The IntAct database: efficient access to fine-grained molecular interaction data.

The IntAct molecular interaction database (https://www.ebi.ac.uk/intact) is a curated resource of molecular interactions, derived from the scientific literature and from direct data depositions. As of August 2021, IntAct provides more than one million binary interactions, curated by twelve global partners of the International Molecular Exchange consortium, for which the IntAct database provides a shared curation and dissemination platform. The IMEx curation policy has always emphasised a fine-grained data and curation model, aiming to capture the relevant experimental detail essential for the interpretation of the provided molecular interaction data. Here, we present recent curation focus and progress, as well as a completely redeveloped website which presents IntAct data in a much more user-friendly and detailed way.

The gene regulation knowledge commons: the action area of GREEKC.

As computational modeling becomes more essential to analyze and understand biological regulatory mechanisms, governance of the many databases and knowledge bases that support this domain is crucial to guarantee reliability and interoperability of resources. To address this, the COST Action Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC, CA15205, www.greekc.org) organized nine workshops in a four-year period, starting September 2016. The workshops brought together a wide range of experts from all over the world working on various steps in the knowledge management process that focuses on understanding gene regulatory mechanisms. The discussions between ontologists, curators, text miners, biologists, bioinformaticians, philosophers and computational scientists spawned a host of activities aimed to standardize and update existing knowledge management workflows and involve end-users in the process of designing the Gene Regulation Knowledge Commons (GRKC). Here the GREEKC consortium describes its main achievements in improving this GRKC.

The Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST).

MOTIVATION: A large variety of molecular interactions occurs between biomolecular components in cells. When a molecular interaction results in a regulatory effect, exerted by one component onto a downstream component, a so-called 'causal interaction' takes place. Causal interactions constitute the building blocks in our understanding of larger regulatory networks in cells. These causal interactions and the biological processes they enable (e.g. gene regulation) need to be described with a careful appreciation of the underlying molecular reactions. A proper description of this information enables archiving, sharing and reuse by humans and for automated computational processing. Various representations of causal relationships between biological components are currently used in a variety of resources. RESULTS: Here, we propose a checklist that accommodates current representations, called the Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST). This checklist defines both the required core information, as well as a comprehensive set of other contextual details valuable to the end user and relevant for reusing and reproducing causal molecular interaction information. The MI2CAST checklist can be used as reporting guidelines when annotating and curating causal statements, while fostering uniformity and interoperability of the data across resources. AVAILABILITY AND IMPLEMENTATION: The checklist together with examples is accessible at https://github.com/MI2CAST/MI2CAST. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Towards a unified open access dataset of molecular interactions.

The International Molecular Exchange (IMEx) Consortium provides scientists with a single body of experimentally verified protein interactions curated in rich contextual detail to an internationally agreed standard. In this update to the work of the IMEx Consortium, we discuss how this initiative has been working in practice, how it has ensured database sustainability, and how it is meeting emerging annotation challenges through the introduction of new interactor types and data formats. Additionally, we provide examples of how IMEx data are being used by biomedical researchers and integrated in other bioinformatic tools and resources.

Non-coding RNA regulatory networks.

It is well established that the vast majority of human RNA transcripts do not encode for proteins and that non-coding RNAs regulate cell physiology and shape cellular functions. A subset of them is involved in gene regulation at different levels, from epigenetic gene silencing to post-transcriptional regulation of mRNA stability. Notably, the aberrant expression of many non-coding RNAs has been associated with aggressive pathologies. Rapid advances in network biology indicates that the robustness of cellular processes is the result of specific properties of biological networks such as scale-free degree distribution and hierarchical modularity, suggesting that regulatory network analyses could provide new insights on gene regulation and dysfunction mechanisms. In this study we present an overview of public repositories where non-coding RNA-regulatory interactions are collected and annotated, we discuss unresolved questions for data integration and we recall existing resources to build and analyse networks.

Phospho-peptide binding domains in S. cerevisiae model organism.

Protein phosphorylation is one of the main mechanisms by which signals are transmitted in eukaryotic cells, and it plays a crucial regulatory role in almost all cellular processes. In yeast, more than half of the proteins are phosphorylated in at least one site, and over 20,000 phosphopeptides have been experimentally verified. However, the functional consequences of these phosphorylation events for most of the identified phosphosites are unknown. A family of protein interaction domains selectively recognises phosphorylated motifs to recruit regulatory proteins and activate signalling pathways. Nine classes of dedicated modules are coded by the yeast genome: 14-3-3, FHA, WD40, BRCT, WW, PBD, and SH2. The recognition specificity relies on a few residues on the target protein and has coevolved with kinase specificity. In the present study, we review the current knowledge concerning yeast phospho-binding domains and their networks. We emphasise the relevance of both positive and negative amino acid selection to orchestrate the highly regulated outcomes of inter- and intra-molecular interactions. Finally, we hypothesise that only a small fraction of yeast phosphorylation events leads to the creation of a docking site on the target molecule, while many have a direct effect on the protein or, as has been proposed, have no function at all.

The yeast noncoding RNA interaction network.

This article describes the creation of the first expert manually curated noncoding RNA interaction networks for S. cerevisiae The RNA-RNA and RNA-protein interaction networks have been carefully extracted from the experimental literature and made available through the IntAct database (www.ebi.ac.uk/intact). We provide an initial network analysis and compare their properties to the much larger protein-protein interaction network. We find that the proteins that bind to ncRNAs in the network contain only a small proportion of classical RNA binding domains. We also see an enrichment of WD40 domains suggesting their direct involvement in ncRNA interactions. We discuss the challenges in collecting noncoding RNA interaction data and the opportunities for worldwide collaboration to fill the unmet need for this data.

Profiling Phosphopeptide-Binding Domain Recognition Specificity Using Peptide Microarrays.

Cellular organization and response to internal and external stimuli are mediated by an intricate web of protein interactions. Some of these interactions are regulated by covalent posttranslational modifications such as phosphorylation and acetylation. These modifications can change the chemical nature of the interaction interfaces and modulate the binding affinity of the interacting partners. In signal transduction, the most frequent modification is reversible phosphorylation of tyrosine, serine or threonine residues. Protein phosphorylation may modulate the activity of enzymes by modifying their conformation, or regulate the formation of complexes by creating docking sites to recruit downstream effectors. Families of modular domains, such as SH2, PTB, and 14-3-3, act as "readers" of the modification event. Specificity between closely related domains of the same family is mediated by the chemical properties of the domain binding surface that, aside from offering a hydrophilic pocket for the phosphorylated residue, shows preference for specific sequences. Although the protein structure and the cell context are also important to ensure specificity, the amino acid sequence flanking the phosphorylation site defines the accuracy of the recognition process, and it is therefore essential to define the binding specificity of phosphopeptide binding domains in order to understand and to infer the interaction web mediated by phosphopeptides. Methods commonly used to discover new interactions (such as yeast two hybrid and phage display) are not suited to study interactions with phosphorylated proteins. On the other hand, peptide arrays are a powerful approach to precisely identify the binding preference of phosphopeptide recognition domains. Here we describe a detailed protocol to assemble arrays of hundreds to thousands phospho-peptides and to screen them with any modular domain of interest.

A subset of RAB proteins modulates PP2A phosphatase activity.

Protein phosphatase 2A (PP2A) is one of the most abundant serine-threonine phosphatases in mammalian cells. PP2A is a hetero-trimeric holoenzyme participating in a variety of physiological processes whose deregulation is often associated to cancer. The specificity and activity of this phosphatase is tightly modulated by a family of regulatory B subunits that dock the catalytic subunit to the substrates. Here we characterize a novel and unconventional molecular mechanism controlling the activity of the tumor suppressor PP2A. By applying a mass spectrometry-based interactomics approach, we identified novel PP2A interacting proteins. Unexpectedly we found that a significant number of RAB proteins associate with the PP2A scaffold subunit (PPP2R1A), but not with the catalytic subunit (PPP2CA). Such interactions occur in vitro and in vivo in specific subcellular compartments. Notably we demonstrated that one of these RAB proteins, RAB9, competes with the catalytic subunit PPP2CA in binding to PPP2R1A. This competitive association has an important role in controlling the PP2A catalytic activity, which is compromised in several solid tumors and leukemias.

The adapter protein CD2AP binds to p53 protein in the cytoplasm and can discriminate its polymorphic variants P72R.

Proline-rich motifs are widely distributed in eukaryotic proteomes and are usually involved in the assembly of functional complexes through interaction with specific binding modules. The tumour-suppressor p53 protein presents a proline-rich region that is crucial for regulating apoptosis by connecting the p53 with a complex protein network. In humans, a common polymorphism determines the identity of residue 72, either proline or arginine, and affects the features of the motifs present in the polyproline domain. The two isoforms have different biochemical properties and markedly influence cancer onset and progression. In this article, we analyse the binding of the p53 proline-rich region with a pool of selected polyproline binding domains (i.e. SH3 and WW), and we present the first demonstration that the purified SH3 domains of the CD2AP/Cin85 protein family are able to directly bind the p53 protein, and to discriminate between the two polymorphic variants P72R.

Searching for repetitions in biological networks: methods, resources and tools.

We present here a compact overview of the data, models and methods proposed for the analysis of biological networks based on the search for significant repetitions. In particular, we concentrate on three problems widely studied in the literature: 'network alignment', 'network querying' and 'network motif extraction'. We provide (i) details of the experimental techniques used to obtain the main types of interaction data, (ii) descriptions of the models and approaches introduced to solve such problems and (iii) pointers to both the available databases and software tools. The intent is to lay out a useful roadmap for identifying suitable strategies to analyse cellular data, possibly based on the joint use of different interaction data types or analysis techniques.

Transport mechanism and regulatory properties of the human amino acid transporter ASCT2 (SLC1A5).

The kinetic mechanism of the transport catalyzed by the human glutamine/neutral amino acid transporter hASCT2 over-expressed in P. pastoris was determined in proteoliposomes by pseudo-bi-substrate kinetic analysis of the Na(+)-glutamineex/glutaminein transport reaction. A random simultaneous mechanism resulted from the experimental analysis. Purified functional hASCT2 was chemically cross-linked to a stable dimeric form. The oligomeric structure correlated well with the kinetic mechanism of transport. Half-saturation constants (Km) of the transporter for the other substrates Ala, Ser, Asn and Thr were measured both on the external and internal side. External Km were much lower than the internal ones confirming the asymmetry of the transporter. The electric nature of the transport reaction was determined imposing a negative inside membrane potential generated by K(+) gradients in the presence of valinomycin. The transport reaction resulted to be electrogenic and the electrogenicity originated from external Na(+). Internal Na(+) exerted a stimulatory effect on the transport activity which could be explained by a regulatory, not a counter-transport, effect. Native and deglycosylated hASCT2 extracted from HeLa showed the same transport features demonstrating that the glycosyl moiety has no role in transport function. Both in vitro and in vivo interactions of hASCT2 with the scaffold protein PDZK1 were revealed.

Combining affinity proteomics and network context to identify new phosphatase substrates and adapters in growth pathways.

Protein phosphorylation homoeostasis is tightly controlled and pathological conditions are caused by subtle alterations of the cell phosphorylation profile. Altered levels of kinase activities have already been associated to specific diseases. Less is known about the impact of phosphatases, the enzymes that down-regulate phosphorylation by removing the phosphate groups. This is partly due to our poor understanding of the phosphatase-substrate network. Much of phosphatase substrate specificity is not based on intrinsic enzyme specificity with the catalytic pocket recognizing the sequence/structure context of the phosphorylated residue. In addition many phosphatase catalytic subunits do not form a stable complex with their substrates. This makes the inference and validation of phosphatase substrates a non-trivial task. Here, we present a novel approach that builds on the observation that much of phosphatase substrate selection is based on the network of physical interactions linking the phosphatase to the substrate. We first used affinity proteomics coupled to quantitative mass spectrometry to saturate the interactome of eight phosphatases whose down regulations was shown to affect the activation of the RAS-PI3K pathway. By integrating information from functional siRNA with protein interaction information, we develop a strategy that aims at inferring phosphatase physiological substrates. Graph analysis is used to identify protein scaffolds that may link the catalytic subunits to their substrates. By this approach we rediscover several previously described phosphatase substrate interactions and characterize two new protein scaffolds that promote the dephosphorylation of PTPN11 and ERK by DUSP18 and DUSP26, respectively.

Specificity of ε and non-ε isoforms of arabidopsis 14-3-3 proteins towards the H+-ATPase and other targets.

14-3-3 proteins are a family of ubiquitous dimeric proteins that modulate many cellular functions in all eukaryotes by interacting with target proteins. 14-3-3s exist as a number of isoforms that in Arabidopsis identifies two major groups named ε and non-ε. Although isoform specificity has been demonstrated in many systems, the molecular basis for the selection of specific sequence contexts has not been fully clarified. In this study we have investigated isoform specificity by measuring the ability of different Arabidopsis 14-3-3 isoforms to activate the H+-ATPase. We observed that GF14 isoforms of the non-ε group were more effective than ε group isoforms in the interaction with the H+-ATPase and in the stimulation of its activity. Kinetic and thermodynamic parameters of the binding of GF14ε and GF14ω isoforms, representative of ε and non-ε groups respectively, with the H+-ATPase, have been determined by Surface Plasmon Resonance analysis demonstrating that the higher affinity of GF14ω is mainly due to slower dissociation. The role of the C-terminal region and of a Gly residue located in the loop 8 and conserved in all non-ε isoforms has also been studied by deletion and site-specific mutagenesis. The C-terminal domains, despite their high divergence, play an auto-inhibitory role in both isoforms and they, in addition to a specific residue located in the loop 8, contribute to isoform specificity. To investigate the generality of these findings, we have used the SPOT-synthesis technology to array a number of phosphopeptides matching known or predicted 14-3-3 binding sites present in a number of clients. The results of this approach confirmed isoform specificity in the recognition of several target peptides, suggesting that the isoform specificity may have an impact on the modulation of a variety of additional protein activities, as suggested by probing of a phosphopeptide array with members of the two 14-3-3 groups.

HuPho: the human phosphatase portal.

Phosphatases and kinases contribute to the regulation of protein phosphorylation homeostasis in the cell. Phosphorylation is a key post-translational modification underlying the regulation of many cellular processes. Thus, a comprehensive picture of phosphatase function and the identification of their target substrates would aid a systematic approach to a mechanistic description of cell signalling. Here we present a website designed to facilitate the retrieval of information about human protein phosphatases. To this end we developed a search engine to recover and integrate information annotated in several publicly available web resources. In addition we present a text-mining-assisted annotation effort aimed at extracting phosphatase related data reported in the scientific literature. The HuPho (human phosphatases) website can be accessed at http://hupho.uniroma2.it.

Asymmetric comparison and querying of biological networks.

Comparing and querying the protein-protein interaction (PPI) networks of different organisms is important to infer knowledge about conservation across species. Known methods that perform these tasks operate symmetrically, i.e., they do not assign a distinct role to the input PPI networks. However, in most cases, the input networks are indeed distinguishable on the basis of how the corresponding organism is biologically well characterized. In this paper a new idea is developed, that is, to exploit differences in the characterization of organisms at hand in order to devise methods for comparing their PPI networks. We use the PPI network (called Master) of the best characterized organism as a fingerprint to guide the alignment process to the second input network (called Slave), so that generated results preferably retain the structural characteristics of the Master network. Technically, this is obtained by generating from the Master a finite automaton, called alignment model, which is then fed with (a linearization of) the Slave for the purpose of extracting, via the Viterbi algorithm, matching subgraphs. We propose an approach able to perform global alignment and network querying, and we apply it on PPI networks. We tested our method showing that the results it returns are biologically relevant.

Combining peptide recognition specificity and context information for the prediction of the 14-3-3-mediated interactome in S. cerevisiae and H. sapiens.

Large-scale interaction studies contribute the largest fraction of protein interactions information in databases. However, co-purification of non-specific or indirect ligands, often results in data sets that are affected by a considerable number of false positives. For the fraction of interactions mediated by short linear peptides, we present here a combined experimental and computational strategy for ranking the reliability of the inferred partners. We apply this strategy to the family of 14-3-3 domains. We have first characterized the recognition specificity of this domain family, largely confirming the results of previous analyses, while revealing new features of the preferred sequence context of 14-3-3 phospho-peptide partners. Notably, a proline next to the carboxy side of the phospho-amino acid functions as a potent inhibitor of 14-3-3 binding. The position-specific information about residue preference was encoded in a scoring matrix and two regular expressions. The integration of these three features in a single predictive model outperforms publicly available prediction tools. Next we have combined, by a naïve Bayesian approach, these "peptide features" with "protein features", such as protein co-expression and co-localization. Our approach provides an orthogonal reliability assessment and maps with high confidence the 14-3-3 peptide target on the partner proteins.

Bayesian modeling of the yeast SH3 domain interactome predicts spatiotemporal dynamics of endocytosis proteins.

SH3 domains are peptide recognition modules that mediate the assembly of diverse biological complexes. We scanned billions of phage-displayed peptides to map the binding specificities of the SH3 domain family in the budding yeast, Saccharomyces cerevisiae. Although most of the SH3 domains fall into the canonical classes I and II, each domain utilizes distinct features of its cognate ligands to achieve binding selectivity. Furthermore, we uncovered several SH3 domains with specificity profiles that clearly deviate from the two canonical classes. In conjunction with phage display, we used yeast two-hybrid and peptide array screening to independently identify SH3 domain binding partners. The results from the three complementary techniques were integrated using a Bayesian algorithm to generate a high-confidence yeast SH3 domain interaction map. The interaction map was enriched for proteins involved in endocytosis, revealing a set of SH3-mediated interactions that underlie formation of protein complexes essential to this biological pathway. We used the SH3 domain interaction network to predict the dynamic localization of several previously uncharacterized endocytic proteins, and our analysis suggests a novel role for the SH3 domains of Lsb3p and Lsb4p as hubs that recruit and assemble several endocytic complexes.

VirusMINT: a viral protein interaction database.

Understanding the consequences on host physiology induced by viral infection requires complete understanding of the perturbations caused by virus proteins on the cellular protein interaction network. The VirusMINT database (http://mint.bio.uniroma2.it/virusmint/) aims at collecting all protein interactions between viral and human proteins reported in the literature. VirusMINT currently stores over 5000 interactions involving more than 490 unique viral proteins from more than 110 different viral strains. The whole data set can be easily queried through the search pages and the results can be displayed with a graphical viewer. The curation effort has focused on manuscripts reporting interactions between human proteins and proteins encoded by some of the most medically relevant viruses: papilloma viruses, human immunodeficiency virus 1, Epstein-Barr virus, hepatitis B virus, hepatitis C virus, herpes viruses and Simian virus 40.

A technique to search for functional similarities in protein-protein interaction networks.

We describe a method to search for similarities across protein-protein interaction networks of different organisms. The technique core consists in computing a maximum weight matching of bipartite graphs resulting from comparing the neighbourhoods of proteins belonging to different networks. Both quantitative and reliability information are exploited. We tested the method on the networks of S. cerevisiae, D. melanogaster and C. elegans. The experiments showed that the technique is able to detect functional orthologs when the sole sequence similarity does not prove itself sufficient. They also demonstrated the capability of our approach in discovering common biological processes involving uncharacterised proteins.