# IntAct Database for Accessing IMEx's Contextual Metadata of Molecular Interactions

Kalpana Panneerselvam, Pablo Porras, Noemí Del-Toro, Livia Perfetto, Anjali Shrivastava, Eliot Ragueneau, Juan Jose Medina Reyes, Sandra Orchard, Henning Hermjakob, Imex Consortium

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Kalpana Panneerselvam, Pablo Porras, Noemí Del-Toro, Livia Perfetto, Anjali Shrivastava, et al. IntAct Database for Accessing IMEx's Contextual Metadata of Molecular Interactions. Current Protocols, 2024, 4 (10), 10.1002/cpz1.70018. hal-04998410

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# IntAct Database for Accessing IMEx's Contextual Metadata of Molecular Interactions

Kalpana Panneerselvam,<fnref n="1" />,<fnref n="6" /> ID Pablo Porras,<fnref n="2" /> Noemí del-Toro,<fnref n="2" />,<fnref n="4" />

Livia Perfetto,<fnref n="3" /> Anjali Shrivastava,<fnref n="2" />,<fnref n="5" /> Eliot Ragueneau,<fnref n="2" />

Juan Jose Medina Reyes,<fnref n="1" /> Sandra Orchard,<fnref n="1" /> ID Henning Hermjakob,<fnref n="1" />
and IMEx Consortium Curators

Published in the Bioinformatics section

The International Molecular Exchange Consortium (IMEx) has evolved into a vital partnership of open resources dedicated to curating molecular interaction data from the scientific literature. This consortium, which includes IntAct, MINT, MatrixDB, and DIP, is a collaborative effort with a central mission of aggregating detailed molecular interaction experimental evidence in a machine-readable format, supported by controlled vocabularies and standard ontologies. The IntAct molecular interaction database (www.ebi.ac.uk/intact), as an IMEx partner, serves as a valuable portal for accessing IMEx data through user-friendly search options and an array of interactive filters. The resource currently hosts an extensive repository of 1,293,508 binary interactions meticulously captured from 75,098 experiments documented in 23,366 publications (as of the February 2024 release), with this corpora being added to by regular data releases. IMEx curation policy has consistently prioritized a fine-grained data and curation model, with a focus on capturing the relevant experimental details essential for interpreting molecular interaction data effectively. Our curation process is designed to support the generation of interactomes tailored to contexts such as disease-specific or tissue-/cell-type-specific interactomes. These interactions are ranked according to a scoring system based on the Proteomics Standard Initiative Molecular Interaction (PSI MI) standards. This scoring system allows users to assess the degree of confidence in binary interactions, enhancing the value of the data. The resource provides insights into the nature of relationships among interacting partners as defined by the experimental setup and the associated biological context. Interactive filters enable users to navigate these rich, multilayered data, promoting a deeper understanding of biological complexity. Additionally, the IntAct website fosters the creation of networks for collaborative analyses by the scientific community. The recent transformation of the IntAct website, supported by a graph-type database, empowers users to execute custom queries tailored to their specific research interests. This article illustrates the diverse levels of annotations available for interactions and the multiple search options at users' disposal to access data of interest. © 2024 European Molecular Biology Laboratory, European Bioinformatics Institute. Current Protocols published by Wiley Periodicals LLC.

**Basic Protocol 1:** Using Quick Search, network visualization, and filters

**Support Protocol:** Accessing fine annotations from intact: Unlocking the molecular details

<footnote n="1">¹European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK</footnote>

<footnote n="2">²Data Curation Director, AstraZeneca</footnote>

<footnote n="3">³Sapienza University of Rome, Rome, Italy</footnote>

<footnote n="4">⁴Current address: Healx, UK</footnote>

<footnote n="5">⁵Current address: Wellcome Sanger Institute, UK</footnote>

<footnote n="6">⁶Corresponding author: kalpanap@ebi.ac.uk</footnote>
**How to cite this article:**

Panneerselvam, K., Porras, P., del-Toro, N., Perfetto, L., Shrivastava, A., Ragueneau, E., Reyes, J. J. M., Orchard, S., Hermjakob, H., & IMEx Consortium Curators. (2024). IntAct database for accessing IMEx's contextual metadata of molecular interactions. *Current Protocols*, *4*, e70018. doi: 10.1002/cpz1.70018

## INTRODUCTION

Since its inception, the IntAct portal has undergone significant expansion in its coverage of protein-protein interactions (PPI) and interactions between other molecule types (e.g., protein-small molecule, mRNA-miRNA) across diverse biological and experimental contexts (Del Toro et al., 2022). This growth has prompted the development of advanced search and visualization capabilities, enriching the user experience. IntAct serves as a gateway to the entire IMEx consortium dataset (Porras et al., 2020), offering a comprehensive repository of interaction data with extensive options for filtering, visualization, and comparative analysis.

All interactions curated within IMEx carry a wealth of information spanning multiple layers, including the publication, experimental, and interaction levels. These interactions are systematically categorized into datasets based on clinical relevance, species, and the inclusion of mutation-specific data (IMEx Consortium Curators et al., 2019; Porras et al., 2020). Proteins are meticulously mapped to canonical or isoform levels, with a keen eye on inherent genetic variants and their influence on interaction strength. Details such as the length of protein constructs affecting binding are documented. Additional evidence described in the publications, including post-translational modifications, as well as experimental and environmental factors impacting interactions, are fully described. This depth of curation is designed to capture the dynamic nature of molecular interactions as they orchestrate various cellular processes across different stages of the cell cycle.

Manual curation of experimentally proven interactions is a labor-intensive endeavor, demanding substantial time and expert resources, yet is essential for creating a rich biological and experimental context, particularly for applications such as large-scale network analysis. The IMEx consortium, represented by key members including IntAct (Del Toro et al., 2022), DIP (Xenarios et al., 2002), UniProt (UniProt Consortium, 2021), MINT (Licata et al., 2012), MatrixDB (Chautard et al., 2011), and IID (Kotlyar et al., 2016), has embraced this formidable challenge for over a decade, successfully sharing the responsibilities necessary for its sustained operation. IMEx's robust annotation strategy, supported by experimental evidence and adherence to HUPO-PSI data standards and controlled vocabularies (https://www.psidev.info/molecular-interactions), empowers users to seamlessly access and exchange molecular interaction data.

## USING QUICK SEARCH, NETWORK VISUALIZATION, AND FILTERS

IntAct offers an array of tools and features to streamline the process of accessing molecular interactions. In this protocol, we will walk you through the steps for conducting a Quick Search, using filters, and leveraging network visualization tools to efficiently retrieve interaction data. Whether you are interested in a specific protein, its isoforms, or post-processed chains, IntAct's user-friendly interface simplifies the task of accessing and downloading the data.
<figure><img src="image_2.png" /><figcaption>**Figure 1** IntAct Quick Search for amyloid-beta precursor protein (gene symbol: APP).</figcaption></figure>

## Necessary Resources

Hardware: computer with internet access.

Software: up-to-date web browser, such as Google Chrome (https://www.google.com/chrome/), Mozilla Firefox (https://www.mozilla.com/), or Apple Safari (https://www.apple.com/safari/), to access IntAct website (https://www.ebi.ac.uk/intact/).

## Quick Search for single proteins by gene name

The first step in accessing molecular interactions through IntAct is to perform a simple search. This approach is tailored to those seeking interactions involving a single protein of interest. Begin by entering the name of the protein in the search bar located prominently on the IntAct portal. IntAct's auto-suggestion feature will assist you in narrowing down potential interactors by interactor type and organism. This helps you pinpoint the specific protein you wish to explore.

For clarity and a real-world demonstration, let's consider a sample search. In this example, we will fetch interactors of the human amyloid-beta precursor (APP) protein from the IntAct website using the gene name (Fig. 1).

As shown, the users can choose to proceed with fetching results for all the interactors shown or select specific interactors for more targeted investigations. This adaptability ensures that researchers can tailor their queries based on the scope and objectives of their analyses.

## Quick Search by UniProtKB accession number

An even more precise method to retrieve unique interactors is by using UniProtKB accession numbers. This approach ensures a more precise query, bypassing potential ambiguities associated with protein names or identifiers. Once a UniProtKB accession number is entered, IntAct's Quick Search generates auto-suggestions that fetch interactors specifically associated with relevant isoforms and post-processed chains. This level of granularity ensures that users obtain interactions tailored to the unique characteristics of their protein. In Figure 2, we showcase the practical application of Quick Search by UniProtKB accession number for the amyloid-beta precursor protein, using the unique
<figure><img src="image_3.png" /><figcaption>**Figure 2** Interactors fetched for APP protein (UniProtKB identifier: P05067).</figcaption></figure>

UniProtKB identifier P05067. The auto-suggestions promptly fetch interactors specific to this identifier, demonstrating the efficiency and precision of this retrieval method.

### Network Viewer: Visualizing interactions

The resulting interactions, which can be further refined using IntAct's versatile filter options, come to life in the Network Viewer—a dynamic platform designed to provide users with an intuitive and visually engaging representation of molecular interactions. The Network Viewer is built upon Cytoscape.js (Franz et al., 2016; Shannon et al., 2003), a powerful and adaptable framework for network visualization, ensuring an effective display of the intricate relationships between filtered data and their network representation.

### Dynamic synchronization

The Network Viewer dynamically synchronizes the filter selections with the resulting network. As users apply filters to their data, the viewer responds in real time, updating the network to reflect the refined dataset. This immediate feedback allows users to explore and analyze their data with precision and efficiency. Network nodes and edges adapt to the evolving dataset, allowing the user to explore interactions of interest, whether investigating high-confidence interactions, specific interaction types, or interactions within particular host organisms. In the course of exploring and analyzing molecular interactions within the Network Viewer, the capability to export the resulting network takes center stage. IntAct ensures that users have the means to preserve and share their insights by offering export options in two standard formats: PNG and GraphML.

In Figure 3, we present the network generated for amyloid-beta precursor isoform 4 (APP695), identified by the UniProtKB accession number P05067-4.

### Network Tools: Enhancing visualization and analysis

The Network Viewer in IntAct empowers users with a suite of tools and features designed to enhance the visualization and analysis of molecular interaction networks. These tools facilitate a deeper understanding of network structure, evidence, and associated attributes.

**Network layouts:** The Network Viewer offers diverse layout options to visualize interactions according to user preferences. “Force directed” layout emphasizes the structural
<figure><img src="image_4.png" /><figcaption>**Figure 3** Network generation for APP695 (UniProtKB identifier: P05067-4).</figcaption></figure>

relationships within the network, often revealing hubs and clusters. “Circular” layout organizes nodes in a circular arrangement, particularly suitable for networks in which nodes are evenly distributed. “Bubbles” layout groups nodes into clusters by species represented as bubbles, making it easier to discern functional modules within the network (Fig. 3).

**Edges:** By default, the Network Viewer displays a summary view of edges, in which all the available evidence between nodes is consolidated into a single edge as shown in Figure 3. This concise representation simplifies the network without losing the underlying evidence, making it easier to interpret complex networks. Edges are color-coded based on the number of evidence sources associated with each interaction, providing a visual indicator of the level of support for a given interaction.

The Network Viewer allows users to explore interactions at a granular level by expanding edges to reveal individual pieces of evidence associated with interacting pairs. This view also supports the visualization of nodes and edges affected by mutation (Fig. 4). Nodes carrying mutation data and the corresponding edges affected are highlighted in pink.

**Node and edge coloring:** Nodes in the network are color-coded based on the species they represent, aiding in the identification of interactions involving different organisms. This feature is particularly useful for users interested in distinguishing interactions in multi-species datasets (Fig. 4).

**Fostering collaboration through shared query URLs:** The query URLs of the resulting networks serve as valuable resources that can be easily shared among users, as they act as direct links to the specific interaction queries. This enables other researchers to replicate results on their own machines, fostering collaboration and facilitating discussions around the molecular interaction data obtained. Example URLs from Figures 3 and 4 can be found here: https://www.ebi.ac.uk/intact/search?query=P05067-4; https://www.ebi.ac.uk/intact/search?query=P05067-4&mutationFilter=true.

**Network tables:** Complementing the dynamic visualization of molecular interactions in the Network Viewer, IntAct provides Network tables (Fig. 5), offering structured and detailed information at both the interaction and interactors levels. At the
<figure><img src="image_5.png" /><figcaption>**Figure 4** Expanded edges of APP695 (UniProtKB P05067-4).</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 5** APP695 (UniProtKB P05067-4) network table.</figcaption></figure>

interaction level, Network tables provide a comprehensive overview of each interaction within the network. The information presented in these tables includes but is not limited to unique identifiers, experimental details, and the biological roles of interactors involved in the interaction. Users can utilize column toggles to customize the information displayed, tailoring the table to their specific research needs. Network tables can be exported in miTAB (https://www.psidev.info/molecular-interactions) or miXML (Kerrien et al., 2007; Sivade Dumousseau et al., 2018) formats.

## Advanced filter options and network visualization in IntAct

IntAct's advanced filter options are tailored to accommodate the needs of both novice explorers and seasoned professionals, providing an effective means to refine interaction data. These filters enable users to cluster interactions and generate networks based on various contextual layers, enhancing the precision and relevance of their queries.

The resulting interactions are displayed in the Network Viewer to effectively show the dynamic sync between the filter and the resulting network.
<figure><img src="image_7.png" /><figcaption>**Figure 6** APP695 (UniProtKB P05067-4) high-confidence network (MIscore: 0.5).</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Figure 7** True binary interactors of APP695 (UniProtKB P05067-4).</figcaption></figure>

**Molecular interaction (MI) score filter:** IntAct offers the user the ability to filter interactions based on their level of confidence. This confidence score (MIscore) is derived from a heuristic molecular interaction scoring system, which takes into account available experimental evidence associated with a given interaction (Villaveces et al., 2015). Factors such as the interaction detection method, the type of interaction, and the number of publications reporting the interaction contribute to this score. High-confidence interactions, backed by strong experimental support, can be selectively accessed using the “MI Score” filter. This filter empowers users to identify interactions with a higher degree of reliability. Figure 6 shows the amyloid-beta precursor 695 (APP695, P05067-4) network filtered to display high-confidence interactions based on a threshold of MIScore 0.5.

**Type of expansion model:** N-ary association data are subject to expansion models that involve spoke-expanded associations. Data coming from high-throughput proximity assays and affinity purification–mass spectrometry (AP-MS) experiments mainly generate such associations. Users seeking only true binary interactions, in which two entities directly interact without intermediaries, can leverage the “Expansion” filter. This filter allows users to filter out spoke-expanded associations, focusing solely on binary interactions (Fig. 7).
<figure><img src="image_9.png" /><figcaption>**Figure 8** Interactors of APP695 (UniProtKB P05067-4) detected in the human neuroblastoma cell line SH-SY5Y.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**Figure 9** APP695 (UniProtKB P05067-4) network filtered for human interactors.</figcaption></figure>

**Host organism filter:** Interactions may occur in various host organisms, and users can choose to filter interactions based on the specific host organism in which the interaction was detected. This capability is particularly valuable for generating tissue- or cell-type-specific interactomes, and it has significant applications in drug discovery, as interactions detected in a particular cell or tissue are more likely to reveal drug targets (Figure 8).

**Interactor species filter:** IntAct recognizes the importance of distinguishing between interactions occurring in different host organisms and also of distinguishing cases in which constructs from a mix of species have been used to demonstrate an interaction. This filter allows users to choose interactions based on whether they are inter-species or intra-species interactions. As an example, Figure 9 shows an APP695 (P05067-4) network with only human proteins as interactors.

**Interaction method filter:** IntAct's filters extend to the categorization of interactions based on the methods used by authors to detect them. This categorization considers not only the entities involved but also the specific techniques employed to ascertain these interactions. These interaction methods can vary widely, encompassing techniques such as yeast two-hybrid assays, co-immunoprecipitation, affinity chromatography, and more (Fig. 10).
<figure><img src="image_11.png" /><figcaption>**Figure 10** APP695 (UniProtKB P05067-4) network filtered for interactions detected by two-hybrid assay.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>**Figure 11** APP695 (UniProtKB P05067-4) network filtered for physical associations.</figcaption></figure>

**Interaction type filter:** IntAct also offers filters based on the type of relation that in-teractions entail. This approach considers the qualitative aspect of interactions, delving into how the interacting entities relate to each other. The categorization types can be di-rect, physical, or association, or proximal or colocalization (Fig. 11). A full description of these interaction types, and the relationships between them, can be accessed in the HUPO PSI-MI controlled vocabulary (http://purl.obolibrary.org/obo/MI_0190). This classifi-cation also plays a crucial role in the assessment of the strength of binary interaction pairs, influencing how interactions are weighted and interpreted.

**Interactor type:** IMEx's curation strategy extends to molecules other than proteins. The categorization of these interaction partners (http://purl.obolibrary.org/obo/MI_0313) al-lows users to filter interactions based on the type of interaction partner. This filter is es-pecially beneficial for users seeking specific molecular associations between proteins, protein complexes, nucleic acids, or small molecules (Fig. 12).

These advanced filter options provide users with a versatile toolkit to navigate and dissect the intricate landscape of molecular interactions. Whether you are conducting fundamen-tal explorations or in-depth analyses, IntAct filters empower you to tailor your searches to meet your specific research goals.
SUPPORT
PROTOCOL

<figure><img src="image_13.png" /><figcaption>**Figure 12** APP695 (UniProtKB P05067-4) network filtered for interactor type "protein."</figcaption></figure>

ACCESSING FINE ANNOTATIONS FROM INTACT: UNLOCKING THE MOLECULAR DETAILS

The IMEx Consortium’s commitment to comprehensive data annotation extends to providing fine-grained annotations that offer rich insights into molecular interactions. These annotations cover a spectrum of attributes, including kinetic parameters, binding domains involved in interactions, mutations affecting interaction outcomes, the use of affinity tags, and external influences originating from the experimental setup. Researchers can access this wealth of information at both the interaction and participant levels, fostering a deeper understanding of the molecular intricacies.

**Expand by mutation:** A mutational change to a single amino acid in a protein may disrupt a protein binding site, thus altering the composition of an interaction sub-network or the formation of a protein complex. The IMEx Consortium diligently captures the impact of genetic variants, predominantly point mutations, on protein-protein interactions. These outcomes are categorized as “disrupting,” “causing,” “decreasing,” or “no effect” compared to the wild-type protein. This valuable information is documented using PSI MI controlled vocabulary terms (https://www.ebi.ac.uk/ols4/ontologies/mi/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FMI_0118). For those interested in exploring interactions involving mutant nodes and their impact, IntAct offers the “Expand by Mutation” option within the Network Viewer. Enabling this option allows users to selectively expand interactions involving mutant nodes, providing a focused view of interactions influenced by genetic variations (Figs. 13 and 14).

**Network table selection:** Within the Network Viewer, users can interact with the network table by selecting one of the highlighted edges, as demonstrated in the provided example (Fig. 13). This selection results in the Network Table displaying detailed information specific to the chosen edge, ensuring that researchers can readily access and scrutinize relevant data.

Interaction viewer

* To obtain a more in-depth view of a selected interaction, users can choose the “Show Details” icon associated with the interaction. This action triggers the appearance of the Feature Viewer, a dedicated tool designed to offer a comprehensive overview of features, including mutations and other relevant attributes.

* The interaction viewer, referred to as the ComplexViewer (Combe et al., 2017), boasts the capability to visualize various types of interactors, spanning proteins, small
<figure><img src="image_14.png" /><figcaption>**Figure 13** Selecting and exploring details of an interaction from the network. The minimum MI score has been set to 0.5.</figcaption></figure>

molecules, and nucleic acids. This comprehensive view enables researchers to understand the intricacies of interactions involving diverse molecular entities.

* Within the ComplexViewer, users can explore a wealth of sequence features of participants. These features include binding regions, mutations, and post-translational modifications that hold relevance to the interaction between participants. The viewer allows researchers to gain insights into how these sequence attributes influence the molecular partnership.

* Hovering the mouse over highlighted mappings reveals positional information on the UniProt amino acid sequence, facilitating a granular examination of the interactions (Fig. 14).

* Further enriching the experience, IntAct enables the integration of features obtained from external sources such as UniProt (Wang, Zhang, and UniProt Consortium, n.d.) and SCOP Superfamily (Andreeva et al., 2020). This ensures that researchers have access to a broader spectrum of information, enhancing the context and depth of their analyses.

**Features tab:** Finally, beneath the viewer, a “Features” tab is readily available. This tab provides an exhaustive list of features associated with the participants, consolidating all the available information into a structured format that is easy to navigate and reference (Fig. 14).

These tools and features collectively empower researchers to unlock the fine annotations associated with molecular interactions, offering a deeper and more comprehensive understanding of the intricacies of biological networks. In the subsequent sections, we explore practical examples of how to access and utilize these annotations for research and analysis.
<figure><img src="image_15.png" /><figcaption>**Figure 14** Details of selected interaction EBI-21181230 supported by ComplexViewer for visualizing features of the participants.</figcaption></figure>

## USING BATCH SEARCH: QUERYING MULTIPLE INTERACTORS

IntAct recognizes the diversity of user needs and the often-intricate nature of research inquiries. To streamline the process of querying multiple interactors simultaneously, IntAct introduces the Batch Search feature. This powerful tool is designed to handle a variety of interactors, making it convenient for users to upload a list of interactors of interest or utilize the search box provided.

Key features of batch search are summarized below.

### Step 1: Upload list of interactors

Users have the flexibility to upload a list of interactors of interest (as shown in Fig. 15) or just provide query terms in the query box (Fig. 16), simplifying the process of querying multiple entities simultaneously. This feature is particularly advantageous when dealing with large-scale datasets or when researchers have a predefined list of interactors for analysis.

### Handling heterogeneous interactors

Batch search is optimized to handle heterogeneous types of interactors. Whether users are querying proteins with known UniProtKB identifiers (UniProt Consortium, 2021), Ensembl identifiers (Martin et al., 2023) for genes and transcripts, ChEBI identifiers (Hastings et al., 2016), or RNAcentral identifiers (The RNAcentral Consortium, 2019), the system is equipped to handle diverse input types efficiently (Fig. 16).
<figure><img src="image_16.png" /><figcaption>**Figure 15** Batch search: Uploading file containing list of query terms for simultaneous search.</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>**Figure 16** Batch search for list of query terms using the search box.</figcaption></figure>

## Step 2: Disambiguation box

Recognizing the potential for generic query terminologies, IntAct incorporates a Disambiguation box. This feature assists users in narrowing down their queries by selecting the specific interactors of interest. This step ensures clarity in the querying process, reducing ambiguity and enhancing the precision of search results. For proteins containing post-processed chains cleaved from a longer transcript, such as amyloid-beta precursor protein (UniProtKB: P05067) and C3 complement protein (UniProtKB: P01024), UniProtKB PRO-chains are listed for the user to specify the query terms (Fig. 17).

The Disambiguation box provides a critical function for refining searches involving generic terms such as “GTP” and “APOE.” These terms may have partial matches across various interactor types, including small molecules, gene names, protein names, or descriptions. By leveraging the Disambiguation box, users can effortlessly fetch interactors with matching terms, paving the way for further querying and precise data retrieval (Fig. 18).
<figure><img src="image_18.png" /><figcaption>**Figure 17** Batch search: Disambiguation box allowing a user to narrow down search to more specific options. For amyloid-beta precursor protein (UniProtKB P05067) and C3 complement protein (UniProtKB P01024). UniProtKB PRO-chain accessions are listed for selection by users.</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>**Figure 18** Batch search: Disambiguation choice for more generic query terms such as “GTP” and “APOE.”</figcaption></figure>
Key features of advanced search are summarized below.

## Molecular Interaction Query Language (MIQL)

The Advanced Search field accommodates Molecular Interaction Query Language (MIQL), enabling users to construct detailed and nuanced queries. MIQL allows users to articulate specific criteria, fields, and grouping preferences, providing a granular level of control over the search parameters.

## Field-specific search restrictions

Advanced Search provides users with a range of options for restricting searches based on the field being queried. Depending on the nature of the field, users can apply specific constraints to refine their search results. This level of customization ensures that searches align precisely with the intended criteria.

<figure><img src="image_20.png" /><figcaption>**Figure 19** MIQL query to fetch interactions with minimal MIscore of 0.5 and with feature type "binding region" from Alzheimer's disease dataset.</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>**Figure 20** MIQL query for fetching interactions between human (NCBI tax-id: 9606) proteins and SARS-CoV-2 (NCBI tax-id: 2697049) proteins.</figcaption></figure>
<table><caption>**Table 1** Troubleshooting</caption><thead><tr><td>Problem</td><td>Possible cause</td><td>Solution</td></tr></thead><tbody><tr><td>Network Viewer is not showing the network</td><td>Your resulting network has more than 1500 interactions</td><td>You can use our advanced filter options to narrow down the results for visualization</td></tr><tr><td>I have mixed species in my network, even after selecting only the human dataset</td><td>The dataset contains at least one nonhuman partner for a binary interaction</td><td>Use the species filter and, by using the toggle at top right, select the “Intra” option to restrict the analysis to only your species of choice</td></tr><tr><td>I find nodes with self-loops in the network—what does that mean?</td><td>Oligomerization of was proteins detected in the interactions</td><td>You can use advanced search options to filter out self-interactions.</td></tr><tr><td>No interactions are found for my query protein</td><td>We are still working toward comprehensive coverage of molecular interactions</td><td>Send your curation requests to intact-help@ebi.ac.uk</td></tr></tbody></table>

**Customizable query structure**

Users can structure their queries in a way that suits the complexity of their research inquiries. The ability to specify fields and group them according to user-defined criteria enhances the adaptability of Advanced Search, catering to a diverse range of research scenarios.

**Tailored search parameters**

The flexibility of Advanced Search extends to offering tailored search parameters. Users can define not only what fields they want to include in the query but also how these fields should be grouped, providing a fine-tuned approach to extracting relevant molecular interaction data.

Further details are available at https://www.ebi.ac.uk/intact/documentation/user-guide#advanced_search.

In Figure 19, we showcase an MIQL query designed to fetch interactions from the Alzheimer's disease dataset with specific criteria. This query is constructed to narrow down the results based on the molecular interaction (MI) score and the presence of a binding domain feature in at least one of the interaction partners.

A second example, in Figure 20, is designed to show how to fetch human target proteins interacting with SARS-CoV2 viral proteins.

**COMMENTARY**

**Critical Parameters**

For obtaining robust results, using unique accession numbers of the query proteins is essential. The resulting interactions are protein isoform and or post-processing chain specific, the number of interactions will vary accordingly.

**Troubleshooting**

A list of common problems, along with their causes and solutions, is provided in Table 1.

**Author Contributions**

**Kalpana Panneerselvam:** Writing—original draft; **Pablo Porras:** Data curation;

**Noemí del-Toro:** Resources; software; **Livia Perfetto:** Data curation; **Anjali Shrivastava:** Software; **Eliot Ragueneau:** Software; **Juan Jose Medina Reyes:** Software; **Sandra Orchard:** Data curation; **Henning Hermjakob:** Project administration.

**Acknowledgements**

This work was supported by the European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI); Open Targets (OTAR-044, OTAR02-048, OTAR02-066); and the Wellcome Trust (Biomedical Resources grants INVAR 3367 and 218294 and Centre grant 203149). The MINT database is supported by a grant from
the Italian Association for Cancer Research (AIRC IG 2017 no. 20322 to G.C.); the MatrixDB database is supported by the Institut Français de Bioinformatique (ANR-11-INBS-0013, Glycomatrix project, to S.R.B.); and the DIP database is supported by US National Institutes of Health, National Institute of General Medical Sciences (NIH NIGMS) award (GM123126 to M.P.). Funding for open access charge was provided by the European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI).

Open access funding enabled and organized by Projekt DEAL.

## Conflict of Interest

The authors declare no conflict of interest.

## Data Availability Statement

IntAct is open source, open data. The source code is available from https://github.com/intact-portal, and all data are freely available through the web interface (API) and from https://www.ebi.ac.uk/intact/download under a CC BY 4.0 license.

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