# SLiMAn 2.0: meaningful navigation through peptide-protein interaction networks

Victor Reys, Jean-Luc Pons, Gilles Labesse

► To cite this version:

Victor Reys, Jean-Luc Pons, Gilles Labesse. SLiMAn 2.0: meaningful navigation through peptide-protein interaction networks. Nucleic Acids Research, 2024, 52 (W1), pp.W313-W317. 10.1093/nar/gkae398. hal-04705909

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# SLiMAn 2.0: meaningful navigation through peptide-protein interaction networks

Victor Reys, Jean-Luc Pons and Gilles Labesse*

Centre de Biologie Structurale, CNRS, INSERM, Univ. Montpellier, Montpellier, France

*To whom correspondence should be addressed. Tel: +33 4 67 41 77 12; Fax: +33 4 67 41 79 13; Email: gilles.labesse@cbs.cnrs.fr

## Abstract

Among the myriad of protein-protein interactions occurring in living organisms, a substantial amount involves small linear motifs (SLiMs) recognized by structured domains. However, predictions of SLiM-based networks are tedious, due to the abundance of such motifs and a high portion of false positive hits. For this reason, a webserver SLiMAn (Short Linear Motif Analysis) was developed to focus the search on the most relevant SLiMs. Using SLiMAn, one can navigate into a given (meta-) interactome and tune a variety of parameters associated to each type of SLiMs in attempt to identify functional ELM motifs and their recognition domains. The IntAct and BioGRID databases bring experimental information, while IUPred and AlphaFold provide boundaries of folded and disordered regions. Post-translational modifications listed in PhosphoSite+ are highlighted. Links to PubMed accelerate scrutiny into the literature, to support (or not) putative pairings. Dedicated visualization features are also incorporated, such as Cytoscape for macromolecular networks and BINANA for intermolecular contacts within structural models generated by SCWRL 3.0. The use of SLiMAn 2.0 is illustrated on a simple example. It is freely available at https://sliman2.cbs.cnrs.fr.

## Graphical abstract

<figure><img src="image_2.png" /></figure>

## Introduction

Living cells produce diverse macromolecules tightly regulated through multiple interactions. This is especially true for proteins that appear to be involved in numerous interactions among which many imply other proteins. Those interactions may rely on domain-domain interfaces or involve a Short Linear Motif (SLiM) and a recognition domain. With roughly 40% of their polypeptide sequence comprising unfolded region, eukaryotic cells evolved intricate networks of protein-protein interactions (PPIs) allowing complex regulations based on SLiMs (1,2).

The importance of these networks is well recognized and numerous experiments are performed to unravel their properties.

Most of the high-throughput experiments reveal which are the possible partners but do not provide the precise interface at atomic details, while structural biology can do so but at low throughput. Structural bioinformatics can leverage on both information to extend experimentally determined interfaces to multiple partners and PPIs.

SLiMs are defined by regular expressions gathered in the ELM database (3). Automatic detection of SLiMs have been proposed to cope with large-scale and high-throughput interactomics studies (4). However, each SLiM has its particular properties (length, folding tendency, ...) making systematic analyses difficult. Manual curation is also tedious due to high-frequency hits (~270 ELM SLiMs per human protein) while a
(sub-)network often contains hundred(s) of proteins. To help deciphering such networks, one can filter putative SLiMs using various predictors for accessibility and/or local disorder. However, a large number of potential SLiMs remain and more focused searches are needed.

The webserver SLiMAn was developed to facilitate identification of functional SLiMs within a given interactome. It combines several databases and tools dedicated to PPIs and SLiMs analyses. It provides an interface to navigate potential pairings between a list of SLiMs and their recognition domains contained in the same interactome. This remarkably reduces the number of SLiMs to interrogate and allows more thorough investigations including model building of the interface. Multiple parameters can be tuned to focus the searches to the most relevant interactions and partners.

SLiMAn 2.0 is a completely rewritten version compared to the former one (5). It brings better interactivity through a new JavaScript interface and now includes experimental data from IntAct (6), structural information from Alphafold models (7) and from HSM (8). Visualization of sequence motif signature, protein networks and 3D models are now based on LogoJS, CytoscapeJS (9) and BINANA (10). Direct links to PubMed (11) accelerate comprehensive validation of predicted hits.

We believe that such a new and rather unique tool will be useful for the whole community and the new webserver will help digging efficiently in the massive deluge of interaction data currently produced at high pace.

# Materials and methods

## Databases

SLiMAn extracts sequence annotations for 'reviewed' entries in UniProtKB (12), which corresponds to 564 277 entries (update February 2023). Boundaries and description of domains come from Pfam annotations (13). Motifs are extracted from the ELM database (3) (http://elm.eu.org; release February 2023). Now, a dedicated menu ('Add new Class'), allows users to build a new class of motifs, with its class type, its regular expression, an e-value and a recognition domain (defined as a Pfam identifier). However, such an added class does not give access to template selection or to comparative modeling.

Post-translational modifications are obtained from the PhosphoSitePlus® database (hereafter, PSP+) (14) (https://www.phosphosite.org; release February 2023) which is focused on a few vertebrates (mainly mouse, rat and human).

In SLiMAn 2.0, experimental protein-protein interaction data are, extracted from two databases: BioGRID (15) (release from February 2023; http://www.thebiogrid.org) and IntAct (6). From BioGRID, only physical interactions are retained, and split into low and high throughput experiments while the data from HuRI (16) within IntAct are set apart.

## Embedded software

In SLiMAn 2.0, disorder predictions are performed using both IUPred2A (17) and the pLDDT scores of the models stored in AlphaFold Database (7). For a subset of SLiMs (e.g.: PDZ binding motifs), putative pairings are now scored using HSM (8). Consensus for SLiMs on the main page are now computed and displayed using LogoJS (18).

Sequence alignments between the domain sequence and template structure sequence are performed using blastp (19)

and MAFFT (20) with the local-pair alignment option, limited to 1000 max-iterations (L-INS-i). For motif alignments, only MAFFT is used with gap opening and extension penalties set to 10 and 0.2, respectively. Four different alignment metrics help in selecting the most suitable templates for comparative modeling: Sequence Identity, Query Coverage, Template Coverage and Contact Conservation Score.

## Network visualization

While the main results are displayed in a table connecting ELM motifs and PFam domains, these connections and those supported by experimental data from BioGRID and IntAct can be visualized using CytoscapeJS (9) in the new version of SLiMAn. Each protein in the network is shown as a box containing its annotated PFam domains. Partners are linked when putative ELM-Pfam pairing, HSM scoring and/or experimental interactions are known. These links are connected to functional annotations extracted from UniProt as well as to PubMed pre-formatted queries (including multiple naming for each partner). The network can be saved as a picture or JSON file.

## Comparative modeling

For the 291 ELM-Pfam associations, the Protein Data Bank (PDB) (21) was parsed to select the corresponding domain chains. For each referenced domain chain, other chains shorter than 35 residues are considered as binding peptides and searched for ELM regular expressions. Modified residues (e.g. phosphorylation) found in the structure are converted to the one-letter code of the corresponding unmodified aminoacids. To validate a peptide-domain complex, one atom from residues that matched the regular expression must be found below 4 Å distance from an atom of the domain chain. Complexes encompassing a peptide matching an ELM motif in close contact with a given Pfam domain are retained in a dedicated structure database (SLiMAn-DB). After template extraction, domain sequences are gathered in a FASTA file and prepared for BLAST searches (19) using makeblastdb. This parsing yielded 5064 templates (from 2228 distinct structures) for 201 ELM-Pfam associations. Three-dimensional models of new complexes can be built using SCWRL3 (22), with backbone atoms and strictly conserved side-chains kept fixed, in a two-step procedure. First, the domain is modeled from the extracted template using the template-bound peptide as a constraint. Then, the resulting domain model is used as a fixed constraint, to optimize side-chains of the interacting peptide. In SLiMAn 2.0, the resulting models can be visualized using 3DmolJS (23) with interaction annotations provided by BINANA (10).

## Implementation

SLiMAn 2.0 uses Apache (v2.4.41) running on a virtual machine with 8 CPU and 12 Go of RAM under Ubuntu 20.04 environment. Python3.8 and R programming languages are used for computations while rendering is done using HTML, CSS, JavaScript and CGI scripts. Databases are stored in CSV or JSON files format while computed data are stored in conventional text formats (TSV, JSON, PDB and FASTA) and available for downloads.
# Results

## General overview

SLiMAn was designed as a web application, enabling a visual and interactive representation of the potential interactions involving SLiMs within a set of proteins. Instead of highlighting all the ELM motifs for each protein, SLiMAn selects only the most relevant SLiMs for which recognition domains are present in the very same list of proteins. This simple rule remarkably reduce the number of SLiMs to analyze.

The new webserver allows one to upload a list of proteins corresponding to a given interactome or to interrogate the databases BioGRID and IntAct and extract a list of partners constituting a (meta-)interactome (i.e. coming from multiple interactomics studies). Then it looks for relevant SLiMs that can be further filtered in or out using various parameters (experimental evidences, e-values, local disorder, name, ...). Due to memory limitation, this interactome should currently contain less than 150 proteins in general.

Regular expressions from the linear motifs referenced in ELM (and user defined motifs) are used to parse the input sequences. For each SLiM detected, all Pfam domains found in the input sequences and matching the ELM class are also extracted. If no Pfam domain matches a given ELM motif, the latter is dropped out.

For each motif, IUPred2A disorder scores and pLDDT values from AlphFoldDB are averaged over the motif length. For each motif-domain pairing, the BioGRID database is searched and high and low throughput experimental interactions are counted. Similar information extraction is performed on IntAct data. The final prediction, for a given motif-domain association (hit), is a set of 33 descriptors (see Supplementary Table S1). Some descriptors (e.g.: ELM e-value, IUPred Strict-Disorder, BioGRID LowThroughput, ...) are used to compute a confidence score (Supplementary Table S2), ranging from 1 to 8 (from lowest to highest confidence). On the main result page (named SLiMAn-Pairings), three summary tables are displayed (see Supplementary Table S3) to recapitulate the number of ELM motifs and Pfam domains found and filtered in or out, the ELM classes detected and the total number of interactions predicted. Below, an interactive filtering panel is displayed, where 56 parameters (see Supplementary Table S4) are tunable to navigate into an interactome with distinct stringency.

Hit predictions are displayed in the main result page, as a table listing ELM motifs in the left column and interacting domains in the top row. Sequence segments corresponding to an ELM motifs are displayed with their PTM annotation from PSP+ (14). PTM are highlighted using a code indicating whether the PTM is mandatory for recognition and present (or not) or not required but present. In those cases, a link gives access to information available on to PSP+ website.

The ELM-Pfam matches are highlighted by colored boxes (according to the confidence score). In the upper-right corner of this box, a link (named hit) connects to a pop-up window recapitulating the information computed by SLiMAn for the given pair. This window provides also links to the literature corresponding to the original experiments supporting the pair (as extracted from BioGRID and/or IntAct). In addition, links to PubMed, allow dedicated searches in the literature for the two connected proteins (using all their alternative names listed in UniProt). The pair box also contains a link to sequence-structure alignments when templates are available in

SLiMAn-DB and another link connects to 3D models built by SLiMAn.

By clicking on the 'Alignments' link, a alignment query of the corresponding hit is launched and 3D models can be built on-the-fly from these alignments. For a selected hit, motif and domain sequences are aligned with templates corresponding to the same ELM-Pfam class combination. Alignments are displayed on the SLiMAn-Alignments page. Residues in the selected motif and domain are highlighted (in green) on the full-length sequences. Domain/motif boundaries can be modified at will and the alignment subsequently updated. Twin sequence-structure alignments, for the motif and the domain, are displayed in a dedicated table with color codes indicating either the local conservation (in the query sequences) and the contacts (template sequences). Alignments can be ranked by the different metrics listed above. Users can select and submit the desired pairs of motif-domain for comparative modelling. Quick modelling is based on optimization of side-chain conformations within the Pfam domain and then the ELM motif using SCWRL3 (22). Models are listed in a table and can be visualized online using 3Dmol.js (23) or downloaded. They can be earmarked as promising or incorrect, and this information is brought back into the main table.

In parallel, the macromolecular network corresponding to the full interactome under study (or part of it according to user selections) can be visualized using CytoscapeJS (9). This interface display all the connections between protein from the submitted interactome, especially those based on experimental evidence (from BioGRID and IntAct). Dense sub-networks often correspond to macromolecular assemblies based on domain-domain interactions which will not appear as such on the main page. In addition, ELM-Pfam connections are shown. The links drawn into the cytoscape panel are connected to information on the paired proteins (domain and motif involved, data from BioGRID and/or Intact if available). Again, links to PubMed are provided to help functional annotation and validation of the putative connections. An example of application focusing on human tankyryses was recently described to highlight the power of a hierarchical search for dedicated motifs (here, the tankyrase binding motifs) and the interest of user-defined motifs with alternative signature to find novel binders (https://doi.org/10.21203/rs.3.rs-3973092/v1).

## Illustrative example

To illustrate the power of SLiMAn, we analyzed the interactome (Supplementary Table S5) of human protein GIPC1 which is involved in the internalization of various membrane receptors. It is connected to an unconventional myosin VI while it recognizes the C-terminal tail of numerous receptors through its PDZ domain. Here, we studied the molecular determinants of the interactions involved in the specific recognition of some PDZ motifs by GIPC1.

A hierarchical survey of the PDZ motifs was focused first on well characterized pairs and then extended to less well characterized partners of GIPC1. This unravels rapidly the specificity determinants at the molecular level, allowing confident detection of likely binding of GIPC1 to several receptors (see Figure 1A). Using stringent criteria (confidence above 5, HSM score above 0.50, pLDDT less than 0.65), SLiMAn highlights the interaction of GIPC1 with three partners (namely LRP2_HUMAN, SEM4C_HUMAN and DP13B_HUMAN). Modeling the complexes associated to the pairing highlighted
<figure><img src="image_3.png" /><figcaption>**Figure 1.** GipC1 meta-interactome analysis through SLiMAn 2.0. (A) Main SLiMAn-Pairings table for ELM-Pfam pairing. (B) Consensus sequence of selected motifs as shown by LogoJS. (C) SLiMAn-Alignment table displaying twinned alignment for an ELM and its recognition domain for a complex to be modeled. (D) Visualization of a complex built by SCWRL, using 3DjmolJS and BINANA.</figcaption></figure>

the structure basis for the specificity of the GIPC1 PDZ. Indeed, a high proportion of basic residues lie around the PDZ groove accommodating the PDZ-binding motif while an enrichment in acidic residues is observed in known partners of GIPC1 (Figure 1B). This survey also revealed a general scheme of regulation by multiple phosphorylation of the GIPC1-recognized PDZ motif in various receptors. Indeed, when lowering the stringency using lower confidence (3) and HSM score (0.35), more PDZ-binding motifs (17) appear. They harbor a lower enrichment in acidic residues (hence a lower HSM score) but a higher content in serine or threonine among which a number appeared to be phosphorylated. Interestingly, phosphorylation brings a negative charge which would enhance GIPC1 binding. The literature validated this hypothesis as the transporter GLUT1 has been shown to interact more strongly with GIPC1 upon phosphorylation (24).

Further analysis at lower stringency (HSM below 0.05) revealed that the protein N from SARS-COV2 was found to interact with GIPC1 (25). Interestingly, its PDZ-binding motif harbors a threonine whose phosphorylation has been detected (26). Modeling the corresponding complex through the SLiMan interface confirmed this association (Figure 1C, D). It suggested a phospho-regulated binding of the viral protein by GIPC1 that is worth further investigation.

## Discussion

SLiMAn 2.0 is a new web-server for analyzing small interactomes containing multiple SLiMs to be discovered. While other tools (e.g.: PrePPI) allow predictions of SLiMs and their partners, but they performed their analysis using fixed parameters learned or set up at one time, while regularly new interfaces, new binding modes or new specificity are brought to light. Hence, interactive surveys may be beneficial to discover new interfaces. In parallel, multiple features are to be combined to carefully annotate a SLiM. SLiMAn 2.0 allows visualizations of the corresponding networks and modeling of some of the underlying interfaces. This was illustrated above for the impact of phosphorylation on various PDZ-binding motifs for GIPC1 recognition. More intensive use of AlphaFold modeling or a better selection scheme for templates will prove helpful in the near future. Similarly, the use of sequence profile instead of regular expression may better detect some motif variants.

This website is free and open to all users and there is no login requirement.

## Data availability

SLiMAn 2.0 is freely available at https://sliman2.cbs.cnrs.fr.

## Supplementary data

Supplementary Data are available at NAR Online.

## Acknowledgements

We acknowledge Alexandre Mezgrhani and Muriel Gelin for helpful discussion and the charity Ligue contre le Cancer for providing a PhD grant to VR. This work was supported by the French Infrastructure for Integrated Structural Biology (FRISBI; ANR-10-INBS-0005) and the infrastructure for screening (ChemBioFrance).

## Funding

Ligue Contre le Cancer [PhD grant to V.R.]; French Infrastructure for Integrated Structural Biology [ANR-10-INBS-0005]; ChemBioFrance; Agence Nationale de la Recherche [ANR-20-CE18-025]. Funding for open access charge: None or CNRS if applicable.
**Conflict of interest statement**

None declared.

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