Short linear motifs – ex nihilo evolution of protein regulation
© Davey et al. 2016
Received: 21 June 2015
Accepted: 13 November 2015
Published: 21 November 2015
Short sequence motifs are ubiquitous across the three major types of biomolecules: hundreds of classes and thousands of instances of DNA regulatory elements, RNA motifs and protein short linear motifs (SLiMs) have been characterised. The increase in complexity of transcriptional, post-transcriptional and post-translational regulation in higher Eukaryotes has coincided with a significant expansion of motif use. But how did the eukaryotic cell acquire such a vast repertoire of motifs? In this review, we curate the available literature on protein motif evolution and discuss the evidence that suggests SLiMs can be acquired by mutations, insertions and deletions in disordered regions. We propose a mechanism of ex nihilo SLiM evolution – the evolution of a novel SLiM from “nothing” – adding a functional module to a previously non-functional region of protein sequence. In our model, hundreds of motif-binding domains in higher eukaryotic proteins connect simple motif specificities with useful functions to create a large functional motif space. Accessible peptides that match the specificity of these motif-binding domains are continuously created and destroyed by mutations in rapidly evolving disordered regions, creating a dynamic supply of new interactions that may have advantageous phenotypic novelty. This provides a reservoir of diversity to modify existing interaction networks. Evolutionary pressures will act on these motifs to retain beneficial instances. However, most will be lost on an evolutionary timescale as negative selection and genetic drift act on deleterious and neutral motifs respectively. In light of the parallels between the presented model and the evolution of motifs in the regulatory segments of genes and (pre-)mRNAs, we suggest our understanding of regulatory networks would benefit from the creation of a shared model describing the evolution of transcriptional, post-transcriptional and post-translational regulation.
Over the past 20 years our understanding of genome organisation expanded rapidly as researchers leveraged breakthroughs in sequencing technology to determine the complete DNA sequence of numerous eukaryotic genomes. It quickly became clear that these genomes differed in several important ways from the prokaryotic genomes that preceded them. Perhaps the most obvious difference was that eukaryotic genomes contained a much larger proportion of non-coding DNA than their distant prokaryotic relatives. In the first decade of the 21st century, the genomics community turned to identifying the complete repertoire of functional elements in these non-coding regions. This led to a flurry of research to understand the function and evolution of the human genome’s vast “heart of darkness” , culminating with ENCODE and related projects [2–4]. Over the same period of time surprising discoveries were causing a similar transition in thinking about the protein products of the eukaryotic genomes [5, 6]. Structural studies were revealing that a substantial number of proteins or segments of proteins in complex organisms are intrinsically disordered, lacking a stable well-defined tertiary structure in their native state [7, 8]. Moreover, these regions were shown to perform numerous functions - directly contradicting the structure-function paradigm, a basic tenet of structural biology [6, 9–11]. These observations, like the analogous discovery of the extensive functionality of non-coding regions, forced a paradigm shift and sparked an interest in these hitherto underappreciated regions.
Many of the interactions mediated by these regions were observed to be low-affinity. Consequently, they often mediate interactions where the biological requirements are such that a transient or dynamic binding event is preferable [10, 12]. Unexpectedly, the vast majority of these modules were shown to be encoded in short regions, what we now describe as short linear motifs (or SLiMs), of less than ten amino acids that mediate transient interactions with peptide binding domains . Furthermore, within these peptides, as few as three or four residues typically encoded the majority of affinity and specificity of binding [10, 14]. Despite these barriers to motif discovery the census of modules rapidly expanded and thousands of SLiMs have now been functionally characterised . They are known to be involved in a diverse array of functions: they assist in protein complex assembly; recruit substrates to modifying enzymes; control protein stability; direct trafficking to and anchoring in specific subcellular locations; and act as sites of post-translational modification (PTM) moiety addition or removal, proteolytic cleavage and structural modification [9, 10, 12, 13]. However, despite increasing appreciation of their abundance and importance [10, 15], little was known until recently about SLiM evolution: especially in comparison to globular domain evolution whose duplication, divergence and recombination was already textbook knowledge [16, 17]. Nevertheless, consideration of the potential evolutionary plasticity of the compact and degenerate SLiMs led to the hypothesis that they could play key roles in protein evolution : acquiring a novel SLiM is an appealing mechanism whereby a protein can gain important regulatory functions. Therefore protein networks could acquire new interactions with only a few amino acid changes . Indeed, short DNA regulatory motifs were thought to be key substrates for transcriptional regulatory evolution , and a parallel with protein motifs seemed possible .
In the past 10 years, there has been much progress in testing the hypothesis that the gain and loss of SLiMs can underlie evolutionary changes in protein function. Here, we review illustrative examples of SLiM evolution and large-scale efforts to characterise the evolutionary diversity of SLiMs. In doing so, we identify several outstanding questions about the origin and evolution of SLiMs: What are the evolutionary forces that drive motif evolution? What is the mechanism of motif binding pocket evolution? When did extensive motif use evolve? Finally, we discuss the parallels in motif evolution at the transcriptional, post-transcriptional and post-translational regulation level.
The evolutionary properties of short linear motifs
Table of characterised examples of motif gain and loss modulating protein function
Species protein (Gene)
Ex nihilo motif acquisition
H. sapiens Leucine-rich repeat protein SHOC-2 (SHOC2)
1 MGSSLG 6
Allele with a single S2- > G mutation in SHOC2 in Noonan-like syndrome patients “knocks in” a motif .
N-myristoylation of SHOC2
H. sapiens Adapter molecule crk (CRK)
ABL1 SH3 domain binding motif
Ex nihilo acquisition in early mammals .
Recruitment of ABL1 to CRK
S.cerevisiae Cell division control protein 6 (Cdc6)
SCF Cdc4 degrons
Ex nihilo acquisition in Saccharomyces sensu stricto clade .
Degradation of Cdc6 by the SCF E3 Ub ligase
M. musculus E3 ubiquitin-protein ligase Mdm2 (Mdm2)
Cyclin binding motif
Acquisition in the rodent lineage via a four amino acid deletion .
Recruitment of and phosphorylation by CDK2
D. melanogaster Segmentation polarity homeobox protein engrailed (en)
Groucho interacting motif
Ex nihilo acquisition in diptera/lepidoptera .
Recruitment of groucho
S.cerevisiae Serine/threonine-protein kinase ELM1 (Elm1)
Calcineurin docking motif
Ex nihilo acquisition in Saccharomyces sensu stricto clade .
Recruitment of and dephosphorylation by calcineurin
Influenza hemagglutinin (H3)
Five NxT sites
Strains spanning the last 40 years have shown gradual acquisition of five novel N-glycosylation sites .
Increased immune system evasion and decreased infectivity
Motif gain/loss post duplication
S. cerevisiae Spindle assembly checkpoint component MAD3 (Mad3)
APC/C Cdc20 binding KEN box
Lost in the Bub1 functional homologues after Mad3-like/Bub1-like duplications .
Loss of APC/C inhibitory function
S. cerevisiae Metallothionein expression activator (Ace2)
Cbk1 docking motif
Lost in Swi5 after Swi5/Ace2 duplication .
Loss of Cbk1 regulated localisation
H. sapiens Cyclin-A2 (CCNA2)
APC/C CDC20 binding ABBA motif
Ex nihilo acquisition in Cyclin A after the Cyclin A/Cyclin B duplication .
Early degradation during an active spindle assembly checkpoint
Tuning of motif specificity/affinity
S. cerevisiae MAP kinase kinase Pbs2 (Pbs2)
Sho1 SH3 domain binding motif
Only binds the SH3 domain of yeast Sho1 but can be recognised by multiple non-yeast SH3 domains .
Specific interaction with Sho1
S. cerevisiae Peroxisomal membrane protein PEX14 (Pex14)
Pex14 SH3 domain binding motif
Promiscuous in vitro but only binds the SH3 domain of co-localised Pex13 in vivo .
Promiscuous in vitro interactions
Ex nihilo co-operative/competitive interface evolution
S. cerevisiae N-acetyltransferase ECO1 (Eco1)
SCF Cdc4 degron
Ex nihilo acquisition in the Saccharomycetaceae clade of four motifs required for sequential kinase and ubiquitin ligase recruitment .
Degradation of Eco1 by the SCF E3 Ub ligase
Mck1 modification site
Cdc7 modification site
Cdk1 modification site
S. cerevisiae DNA replication licensing factor MCM3 (Mcm3)
Cdk1 modification sites
Ex nihilo acquisition of a cluster of Cdk1 modification sites in the Saccharomycetaceae family .
Regulation of nucleocytoplasmic shuttling of Mcm3
S. cerevisiae DIG2 (Dig2)
Competitive recognition of substrate by kinase and phosphatase
H. sapiens Retinoblastoma-associated protein RB (RB1)
Cyclin A docking site
873 KKLRF 875
Competitive recognition of substrate by kinase and phosphatase
PP1 binding RVxF
Motif gain/loss post de novo gene birth
Human immunodeficiency virus type 1 (HIV-1) Protein Vpu (vpu)
SCF β-TrCP degron
Highjacking of the host SCF-β-TrCP E3 Ub ligase
The degeneracy of motif-binding domain specificity provides substantial flexibility for a motif-containing peptide to encode a range of binding attributes. Consequently, evolution can adjust the affinity, specificity and selectivity of each domain-motif interaction in the network [10, 47–49]. For example, the affinities of PxIxIT docking motifs for calcineurin can range over two orders of magnitude ; artificially increasing the affinity of the PxIxIT motif in the calcineurin-activated transcriptional regulator CRZ1 (Crz1) results in constitutive dephosphorylation, transcriptional hyperactivity, and disruption of other calcineurin-dependent events . This suggests that motif instances in the calcineurin substrate network may have been tuned to optimally regulate substrate modification state. Similarly, the affinity of a PxxP motif in the MAP kinase kinase PBS2 (Pbs2) for its target SRC Homology 3 (SH3) domain in yeast high osmolarity signaling protein SHO1 (Sho1) correlates linearly with the biological output of the high osmolarity glycerol pathway, suggesting that evolution tuned this response by optimising the strength of the interaction . The same motif was shown to bind exclusively to the Sho1 SH3 domain in yeast, but to multiple non-yeast SH3 domains, indicating that evolution has tweaked the motif-domain interface to reduce deleterious promiscuous binding to other co-localised SH3 domains in the yeast proteome . A further level of motif tuning occurs through the acquisition of additional, co-operative motifs (Fig. 2d-f) (see Table 1). For example, the addition of a cluster of Cdk1 consensus sites to the flanks of a pre-existing nuclear localisation signal (NLS) adds a novel level of regulation to the nucleocytoplasmic shuttling of DNA replication licensing factor MCM3 (Mcm3) in yeast . Similar switching mechanisms involving co-operative and competitive use of motifs have evolved on numerous occasions [12, 27, 55, 56]. Remarkably, complete multi-motif interfaces can be acquired relatively rapidly on an evolutionary timescale, for example, the sequential recruitment of motif-binding partners to the multi-motif interfaces regulating the degradation of yeast Cell division control protein 6 (Cdc6)  and N-acetyltransferase ECO1 (Eco1) .
What are the evolutionary forces that drive specific motif evolution?
Ex nihilo motif birth
Motif birth occurs as a single mutation in a single allele in a single member of a species. When studying motifs, we generally consider a motif present in a fixed allele (i.e. it is present in all members of the population – SLiM-containing alleles may also be subject to balancing selection though no examples are known). On a population level, the steps from the ex nihilo birth of a motif to fixation or loss can follow several paths (Fig. 3e). The likelihood of motif fixation or loss will be dependent on the phenotype of the motif and the effective population size . For clarity three basic groupings can be used to describe a continuum of motif phenotypes: beneficial motifs are those that have an adaptive phenotype; neutral motifs are those that do not have any selectable positive or negative phenotype; and deleterious motifs are those that have a selectable negative phenotype. As a general model, alleles with beneficial motifs will be under positive selection and will become fixed in the population; those with neutral motifs can become fixed or lost by genetic drift; and those with deleterious motifs will be lost by negative selection. However, due to stochasticity in the evolutionary process, exceptions will occur. For example, beneficial motifs can be lost by genetic drift before they reach appreciable frequencies and deleterious motifs can become fixed in small populations. Once a motif has become fixed, negative (or purifying) selection will retain beneficial motifs, and subsequent mutations that become fixed by genetic drift will tend to remove neutral motifs over time. Substitutions that deleteriously affect the affinity, specificity and selectivity of a beneficial motif will generally be under negative selection and will fail to spread through the population. Conversely, those that result in a superior phenotype will be under positive selection and can become fixed. The interplay of this positive and negative selection might give directionality to the evolution of a motif and could in effect act as a ratchet to optimise the motif’s binding attributes (Fig. 3f).
Motif optimization in a network
Multiple motif-containing proteins are often competing for a finite pool of a given motif-binding pocket-containing protein. The optimisation of each motif must thus be considered in the context of the whole interaction network: to balance competition between motif-containing proteins and define the proportion of each motif-containing protein that occupies a given motif-binding pocket. These systems must consider the timing/strength of expression of the motif-containing and motif-binding partners and, as many motifs function in multiprotein complexes and cannot sustain interactions without co-operativity, changes in expression of scaffolding molecules. Such a model would require co-evolution of the network to tune the attributes of each interface in reaction to changes to the network. These network changes can include: an increase or decrease in the abundance of a component of the network; the gain or loss of a motif; mutations that alter the affinity, specificity and selectivity of a motif; or the addition of intramolecular co-operativity between motifs that can increase the avidity of an interaction, increase the specificity of an interaction, or add regulatory constraints that act as conditional modulators of an interaction [51, 66]. Many inhibitors of motif-mediated systems, both endogenous and pathogenic, take advantage of the delicate balance of these systems by utilising high affinity motifs, or high avidity co-operative multi-motif interfaces, to titrate the available motif-binding proteins [46, 67–69]. A related question is whether the cumulative effect of all presumably individually neutral motifs on the network level can have an appreciable phenotype by titrating the motif-binding partner away from motif-containing proteins. A consequence of this would be that there exists an upper limit to the number of instances of a motif in a proteome. It is evident that large numbers of motif instances for a single motif-binding partner are possible, for example, NLS motifs are present in hundreds of proteins yet they function without issue . However, it has also been shown that motif–containing peptides in high concentrations can act as potent inhibitors . Similar inhibitory effects have been observed for motifs with artificially increased affinities . Several motif networks have been shown to recruit targets with a hierarchy driven by the intrinsic affinity for their motif-containing binding partner. In some cases, these networks regulate recruitment using competitive mechanisms facilitated by limiting amounts of the motif-binding domains . So can evolutionarily neutral motif instances in sufficiently high quantities or with sufficiently high affinities act as inhibitors? Or would the set of novel untuned, and therefore possibly lower affinity, motifs be outcompeted by the key biological targets? This is currently unclear. However, the upper limit of instances of a functionally important motif is likely correlated with the abundance of the motif-binding protein and the abundance and relative affinities of the motif-containing proteins. An important consideration is that motif-binding domains instances, in excess, can significantly bind a pool of weaker motifs beyond their normal targets . Perhaps the expansion of a motif network is the result of an increase in the abundance of the motif-binding partner, and thus an expansion of the number of recruited motif-containing proteins, followed by a wave of selection. These concepts illustrate that when considering the evolutionary forces of mutations in motifs it is important to consider both protein autonomous effects (i.e., changes in the regulation of that protein) and effects due to modulation of the larger protein interaction network.
What is the mechanism of motif-binding pocket evolution?
Some motif-binding domains are members of large domain families. Members of most of these motif-binding domain families, while utilising the same binding pocket, have diverged specificities to recognise distinct, often overlapping, sets of peptides (Fig. 4b) [10, 75, 79, 80]. For example, the optimal specificities of kinases [81–83] and SRC Homology 2 (SH2) domains [84, 85] have diversified during family expansions. The specificity of a motif-binding pocket is dependent on its physicochemical properties. Evolutionary refinement of the domain surface post-duplication can modulate these physicochemical properties and thus the binding preferences of the motif-binding domain. For example, dependent on the biological requirement, amino acid changes in the binding surfaces can shift the binding preferences to allow a given peptide bind to one of the duplicated domains but not the other, or less drastically, bind with different affinities to each domain. Both mechanisms result in diverged specificities for the novel binding domains and over time the specificity of the domains can drift extensively. When overlapping specificity with homologous, or non-homologous, co-localised domains results in deleterious motif-binding events the specificities of motif-binding pockets will evolve to reduce this overlap [48, 53, 83, 86]. For example, mitotic kinases have been observed to target the correct substrates by a combination of substrate co-localisation and kinase specificity. The specificity of several of these kinases have evolved to specifically disfavour the motifs of other co-localised mitotic kinases [48, 87].
When did extensive motif use evolve?
Table of several classical SLiM-binding domain families, and representative DNA and RNA motif-binding domain familiesa
C2H2/C2HC zinc finger
Do common principles of regulatory evolution unite motifs in DNA, RNA and Protein?
Many parallels have been observed for motif use at the transcriptional, post-transcriptional and post-translational level. For example, specification of responses through the co-operative action of multiple motif recruited regulators is a theme at all levels of regulation (transcription: , splicing: , miRNA , signalling ). Much like combinations of SLiMs in disordered regions that lead to combinatorial post-translational regulatory switches , enhancers integrate complex transcriptional circuitry to individual genes . Like the regulatory regions of DNA and (pre-)mRNA, disordered regions containing multiple SLiMs are key foci where the gain and loss of motifs can lead to complex changes in cell regulation and physiology [38, 68]. Another example is the analogy of SLiM-binding pocket and SLiM co-evolution with DNA-binding domain - DNA regulatory element co-evolution. Because of the predicted pleiotropy of DNA-binding domain specificity changes, it was argued that such changes (in trans) should be comparatively rare relative to changes in the modular DNA binding sites (in cis ). Nevertheless, several examples of such changes and the corresponding co-evolution of DNA binding sites were subsequently identified (e.g., ). Once again, examples of pocket-SLiM co-evolution exist [40, 77, 78]. Finally, recent genome-scale chromatin immunoprecipitation and DNase hypersensitivity mapping experiments have indicated that DNA-protein interactions evolve rapidly between species. These results suggest that many DNA motif - protein interactions in complex genomes are not preserved over evolution while a small subset of functional binding sites is preserved near key target genes . This is analogous to the evolutionary reservoir model described above, where most SLiMs are evolutionarily transient, and a few core SLiMs are preserved by natural selection. The rapid evolutionary turnover of a large fraction of regulatory interactions is consistent with a model where most of the changes are nearly neutral with respect to selection [65, 102] (although we note that extensive lineage-specific selection could also produce similar patterns ). If the mostly neutral model is correct, only a small fraction of the evolutionary reservoir created by non-adaptive processes will be preserved by natural selection. Due to the size and complexity of eukaryotic genomes and proteomes and the short, degenerate nature of motifs, the rate of ex nihilo motif gain may be rapid enough that a large number of neutral regulatory interactions are present at all levels (DNA, RNA and proteins).
Every motif will be subjected to unique evolutionary pressures and novel motifs will fall along a phenotypic continuum rather than a neatly classifiable trinity of positive, neutral or negative phenotypes. Nevertheless, we have described a general model for the mechanism of motif evolution where the dynamic equilibrium of motifs being rapidly created ex nihilo in disordered regions and then destroyed by mutations provides a reservoir of functional diversity in protein interaction networks. We believe this diversity represents a key raw material exploited by evolution as it elaborates the complexity of the cell. This advocates a model of protein evolution resulting from both domain duplication and ex nihilo motif evolution.
The expansion of motif-binding domains linking compact and degenerate peptides to important functions greatly increased the information processing potential of the cell by simplifying access to regulatory pathways and cell state information. This expansion of functional motif space has allowed mutations, insertions and deletions to act as a powerful mechanism to add novel functional modules to a protein. Such a simple evolutionary mechanism to create selectable phenotypic diversity appears to have been advantageous to many organisms as it was extensively expanded and exploited resulting in an explosion in network connectivity and an increase in the regulatory complexity of the cell. The large functional motif space also increased the evolvability of these organisms by offering huge potential future adaptive evolution. Thus, it is tempting to assume that increasing motif usage is beneficial to complex organisms. However, as the Noonan-like syndrome motif “knock in” example shows, on an individual level, the deleterious effect of motif birth can be severe. The relative likelihood of motif gain and loss is still unknown, however, it is possible that if the effective population size becomes small for complex organisms, and interactions may appear ex nihilo in disordered regions at a high enough rate, natural selection might simply not be strong enough to purge them. [65, 104].
Many basic questions remain regarding the extent of motif use. How many motifs specifically bind each motif-binding pocket? How many of these motif-binding events are biologically important? How many are “evolutionary noise” ? These unknowns complicate our quest to understand motif evolution and consequently numerous unanswered evolutionary questions also exist. How often do motifs arise ex nihilo? What proportion of these novel motifs are advantageous, deleterious and neutral? What is the cumulative cost of multiple neutral motifs? If the acquisition of a given motif class is advantageous to a particular protein will it eventually acquire it? How does evolution optimise the binding attributes of a motif? How do co-operative sets of motifs evolve (Does the presence of a motif increase the likelihood of the acquisition of a co-operative motif)? Further experimental and theoretical exploration is needed to answer these questions. This will be confounded by experimental limitations (perhaps “biologically irrelevant” motifs haven’t been tested under the correct lab conditions) and the weak phenotypes, redundancy and co-operativity of many motifs. This remains a key area of research and will require numerous experimental and analytical advances. A key step will be the creation of unbiased, proteome-wide approaches to identify SLiMs, such as proteomic phage display [105, 106]. Although the experimental and analytical techniques will be specific to SLiMs, in light of the parallels between regulatory motifs in all the major macromolecules, we suggest that studies aimed at understanding the mechanisms of SLiM evolution should consider their evolutionarily analogous motifs in the regulatory regions of DNA and (pre-)mRNA. Ultimately, our understanding of cell regulation could benefit greatly through the use of shared concepts and models for motif evolution at the transcriptional, post-transcriptional and post-translational level (e.g., [35, 65, 107]).
We apologise to all colleagues whose work could not be cited here owing to space restrictions. NED is supported by a SFI Starting Investigator Research Grant (13/SIRG/2193). MSC is supported by NIH grant GM-48728. AMM is supported by grants from the National Sciences and Engineering Research Council (NSERC). We thank Richard Edwards, Hunter Fraser, Toby Gibson, Aino Järvelin, Christian Landry, Denis Shields, Kim Van Roey and Taraneh Zarin for fruitful discussions and critically reading the manuscript.
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