes by repressing hedgehog signaling; it is however unclear whether hedgehog inhibition is direct or indirect (By similarity). Appears to modulate insulin sensitivity. Also involved in melanogenesis (By similarity). Also able to hydrolyze 2',3'-cGAMP (cyclic GMP-AMP), a second messenger that activates TMEM173/STING and triggers type-I interferon production (By similarity). 2',3'-cGAMP degradation takes place in the lumen or extracellular space, and not in the cytosol where it is produced; the role of 2',3'-cGAMP hydrolysis is therefore unclear. Not able to hydrolyze the 2',3'-cGAMP linkage isomer 3'-3'-cGAMP (By similarity)

The PPI view displays H-InvDB human protein-protein interaction (PPI) information. It is constructed by assigning interaction data to H-InvDB proteins which were originally predicted from transcriptional products generated by the H-Invitational project. The PPI view is now providing 32,198 human PPIs comprised of 9,268 H-InvDB proteins. H-Invitational Database (H-InvDB) is an integrated database of human genes and transcripts. By extensive analyses of all human transcripts, we provide curated annotations of human genes and transcripts that include gene structures, alternative splicing isoforms, non-coding functional RNAs, protein functions, functional domains, sub-cellular localizations, metabolic pathways, protein 3D structure, genetic polymorphisms (SNPs, indels and microsatellite repeats) , relation with diseases, gene expression profiling, molecular evolutionary features, protein-protein interactions (PPIs) and gene families/groups. Sponsors: This research is financially supported by the Ministry of Economy, Trade and Industry of Japan (METI), the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT) and the Japan Biological Informatics Consortium (JBIC). Also, this work is partly supported by the Research Grant for the RIKEN Genome Exploration Research Project from MEXT to Y.H. and the Grant for the RIKEN Frontier Research System, Functional RNA research program.

In this work, we introduce a novel network synthesis model that can generate families of evolutionarily related synthetic protein–protein interaction (PPI) networks. Given an ancestral network, the proposed model generates the network family according to a hypothetical phylogenetic tree, where the descendant networks are obtained through duplication and divergence of their ancestors, followed by network growth using network evolution models. We demonstrate that this network synthesis model can effectively create synthetic networks whose internal and cross-network properties closely resemble those of real PPI networks. The proposed model can serve as an effective framework for generating comprehensive benchmark datasets that can be used for reliable performance assessment of comparative network analysis algorithms. Using this model, we constructed a large-scale network alignment benchmark, called NAPAbench, and evaluated the performance of several representative network alignment algorithms. Our analysis clearly shows the relative performance of the leading network algorithms, with their respective advantages and disadvantages. The algorithm and source code of the network synthesis model and the network alignment benchmark NAPAbench are publicly available at http://www.ece.tamu.edu/bjyoon/NAPAbench/.

All-to-all prediction scores between Human and HIV1 for three independent sequence-based PPI predictors: PIPE4, SPRINT, SPPS. Each algorithm was trained on intra-species PPIs (Human-Human & HIV1-HIV1) to generate the inter-species predictions. The training samples were obtained from BioGRID. The human proteome was obtained from Uniprot (Proteome Id: UP000005640) and filtered for Reviewed/Swiss-Prot status; resulting in 20,350 proteins (7 proteins excluded due to sequence length). The HIV1 proteome was similarly obtained (Proteome Id: UP000002241); resulting in 9 proteins. All 183,087 predictions are provided for each method except SPPS for which 25 human protein sequences were excluded for having non-standard amino acids. Each file contains three columns of comma separated values representing: human-protein,hiv1-protein,score where the score column represents the likelihood of interaction for that given PPI. Files are sorted on the Human protein and then on the HIV1 protein.

Nuclear proteins are major constituents and key regulators of the topological organization of nucleome and manipulators of nuclear events. To decipher the global connectivity of nuclear proteins and the hierarchically organized modules of nuclear protein interactions, the double chemical-crosslinking coupled with mass spectrometry (in vivo XL-MS) analysis was integrated with the dimethyl-labelling to generate a qXL-MS workflow, which consequently identified 5,340 cross-linked peptides (or crosslinks) from the nuclei of soybean seedlings. These crosslinks were converted into 1,297 nuclear protein-protein interactions (PPIs), 1220 (94%) of which were non-confirmative (or novel) nuclear PPIs compared to those in repositories. There were 250 and 26 novel interactors of histones and nucleolar box C/D snoRNP complex, respectively. Modulomic analysis of the Arabidopsis orthoglous PPIs constructed 27 and 24 master nuclear protein-protein interaction modules (NPIMs) that contain the condensate-forming protein(s) and the intrinsically disordered region (IDR)-containing proteins, respectively. These NPIMs successfully captured the previously reported nuclear protein complexes and nuclear bodies in nucleus. Surprisingly, these NPIMs are hierarchically assorted into 4 higher order communities in nucleomic graph, including Genome and Nucleolus Community. This combinatorial pipeline of 4C quantitative interactomics and PPI network modularization revealed 17 ethylene hormone-specific module variants participating in a broad range of nuclear events. It is capable of capturing both nuclear protein complexes and nuclear bodies, of constructing the topological architectures of both PPI modules and module variants in nucleome and of probably mapping the protein compositions of biomolecular condensates.

G-17: gastrin-17, PPI: proton pump inhibitor, IQR: interquartile range.*Fundic atrophy was defined by histology gold standard.

The Predictive Phenomics Science & Technology Initiative (PPI) at Pacific Northwest National Laboratory are tackling the grand challenge of understanding and predicting phenotype by identifying the molecular basis of function and enable function-driven design and control of biological systems. Research projects within this initiative are divided into three Thrust Areas (TAs): TA1) Enhancing Multi-Scale Phenomics Measurements, TA2) Identifying Molecular Patterns of Biological Function, and TA3) Computational Methods - Phenotypic Signatures. In efforts to enable discovery, reproducibility, and reuse of PPI-funded digital research data generated or used through the course of the proposed research-funded lifecycles, all corresponding digital data assets conducted under the Laboratory Directed Research and Development Program at PNNL are linked to this PPI dataset catalog collection.

The global PPI NAND Flash market size was valued at USD 2.5 billion in 2019 and is projected to reach USD 4.2 billion by 2027, exhibiting a CAGR of 6.7% during the forecast period. The growing demand for consumer electronics, such as smartphones, tablets, and laptops, is a major factor driving the demand for PPI NAND Flash memory. Additionally, the increasing popularity of cloud-based services and the proliferation of 5G networks are further fueling the growth of the market. Key trends in the PPI NAND Flash market include the increasing adoption of 3D NAND technology, which offers higher storage density and performance compared to traditional 2D NAND. Additionally, the emergence of new applications, such as artificial intelligence (AI) and machine learning (ML), is expected to create new growth opportunities for the PPI NAND Flash market. Some of the leading companies in the market include Dosilicon, Deutron Electronics, Wuhan Yuxin Semiconductor, UMT, GigaDevice, and Shanghai Fudan Microelectronics.

Protein-Protein, Genetic, and Chemical Interactions for Gao J (2008):A protein-protein interaction network of transcription factors acting during liver cell proliferation. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Liver regeneration is a complex process that involves a multitude of cellular functions, including primarily cell proliferation, apoptosis, inflammation, and metabolism. A number of signaling pathways that control these processes have been identified, and cross communication between them by direct protein-protein interactions has been shown to be crucial in orchestrating liver regeneration. Previously, we have identified a group of transcription factors capable of regulating liver cell growth and that may be involved in liver cancer development. The expression of some of their mouse counterpart genes was altered dramatically after liver injury and regeneration induced by CCl(4) in mice. In an effort to elucidate the molecular basis for liver regeneration through protein-protein interactions (PPI), a matrix mating Y2H approach was produced to generate a PPI network between a set of 32 regulatory proteins. Sixty-four interactions were identified, including 4 that had been identified previously. Ten of the interactions were further confirmed with GST pull-down and coimmunoprecipitation assays. Information provided by this PPI network may shed further light on the molecular mechanisms that regulate liver regeneration at the protein interaction level and ultimately identify regulatory factors that may serve as candidate drug targets for the treatment of liver diseases.

PPI OnlyFaces

## Overview

PPI OnlyFaces is a dataset for object detection tasks - it contains Signature Passport Face Plates annotations for 9,744 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Diabetes-related diseases (DRDs), especially cancers pose a big threat to public health. Although people have explored pathological pathways of a few common DRDs, there is a lack of systematic studies on important biological processes (BPs) connecting diabetes and its related diseases/cancers. We have proposed and compared 10 protein–protein interaction (PPI)-based computational methods to study the connections between diabetes and 254 diseases, among which a method called DIconnectivity_eDMN performs the best in the sense that it infers a disease rank (according to its relation with diabetes) most consistent with that by literature mining. DIconnectivity_eDMN takes diabetes-related genes, other disease-related genes, a PPI network, and genes in BPs as input. It first maps genes in a BP into the PPI network to construct a BP-related subnetwork, which is expanded (in the whole PPI network) by a random walk with restart (RWR) process to generate a so-called expanded modularized network (eMN). Since the numbers of known disease genes are not high, an RWR process is also performed to generate an expanded disease-related gene list. For each eMN and disease, the expanded diabetes-related genes and disease-related genes are mapped onto the eMN. The association between diabetes and the disease is measured by the reachability of their genes on all eMNs, in which the reachability is estimated by a method similar to the Kolmogorov–Smirnov (KS) test. DIconnectivity_eDMN achieves an area under receiver operating characteristic curve (AUC) of 0.71 for predicting both Type 1 DRDs and Type 2 DRDs. In addition, DIconnectivity_eDMN reveals important BPs connecting diabetes and DRDs. For example, “respiratory system development” and “regulation of mRNA metabolic process” are critical in associating Type 1 diabetes (T1D) and many Type 1 DRDs. It is also found that the average proportion of diabetes-related genes interacting with DRDs is higher than that of non-DRDs.