Protein-metabolite Interactions Research Articles

MetalinksDB: a flexible and contextualizable resource of metabolite-protein interactions.

From the catalytic breakdown of nutrients to signaling, interactions between metabolites and proteins play an essential role in cellular function. An important case is cell-cell communication, where metabolites, secreted into the microenvironment, initiate signaling cascades by binding to intra- or extracellular receptors of neighboring cells. Protein-protein cell-cell communication interactions are routinely predicted from transcriptomic data. However, inferring metabolite-mediated intercellular signaling remains challenging, partially due to the limited size of intercellular prior knowledge resources focused on metabolites. Here, we leverage knowledge-graph infrastructure to integrate generalistic metabolite-protein with curated metabolite-receptor resources to create MetalinksDB. MetalinksDB is an order of magnitude larger than existing metabolite-receptor resources and can be tailored to specific biological contexts, such as diseases, pathways, or tissue/cellular locations. We demonstrate MetalinksDB's utility in identifying deregulated processes in renal cancer using multi-omics bulk data. Furthermore, we infer metabolite-driven intercellular signaling in acute kidney injury using spatial transcriptomics data. MetalinksDB is a comprehensive and customizable database of intercellular metabolite-protein interactions, accessible via a web interface (https://metalinks.omnipathdb.org/) and programmatically as a knowledge graph (https://github.com/biocypher/metalinks). We anticipate that by enabling diverse analyses tailored to specific biological contexts, MetalinksDB will facilitate the discovery of disease-relevant metabolite-mediated intercellular signaling processes.

Unveiling Actin Cytoskeleton Role in Mediating Chikungunya-Associated Arthritis: An Integrative Proteome-Metabolome Study.

Background: Chikungunya is a zoonotic disease caused by the Chikungunya virus (CHIKV), primarily transmitted to humans through infected Aedes mosquitoes. The infection is characterized by symptoms such as high fever, musculoskeletal pain, polyarthritis, and a rash, which can lead to severe complications such as encephalitis, meningitis, and even fatalities. While many disease manifestations resemble those of other viral infections, chronic arthritis caused by CHIKV is unique, and its molecular mechanisms remain ill-defined. Materials and Methods: Proteomics data from both cellular and patient levels of CHIKV infection were curated from PubMed and screened using inclusion and exclusion criteria. Patient serum proteomics data obtained from P RIDE underwent reanalysis using Proteome Discoverer 2.2. Enrichment and protein-protein interaction network analysis were conducted on differentially expressed proteins from both serum and cellular datasets. Metabolite data from CHIKV-infected patients were further retrieved, and their protein binding partners were identified using BindingDB. The protein-metabolite interaction pathway was further developed using MetaboAnalyst. Results: The proteomics data analysis revealed differential expression of proteins involved in critical host mechanisms, such as cholesterol metabolism and mRNA splicing, during CHIKV infection. Consistent upregulation of two actin cytoskeleton proteins, TAGLN2 and PFN1, was noted in both serum and cellular datasets, and their upregulations are associated with arthritis. Furthermore, alterations in purine metabolism were observed in the integrative proteome-metabolome analysis, correlating with cytoskeletal remodelling. Conclusion: Collectively, this integrative view sheds light on the involvement of actin cytoskeleton remodeling proteins and purine metabolic pathways in the development of arthritis during CHIKV infection.

Abstract 913: Automated reasoning artificial intelligence (AI) to model the cell-cell biochemical and cellular interactions in the tumor microenvironment (TME) of oral cancer (OC)

Abstract Our goal was to study the spatial complexity of HPV negative OC by integrating scRNAseq and spatial transcriptomics from 11 samples including 5 from never smokers never drinkers (NSND). We used Turing Biosystems proprietary graph database engine and automated reasoning AI to analyze and model the cell-cell biochemical and cellular interactions in the TME. The tumors H&E images, Visium 10X, and single cell 10X data were pre-processed using standard methods. QC-based genes and cells filtering, gene expression normalization, dimensionality reduction, clustering of Visium spots, and identification of spatially variable genes were performed. We generated multiple networks from tumor data and prior biological knowledge to integrate different layers of information: single-cell and spatial transcriptomics data, cell-cell interactions networks, cell neighboring, spatial correlation networks. We first used a novel network-based technique to identify the spatial distribution of any cell subtypes (e.g. CAF, T cells), by integrating differentially expressed genes from scRNAseq, prior knowledge and prior data (e.g. TCGA). We used a multilayer network approach to integrate the different networks of data and an automated reasoning AI analysis which allows to interpret the results based on a reasoning on biological and clinical knowledge in opposition to machine learning methods based on statistical patterns. We identified CAFs/T cells/IFN-g to be strongly correlated within the tumor islets interacting with the stroma in NSND while they were more correlated within the stromal regions in smokers. We also identified 2 or 3 subtypes of tumor islets areas based on their functional states (e.g., immune pathways activated or metabolic differences), allowing us to unravel a more detailed map of the intratumoral heterogeneity when compared to histopathology annotations. In order to get some insights into the functional impact of the spatial distribution of cells, the tumors were then represented as multilayer networks to identify all possible cell-cell interactions in the TME. This was followed by a spatial simulation of these interactions using logic (e.g., inhibition, activation) and correlative (e.g., gene expression, imaging features correlations) interactions in the networks to identify spatial interactions. From that we analysed all the possible known protein-protein and metabolite-protein interactions between the cell types analysed and mapped the tumors with the gene expressions corresponding to the pairs of interacting proteins (or enzymes linked to metabolites). We found an interaction between CAF and Tregs via CXCL12 (CAFs) and CXCR4 (Tregs). In conclusion, automated reasoning AI integrating scRNAseq and spatial transcriptomics allows an in-depth analysis of cells spatial distribution and its functional impact in OC. Citation Format: Karène Mahtouk, Maxime Vincent, Yannick Le Meitour, Béatrice Vanbervliet-Defrance, Sonia Canjura, Cyril Degletagne, Laurie Tonon, Lucas Michon, Jebrane Bouaoud, Aude Excoffier, Philippe Zrounba, Rémy Boutonnet, Adam Amara, Pierre Saintigny. Automated reasoning artificial intelligence (AI) to model the cell-cell biochemical and cellular interactions in the tumor microenvironment (TME) of oral cancer (OC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 913.

Phytohormones in a universe of regulatory metabolites: lessons from jasmonate.

Small-molecule phytohormones exert control over plant growth, development, and stress responses by coordinating the patterns of gene expression within and between cells. Increasing evidence indicates that currently recognized plant hormones are part of a larger group of regulatory metabolites that have acquired signaling properties during the evolution of land plants. This rich assortment of chemical signals reflects the tremendous diversity of plant secondary metabolism, which offers evolutionary solutions to the daunting challenges of sessility and other unique aspects of plant biology. A major gap in our current understanding of plant regulatory metabolites is the lack of insight into the direct targets of these compounds. Here, we illustrate the blurred distinction between classical phytohormones and other bioactive metabolites by highlighting the major scientific advances that transformed the view of jasmonate from an interesting floral scent to a potent transcriptional regulator. Lessons from jasmonate research generally apply to other phytohormones and thus may help provide a broad understanding of regulatory metabolite-protein interactions. In providing a framework that links small-molecule diversity to transcriptional plasticity, we hope to stimulate future research to explore the evolution, functions, and mechanisms of perception of a broad range of plant regulatory metabolites.

Abstract B075: Identifying functional allosteric binding sites using a systematic and scalable AMPS screening platform for drug discovery

Abstract Endogenous cellular metabolites have been shown to bind and regulate proteins involved in tumorigenesis. These low-affinity metabolite-protein interactions can modulate protein function through binding at allosteric sites. As an example, in normal cells, the ABL1 protein is regulated by N-terminal myristylation, which binds to a hydrophobic pocket in the C-terminal lobe of the kinase domain autoinhibiting its activity. Fusion of BCR to ABL1 leads to deletion of the N-terminal myristylation site activating BCR-ABL oncoprotein leading to chronic myeloid leukemia (CML). Similarly, autopalmitoylation of TEAD proteins regulates transcriptional output of Hippo signaling and tumorigenesis. Allosteric metabolite binding sites on these targets have been successfully used to generate new medicines (e.g.: Asciminib for BCR-ABL) and investigational cancer therapies (e.g.: IK-930, VT3989 for TEAD). However, there are limited approaches that can be broadly deployed to discover functional regulatory sites on precision oncology targets. Atavistik Bio has developed the AMPS (Atavistik Metabolite Protein Screening) platform that enables systematic and scalable screening of metabolites against proteins or protein complexes of interest. The AMPS platform technology integrates equilibrium dialysis with sensitive mass-spectrometry to screen a proprietary metabolite library. A state-of-the-art computational workflow coupled with deep mechanistic structure-function characterization enables us to discover novel functional allosteric modulators. To validate the platform, we have screened several precision oncology targets and characterized the AMPS metabolite hits with functional assays. For ABL1 tyrosine kinase, we have applied AMPS on distinct enzyme forms to probe the role of metabolite binding in the context of phosphorylation states and drug resistant T315I mutant forms. In addition, AMPS allows us to investigate novel metabolite sensitization of ABL1 in the presence of existing drugs Imatinib and Asciminib. Results from AMPS and follow up experiments highlight phosphorylation state dependency on myristate binding to ABL1. In conclusion, we demonstrate the AMPS platform can identify highly conserved allosteric protein-metabolite interactions to advance small molecule drug discovery efforts for new precision medicines. Citation Format: Anil K Padyana, Anna Schinzel, Gordon Murray, Joseph H LaPointe, Kaitlyn Selmer, Truc Pham, Melissa J Buskes, Shomit Sengupta, Maria-Jesus Blanco, Thomas Roddy, Marion Dorsch. Identifying functional allosteric binding sites using a systematic and scalable AMPS screening platform for drug discovery [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr B075.

Metabolomics and mitochondrial dysfunction in cardiometabolic disease

Circulating metabolites are indicators of systemic metabolic dysfunction and can be detected through contemporary techniques in metabolomics. These metabolites are involved in numerous mitochondrial metabolic processes including glycolysis, fatty acid β-oxidation, and amino acid catabolism, and changes in the abundance of these metabolites is implicated in the pathogenesis of cardiometabolic diseases (CMDs). Epigenetic regulation and direct metabolite-protein interactions modulate metabolism, both within cells and in the circulation. Dysfunction of multiple mitochondrial components stemming from mitochondrial DNA mutations are implicated in disease pathogenesis. This review will summarize the current state of knowledge regarding: i) the interactions between metabolites found within the mitochondrial environment during CMDs, ii) various metabolites' effects on cellular and systemic function, iii) how harnessing the power of metabolomic analyses represents the next frontier of precision medicine, and iv) how these concepts integrate to expand the clinical potential for translational cardiometabolic medicine.

Metabolite interactions in the bacterial Calvin cycle and implications for flux regulation

Metabolite-level regulation of enzyme activity is important for microbes to cope with environmental shifts. Knowledge of such regulations can also guide strain engineering for biotechnology. Here we apply limited proteolysis-small molecule mapping (LiP-SMap) to identify and compare metabolite-protein interactions in the proteomes of two cyanobacteria and two lithoautotrophic bacteria that fix CO2 using the Calvin cycle. Clustering analysis of the hundreds of detected interactions shows that some metabolites interact in a species-specific manner. We estimate that approximately 35% of interacting metabolites affect enzyme activity in vitro, and the effect is often minor. Using LiP-SMap data as a guide, we find that the Calvin cycle intermediate glyceraldehyde-3-phosphate enhances activity of fructose-1,6/sedoheptulose-1,7-bisphosphatase (F/SBPase) from Synechocystis sp. PCC 6803 and Cupriavidus necator in reducing conditions, suggesting a convergent feed-forward activation of the cycle. In oxidizing conditions, glyceraldehyde-3-phosphate inhibits Synechocystis F/SBPase by promoting enzyme aggregation. In contrast, the glycolytic intermediate glucose-6-phosphate activates F/SBPase from Cupriavidus necator but not F/SBPase from Synechocystis. Thus, metabolite-level regulation of the Calvin cycle is more prevalent than previously appreciated.

Clinical prognosis and related molecular features of hepatitis B-associated adolescent and young adult hepatocellular carcinoma

BackgroundInattention has been given to the pathogenesis of adolescent and young adult (AYA) hepatocellular carcinoma (HCC). Due to the more advanced tumor progression and poorer prognosis of AYA-HCC, together with a better tolerance ability, noncirrhotic background, and a stronger willingness to treat AYA-HCC, clinical and molecular biology studies are urgent and necessary, especially for those with hepatitis B infection.MethodsFor clinical aspects, the overall survival, the recurrence-free survival, and the Cox analyses were performed. Then, functional analysis, gene clustering, metabolic-related analysis, immune infiltration and competing endogenous RNA (ceRNA) construction were carried out using whole transcriptome sequencing technique.ResultsBased on the clinical information of our HCC cohort, the overall survival and recurrence-free survival rates were worse in the AYA group than in the elderly group as previously described. According to our whole transcriptome sequencing results, functional analysis revealed that metabolism-related pathways as well as protein translation and endoplasmic reticulum processing were enriched. Then the hub metabolism-related genes were screened by metabolite–protein interactions (MPIs) and protein–protein interactions (PPIs). Fatty acid metabolism is a crucial component of metabolic pathways, abnormalities of which may be the reason for the worse prognosis of HBV-AYA HCC. Finally, the relationship of disrupted expression of metabolism-related genes with immune infiltration was also analyzed, and the lncRNA‒miRNA‒mRNA-related ceRNA network for HBV-AYA HCC was constructed, which may provide new cues for HBV-AHA HCC prevention.ConclusionThe worse prognosis and recurrence rate of HBV-AYA HCC may be related to abnormalities in metabolism-related pathways, especially disorders of fatty acid metabolism.

Protein-Metabolite Interactions: Discovery and Significance.

Metabolites orchestrate cellular processes as either substrates, co-enzymes, inhibitors, or activators of cellular proteins such as enzymes and receptors. Although traditional biochemical and structural biology-based approaches have been successfully employed for the discovery of protein-metabolite interactions, they often fail to detect transient and low-affinity biomolecular relationships. Another limitation of these approaches is that they are performed under in vitro conditions lacking the physiological context. Recently developed mass spectrometry-based methodologies overcome both these shortcomings, and have resulted in the discovery of global protein-metabolite cellular interaction networks. Herein, we describe traditional and modern approaches for the discovery of protein-metabolite interactions, and discuss the impact of these discoveries on our understanding of cellular physiology and on drug development.

The involvement of allosteric effectors and post-translational modifications in the control of plant central carbon metabolism.

Plant metabolism is finely orchestrated to allow the occurrence of complementary and sometimes opposite metabolic pathways. In part this is achieved by the allosteric regulation of enzymes, which has been a cornerstone of plant research for many decades. The completion of the Arabidopsis genome and the development of the associated toolkits for Arabidopsis research moved the focus of many researchers to other fields. This is reflected by the increasing number of high-throughput proteomic studies, mainly focused on post-translational modifications. However, follow-up 'classical' biochemical studies to assess the functions and upstream signaling pathways responsible for such modifications have been scarce. In this work, we review the basic concepts of allosteric regulation of enzymes involved in plant carbon metabolism, comprising photosynthesis and photorespiration, starch and sucrose synthesis, glycolysis and gluconeogenesis, the oxidative pentose phosphate pathway and the tricarboxylic acid cycle. Additionally, we revisit the latest results on the allosteric control of the enzymes involved in these pathways. To conclude, we elaborate on the current methods for studying protein-metabolite interactions, which we consider will become crucial for discoveries in the future.

NMR proven albumin interaction with metabolites in complex mixtures

One critical step in new drugs development is the investigation of the interactions between drug candidate and target protein. Nuclear Magnetic Resonance Spectroscopy (NMR) is a well-established technique for studding these interactions. Due to its availability and structural similarities to human albumin, bovine serum albumin (BSA) is widely accepted as a model for investigating the binding of small molecules to serum albumin. We report here on the evaluation of binding interactions between BSA and 18 metabolites using saturation transfer difference (STD) NMR experiments. Positive STD signals that indicate metabolite-protein interactions were obtained for leucine, pyruvic acid, valine, threonine, alanine, 4-aminohippuric acid and tryptophan.

SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy

Drug combinations can be the prime strategy for increasing the initial treatment options in cancer therapy. However, identifying the combinations through experimental approaches is very laborious and costly. Notably, in vitro and/or in vivo examination of all the possible combinations might not be plausible. This study presented a novel computational approach to predicting synergistic drug combinations. Specifically, the deep neural network-based binary classification was utilized to develop the model. Various physicochemical, genomic, protein–protein interaction and protein-metabolite interaction information were used to predict the synergy effects of the combinations of different drugs. The performance of the constructed model was compared with shallow neural network (SNN), k-nearest neighbors (KNN), random forest (RF), support vector machines (SVMs), and gradient boosting classifiers (GBC). Based on our findings, the proposed deep neural network model was found to be capable of predicting synergistic drug combinations with high accuracy. The prediction accuracy and AUC metrics for this model were 92.21% and 97.32% in tenfold cross-validation. According to the results, the integration of different types of physicochemical and genomics features leads to more accurate prediction of synergy in cancer drugs.

Proteins and metabolites fingerprints of gestational diabetes mellitus forming protein-metabolite interactomes are its potential biomarkers.

Gestational diabetes mellitus (GDM) is a consequence of glucose intolerance with an inadequate production of insulin that happens during pregnancy and leads to adverse health consequences for both mother and fetus. GDM patients are at higher risk for preeclampsia, and developing diabetes mellitus type 2 in later life, while the child born to GDM mothers are more prone to macrosomia, and hypoglycemia. The universally accepted diagnostic criteria for GDM are lacking, therefore there is a need for a diagnosis of GDM that can identify GDM at its early stage (first trimester). We have reviewed the literature on proteins and metabolites fingerprints of GDM. Further, we have performed protein-protein, metabolite-metabolite, and protein-metabolite interaction network studies on GDM proteins and metabolites fingerprints. Notably, some proteins and metabolites fingerprints are forming strong interaction networks at high confidence scores. Therefore, we have suggested that those proteins and metabolites that are forming protein-metabolite interactomes are the potential biomarkers of GDM. The protein-metabolite biomarkers interactome may help in a deep understanding of the prognosis, pathogenesis of GDM, and also detection of GDM. The protein-metabolites interactome may be further applied in planning future therapeutic strategies to promote long-term health benefits in GDM mothers and their children.

Protein-metabolite interactomics of carbohydrate metabolism reveal regulation of lactate dehydrogenase.

Metabolic networks are interconnected and influence diverse cellular processes. The protein-metabolite interactions that mediate these networks are frequently low affinity and challenging to systematically discover. We developed mass spectrometry integrated with equilibrium dialysis for the discovery of allostery systematically (MIDAS) to identify such interactions. Analysis of 33 enzymes from human carbohydrate metabolism identified 830 protein-metabolite interactions, including known regulators, substrates, and products as well as previously unreported interactions. We functionally validated a subset of interactions, including the isoform-specific inhibition of lactate dehydrogenase by long-chain acyl-coenzyme A. Cell treatment with fatty acids caused a loss of pyruvate-lactate interconversion dependent on lactate dehydrogenase isoform expression. These protein-metabolite interactions may contribute to the dynamic, tissue-specific metabolic flexibility that enables growth and survival in an ever-changing nutrient environment.

The Knowns and Unknowns in Protein–Metabolite Interactions

Increasing attention has been focused on the study of protein-metabolite interactions (PMI), which play a key role in regulating protein functions and directing an orchestra of cellular processes. The investigation of PMIs is complicated by the fact that many such interactions are extremely short-lived, which requires very high resolution in order to detect them. As in the case of protein-protein interactions, protein-metabolite interactions are still not clearly defined. Existing assays for detecting protein-metabolite interactions have an additional limitation in the form of a limited capacity to identify interacting metabolites. Thus, although recent advances in mass spectrometry allow the routine identification and quantification of thousands of proteins and metabolites today, they still need to be improved to provide a complete inventory of biological molecules, as well as all interactions between them. Multiomic studies aimed at deciphering the implementation of genetic information often end with the analysis of changes in metabolic pathways, as they constitute one of the most informative phenotypic layers. In this approach, the quantity and quality of knowledge about PMIs become vital to establishing the full scope of crosstalk between the proteome and the metabolome in a biological object of interest. In this review, we analyze the current state of investigation into the detection and annotation of protein-metabolite interactions, describe the recent progress in developing associated research methods, and attempt to deconstruct the very term "interaction" to advance the field of interactomics further.

Don't let go: co-fractionation mass spectrometry for untargeted mapping of protein-metabolite interactomes.

The chemical complexity of metabolomes goes hand in hand with their functional diversity. Small molecules have many essential roles, many of which are executed by binding and modulating the function of a protein partner. The complex and dynamic protein-metabolite interaction (PMI) network underlies most if not all biological processes, but remains under-characterized. Herein, we highlight how co-fractionation mass spectrometry (CF-MS), a well-established approach to map protein assemblies, can be used for proteome and metabolome identification of the PMIs. We will review recent CF-MS studies, discuss the main advantages and limitations, summarize the available CF-MS guidelines, and outline future challenges and opportunities.

Prediction of metabolite-protein interactions based on integration of machine learning and constraint-based modeling.

Metabolite-protein interactions play an important role in regulating protein functions and metabolism. Yet, predictions of metabolite-protein interactions using genome-scale metabolic networks are lacking. Here, we fill this gap by presenting a computational framework, termed SARTRE, that employs features corresponding to shadow prices determined in the context of flux variability analysis to predict metabolite-protein interactions using supervised machine learning. By using gold standards for metabolite-protein interactomes and well-curated genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae, we found that the implementation of SARTRE with random forest classifiers accurately predicts metabolite-protein interactions, supported by an average area under the receiver operating curve of 0.86 and 0.85, respectively. Ranking of features based on their importance for classification demonstrated the key role of shadow prices in predicting metabolite-protein interactions. The quality of predictions is further supported by the excellent agreement of the organism-specific classifiers on unseen interactions shared between the two model organisms. Further, predictions from SARTRE are highly competitive against those obtained from a recent deep-learning approach relying on a variety of protein and metabolite features. Together, these findings show that features extracted from constraint-based analyses of metabolic networks pave the way for understanding the functional roles of the interactions between proteins and small molecules. https://github.com/fayazsoleymani/SARTRE.

Self- and cross-attention accurately predicts metabolite-protein interactions

Self- and cross-attention accurately predicts metabolite-protein interactions

Rewiring of the protein–protein–metabolite interactome during the diauxic shift in yeast

In budding yeast Saccharomyces cerevisiae, the switch from aerobic fermentation to respiratory growth is separated by a period of growth arrest, known as the diauxic shift, accompanied by a significant metabolic rewiring, including the derepression of gluconeogenesis and the establishment of mitochondrial respiration. Previous studies reported hundreds of proteins and tens of metabolites accumulating differentially across the diauxic shift transition. To assess the differences in the protein–protein (PPIs) and protein–metabolite interactions (PMIs) yeast samples harvested in the glucose-utilizing, fermentative phase, ethanol-utilizing and early stationary respiratory phases were analysed using isothermal shift assay (iTSA) and a co-fractionation mass spectrometry approach, PROMIS. Whereas iTSA monitors changes in protein stability and is informative towards protein interaction status, PROMIS uses co-elution to delineate putative PPIs and PMIs. The resulting dataset comprises 1627 proteins and 247 metabolites, hundreds of proteins and tens of metabolites characterized by differential thermal stability and/or fractionation profile, constituting a novel resource to be mined for the regulatory PPIs and PMIs. The examples discussed here include (i) dissociation of the core and regulatory particle of the proteasome in the early stationary phase, (ii) the differential binding of a co-factor pyridoxal phosphate to the enzymes of amino acid metabolism and (iii) the putative, phase-specific interactions between proline-containing dipeptides and enzymes of central carbon metabolism.

Databases and Tools to Investigate Protein-Metabolite Interactions.

Protein-metabolite interactions (PMIs) are directly responsible for the regulation of numerous processes. From the direct regulation of enzymes to complex developmental processes intermediated by hormones, PMIs are central to understanding the molecular mechanisms of important physiological phenomena. Still, proving such interactions experimentally has proven an arduous task. We discuss here some of the current technologies contributing to expand our knowledge on PMIs, with particular emphasis on platforms and databases to explore the highly heterogenous nature of characterized PMIs, which is likely to be an essential resource on the development of new computational approaches to predict and validate interactions based on large-scale PMI screenings.