Experimental detection of short regulatory motifs in eukaryotic proteins: tips for good practice as well as for bad
© Gibson et al. 2016
Received: 17 July 2015
Accepted: 13 November 2015
Published: 18 November 2015
It has become clear in outline though not yet in detail how cellular regulatory and signalling systems are constructed. The essential machines are protein complexes that effect regulatory decisions by undergoing internal changes of state. Subcomponents of these cellular complexes are assembled into molecular switches. Many of these switches employ one or more short peptide motifs as toggles that can move between one or more sites within the switch system, the simplest being on-off switches. Paradoxically, these motif modules (termed short linear motifs or SLiMs) are both hugely abundant but difficult to research. So despite the many successes in identifying short regulatory protein motifs, it is thought that only the “tip of the iceberg” has been exposed. Experimental and bioinformatic motif discovery remain challenging and error prone. The advice presented in this article is aimed at helping researchers to uncover genuine protein motifs, whilst avoiding the pitfalls that lead to reports of false discovery.
KeywordsLinear motifs Bioinformatics Molecular switches Protein complexes Cell regulation Experimental design
The molecular deconstruction of cell signalling began in earnest with the identification of regulatory protein kinases and the cloning of the first viral oncogenes, some of which themselves encoded protein kinases captured from cellular signalling systems [1, 2]. During the following decades, a trio of methods-transient overexpression, mutagenesis and western blot-were harnessed together into the main workflow used to investigate regulatory proteins in the cell. In recent years, it has become clear that these methods are inadequate to address the complexity of cell systems, not least because most cellular systems operate under finely balanced gene dosage requirements [3–5] that are obliterated when any one protein is massively overexpressed .
A more modern view of cell signalling holds that its elements are highly restricted in space and time . Systematic proteomic studies have forced us to accept that most regulatory proteins spend most of their time in large multi-protein complexes [8–11], increasingly found to be associated with RNA gene products (which we will not address further here) . These complexes are highly dynamic and may coalesce, split apart, relocate, gain and lose individual proteins and, when no longer needed, be fully dismantled. The regulatory decisions emanating from the complexes must then be transmitted to other parts of the cell, for example by detaching a protein from a signalling complex at the plasma membrane and transporting it into the nucleus where it can modulate gene expression, as typified by beta-catenin under Wnt signalling .
For the most part, these regulatory complexes are so poorly understood that they are effectively black box input/output devices with little knowledge of the internal workings. Nevertheless, researchers have now provided many examples where small parts of the machinery within subcomplexes have yielded details of information processing mechanisms [14–16]. It turns out that cellular regulatory complexes primarily operate through the assembly and operation of molecular switching mechanisms [17–21]. Therefore, if we desire to fully understand cellular systems, our challenge will be to reveal the full complement of molecular switches specified by the proteome. This number is vast and presently incalculable, but this is our challenge.
Why are there so many SLiMs?
Although there are only around 20,000 protein-coding genes in the human genome, we estimate that the proteome will contain over a million PTM sites plus hundreds of thousands of peptide elements that will become defined as linear motifs . These elements primarily, but not exclusively, reside in segments of intrinsically disordered polypeptide (IDP), i.e., parts of proteins that lack the capability to fold into globular domains. It is estimated that some 30 % of the human proteome cannot adopt a stable, natively folded structure [28, 29]. IDP massively increases the available interaction surface of the proteome with many of those interactions utilising short peptide segments, the linear motifs [30–32]. (In this respect, Eukaryotes are quite different to bacteria, which have limited amounts of intracellular IDP, although there are interesting exceptions such as the degradosome, a very “eukaryotic-like” regulatory complex ).
Natural selection acts to optimise organisms to their environment. Over long periods of time, organisms may become increasingly robust to a large variety of environmental parameters. As C. H. Waddington emphasised, natural selection primarily acts to fine-tune weak phenotypes in a process that is both iterative and parallel, such that over time significant phenotypic changes result [34, 35]. As is well understood by engineers, increases in multi-parameter robustness always require increases in system complexity. In the biological context, long-term selection for organismal robustness has been directly responsible for driving an increase in complexity in cell regulatory systems . This has resulted in the modern eukaryotic cell that is full of protein complexes sampling multiple inputs and processing the received information to tune the levels of multiple outputs.
The amount of switching circuitry needed for cellular information processing could not be achieved by complexes consisting solely of globular proteins, which would lack the number of alternative conformational states and alternative interactions needed to control information flow. Instead, it is the IDP elements in regulatory proteins that provide the interaction surfaces enabling system complexity. On their own, however, the flexible IDP elements would confer insufficient precision to the interactions needed to build reliable information processing systems. Therefore, regulatory complexes have an intrinsic duality: structurally precise globular folded domains working with flexible IDPs that enable high information storage, in particular as conditional PTMs . Together they assemble the interconnected dynamic molecular switches that make the regulatory decisions .
If they are so abundant, why are they so hard to find?
A typical short linear motif will have three to four amino acid residues that interact with a part of the surface of the ligand domain . This functionality dictates that these residue positions will be evolutionarily conserved, although some positions may allow a flexible subset of amino acids such as similarly sized hydrophobic side chains (e.g., Ile, Leu, Val) or side chains with similar charge (e.g., Asp, Glu) . A bioinformatician quickly realises that the information content of the sequence space for a given motif (which can be represented by Shannon’s entropy) is remarkably poor and that a proteome will contain such vast numbers of short sequences matching the motif patterns that most cannot be functional. When the number of false positives greatly exceeds the number of true motifs, the poor signal-to-noise ratio will greatly hamper computational discovery of novel motif instances. Consequently, there are still rather few examples of bioinformatic discovery and subsequent experimental validation [39–41]. Similarly, the experimentalist cherry-picking a motif candidate in their favourite protein is also in great danger of going after an invalid target site.
There are at least three reasons why the cell does not get confused by the superabundance of false motif sequences. The first is that signalling is tightly restricted in space and time, such that most false motif-ligand candidates can never physically meet . The second is that many candidate motifs are buried in folded proteins and completely inaccessible to the ligand domain. The third is that even if one false motif were to bind to a partner domain, it will not result in a regulatory event. This is because the typical dissociation constant Kd is low micromolar so that the time bound, usually just a few seconds, is far too transient to cause a state change. It is critical to remember that SLiMs always operate cooperatively [8, 20, 32].
What are the worst mistakes made by experimentalists?
Experimentalists start to go wrong when they overestimate the (normally low) likelihood that any given candidate motif might be real. A lack of understanding of protein sequence/structure relationships and of how sequence evolution and residue conservation can help assessing candidates will mean that the chance to evaluate the protein context will be passed up. There has been a historic tendency to underestimate and even ignore space-time compartmentalisation, naively assuming that a protein with a peptide motif will freely diffuse to find a protein with a partner domain. And there has been a tendency to over-interpret the results of in-cell experiments, which, on their own, can never validate a proposed SLiM-mediated interaction. In past decades, many labs working on signalling protein function used almost exclusively cell cultures and have been unwilling to deploy biochemical, biophysical or structural methodologies. This is unfortunate, as our experience over many years of reviewing the experimental literature for ELM has forced us to conclude that it is essential to undertake in vitro validation of the findings from in-cell work. Given the complexity of macromolecular complexes, a token co-immunoprecipitation using an overexpressed, tagged protein is by no means proof of a motif interaction. While in-cell work is insufficient, so too are purely in vitro binding studies. It is perfectly possible to get an artefactual binding event when combining proteins that never see each other in the cell. For example, actin was first crystallised tightly bound to the secreted bovine gut protein DNAse1 .
The key to reliable motif detection is interdisciplinarity: in-cell and in vitro analyses are both needed. If your laboratory is too specialized to handle this, then collaboration with a partner who brings in the complementary expertise is going to be needed.
A key in vitro requirement is to validate the structural integrity of a protein where a candidate motif has been mutated. A significant fraction of SLiMs has two or more conserved hydrophobic residues, for instance, the nuclear export sequence (NES) has four . Most sequence matches to the NES motif are therefore buried in globular protein domains. We have discussed earlier the logical trap where failure to export a mutated protein from the nucleus is taken as proof that a functional NES has been identified . An alternative scenario doesn’t get considered which is that an unfolding mutant of a nuclear protein may accumulate in the nucleus where, if it aggregates, it can no longer leave the compartment. This type of logical error, where a negative result is assumed to provide positive proof of a functional site, can apply to other classes of motif. For example, the D-box anaphase degron has two conserved hydrophobic residues, and thus many candidates are in folded domains. Because amyloids are refractory to proteasomal targeting and destruction , persistence of unfolding mutants may be reported as indicative of degron function, when there is no degron at that site .
So the worst mistakes made by experimentalists are when they fail to adequately control their experiments by not ensuring that consistent results are obtained from both in vitro and in-cell methods, as well as not checking structural integrity of the mutated proteins.
Bioinformatics tools that may help motif investigations
Bioinformatics tools useful for motif discovery. Each resource is listed with its name, weblink, main reference, and short description
To explore candidate functional sites in proteins and to learn about known motifs
To analyse protein queries for the presence of short contiguous peptide motifs that have a known function in at least one other protein
To identify short protein sequence motifs that are recognized by modular signalling domains, phosphorylated by protein Ser/Thr- or Tyr-kinases or mediate specific interactions with proteins or phospholipids
To predict binding of a given peptide to a protein structure
To find short, over-represented peptide patterns/linear motifs, in a set of proteins
To find novel, significantly over-represented, short protein motifs
To identify regions of local similarity between nuleotide or protein sequences, which can be used to infer functional and evolutionary relationships between sequences as well as help identify members of gene families
Provides free software and data services to foster scientific collaboration and facilitate the scientific discovery proces; the project adheres to the open source philosophy that promotes collaboration and code reuse
General purpose DNA or protein multiple sequence alignment program
Multiple alignment program for amino acid or nucleotide sequences
Lightweight Java applet for use in web applications, and a powerful desktop application that employs web services for sequence alignment
Database composed of phylogenetic trees inferred from animal genomes, providing orthology/paralogy predictions as well the evolutionary history of genes
Database of orthologous groups of genes annotated with functional categories derived from COG/KOG categories
Database providing phylogenetic classification of proteins encoded in complete genomes
Linear motif conservation filter
To identify functional regions in proteins
De novo motif discovery tool to identify relatively over-constrained proximal groupings of residues within intrinsically disordered regions, indicative of a putatively functional motif
To identify and annotate genetically mobile domains and to analyse domain architectures
Database providing a large collection of protein families, each represented by multiple sequence alignments and hidden Markov models
To classify sequences into protein families and to predict the presence of important domains and sites
Single worldwide repository of information about the 3D structures of large biological molecules, including proteins and nucleic acids
Pictorial database providing an at-a-glance overview of the contents of each 3D structure deposited in PDB
To predict intrinsically unstructured regions in proteins
Community resource, providing pre-computed disorder predictions on a large library of proteins from completely-sequenced genomes
Centralized resource for annotations of intrinsic protein disorder
Database providing information about proteins that lack fixed 3D structure in their putatively native states, either in their entirety or in part
Online interaction respository with data compiled through comprehensive curation efforts
Provides known and predicted protein-protein interactions
Freely available, open source database system and analysis tools for molecular interaction data; all interactions are derived from literature curation or direct user submissions and are freely available
Web-based database of protein interaction sites, providing information on interaction sites of a protein from multiple PDB entries
Database of domain-peptide interactions
Cellular compartment-specific database for protein-protein interaction network analysis
Web server to explore short linear motif-mediated interactions
Database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies
Collection of experimentally verified mammalian protein complexes
Web server for protein subcellular localization prediction with functional gene ontology annotation
Database that collects experimental annotations for the subcellular localization of proteins in Homo sapiens and Arabidopsis thaliana
Collaborative effort to address the need for consistent descriptions of gene products across databases
Database of protein subcellular localization data manually curated from the literature or obtained from high-throughput microscopy-based screens
Curated database providing data that describe the membrane organization and subcellular localization of proteins from the RIKEN FANTOM4 mouse and human protein sequence set
Publicly available database with millions of high-resolution images showing the spatial distribution of proteins in 44 different normal human tissues and 20 different cancer types, as well as 46 different human cell lines
Resource integrating evidence on tissue expression from manually curated literature, proteomics and transcriptomics screens, and automatic text mining
Manually annotated, non-redundant protein sequence and sequence isoform database; related information about the biological function of protein are curated from the scientific literature
Open-access database of publicly available antibodies against human protein targets; contains data on the antibody efficacy in a range of biochemical and cell biological techniques
Serves to advance the worldwide aspects of the chemical sciences and to contribute to the application of chemistry in science
The key goal is to retrieve as much information as possible about the protein sequence containing the putative motif. A multiple sequence alignment is essential. Sequences can be collected by BLAST-ing  with the reference protein. Jalview  provides a platform for handling alignments, colour-coding by amino acid similarity and provides web services to remotely interface with alignment software such as Clustal Omega  and secondary structure prediction tools such as JPred . Separately, known protein domains can be retrieved from Pfam , SMART  and InterPro . Native disorder predictors, such as IUPred , complement the protein domain and secondary structure predictors. Most (but not all) SLiMs and PTMs are present in IDP. Any site that has been functional over significant evolutionary time periods will show sequence conservation. In fact, it is useful to remember that ALL conserved residues in segments of IDP are functional, whereas many of the conserved residues in globular domains are structural, with primarily those residues at conserved regions of the domain surface being directly functional. The protein structure databank (PDB)  should also be checked, as any direct structural knowledge will reinforce (or overrule) the information from the other resources. Protein complex databases like Corum  and network/interaction resources such as STRING  should be consulted for the known interactors.
Besides the core tools that will always apply for motif discovery, a large number of bioinformatics utilities may optionally come into play (Table 1). For example, if it is not certain whether two proteins are co-expressed in the same cells, the Human Protein Atlas  and CELLO2GO  might be informative for shared tissue and cellular location. If an antibody is needed for in-cell work, it is worth checking Antibodypedia  for user evaluations of antibody quality. Do remember, though, that the information stored in bioinformatics resources is NOT always accurate! Look for synergy between different types of information (as an obvious example, a DNA-binding domain in the protein sequence would synergise with antibody staining that indicated the protein was located in the nuclear compartment). The more critical it is to your project, the more effort you should put into checking up with the primary literature. The next section addresses a specific example of data quality that routinely affects motif discovery.
Multiple alignments and the choppy state of public sequence data
Most protein sequences in UniProt have been automatically translated from the DNA generated by whole genome sequencing projects using gene prediction algorithms and/or homology to reference sequences. Have you ever wondered how many high quality eukaryotic genome sequences have been produced so far? There are legions of partially finished genomes  but the good ones will fit on the fingers of one hand (see also ). The way science is set up currently, once the grant has finished, the genome (in whatever state) gets published, usually in a flagship journal, and that is the end of it. There tends to be neither money nor desire to do the unglamorous work needed to finish the job.
It is of course wonderful that we have so much diverse genomic sequence data, allowing research work to be undertaken that was not feasible a few years ago. But the quality issue cannot be avoided and, for most species’ genomes, any gene that is important to your projects should, as a matter of course, be resequenced.
A particular problem for aligning motif-rich sequences is that the alignment programs do not handle natively disordered sequences very well. This is partly because the programs have been optimised to work with globular protein sequences and partly because they expect collinearity of the sequences. An IDP sequence is often more free to tolerate residue substitutions as well as undergo assorted genetic rearrangements. There are likely to be alternatively spliced isoforms, too. Because of these confounding issues, it should not be assumed that the motifs will always be correctly aligned. Even worse, motifs can change position within sequences (probably by duplication and loss of the original) while some motifs are typically found in multiple copies and can vary in number across species . Since motif presence/absence tends to be rather dynamic over long evolutionary timescales, it is generally not useful to align sequences that are too divergent. It should not usually be necessary to drop below ~40 % identity and below ~30 % should be avoided unless there is no choice.
To summarise this section, it is essential to work with multiple sequence alignments. Examine them carefully  but at the same time be alert for the many ways that they can also be misleading in the study of motifs.
Work flows for discovery and validation of short linear motifs
(a) Developing a work flow for discovery of a new instance of a known motif
Normally the starting point is identification of a candidate motif in a protein of interest. That protein may already be known to interact with the partner protein, or there may be biological plausibility that they might work together, though not yet direct evidence.
(b) Developing a work flow for de novo motif discovery
Possible starting points for discovering a hitherto unknown variety of protein motif may be a bioinformatics network analysis that places interesting proteins in proximity or, more often, two proteins that are known to directly interact. Subsequently, the two proteins of interest are being chopped up to narrow down the interacting region, guided by the available knowledge of their modular domain architectures, including any solved structures of individual components. If one of those proteins interacts with a region predicted to be within an IDP segment, there may be an embedded linear motif. (If both proteins interact through IDP regions, there may be interacting IDDs - intrinsically disordered domains - as for example in E2F and DP1 and Rb .)
Again, performing the bioinformatics analyses (Fig. 5, Table 1) before too much experimentation has been undertaken may be informative for experimental design, as well as saving money and effort if the candidate motif seems implausible. The most conserved region in an interacting IDP segment might include the binding motif.
The experiments are mostly similar to those used to define a new example of an existing motif (Fig. 6, Additional file 1: Table S1). The key difference is the greater uncertainty in the interacting region. As it gets narrowed down, overlapping peptides could be used in binding assays to define the boundaries. Structural studies are extremely desirable, though not always practical in the early rounds of experiments. Nevertheless, there are a number of examples where a solved structure was included in the paper that first defined a novel linear motif [69, 70]. High resolution crystal structures provide the most detailed information of the interaction interface but cannot always be obtained. However, there are also many valuable NMR structures of domain:motif complexes. Again, you need to show that there is a relationship between the two proteins being tested, using several different experiments, both in vitro and in-cell. And you need to show that this relationship involves the motif (though of course the interaction doesn’t have to be limited to a single site, given the cooperative nature of these systems).
If you successfully define a novel linear motif, it is worth using some motif-hunting bioinformatics tools to search for other likely candidates. SLiMSearch for example will rank matches by disorder prediction and conservation . Not all motifs are abundant in the proteome, so there is no guarantee of finding anything. The true motif signal may also be confounded by the noise in the searches. But if you find some candidates, even if you don’t test many or any of them, they will add value when you publish and if others test them, they will increase the citations of your paper.
Examples of actual linear motif discovery
The ELM resource has over 2400 links to papers either directly detailing SLiM discovery or being relevant to the research area. Thus, researchers can educate themselves on any aspect of experimental motif detection. Still, it might be worth mentioning a couple of high quality examples.
Discovery of the FFAT motif is a good example of a single paper capturing substantial knowledge for a hitherto unknown linear motif . FFAT binds to VAP protein, targeting the motif-containing proteins to the ER membrane. The motif was visualised initially by comparing a 39-residue targeting fragment with a second ER-targeted protein. A range of in-cell experiments using both yeast and mammalian cell systems, such as motif transplantation to GFP and motif mutation, confirmed the motif’s cellular function, targeting to the ER membrane. In vitro binding studies revealed a typical, low-micromolar dissociation constant, while a mutated motif did not bind. A database search using a sequence motif derived from the aligned proteins detected a total of 17 FFAT-containing proteins in vertebrate proteomes with lipid-related functions. Since the motif has six very highly conserved core residues, sequence searches are more informative than for many motifs and so the first paper to discover the motif essentially reported the full set.
We want to conclude this section by noting that methods to show proximity of proteins in-cell are becoming increasingly sophisticated. This means that in future, in-cell proximity might have been “validated” before a motif discovery project is undertaken. In-cell cross-linking Mass Spectrometry is now being performed by a number of labs [73–75]. This technique is undoubtedly challenging but might be indispensible in revealing enzyme-substrate relationships for the ~500 mammalian protein kinases, which fall into large groups with identical or similar target site motifs but very different substrate proteins. Another exciting new method is proximity labelling by biotinylation, BioID , which was successfully used recently to identify new substrates targeted to the proteasome for degradation by the betaTrCP E3 ligase .
A rule of thumb 1-2-3 reliability scoring system
Rule of thumb quality scoring scheme
Indirect supporting evidence
Direct supporting evidence for binding but not for in-cell function
Evidence in-cell that proteins associate, but direct supporting evidence for motif binding in vitro is lacking
Direct supporting evidence for both binding and in-cell function
SLiM discovery will continue for many years to be a major activity in research into how cell regulation works. As we have seen, the process has in the past been inefficient and error-prone, so that the literature is full of inadequately characterised motif instances as well as hundreds of false positive identifications. Most of the linear motifs that have been correctly identified so far are in mammalian systems and this bias is reflected in the cellular experimental assays listed. However, yeast and plant researchers will generally have access to equivalent experimental strategies. It is our hope that this article will help researchers to approach motif discovery with good scientific technique, increasing their success rate with the corollary of reducing the wastage of resources that has at times occurred. Their low binding affinities and inherently cooperative nature mean that this is still not necessarily going to be straightforward. But of the million or so motifs used by the cell, the number that are well characterised still just amounts to a rounding error. Good luck hunting them and remember that in science you partly create your own luck according to the quality of the work that you do and the thinking that you put into it.
Eukaryotic linear motif resource
Intrinsically disordered polypeptide
Short linear motif
Nuclear export signal
Src Homology 2 domain/motif
Src Homology 3 domain/motif
ELM category for cleavage motifs
ELM category for degradation motifs (degrons)
ELM category for docking motifs
ELM category for ligand binding motifs
Non-specific lethal complex
ELM category for modification sites
ELM category for targeting/trafficking motifs
We thank Norman Davey and Stephan Feller for encouraging us to write up these guidelines. We are grateful to the many people who have helped us to support the ELM resource.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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