elm.eu.org

The ELM resource at elm.eu.org is organized into five main areas: ELM Prediction, ELM DB, ELM Candidates, ELM Information, and ELM Downloads. Use ELM Prediction when you want to search for or predict motif matches in a protein sequence; use ELM DB when you need curated, literature-backed motif classes and instances; use ELM Candidates for proposed but not yet fully curated entries; use ELM Information for help, news, statistics, plots, links, API access, and background on the resource; and use ELM Downloads when you need bulk data files rather than browsing records one at a time.

ELM Prediction

This section is the entry point for querying sequences against the ELM resource. It is the section to start from if your goal is to find linear motif matches rather than read documentation about known motifs.

ELM DB

ELM DB is the curated core of the resource. Its sub-sections cover:

• ELM classes — the defined motif classes
• ELM instances — individual occurrences of those classes
• ELM pdb structures — motif-related structural entries
• ELM binding domains — domains that interact with motifs
• ELM switches — motif-based switching elements
• ELM pathways — pathway associations
• ELM references — literature supporting entries
• ELM related diseases — disease links
• ELM viral instances — viral motif occurrences
• ELM pathogenic abuse — pathogen-driven misuse of motifs
• ELM experiments — experimental validation records
• ELM GO Terms — Gene Ontology annotations

ELM Candidates

This section holds candidate classes and candidate instances — entries proposed for the resource but not yet at the same status as the curated ELM DB records. Treat it as a staging area rather than the final curated set.

ELM Information

This section collects the supporting material around the data:

• Help — guidance on using the resource
• News — release and update announcements
• Stats and Plots — quantitative overviews of the resource
• Links — related external resources
• API — programmatic access
• About — background on ELM

The homepage also highlights a third-party tool, gget elm, described as a way to run fast, scalable queries of eukaryotic linear motifs locally. That is a separate tool referenced by ELM, not one of the five main sections.

ELM Downloads

ELM Downloads provides bulk files rather than browsable pages. The listed download categories are:

Which section should you use?

• You have a protein sequence and want motif matches → ELM Prediction.
• You want a specific curated motif class or its known instances → ELM DB.
• You want entries that are proposed but not yet fully curated → ELM Candidates.
• You need documentation, statistics, or programmatic access → ELM Information.
• You need the whole dataset for local analysis → ELM Downloads.

The homepage also surfaces recent database updates, including newly released and revised classes. Examples from those announcements include new classes such as DOC_TBK1_STING_1, DEG_Cend_DCAF12_1, LIG_TRAF3_MATH_PxP_3, and MOD_AAK1BIKe_LxxQxTG_1, along with revised classes such as LIG_TRAF2like_MATH_shPxQ_1 and LIG_SH3_PxxDY_5. Checking the News section under ELM Information is the way to track these changes.

ELM (the Eukaryotic Linear Motif resource at elm.eu.org) organizes disease- and pathogen-related motif data into three dedicated sections under its ELM DB: ELM related diseases, ELM viral instances, and ELM pathogenic abuse. Together these connect short linear motifs (SLiMs) to human disease associations, to motifs found in viral proteins, and to cases where pathogens exploit host motifs. If you are studying how a motif contributes to infection or disease, these are the entry points to check before running a prediction.

The three disease- and pathogen-focused sections

These sit alongside the core ELM DB sections (ELM classes, ELM instances, ELM experiments, ELM GO Terms, ELM PDB structures, ELM binding domains, ELM interactions, ELM pathways, and renamed ELM entries), so you can cross-reference a disease- or virus-linked motif against its structural and functional annotation.

How the viral and pathogenic content is structured

• Viral instances are catalogued as instances, meaning they are concrete occurrences of a motif in a specific viral protein, tied back to the parent ELM class that defines the pattern.
• Pathogenic abuse entries describe motifs that pathogens use to interfere with host processes, rather than motifs that are simply present in a pathogen.
• Related diseases links motif data to disease context, useful when you want to move from a sequence pattern to a possible clinical or functional relevance.

Because these are database sections rather than separate tools, you browse or query them the same way as other ELM DB content, and you can pull them programmatically through the ELM API or in bulk via ELM downloads.

SARS-CoV-2 and COVID-19 content

ELM has featured SARS-CoV-2 work directly. The site highlights:

• COVID-19 SLiM Links, collecting motif-related resources for SARS-CoV-2.
• An article on SLiM signatures in the SARS-CoV-2 entry system, published in Science Signaling.
• Validation experiments for SARS-CoV-2 entry system SLiMs, covering the SPIKE CendR and NRP1 receptor in SARS-CoV-2 cell entry.
• An HSTalks interview (Gibson and Yamauchi) on SARS-CoV-2 entry SLiMs.

This makes ELM useful if you want worked examples of how viral entry motifs are identified and then experimentally validated, not just listed.

Using these sections in practice

1. Start from the ELM DB menu and open ELM related diseases, ELM viral instances, or ELM pathogenic abuse depending on whether your question is about disease association, viral proteins, or host exploitation.
2. Cross-check the parent ELM class for any instance you find, to see the defining pattern, binding domains, and switches.
3. Confirm with experiments using the ELM experiments section, since database entries and validated instances are tracked separately.
4. Scale up if needed with the ELM API or ELM downloads, or use gget elm for large-scale local queries as highlighted in the featured paper.

A practical example: if you are investigating how a viral protein disrupts an immune pathway, you would look for its motif in ELM viral instances, check whether a matching entry appears under ELM pathogenic abuse, and then verify the interaction against ELM interactions and ELM pathways.

What to keep in mind

• These sections describe curated motif–disease and motif–pathogen relationships; they are research annotations, not clinical diagnostic or treatment guidance.
• Viral and pathogenic entries are updated as new classes and instances are released, so check the ELM database updates notes for recent additions and revisions.
• The resource is in English and centers on eukaryotic linear motifs, so non-eukaryotic or non-motif mechanisms fall outside its scope.

An ELM class is the reusable definition of a eukaryotic linear motif — a pattern describing a functional site in proteins. An ELM instance is a concrete, documented occurrence of that class in a specific protein. In short: the class is the rule, the instance is a verified example of the rule being used. You need both concepts to search the ELM resource effectively, because the database is organized around them and lets you browse either level.

The class: a motif definition

A class captures what the motif looks like and what it does. It is the abstract entry — the pattern, the functional site type, and the associated annotation — rather than any single protein.

Classes are named with a structured prefix that signals the motif's role. From the ELM release notes, you can see prefixes such as:

• LIG_ — ligand-binding motifs (for example LIG_SH3_PxxxRxxKP_6)
• DOC_ — docking motifs (for example DOC_PP2B_LxvP_1)
• DEG_ — degradation motifs (for example DEG_Cend_DCAF12_1)
• MOD_ — modification motifs (for example MOD_AAK1BIKe_LxxQxTG_1)
• TRG_ — targeting motifs (for example TRG_NESrev_CRM1_2)

The suffix encodes the interacting partner and a version number, so LIG_SH3_PxxxRxxKP_6 is the sixth defined class of an SH3-binding ligand motif with that pattern.

The instance: a documented occurrence

An instance is where a class meets a real protein. It records that a specific protein contains a match to the class pattern, together with the evidence supporting it. When the ELM release notes say a class was "revised with ~70 new instances," that means 70 additional protein occurrences were added to the evidence base for that motif definition.

This distinction matters because a class can exist with many instances, few instances, or be newly defined and still accumulating them. Instance counts are therefore a rough signal of how well-characterized a motif type is.

How the two levels relate

The homepage illustrates the link directly: it highlights "PDB-Structure 2IWP showing a peptide from ELM class LIG_PDZ_Class_1" — the class is the definition, and the structure is a specific instance of it.

Why the database separates them

Separating definitions from occurrences lets you ask two different questions:

• "What kinds of linear motifs exist?" — browse or search ELM classes.
• "Which proteins actually contain this motif, and what's the evidence?" — look at ELM instances.

It also keeps the resource maintainable. A single class can be updated once and gain many instances, as in the release notes where MOD_PIKK_1 was updated with 18 new instances, or where nine new classes were released alongside roughly 70 new instances across five revised classes.

Practical implications when you use ELM

• If you are scanning a protein of interest for known motifs, you are working at the instance level — matching your sequence against class definitions.
• If you want to understand a motif's consensus pattern, interacting domain, or pathway context, you are working at the class level.
• Class names change over time. The release notes record renamings, such as LIG_TRAF2_1 becoming LIG_TRAF2like_MATH_shPxQ_1, so cite the current class name when referencing a motif.
• The database also maintains candidate classes and candidate instances separately from validated ones, so check which category an entry belongs to before treating it as established.

For large-scale work, the ELM resource points to gget elm for running queries locally rather than through the web interface.

ELM Prediction is the section of the Eukaryotic Linear Motif (ELM) resource at elm.eu.org where you submit a protein sequence and get back candidate eukaryotic linear motifs (also called short linear motifs, or SLiMs) with links to the matching ELM classes and instances. Use it when you have a sequence of interest and want to know which known functional sites it may contain. It is a prediction aid, not proof of function: hits are hypotheses that normally need experimental validation.

What ELM Prediction does

The ELM resource catalogs experimentally validated linear motifs as ELM classes (the motif definition, usually a regular expression) and ELM instances (specific proteins where that motif is documented). ELM Prediction scans your sequence against those class definitions and reports matches.

Because the underlying patterns are short, most matches occur by chance. ELM therefore attaches filters and context information to help you judge which hits are worth following up, rather than returning a flat list of pattern matches.

Step-by-step: running a prediction

1. Get your sequence. Use the protein sequence in FASTA format, ideally the UniProt canonical sequence for the protein you care about.
2. Open the ELM Prediction page. From the ELM home page, go to the ELM Prediction section.
3. Paste your sequence into the input and start the search.
4. Read the results. Each hit is reported against an ELM class, with its position in your sequence and links into the ELM DB (ELM classes, ELM instances, and related annotations such as ELM PDB structures, binding domains, switches, pathways, references, related diseases, viral instances, pathogenic abuse, experiments, and GO terms).
5. Filter and prioritize. Use the structural and context clues in the output to discard matches in disordered regions where the motif could not be presented, or in the wrong compartment, and keep the ones consistent with what you know about the protein.

Expected result: a ranked set of candidate motif occurrences in your sequence, each traceable to a documented ELM class and its instances.

What to check before trusting a hit

• Is the motif accessible? A motif buried in a folded core is unlikely to be functional.
• Is it in the right compartment? Many motifs only function in specific cellular locations.
• Is the motif conserved? Conservation across orthologs strengthens a candidate.
• Does the ELM class have documented instances? A class with many validated instances is a better-supported pattern than a sparse one.

These are the same considerations the ELM documentation raises; treat the prediction as a starting point for that reasoning, not a replacement for it.

Running large or repeated queries locally

The ELM site notes that gget elm can be used for fast, scalable querying of eukaryotic linear motifs locally. If you need to screen many sequences or run the search repeatedly in a pipeline, that is the route to take instead of submitting sequences one at a time through the web interface.

Practical limitations

• Short patterns mean a high false-positive rate by design; expect many matches in a long sequence.
• Prediction says nothing about whether the motif is actually used in a given cell type or condition.
• The database is updated over time — new classes and revised classes are announced on the site — so re-run queries if you are working from an older result set.

If your goal is simply to look up a motif you already know, go to the ELM DB and browse ELM classes or ELM instances directly. Use ELM Prediction when you have a sequence and want candidate motifs proposed for it.

ELM (elm.eu.org) is the Eukaryotic Linear Motif resource, a database and prediction platform for functional sites in proteins known as eukaryotic linear motifs (ELMs). It is aimed at researchers who need to find, predict, or download information about short linear motifs (SLiMs) — compact protein regions that mediate interactions, modifications, and regulatory events. The site organizes its content into five main areas: Prediction, DB (database), Candidates, Information, and Downloads.

What ELM catalogs

ELM collects linear motifs as curated classes and observed instances. A class describes a motif pattern and its function; an instance is a specific occurrence of that motif in a protein. The database also links motifs to supporting evidence and related structures.

The site's database section exposes several browsable categories:

• ELM classes — curated motif definitions
• ELM instances — individual motif occurrences in proteins
• ELM pdb structures — structural context for motifs
• ELM binding domains — domains that recognize motifs
• ELM switches — motifs involved in switching behavior
• ELM pathways — pathway associations
• ELM references — supporting literature
• ELM related diseases — disease links
• ELM viral instances and ELM pathogenic abuse — viral and pathogen-related motifs
• ELM experiments — experimental validation data
• ELM GO Terms — Gene Ontology annotations

Prediction and candidates

The ELM Prediction section lets you search for motifs in a protein sequence. The ELM Candidates area holds candidate classes and candidate instances — motif entries that are proposed but not yet fully curated as confirmed classes. This distinction matters if you need only validated motifs versus exploratory candidates.

Information and downloads

The ELM Information section includes Help, News, Stats, Plots, Links, and API pages, plus an About page. The ELM Downloads section provides bulk data for local analysis, covering classes, instances, experiments, GO terms, PDB structures, binding domains, interactions, related diseases, viral instances, pathways, renamed entries, templates, and media.

How to use it

1. To check whether a motif is known: go to the database and browse or search ELM classes and instances.
2. To scan a sequence for motifs: use the ELM Prediction section with your protein sequence as input; the expected result is a list of predicted motif matches.
3. To work at scale or offline: use the Downloads section or the API, rather than querying the web interface manually.
4. To explore unconfirmed motifs: check ELM Candidates, keeping in mind these are not the same as curated classes.

Points to note

• ELM focuses on eukaryotic linear motifs, so it is not a general protein-domain database.
• The resource distinguishes curated classes from candidate entries; treat them differently in analysis.
• The site is updated — its news items list newly released and revised classes, so checking recent updates helps confirm whether a motif is current.
• The page does not state pricing or login requirements, so access conditions should be verified directly on the site rather than assumed.