New Results

Proteome-wide signatures of function in highly diverged intrinsically disordered regions

Taraneh Zarin, Bob Strome, Alex N Nguyen Ba, Simon Alberti, Julie D Forman-Kay, Alan M Moses
doi: https://doi.org/10.1101/578716

Abstract

Intrinsically disordered regions make up a large part of the proteome, but the sequence-to-function relationship in these regions is poorly understood, in part because the primary amino acid sequences of these regions are poorly conserved in alignments. Here we use an evolutionary approach to detect molecular features that are preserved in the amino acid sequences of orthologous intrinsically disordered regions. We find that most disordered regions contain multiple molecular features that are preserved, and we define these as “evolutionary signatures” of disordered regions. We demonstrate that intrinsically disordered regions with similar evolutionary signatures can rescue function in vivo, and that groups of intrinsically disordered regions with similar evolutionary signatures are strongly enriched for functional annotations and phenotypes. We propose that evolutionary signatures can be used to predict function for many disordered regions from their amino acid sequences.

Introduction

Intrinsically disordered protein regions are associated with a large array of functions (reviewed in (Forman-Kay and Mittag, 2013)), including cell signaling (Iakoucheva et al., 2004; Tompa, 2014; Wright and Dyson, 2014), mediation of protein-protein interactions (Borgia et al., 2018; Tang et al., 2012; Tompa et al., 2015), and the formation of membraneless organelles through phase separation (Banani et al., 2017; Franzmann et al., 2018; Nott et al., 2015; Patel et al., 2015; Riback et al., 2017). These regions are widespread in eukaryotic proteomes (Peng et al., 2013; Ward et al., 2004), but do not fold into stable secondary or tertiary structures, and do not typically perform enzymatic functions (Uversky, 2011). Although intrinsically disordered regions can readily be identified based on their primary amino acid sequence (Dosztányi et al., 2005; Uversky, 2002), it remains a challenge to associate these regions with specific biological and biochemical functions based on their amino acid sequences, limiting systematic functional analysis. In stark contrast, for folded regions, protein function can often be predicted with high specificity based on the presence of conserved protein domains (El-Gebali et al., 2018) or enzymatic active sites (Ondrechen et al., 2001). Analogous methods to assign function to intrinsically disordered regions based on evolutionary conservation (or other sequence properties) are of continuing research interest (reviewed in (Van Der Lee et al., 2014)).

We and others (Davey et al., 2012; Nguyen Ba et al., 2012) have shown that short segments of evolutionary conservation in otherwise rapidly evolving disordered regions point to key functional residues, often important for posttranslational modifications, or other transient protein interactions (Tompa et al., 2014). However, these conserved segments make up a small fraction of disordered regions (5%), and the vast majority of disordered amino acids show little evidence for evolutionary constraint in alignments of primary amino acid sequences (Colak et al., 2013). It is currently unclear how intrinsically disordered regions persist at high frequency in the proteome, given these apparently low levels of evolutionary constraint.

One hypothesis for the preponderance of disordered regions despite high amino acid sequence divergence, is that the “molecular features” of disordered regions that are important for function (such as length (Schlessinger et al., 2011), complexity (Alberti et al., 2009; Halfmann, 2016; Kato et al., 2012; Molliex et al., 2015), amino acid composition (Moesa et al., 2012), and net charge (Mao et al., 2010; Strickfaden et al., 2007; Zarin et al., 2017)) do not lead to detectable similarity in primary amino acid sequence alignments. Indeed, recently, evidence that such molecular features can be under evolutionary constraint has been reported for some proteins (Daughdrill et al., 2007; Lemas et al., 2016; Zarin et al., 2017). For example, we showed that signaling function of a disordered region in the Saccharomyces cerevisiae protein Ste50 appears to depend on its net charge, and we found evidence that this molecular feature is under evolutionary constraint, despite no evidence for homology of the primary amino acid sequence in alignments (Zarin et al., 2017).

Here we sought to test whether evolutionary preservation of molecular features is a general property of highly diverged intrinsically disordered protein regions. To do so, we obtained a set of 82 sequence features reported in the literature to be important for disordered region function (Table S1). We computed these for S.cerevisiae intrinsically disordered regions and their orthologs, and compared them to simulations of molecular evolution where conserved segments (if any) are retained, but where there is no selection to retain molecular features (Nguyen Ba et al., 2014, 2012). Deviations from the simulations indicate that the highly diverged intrinsically disordered regions are preserving molecular features during evolution through natural selection (Zarin et al., 2017).

We find that many intrinsically disordered regions show evidence for selection on multiple molecular features, which we refer to as an “evolutionary signature”. Remarkably, we show that intrinsically disordered regions with similar evolutionary signatures appear to rescue function, while regions with very different signatures cannot, strongly supporting the idea that the preserved molecular features are important for disordered region function. By clustering intrinsically disordered regions based on these evolutionary signatures, we obtain (to our knowledge) the first global view of the functional landscape of these enigmatic protein regions. We recover patterns of molecular features known to be associated with intrinsically disordered region functions such as subcellular organization and targeting signals. We also identify new patterns of molecular features not previously associated with functions of disordered regions such as DNA repair and ribosome biogenesis. Finally, we show that similarity of evolutionary signatures can generate hypotheses about the function of completely disordered proteins. Taken together, our results indicate that evolutionary constraint on molecular features in disordered regions is so widespread that sequence-based prediction of their functions should be possible based on molecular features.

Results

Proteome-wide evolutionary analysis reveals evolutionarily constrained sequence features are widespread in highly diverged intrinsically disordered regions

We identified more than 5000 intrinsically disordered regions (IDRs) in the S.cerevisiae proteome and quantified their evolutionary divergence (see Methods). As expected, we found that the IDRs evolve more rapidly than the regions that were not identified as disordered (Fig. S1). We also confirmed that the vast majority of these IDRs are distinct from Pfam domains (Fig. S2). These results are consistent with previous reports (Brown et al., 2010; Colak et al., 2013; de la Chaux et al., 2007; Khan et al., 2015; Light et al., 2013; Tóth-Petróczy and Tawfik, 2013) that the primary amino acid sequence alignments of IDRs show high levels of divergence and it is not possible to annotate IDR functions using standard homology-based approaches.

To test for selection on molecular features in these IDRs, we applied a method that we recently used to show evidence of selection on an IDR in the S.cerevisiae Ste50 protein (Zarin et al., 2017). We obtained 82 molecular features that have been reported or hypothesized to be important for IDR function (Table S1) and tested whether these molecular features are under selection in the S. cerevisiae IDRs (see Methods for details). Briefly, we compare the distribution of a given molecular feature in a set of orthologous IDRs to a null expectation, which is formed by simulating the evolution of each IDR. When the mean or variance of the molecular feature across the orthologous IDRs deviates from the distribution of means or variances in our null expectation, we predict that this feature is under selection, and thus could be important for the function of the IDR in question. For example, in the Ste50 IDR, as reported previously (Zarin et al., 2017), we found that the variance of the net charge with phosphorylation of the IDR falls outside of our null expectation, while the mean falls within our null expectation (Fig. 1A).

Figure 1.
Figure 1.

Proteome-wide evolutionary analysis reveals evolutionarily constrained sequence features are widespread in highly diverged intrinsically disordered regions. A) Left: Mean versus log variance of the “net charge with phosphorylation” molecular feature for the real Ste50 IDR (a.a. 152-250) ortholog set and simulated Ste50 orthologous IDR sets (N=1000). Right: Example simulated Ste50 orthologous IDR sets (no. 663 and no. 56 out of 1000) and the real Ste50 IDR and its orthologs, coloured according to percent identity in the primary amino acid sequence. B) Percentage of IDRs that are significantly deviating from simulations in mean, log variance, or both mean and log variance of each molecular feature. C) Frequency [1+log(frequency)] of number of significant molecular features per IDR for the real IDRs (yellow) versus the random expectation (blue) obtained from a set of simulated IDRs.

We applied this analysis to 5149 IDRs (see Methods) and computed the percentage of IDRs where the evolution of each molecular feature fell beyond our null expectation (empirical p<0.01, Fig. 1B). We find that charge properties such as net charge and acidic residue content are most likely to deviate from our null expectation (more than 50% of IDRs) (Fig. 1B). This is in contrast to non-conserved motif density, which deviates from our null expectation in 21.6% of IDRs at most (for CDK phosphorylation consensus sites). Other molecular features that frequently deviate from our null expectation are sequence complexity (43.0%), asparagine residue content (43.3%), and physicochemical features such as isoelectric point (53.9%). We also found that the mean of each molecular feature deviates from our null expectation more often than the variance (Fig. 1B). These results suggest that there are many more molecular features that are under selection in IDRs than is currently appreciated (Daughdrill et al., 2007; Lemas et al., 2016; Zarin et al., 2017).

Next, we quantified the number of molecular features that are significant per IDR, assigning significance to a molecular feature if either the mean, variance, or both mean and variance of the molecular feature deviated from our null expectation (empirical p<0.01, Fig. 1C). Surprisingly, many IDRs have many significant molecular features, with a median of 15 significant molecular features per IDR (compared to 1 significant feature expected by chance; see Methods). Although many of our features are correlated (see Discussion), these results suggest that the deviation from our expectations of molecular feature evolution is not due to a few outlier IDRs, but rather that most IDRs tend to have multiple molecular features that are under selection.

Intrinsically disordered regions with similar molecular features can perform similar functions despite negligible similarity of primary amino acid sequences

The analysis above indicates that highly diverged intrinsically disordered regions (IDRs) typically contain multiple molecular features that are under selection. To summarize the set of preserved molecular features in each IDR, we computed Z-scores comparing either the observed mean or variance of each molecular feature in the orthologous IDRs to our simulations (see Methods). We call these summaries of evolution of molecular features (vectors of Z-scores) “evolutionary signatures”. If the features are important for function, IDRs with similar evolutionary signatures are predicted to perform (or at least be capable of performing) similar molecular functions. To test this hypothesis, we replaced the endogenous Ste50 IDR with several IDRs from functionally unrelated proteins: Pex5, a peroxisomal signal receptor (Erdmann and Blobel, 1996), Stp4, a predicted transcription factor (Abdel-Sater et al., 2004), and Rad26, a DNA-dependent ATPase involved in Transcription Coupled Repair (Gregory and Sweder, 2001; Guzder et al., 1996) (Fig. 2A). Ste50 is an adaptor protein in the High Osmolarity Glycerol (HOG) and mating pathways (Hao et al., 2008; Jansen et al., 2001; Tatebayashi et al., 2007; Truckses et al., 2006; Yamamoto et al., 2010) whose IDR is important for basal mating pathway activity (as measured by expression of a reporter driven by the Fus1 promoter) (Hao et al., 2008; Zarin et al., 2017). The IDRs that we used to replace the Ste50 IDR all have negligible similarity when their primary amino acid sequences are aligned, but vary in the similarity of their evolutionary signatures (to the Ste50 IDR, Fig. 2A). We found that the basal mating reporter expression in each strain corresponded to how similar the evolutionary signature of the replacing IDR was to that of the Ste50 IDR (all mutants significantly different from wildtype and each other, Wilcoxon test p<0.05, Fig. 2B). To further assay mating pathway activity, we exposed the wildtype and chimaeric strains with IDRs from Pex5, Stp4 and Rad26 to mating pheromone. We found that the two chimaeric strains that were more similar in their evolutionary signatures to the wildtype (Pex5 and Stp4) began the process of “shmooing”, or responding to pheromone, whereas the strain that had the IDR with the most different evolutionary signature (Rad 26) could not shmoo (Fig. 2C; full micrographs in Fig. S3). That the evolutionary signature of molecular features of IDRs can be used to predict which IDRs can rescue signaling function suggests that these signatures may be associated with IDR function.

Figure 2.
Figure 2.

Intrinsically disordered regions with similar evolutionary signatures can rescue wildtype phenotypes, while those with different evolutionary signatures cannot. A) Multiple sequence alignment of Ste50 iDr (a.a. 152-250), Pex5 IDR (a.a. 77-161), Stp4 (a.a. 144-256), and Rad26 IDR (a.a. 163-239) shows negligible similarity when their primary amino acid sequences are aligned, while evolutionary signatures show that the Pex5 and Stp4 IDRs are more similar to the Ste50 IDR than the Rad26 IDR. IDRs are presented in order of increasing Euclidian distance between their evolutionary signatures. The Ste50 IDR is located between the Sterile Alpha Motif (SAM) and Ras Association (RA) domains in the Ste50 protein. B) Boxplots show distribution of values corresponding to basal Fus1pr-GFP activity in an S.cerevisiae strain with the wildtype Ste50 IDR compared to strains with the Pex5, Stp4, or Rad26 IDR swapped to replace the Ste50 IDR in the genome. Boxplot boxes represent the 25th-75th percentile of the data, the black line represents the median, and whiskers represent 1.5*the interquartile range. Outliers are represented by unfilled circles. C) Brightfield micrographs showing each strain from part B following exposure to pheromone. Shmooing cells are those which have elongated cell shape, i.e. mating projections.

Proteome-wide view of evolutionary signatures in disordered regions reveals association with function

To test the association of function with evolutionary signatures in highly diverged IDRs, we clustered and visualized the evolutionary signatures for 4646 IDRs in the proteome (see Methods) (Fig. 3). Remarkably, the evolutionary signatures reveal a global view of disordered region function. The IDRs fall into at least 23 clusters based on similarity of their evolutionary signatures (groups A through W, Fig. 3) that are significantly associated with specific biological functions (enriched for Gene Ontology (GO) term, phenotype, and/or literature annotations, False Discovery Rate [FDR]=5%, Benjamini-Hochberg corrected) (Table 1; full table of enrichments in supplementary data; clustered IDRs and evolutionary signatures in supplementary data). Given that this level of specificity of biological information has not been previously associated with sequence properties of highly diverged IDRs, we performed a series of controls, ensuring that our clusters are not based on homology between IDRs, that our annotation enrichment results are not due to a mis-specification of the null hypothesis, and to confirm that these annotation enrichment results cannot be obtained simply based on amino acid frequencies of IDRs (Table S2; see Methods).

Figure 3.
Figure 3.

Clustering evolutionary signatures shows that IDRs in the proteome share evolutionary signatures, and that these clusters of IDRs are associated with specific biological functions. A-W show clusters significantly enriched for annotations (see Table 1; full table of enrichments in supplementary data). Cluster names represent summary of enriched annotations.

View this table:
Table 1.

Top 5 enriched GO term annotations and top 3 enriched phenotype annotations for each cluster. Full table of >1300 significant GO term, phenotype, and literature enrichments in supplementary data.

Several of the functions that we find enriched within our clusters have been previously associated with molecular features of IDRs, which we recover in our analysis. For example, we find a cluster that is associated with “nucleocytoplasmic transporter activity” (cluster M) that includes IDRs from FG-NUP proteins Nup42, Nup145, Nup57, Nup49, Nup116, and Nup100 that form part of the nuclear pore central transport channel (Alber et al., 2007). In cluster M, we find molecular features such as increased asparagine content, increased polar residue content, and increased proline and charged residue demixing (“Omega” (Martin et al., 2016)) in addition to the well-known “FG” repeats that are found in the FG-NUP IDRs (reviewed in (Terry and Wente, 2009)). Another interesting example is cluster O, which contains IDRs from proteins that are enriched for a wide range of annotations such as “P-body”, “cytoplasmic stress granule”, “actin cortical patch”, and “DNA binding”. Cluster O contains IDRs from proteins associated with phase separation and membraneless organelles such as Sup35 (Franzmann et al., 2018) and Dhh1 (Protter et al., 2018). The evolutionary signatures for the IDRs in this cluster include features that are typically associated with so-called “prionogenic”, low complexity disordered regions, such as increased mean polyglutamine repeats (Alberti et al., 2009), but also indicate that there are other relevant molecular features for this set of disordered regions (Fig. 4A). For example, in these regions, the variance of the net charge is reduced, and charged residues are depleted during evolution. These sequence features are illustrated in Fig. 4B, where we compare the presence of glutamine and charged residues in an example disordered region from this cluster (Ccr4; a protein that is known to accumulate in P-bodies (Teixeira and Parker, 2007)) to an example from the corresponding simulation (Fig. 4B). Taken together, these results indicate that our analysis captures molecular features that have been previously associated with IDR functions, and suggests additional molecular features in these IDRs that may be important for their functions.

Figure 4.
Figure 4.

Evolutionary signatures in cluster O contain some molecular features that are typically associated with IDRs as well as some that are not. A) Pattern of evolutionary signatures in cluster O. B) Example disordered region from cluster O, Ccr4, with a subset of highlighted molecular features compared between its real set of orthologs and an example set of simulated orthologous IDRs. Species included in phylogeny in order from top to bottom are S.cerevisiae, Saccharomyces mikatae, Saccharomyces kudriavzevii, Saccharomyces uvarum, Candida glabrata, Kazachstania naganishii, Naumovozyma castellii, Naumovozyma dairenensis, Tetrapisispora blattae, Tetrapisispora phaffii, Vanderwaltozyma polyspora, Zygosaccharomyces rouxii, Torulaspora delbrueckii, Kluyveromyces lactis, Eremothecium (Ashbya) cymbalariae, Lachancea waltii.

We also find functions associated with our clusters that have not been previously associated with molecular features of IDRs. For example, cluster D (Fig. 5A) is associated with DNA repair, and its evolutionary signature contains increased mean “Kappa” (Das and Pappu, 2013) and decreased mean “Sequence Charge Decoration” (SCD) (Sawle and Ghosh, 2015), both of which indicate that there is an increased separation of positive and negatively charged residues in these IDRs compared to our null expectation. This is illustrated by the IDR from Srs2, a protein that is known to be involved in DNA repair (Aboussekhra et al., 1989; Yeung and Durocher, 2011), and shows high charge separation compared to an example corresponding simulation (Figure 5B). The evolutionary signature for this cluster also reveals an increased mean fraction of charged residues and negatively charged residues in particular (Fig. 5A), which is also clear in the comparison between the real Srs2 orthologs and the simulation (Fig. 5B). Although acidic stretches have been associated with IDRs in histone chaperones (Warren and Shechter, 2017), to our knowledge, the separation of oppositely charged residues has not been associated with the wider functional class of DNA repair IDRs.

Figure 5.
Figure 5.

Cluster D contains disordered regions associated with DNA repair. A) Pattern of evolutionary signatures in cluster D. B) Example disordered region from cluster D, Srs2, with a subset of highlighted molecular features compared between its real set of orthologs and an example set of simulated orthologous IDRs. Species included in phylogeny in order from top to bottom are S.cerevisiae, S.mikatae, S.kudriavzevii, S.uvarum, C.glabrata, Kazachstania africana, K.naganishii, N.castellii, N.dairenensis, T.phaffii, Z.rouxii, T.delbrueckii, K.lactis, Eremothecium (Ashbya) gossypii, E.cymbalariae, Lachancea kluyveri, Lachancea thermotolerans, L.waltii.

Our analysis also indicates that there is not necessarily a 1:1 mapping between IDRs with shared evolutionary signatures and current protein functional annotations. For example, we find three clusters associated with ribosome biogenesis (cluster A, C, F) that cannot be distinguished based on their enriched GO terms. The largest of these is cluster A, where 201/295 proteins have a “nucleus” annotation, and 110/295 are essential proteins (“inviable” deletion phenotype). This cluster is also enriched for several phenotypes associated with RNA accumulation (Table 1, cluster A; see supplementary data for full list of significant enrichments). Cluster A contains highly acidic IDRs with CKII phosphorylation consensus sites. CKII has been previously associated with nucleolar organization (Louvet et al., 2006), and a previous analysis of non-conserved consensus phosphorylation sites found ribosome biogenesis as strongly enriched in predicted CKII targets (Lai et al., 2012). In contrast, cluster C shares neither of these molecular features with cluster A, and cluster F shares only highly acidic residue content. Interestingly, cluster C contains increased mean polylysine repeats, and is significantly enriched for proteins that have been experimentally verified as targets for lysine polyphosphorylation (Bentley-DeSousa et al., 2018) (p=2.7×10−3, hypergeometric test). Overall, although the IDRs in these clusters share different evolutionary signatures, they are all found in proteins associated with ribosome biogenesis. We hypothesize that these different signatures point to different functions relating to ribosome biogenesis, but we have no indication of what these might be based on current protein annotations (see Discussion).

We find similar observations in multiple clusters that have distinct evolutionary signatures enriched for terms associated with regulation of transcription (clusters I, J, L, N, O, R). These clusters are not clearly separable based on mechanistic steps of transcription (such as sequence-specific DNA binding, chromatin remodeling, etc.). Some of these clusters exhibit molecular features that have been associated with different classes of transcriptional activation domains that are based on amino acid composition (reviewed in (Frietze and Farnham, 2011)). For example, cluster J, O and N have increased glutamine residue content, while cluster N has increased proline residue content. However, clusters I and R have no amino acid composition bias, while cluster N has increased proline-directed phosphorylation consensus sites, suggesting post-translational modifications. This indicates that our analysis reveals new sub-classifications of transcription-associated IDRs. While we hypothesize that these IDRs have different functions, once more we have no indication of what these functions could be based on current protein annotations (see Discussion).

A cluster of evolutionary signatures is associated with N-terminal mitochondrial targeting signals

One of our clusters of intrinsically disordered regions is exceptionally strongly associated with the mitochondrion (144/165 proteins in the cluster) and other annotations that are related to mitochondrial localization and function (for example, 81/165 proteins in the cluster have shown a decreased respiratory growth phenotype) (Table 1, cluster W; see supplementary data for full list of significant enrichments). The vast majority of mitochondrial proteins are synthesized with N-terminal pre-sequences (Maccecchini et al., 1979) (also known as N-terminal targeting signals) that are cleaved upon import (Vögtle et al., 2009) and are thought to sample dynamic structural configurations (Saitoh et al., 2011, 2007) (Fig. 6A). Since 145/165 of the disordered regions in this cluster are N-terminal, we hypothesized that this cluster contains disordered regions that are associated with mitochondrial targeting signals (Vögtle et al., 2009). In line with this hypothesis, we find previously described sequence features of mitochondrial N-terminal targeting signals in our evolutionary signatures; for example, these IDRs are depleted of negatively charged residues, have an abundance of positively charged residues, and are much more hydrophobic than our null expectation (Fig. S4A) (Garg and Gould, 2016; Vögtle et al., 2009). Examples of disordered regions in this cluster include those of the Heme A synthase Cox15 and the mitochondrial inner membrane ABC (ATP-binding cassette) transporter Atm1 (Fig. S4B). In order to test our hypothesis that this cluster of evolutionary signatures identifies mitochondrial N-terminal targeting signals, we used a recently published tool that scores the probability that a sequence is a mitochondrial targeting signal (Fukasawa et al., 2015). Using this tool, we find that the IDRs in cluster W have a much higher probability of being mitochondrial targeting signals than any other cluster with enriched annotations in our analysis (Bonferroni-corrected p ≤ 6.5×10−11, Wilcoxon test) (Fig. 6B, red box). Interestingly, the adjacent cluster V (Fig. 6B, purple box), which we hypothesize to contain targeting sequences for the endoplasmic reticulum, is distinct from cluster W in this analysis.

Figure 6.
Figure 6.

Cluster W is associated with mitochondrial N-terminal targeting signals. A) Schematic (not to scale) showing the path of a mitochondrial precursor peptide (with N-terminal targeting sequence in red) from the cytosol, where it is translated, to the mitochondrial matrix, where the peptide folds and targeting sequence is cleaved. B) Violin plots (median indicated by black dot, thick black line showing 25th-75th percentile, and whiskers showing outliers) show distributions of mitochondrial presequence probability scores for all IDRs in each cluster. The cluster that we predict to contain mitochondrial N-terminal targeting signals is outlined in red, while the cluster that we predict to contain endoplasmic reticulum targeting signals is outlined in purple. C) Micrographs of S.cerevisiae strains in which Cox15 is tagged with GFP, with either the wildtype Cox15 IDR, deletion of the Cox15 IDR, replacement of the Cox15 IDR with the Atm1 IDR (also in the mitochondrial targeting signal cluster), or replacement of the Cox15 IDR with the Emp47 IDR (from the endoplasmic reticulum targeting signal cluster).

If the specificity of the function of the IDRs in this cluster is strong, we predict that swapping an IDR from cluster W with that of a verified mitochondrial targeting sequence would result in correct localization to the mitochondria, while swapping an IDR from a different cluster would not. To test this, we first used the (uncharacterized) disordered region from Atm1 that falls into cluster W to replace that of Cox15, which also falls into cluster W and is an experimentally verified mitochondrial targeting sequence (Vögtle et al., 2009) (Fig. 6C). In accordance with our hypothesis, we find that GFP-tagged Cox15 correctly localizes to the mitochondria when its disordered region is swapped with that of Atm1, but does not localize correctly when its disordered region is deleted (Fig. 6C; full micrographs in Fig. S5). We also repeated this experiment with another protein that has an experimentally verified N-terminal mitochondrial targeting sequence, Mdl2, and found the same results (Fig. S6). Next, we replaced the Cox15 IDR with the disordered region of Emp47, which has an evolutionary signature that we predict to be associated with targeting signals for the endoplasmic reticulum (cluster V). In this case, as we predicted, we found no mitochondrial localization of

Cox15-GFP. Importantly, these putative targeting signals have no detectible similarity when their primary amino acid sequences are aligned, and we therefore suggest that the similarity in their molecular features is preserved by stabilizing selection (see Discussion). These results confirm that IDRs with similar evolutionary signatures can rescue subcellular targeting functions, and suggest that the evolutionary signatures are specific enough to predict function of at least some IDRs.

Evolutionary signatures of function can be used for functional annotation of fully disordered proteins

A major challenge to proteome-wide analysis of IDRs is the limited applicability of homology-based sequence analysis. Proteins with a mixture of disordered regions and structured domains can be assigned function based on homology to their structured domains, but fully disordered proteins are much more difficult to classify (reviewed in (Van Der Lee et al., 2014)). We therefore asked whether hypotheses about functions of fully disordered proteins could be generated using evolutionary signatures. We identified ten yeast proteins of unknown function that are predicted to be most disordered (see Methods). To predict function according to our clustering analysis, we simply assigned them the annotation of the cluster in which they fell (Table 2). For example, Rnq1 has been extensively studied as a “yeast prion”, but there is no clear function associated with this protein under normal conditions (Kroschwald et al., 2015; Sondheimer and Lindquist, 2000; Treusch and Lindquist, 2012). Interestingly, Rnq1 falls into our cluster of disordered regions that are associated with nucleocytoplasmic transport (cluster M) and the nuclear pore central transport channel. While Rnq1 is annotated with a cytosolic localization, an RNQ1 deletion was recently shown to cause nuclear aggregation of the polyQ-expanded huntingtin exon1 (Httex1) in a model of Huntington’s disease (Zheng et al., 2017). Therefore, we propose a role for Rnq1 in nucleocytoplasmic transport. For some of these largely disordered proteins, we obtain large disordered segments falling into multiple clusters (indicated by more than one cluster ID in Table 2), suggesting more than one possible function for the protein (see Discussion). This analysis illustrates how evolutionary signatures can be used to generate hypotheses of function for fully disordered proteins.

View this table:
Table 2.

Evolutionary signatures of function can be used for functional annotation of previously uncharacterized proteins and IDRs.

Discussion

In this work, we tested for evolutionary constraints on highly diverged intrinsically disordered regions proteome-wide. In contrast to the relative lack of constraint on primary amino acid sequence alignments (compared to folded regions, (Brown et al., 2002; Tóth-Petróczy and Tawfik, 2013)), we find that the vast majority of disordered regions contain molecular features that deviate in their evolution from our null expectation (a simulation of disordered region evolution (Nguyen Ba et al., 2014, 2012)). Our discovery that highly diverged disordered regions contain (interpretable) molecular features that are under evolutionary constraint provides researchers with testable hypotheses about molecular features that could be important for function in their proteins of interest. Furthermore, in principle, our framework for the analysis of diverged disordered regions can be extrapolated to proteins from other species.

Importantly, our choice of features was based on previous reports of important sequence features in IDRs that could be easily calculated for protein sequences and scaled to millions of simulated sets of orthologous IDRs. Thus, our evidence for constraint must represent a lower bound on the total amount of functional constraint on highly diverged IDRs: there are very likely to be sequence characteristics that were not captured by our features. Further, even when we do find evidence for constraint on a feature, we do not know whether our feature represents the actual feature required for IDR function, or is simply correlated with it. For example, we found IDRs that show constraint on glycine and arginine content, but these may reflect the real constraint on planar-pi interactions (Vernon et al., 2018) and are not fully captured by either of these features. In the future, we could exhaustively search for protein sequence features that best explain the evolutionary patterns as was done for features of activation domains that explain reporter activity (Ravarani et al., 2018).

Despite the somewhat arbitrary choice of molecular features, we found strong evidence that groups of disordered regions share “evolutionary signatures”, and that these groups of IDRs are associated with specific biological functions. To demonstrate the association of evolutionary signatures with previously known functions, we associated IDRs with protein function. However, many proteins contain multiple IDRs. In these proteins, the IDRs may perform different functions (just as multiple folded domains may perform independent functions), thus complicating the mapping of molecular functions to molecular features of IDRs. Systematic data at the level of individual IDRs would greatly facilitate future progress in this area.

Another challenge in associating specific functions with individual IDRs is that current bioinformatics predictions of IDRs at the proteome level often lead to arbitrary breaks (or merging) of IDRs, as IDR boundaries are very difficult to define precisely (even with sensitive experimental approaches (Jensen et al., 2013)). Whether or not IDRs serve as distinct functional units across a linear peptide sequence, and where the boundaries for these regions lie on a proteome-wide scale, is an area for further research. In our cluster analysis, we find that the vast majority of IDRs in multi-IDR proteins fall into different clusters, and that this matches our expectation from random chance. A small minority of IDRs from very large (>1500 amino acid) disordered proteins cluster together, suggesting that they are “broken up” pieces of larger units.

Despite the caveat of IDR boundaries in proteome-wide analyses, evolutionary signatures of selection on molecular features represent a new way to assign function to the large numbers of currently enigmatic IDRs that have been identified based on protein sequences. This approach is complementary to current bioinformatics approaches to predict IDR function that are based on presence (Edwards et al., 2007) or conservation of SLiMs (Beltrao and Serrano, 2005; Davey et al., 2012; Lai et al., 2012; Nguyen Ba et al., 2012), prediction of interactions (MoRFs) (Fuxreiter et al., 2004; Lee et al., 2012; Mohan et al., 2006; Oldfield et al., 2005; Vacic et al., 2007), or the recently proposed phase separation propensity score (Vernon et al., 2018).

Widespread evidence for shared functions in the highly diverged portions of IDRs also has several evolutionary implications. The lack of homology between most IDRs with similar evolutionary signatures suggests that the molecular features are preserved in each IDR independently. For example, the more than 150 IDRs that we believe represent mitochondrial N-terminal targeting signals share similar constraints on their molecular features, yet these signals have been preserved independently over very long evolutionary time as mitochondrial genes were transferred individually to the nuclear genome (Adams and Palmer, 2003). The preservation of molecular features over long evolutionary time, despite accumulation of amino acid divergence, is consistent with a model of stabilizing selection (Bedford and Hartl, 2009; Hansen, 1997; Lande, 1976), where individual amino acid sites are under relatively weak functional constraints (Landry et al., 2014). In this view, single point mutations are unlikely to dramatically impair IDR function, and therefore large evolutionary divergence can accumulate. This also suggests that disease-causing mutations in disordered regions are more likely to cause gain of function, consistent with at least one recent study (Meyer et al., 2018).

Although current models for the evolution of short linear motifs (well-characterized functional elements in IDRs) also implicate stabilizing selection (Koch et al., 2018; Landry et al., 2014), these motifs represent only a minority of the residues in disordered regions (Nguyen Ba et al., 2012). Our observation of shared evolutionary signatures associated with specific functions in highly diverged IDRs suggests that this evolutionary mechanism is shaping the proteome on a much wider scale than currently appreciated. Further, stabilizing selection stands in contrast to purifying selection, the major evolutionary mechanism thought to preserve function in stably folded regions of the proteome (Taylor and Raes, 2004). Thus, we propose that these two major biophysical classes of protein regions (IDRs vs. folded regions) also evolve under two different functional regimes.

Methods

Multiple sequence alignments and visualization

We acquired orthologs of Saccharomyces cerevisiae from the Yeast Gene Order Browser (Byrne and Wolfe, 2005) and made multiple sequence alignments using MAFFT (Katoh and Standley, 2013) with default settings, as previously described (Nguyen Ba et al., 2014, 2012). We visualized multiple sequence alignments using Jalview (Waterhouse et al., 2009).

Quantification of evolutionary divergence of IDRs and ordered regions of the proteome

We identified IDRs in the S.cerevisiae proteome using DISOPRED3 (Jones and Cozzetto, 2015) and filtered them to include only those that are 30 amino acids or longer. We identified the non-disordered regions of the proteome as the inverse subset of the IDRs, and again only included regions that are 30 amino acids or longer. Using the multiple sequence alignments constructed for these protein regions (as above), and only including those proteins for which there at least 10 species in the alignment and at least 10 amino acids for each species, we calculated evolutionary distances for each region using PAML (Yang, 2007) using the WAG model, with an initial kappa of 2, initial omega of 0.4, and clean data set to 0. We used the sum of branch lengths for each region to estimate the evolutionary divergence, and plotted the distribution of this metric for IDRs and non-IDRs in the S.cerevisiae proteome in Fig. S1.

Quantification of IDR overlap with Pfam annotations

We obtained the list of Pfam (El-Gebali et al., 2018) domain coordinates for S.cerevisiae from the Saccharomyces Genome Database (SGD) (Cherry et al., 2012). We included domain coordinates that had e-values less than or equal to 1, and which occurred in more than one protein in the S.cerevisiae proteome. We then computed the percentage overlap of each IDR (coordinates determined as above) with the Pfam domain coordinates, and plotted the distribution of percent overlap values for all predicted IDRs in the S.cerevisiae proteome in Fig. S2.

Evolutionary analysis of diverged disordered regions

Evolutionary analysis of diverged disordered regions was performed as in (Zarin et al., 2017), with some modifications to facilitate proteome-wide analysis. Using the multiple sequence alignments of S.cerevisiae IDRs and species branch lengths (as described above), we used the previously described phyloHMM software (Nguyen Ba et al., 2012) to estimate the “local rate of evolution”, “column rate of evolution”, and any Short Linear Motif (SLiM) coordinates. For each IDR, we simulated 1000 orthologous sets of IDRs using the S.cerevisiae sequences as the root and a previously described disordered region evolution simulator (Nguyen Ba et al., 2014) that preserves SLiMs and evolves sequences according to disordered region substitution matrices. This simulator requires a scaling factor to convert evolutionary distances from substitutions per site as obtained from PAML (Yang, 2007). We chose the scaling factor such that the average distance between S.cerevisiae and S.uvarum over all the IDR alignments equals 1.

Sequences and trees were read into R using the “seqinr” (Charif and Lobry, 2007) and “ape” (Paradis and Schliep, 2018) packages, respectively. Sequences were parsed in R using the “stringr” (Wickham, 2010) and “stringi” (Gagolewski, 2019) packages. We calculated all the sequence features for the real and simulated set of IDR orthologs using custom functions in R except for “Omega” (Martin et al., 2016), “Kappa” (Das and Pappu, 2013), and Wootton-Federhen complexity (Wootton and Federhen, 1993), which were calculated using the localCider program (Holehouse et al., 2017) called through R using the “rPython” package (Bellosta, 2015). We calculated the mean and log variance of each feature for each real set of orthologous IDRs and each of the 1000 sets of orthologous IDRs. Because simulations sometimes lead to the deletion of the IDR, we did not include those IDRs that had fewer than 950 non-empty simulations. To obtain a random expectation for Fig. 1C, we quantified the number of significant (p<0.01) molecular features in a set of randomly chosen simulated IDRs (one for each real IDR).

To summarize the difference between each real set of orthologous IDRs and its corresponding 1000 simulated sets of orthologous IDRs, we used a standard z-score (Z) where we subtracted the mean of the simulations (μ) from the real value (x) and divided by the standard deviation of the simulations (σ). The formula for the Z-score is as follows: Embedded Image

Strain construction and growth conditions

All strains (Table S3) were constructed in the S. cerevisiae BY4741 background. IDR transformants were constructed using the Delitto Perfetto in vivo site-directed mutagenesis method (Storici et al., 2001). Ste50 IDR mutants were constructed in the ssk22Δ0::HisMx3 ssk2Δ0 background as in (Zarin et al., 2017). Genomic changes in transformed strains were confirmed by Sanger sequencing. For mitochondrial strains, starting strains were acquired from the GFP collection (Huh et al., 2003). The Fus1pr-GFP reporter was constructed as in (Zarin et al., 2017) using Gibson assembly (Gibson et al., 2009), integrated at the HO locus using a selectable marker (URA3), and confirmed by PCR.

All experiments were done on log-phase cells grown at 30°C in rich or synthetic complete media lacking appropriate nutrients to maintain selection of markers, unless otherwise stated. Two percent (wt/vol) glucose was used as the carbon source.

Confocal microscopy and image analysis

We acquired all images with a Leica TCS SP8 microscope using standard, uncoated glass slides with a 100x objective. To quantify basal Fus1pr-GFP expression, single cells in micrographs were segmented using BeerGoggles (http://beergoggles.csb.utoronto.ca/). The segmented masks and corresponding fluorescent images were imported into R using the “EBImage” package (Pau et al., 2010), and GFP intensity for each cell was quantified using a custom R script (sample script available on http://beergoggles.csb.utoronto.ca). To assay shmooing, log phase cells were inoculated with 1 uM alpha factor for 2 hours at 30°C (as in (Kompella et al., 2016)), at which point they were imaged in brightfield as above.

Clustering of proteome-wide evolutionary signatures

Hierarchical clustering was performed using the Cluster 3.0 program (de Hoon et al., 2004). The evolutionary signature data was first filtered to include only those IDRs that had at least one z-score with an absolute value of 3 or more, and with at least 95% data present for the 164 features. This resulted in 4646 IDRs (filtered from the initial 5149) that were then clustered using uncentered correlation distance and average linkage, with “cluster” and “calculate weights” options selected for “genes” (i.e. IDRs), but not for arrays (i.e. molecular features). Clusters were picked manually for further analysis. The full clusterplot is available in supplementary data.

In order to ensure that the clustering was not simply due to homology between the disordered regions, for each cluster, we computed the pairwise distance of its disordered region sequences based on the BLOSUM62 substitution matrix, and compared this to the pairwise distance between all disordered regions outside of that cluster (using the Biostrings R package (Pagès et al., 2018)). We compared the pairwise similarity of the IDRs in each cluster to that of the IDRs outside that cluster, and calculated the percent of disordered regions that fell in the top 1% of pairwise percent identity in all the clusters. This metric is presented for each cluster in Table S2. For example, the cluster with the highest amount of “homologous” IDRs according to this threshold (top 1% homology) is cluster Q, with 8.9% homologous IDRs. However, the vast majority of the clusters have negligibly homologous IDRs; for example, 17/23 clusters have less than 1% homology between IDRs.

Tests for enrichment of annotations

Annotations for Gene Ontology (GO) terms, phenotypes, and literature were acquired from SGD (Cherry et al., 2012) for the S.cerevisiae proteome. We included GO terms that applied to a maximum of 5000 genes in the S.cerevisiae proteome. A test for enrichment of annotations was done using the hypergeometric test for each cluster against all the proteins in the clustering analysis. To obtain Q-values, p-values were corrected using the Benjamini-Hochberg method. Q-values below an FDR of 5% were retained. Because there is not a 1 -to-1 correspondence between IDRs and annotations, which are based on proteins, we also calculated Q-values using permutation tests. To do so, we uniformly sampled 1000 clusters of IDRs for each cluster from the 4646 IDRs included in our clusterplot, and obtained the sum of the top ten – log Q-values associated with each test for enrichment, as above. We compared this test statistic to the observed sum of top ten – log Q-values for each cluster, and reported the difference as a standard z-score in Table S2.

In order to understand how our evolutionary signatures compare to information obtained only from amino acid frequencies, we computed vectors of z-scores for each IDR that represented their amino acid frequencies normalized to the proteome-wide average. We clustered these vectors using k-means (K=25) with the Cluster 3.0 program (de Hoon et al., 2004). We performed a similar permutation test (as above), where the sample of 1000 clusters was not uniform, but drawn to create 1000 random clusters of IDRs with similar amino acid composition for each cluster. For example, for each IDR in a cluster, we found the cluster that it fell into in the amino acid frequency clusterplot, and sampled from that cluster to replace the IDR in our evolutionary signature clusterplot. We did this 1000 times for each cluster, and used the same test statistic as the above-described permutations to report the difference in enriched annotations between our clusterplot based on evolutionary signatures and the clusterplot based on amino acid frequencies (Table S2).

Identification of highly disordered proteins with unknown function

We identified proteins whose biological role is unknown according to their SGD annotation (Cherry et al., 2012). We quantified the percent of residues that were predicted to be disordered in each protein with unknown function, and present the top ten most disordered proteins in Table 2.

Acknowledgements

We thank Alex X Lu, Christiane Iserman, Dr. Iva Pritisanac, Shadi Zabad, and Ian S Hsu for comments on the manuscript. We thank Alex X Lu for stimulating discussions about clustering and Dr. Iva Pritisanac for suggesting analysis of completely disordered proteins. We thank Dr. Helena Friesen and Dr. Brenda Andrews for providing strains from the yeast GFP collection. We thank Canadian Institutes for Health Research (CIHR) for funding to AMM and JDF-K, Canada Foundation for Innovation (CFI) for funding to AMM, and the National Science and Engineering Research Council of Canada (NSERC) for an Alexander Graham Bell scholarship and Michael Smith Foreign Study Supplement to TZ.

References

  1. ↵
    Abdel-Sater F, Iraqui I, Urrestarazu A, André B. 2004. The External Amino Acid Signaling Pathway Promotes Activation of Stp1 and Uga35/Dal81 Transcription Factors for Induction of the AGP1 Gene in Saccharomyces cerevisiae. Genetics 166:1727–1739. doi:10.1534/genetics.166.4.1727
    OpenUrlAbstract/FREE Full Text
  2. ↵
    Aboussekhra A, Chanet R, Zgaga Z, Cassier-Chauvat C, Heude M, Fabre F. 1989. RADH, a gene of Saccharomyces cerevisiae encoding a putative DNA helicase involved in DNA repair. Characteristics of radH mutants and sequence of the gene. Nucleic Acids Res 17:7211–7219. doi:10.1093/nar/17.18.7211
    OpenUrlCrossRefPubMedWeb of Science
  3. ↵
    Adams KL, Palmer JD. 2003. Evolution of mitochondrial gene content: Gene loss and transfer to the nucleus. Mol Phylogenet Evol 29:380–395. doi:10.1016/S1055-7903(03)00194-5
    OpenUrlCrossRefPubMedWeb of Science
  4. ↵
    Alber F, Dokudovskaya S, Veenhoff LM, Zhang W, Kipper J, Devos D, Suprapto A, Karni-Schmidt O, Williams R, Chait BT, Sali A, Rout MP. 2007. The molecular architecture of the nuclear pore complex. Nature 450:695–701. doi:10.1038/nature06405
    OpenUrlCrossRefPubMedWeb of Science
  5. ↵
    Alberti S, Halfmann R, King O, Kapila A, Lindquist S. 2009. A Systematic Survey Identifies Prions and Illuminates Sequence Features of Prionogenic Proteins. Cell 137:146–158. doi:10.1016/j.cell.2009.02.044
    OpenUrlCrossRefPubMedWeb of Science
  6. ↵
    Banani SF, Lee HO, Hyman AA, Rosen MK. 2017. Biomolecular condensates: organizers of cellular biochemistry. Nat Rev Mol Cell Biol 18:285–298. doi:10.1038/nrm.2017.7
    OpenUrlCrossRefPubMed
  7. ↵
    Bedford T, Hartl DL. 2009. Optimization of gene expression by natural selection. Proc Natl Acad Sci U S A 106:1133–8. doi:10.1073/pnas.0812009106
    OpenUrlAbstract/FREE Full Text
  8. ↵
    Bellosta CJG. 2015. rPython: Package Allowing R to Call Python. https://cran.r-project.org/package=rPython
  9. ↵
    Beltrao P, Serrano L. 2005. Comparative genomics and disorder prediction identify biologically relevant SH3 protein interactions. PLoS Comput Biol 1:e26. doi:10.1371/journal.pcbi.0010026
    OpenUrlCrossRefPubMed
  10. ↵
    Bentley-DeSousa A, Holinier C, Moteshareie H, Tseng YC, Kajjo S, Nwosu C, Amodeo GF, Bondy-Chorney E, Sai Y, Rudner A, Golshani A, Davey NE, Downey M. 2018. A Screen for Candidate Targets of Lysine Polyphosphorylation Uncovers a Conserved Network Implicated in Ribosome Biogenesis. Cell Rep 22:3427–3439. doi:10.1016/j.celrep.2018.02.104
    OpenUrlCrossRefPubMed
  11. ↵
    Borgia A, Borgia MB, Bugge K, Kissling VM, Heidarsson PO, Fernandes CB, Sottini A, Soranno A, Buholzer KJ, Nettels D, Kragelund BB, Best RB, Schuler B. 2018. Extreme disorder in an ultrahigh-affinity protein complex. Nature 555:61–66. doi:10.1038/nature25762
    OpenUrlCrossRefPubMed
  12. ↵
    Brown CJ, Johnson AK, Daughdrill GW. 2010. Comparing models of evolution for ordered and disordered proteins. Mol Biol Evol 27:609–21. doi:10.1093/molbev/msp277
    OpenUrlCrossRefPubMedWeb of Science
  13. ↵
    Brown CJ, Takayama S, Campen AM, Vise P, Marshall TW, Oldfield CJ, Williams CJ, Keith Dunker A. 2002. Evolutionary rate heterogeneity in proteins with long disordered regions. J Mol Evol 55:104–110. doi:10.1007/s00239-001-2309-6
    OpenUrlCrossRefPubMedWeb of Science
  14. ↵
    Byrne KP, Wolfe KH. 2005. The Yeast Gene Order Browser: Combining curated homology and syntenic context reveals gene fate in polyploid species. Genome Res 15:1456–1461. doi:10.1101/gr.3672305
    OpenUrlAbstract/FREE Full Text
  15. ↵
    1. Bastolla U,
    2. Porto M,
    3. Roman HE,
    4. Vendruscolo M
    Charif D, Lobry JR. 2007. SeqinR 1.0-2: A Contributed Package to the R Project for Statistical Computing Devoted to Biological Sequences Retrieval and Analysis In: Bastolla U, Porto M, Roman HE, Vendruscolo M, editors. Structural Approaches to Sequence Evolution: Molecules, Networks, Populations. Berlin, Heidelberg: Springer Berlin Heidelberg. pp. 207–232. doi:10.1007/978-3-540-35306-5_10
    OpenUrlCrossRef
  16. ↵
    Cherry JM, Hong EL, Amundsen C, Balakrishnan R, Binkley G, Chan ET, Christie KR, Costanzo MC, Dwight SS, Engel SR, Fisk DG, Hirschman JE, Hitz BC, Karra K, Krieger CJ, Miyasato SR, Nash RS, Park J, Skrzypek MS, Simison M, Weng S, Wong ED. 2012. Saccharomyces Genome Database: The genomics resource of budding yeast. Nucleic Acids Res 40:700–705. doi:10.1093/nar/gkr1029
    OpenUrlCrossRef
  17. ↵
    Colak R, Kim T, Michaut M, Sun M, Irimia M, Bellay J, Myers CL, Blencowe BJ, Kim PM. 2013. Distinct types of disorder in the human proteome: functional implications for alternative splicing. PLoS Comput Biol 9:e1003030. doi:10.1371/journal.pcbi.1003030
    OpenUrlCrossRefPubMed
  18. ↵
    Das RK, Pappu R V. 2013. Conformations of intrinsically disordered proteins are influenced by linear sequence distributions of oppositely charged residues. Proc Natl Acad Sci U S A 110:13392–7. doi:10.1073/pnas.1304749110
    OpenUrlAbstract/FREE Full Text
  19. ↵
    Daughdrill GW, Narayanaswami P, Gilmore SH, Belczyk A, Brown CJ. 2007. Dynamic behavior of an intrinsically unstructured linker domain is conserved in the face of negligible amino acid sequence conservation. J Mol Evol 65:277–288. doi:10.1007/s00239-007-9011-2
    OpenUrlCrossRefPubMedWeb of Science
  20. Davey NE, Cyert MS, Moses AM. 2015. Short linear motifs – ex nihilo evolution of protein regulation. Cell Commun Signal 13:43. doi:10.1186/s12964-015-0120-z
    OpenUrlCrossRefPubMed
  21. ↵
    Davey NE, Van Roey K, Weatheritt RJ, Toedt G, Uyar B, Altenberg B, Budd A, Diella F, Dinkel H, Gibson TJ. 2012. Attributes of short linear motifs. Mol BioSyst 8:268–281. doi:10.1039/C1MB05231D
    OpenUrlCrossRefPubMed
  22. ↵
    de Hoon MJL, Imoto S, Nolan J, Miyano S. 2004. Open source clustering software. Bioinformatics 20:1453–1454. doi:10.1093/bioinformatics/bth078
    OpenUrlCrossRefPubMedWeb of Science
  23. ↵
    de la Chaux N, Messer PW, Arndt PF. 2007. DNA indels in coding regions reveal selective constraints on protein evolution in the human lineage. BMC Evol Biol 7:191. doi:10.1186/1471-2148-7-191
    OpenUrlCrossRefPubMed
  24. ↵
    Dosztányi Z, Csizmók V, Tompa P, Simon I. 2005. The pairwise energy content estimated from amino acid composition discriminates between folded and intrinsically unstructured proteins. J Mol Biol 347:827–839. doi:10.1016/j.jmb.2005.01.071
    OpenUrlCrossRefPubMedWeb of Science
  25. ↵
    Edwards RJ, Davey NE, Shields DC. 2007. SLiMFinder: a probabilistic method for identifying overrepresented, convergently evolved, short linear motifs in proteins. PLoS One 2:e967. doi:10.1371/journal.pone.0000967
    OpenUrlCrossRefPubMed
  26. ↵
    El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, Qureshi M, Richardson LJ, Salazar GA, Smart A, Sonnhammer ELL, Hirsh L, Paladin L, Piovesan D, Tosatto SCE, Finn RD. 2018. The Pfam protein families database in 2019. Nucleic Acids Res 47:427–432. doi:10.1093/nar/gky995
    OpenUrlCrossRef
  27. ↵
    Erdmann R, Blobel G. 1996. Identification of Pex13p, a peroxisomal membrane receptor for the PTS1 recognition factor. J Cell Biol 135:111–121. doi:10.1083/jcb.135.1.111
    OpenUrlAbstract/FREE Full Text
  28. ↵
    Forman-Kay JD, Mittag T. 2013. From sequence and forces to structure, function, and evolution of intrinsically disordered proteins. Structure 21:1492–9. doi:10.1016/j.str.2013.08.001
    OpenUrlCrossRefPubMed
  29. ↵
    Franzmann TM, Jahnel M, Pozniakovsky A, Mahamid J, Holehouse AS, Nüske E, Richter D, Baumeister W, Grill SW, Pappu R V., Hyman AA, Alberti S. 2018. Phase separation of a yeast prion protein promotes cellular fitness. Science (80-) 359. doi:10.1126/science.aao5654
    OpenUrlAbstract/FREE Full Text
  30. ↵
    Frietze S, Farnham PJ. 2011. Transcription factor effector domains. Subcell Biochem 52:261–77. doi:10.1007/978-90-481-9069-0_12
    OpenUrlCrossRefPubMed
  31. ↵
    Fukasawa Y, Tsuji J, Fu S-C, Tomii K, Horton P, Imai K. 2015. MitoFates: Improved Prediction of Mitochondrial Targeting Sequences and Their Cleavage Sites. Mol Cell Proteomics 14:1113–1126. doi:10.1074/mcp.M114.043083
    OpenUrlAbstract/FREE Full Text
  32. ↵
    Fuxreiter M, Simon I, Friedrich P, Tompa P. 2004. Preformed structural elements feature in partner recognition by intrinsically unstructured proteins. J Mol Biol 338:1015–1026. doi:10.1016/j.jmb.2004.03.017
    OpenUrlCrossRefPubMedWeb of Science
  33. ↵
    Gagolewski M. 2019. R package stringi: Character string processing facilities.
  34. ↵
    Garg SG, Gould SB. 2016. The Role of Charge in Protein Targeting Evolution. Trends Cell Biol 26:894–905. doi:10.1016/j.tcb.2016.07.001
    OpenUrlCrossRef
  35. ↵
    Gibson DG, Young L, Chuang R, Venter JC, Iii CAH, Smith HO, America N, Hutchison C a, Smith HO. 2009. Enzymatic assembly of DNA molecules up to several hundred kilobases (supp). Nat Methods 6:343–5. doi:10.1038/NMETH.1318
    OpenUrlCrossRefPubMedWeb of Science
  36. ↵
    Gregory SM, Sweder KS. 2001. Deletion of the CSB homolog, RAD26, yields Spt(-) strains with proficient transcription-coupled repair. Nucleic Acids Res 29:3080–3086.
    OpenUrlCrossRefPubMedWeb of Science
  37. ↵
    Guzder SN, Habraken Y, Sung P, Prakash L, Prakash S. 1996. RAD26, the yeast homolog of human Cockayne’s syndrome group B gene, encodes a DNA-dependent ATPase. J Biol Chem 271:18314–18317. doi:10.1074/jbc.271.31.18314
    OpenUrlAbstract/FREE Full Text
  38. ↵
    Halfmann R. 2016. A glass menagerie of low complexity sequences. Curr Opin Struct Biol 38:9–16. doi:10.1016/j.sbi.2016.05.002
    OpenUrlCrossRefPubMed
  39. ↵
    Hansen TF. 1997. Stabilizing Selection and the Comparative Analysis of Adaptation. Evolution (N Y) 51:1341–1351. doi:10.2307/2411186
    OpenUrlCrossRefPubMedWeb of Science
  40. ↵
    Hao N, Zeng Y, Elston TC, Dohlman HG. 2008. Control of MAPK specificity by feedback phosphorylation of shared adaptor protein Ste50. J Biol Chem 283:33798–802. doi:10.1074/jbc.C800179200
    OpenUrlAbstract/FREE Full Text
  41. ↵
    Holehouse AS, Das RK, Ahad JN, Richardson MOG, Pappu R V. 2017. CIDER: Resources to Analyze Sequence-Ensemble Relationships of Intrinsically Disordered Proteins. Biophys J 112:16–21. doi:10.1016/j.bpj.2016.11.3200
    OpenUrlCrossRefPubMed
  42. ↵
    Huh, K. W, Falvo, V. J, Gerke, C. L, Carroll, S. A, Howson, W. R, Weissman, S. J, O’Shea, K. E. 2003. Global analysis of protein localization in budding yeast. Nature 425:686–691.
    OpenUrlCrossRefPubMedWeb of Science
  43. ↵
    Iakoucheva LM, Radivojac P, Brown CJ, O’Connor TR, Sikes JG, Obradovic Z, Dunker a. K. 2004. The importance of intrinsic disorder for protein phosphorylation. Nucleic Acids Res 32:1037–1049. doi:10.1093/nar/gkh253
    OpenUrlCrossRefPubMedWeb of Science
  44. ↵
    Jansen G, Bühring F, Hollenberg CP, Ramezani Rad M. 2001. Mutations in the SAM domain of STE50 differentially influence the MAPK-mediated pathways for mating, filamentous growth and osmotolerance in Saccharomyces cerevisiae. Mol Genet Genomics 265:102–117. doi:10.1007/s004380000394
    OpenUrlCrossRefPubMedWeb of Science
  45. ↵
    Jensen MR, Ruigrok RWH, Blackledge M. 2013. Describing intrinsically disordered proteins at atomic resolution by NMR. Curr Opin Struct Biol 23:426–435. doi:10.1016/j.sbi.2013.02.007
    OpenUrlCrossRefPubMed
  46. ↵
    Jones DT, Cozzetto D. 2015. DISOPRED3: Precise disordered region predictions with annotated protein-binding activity. Bioinformatics 31:857–863. doi:10.1093/bioinformatics/btu744
    OpenUrlCrossRefPubMed
  47. ↵
    Kato M, Han TW, Xie S, Shi K, Du X, Wu LC, Mirzaei H, Goldsmith EJ, Longgood J, Pei J, Grishin N V., Frantz DE, Schneider JW, Chen S, Li L, Sawaya MR, Eisenberg D, Tycko R, McKnight SL. 2012. Cell-free formation of RNA granules: Low complexity sequence domains form dynamic fibers within hydrogels. Cell 149:753–767. doi:10.1016/j.cell.2012.04.017
    OpenUrlCrossRefPubMedWeb of Science
  48. ↵
    Katoh K, Standley DM. 2013. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol Biol Evol 30:772–780. doi:10.1093/molbev/mst010
    OpenUrlCrossRefPubMedWeb of Science
  49. ↵
    Khan T, Douglas GM, Patel P, Nguyen Ba AN, Moses AM. 2015. Polymorphism Analysis Reveals Reduced Negative Selection and Elevated Rate of Insertions and Deletions in Intrinsically Disordered Protein Regions. Genome Biol Evol 7:1815–26. doi:10.1093/gbe/evv105
    OpenUrlCrossRefPubMed
  50. ↵
    Koch V, Otte M, Beye M. 2018. Evidence for Stabilizing Selection Driving Mutational Turnover of Short Motifs in the Eukaryotic Complementary Sex Determiner (Csd) Protein. G3&…#58; GenesGenomesGenetics g3.200527.2018. doi:10.1534/g3.118.200527
    OpenUrlAbstract/FREE Full Text
  51. ↵
    Kompella PS, Moses AM, Peisajovich SG. 2016. Introduction of Premature Stop Codons as an Evolutionary Strategy To Rescue Signaling Network Function. ACS Synth Biol acssynbio.6b00142. doi:10.1021/acssynbio.6b00142
    OpenUrlCrossRef
  52. ↵
    Kroschwald S, Maharana S, Mateju D, Malinovska L, Nüske E, Poser I, Richter D, Alberti S. 2015. Promiscuous interactions and protein disaggregases determine the material state of stress-inducible RNP granules. Elife 4:1–32. doi:10.7554/eLife.06807
    OpenUrlCrossRefPubMed
  53. ↵
    Lai ACW, Nguyen Ba AN, Moses AM. 2012. Predicting kinase substrates using conservation of local motif density. Bioinformatics 28:962–9. doi:10.1093/bioinformatics/bts060
    OpenUrlCrossRefPubMed
  54. ↵
    Lande R. 1976. Natural Selection and Random Genetic Drift in Phenotypic Evolution. Evolution (N Y) 30:314. doi:10.2307/2407703
    OpenUrlCrossRefWeb of Science
  55. ↵
    Landry CR, Freschi L, Zarin T, Moses AM. 2014. Turnover of protein phosphorylation evolving under stabilizing selection. Front Genet 5:1–6. doi:10.3389/fgene.2014.00245
    OpenUrlCrossRefPubMed
  56. ↵
    Lee S-H, Kim D-H, J. Han J, Cha E-J, Lim J-E, Cho Y-J, Lee C, Han K-H. 2012. Understanding Pre-Structured Motifs (PreSMos) in Intrinsically Unfolded Proteins. Curr Protein Pept Sci 13:34–54. doi:10.1016/j.ijheatmasstransfer.2013.04.020
    OpenUrlCrossRefPubMed
  57. ↵
    Lemas D, Lekkas P, Ballif BA, Vigoreaux JO. 2016. Intrinsic disorder and multiple phosphorylations constrain the evolution of the flightin N-terminal region. J Proteomics 135:191–200. doi:10.1016/j.jprot.2015.12.006
    OpenUrlCrossRef
  58. ↵
    Light S, Sagit R, Ekman D, Elofsson A. 2013. Long indels are disordered: A study of disorder and indels in homologous eukaryotic proteins. Biochim Biophys Acta – Proteins Proteomics 1834:890–897. doi:10.1016/j.bbapap.2013.01.002
    OpenUrlCrossRefWeb of Science
  59. ↵
    Louvet E, Junéra HR, Berthuy I, Hernandez-Verdun D. 2006. Compartmentation of the nucleolar processing proteins in the granular component is a CK2-driven process. Mol Biol Cell 17:2537–46. doi:10.1091/mbc.e05-10-0923
    OpenUrlAbstract/FREE Full Text
  60. ↵
    Maccecchini ML, Rudin Y, Blobel G, Schatz G. 1979. Import of proteins into mitochondria: precursor forms of the extramitochondrially made F1-ATPase subunits in yeast. Proc Natl Acad Sci U S A 76:343–7. doi:10.1073/pnas.76.1.343
    OpenUrlAbstract/FREE Full Text
  61. ↵
    Mao AH, Crick SL, Vitalis A, Chicoine CL, Pappu R V. 2010. Net charge per residue modulates conformational ensembles of intrinsically disordered proteins. Proc Natl Acad Sci U S A 107:81838. doi:10.1073/pnas.0911107107
    OpenUrlAbstract/FREE Full Text
  62. ↵
    Martin EW, Holehouse AS, Grace CR, Hughes A, Pappu R V, Mittag T. 2016. Sequence determinants of the conformational properties of an intrinsically disordered protein prior to and upon multisite phosphorylation. J Am Chem Soc jacs.6b10272. doi:10.1021/jacs.6b10272
    OpenUrlCrossRefPubMed
  63. ↵
    Meyer K, Kirchner M, Uyar B, Cheng J-Y, Russo G, Hernandez-Miranda LR, Szymborska A, Zauber H, Rudolph I-M, Willnow TE, Akalin A, Haucke V, Gerhardt H, Birchmeier C, Kühn R, Krauss M, Diecke S, Pascual JM, Selbach M. 2018. Mutations in Disordered Regions Can Cause Disease by Creating Dileucine Motifs. Cell 0:239–253. doi:10.1016/j.cell.2018.08.019
    OpenUrlCrossRef
  64. ↵
    Moesa HA, Wakabayashi S, Nakai K, Patil A. 2012. Chemical composition is maintained in poorly conserved intrinsically disordered regions and suggests a means for their classification. Mol Biosyst 8:3262. doi:10.1039/c2mb25202c
    OpenUrlCrossRefPubMed
  65. ↵
    Mohan A, Oldfield CJ, Radivojac P, Vacic V, Cortese MS, Dunker AK, Uversky VN. 2006. Analysis of Molecular Recognition Features (MoRFs). J Mol Biol 362:1043–1059. doi:10.1016/j.jmb.2006.07.087
    OpenUrlCrossRefPubMedWeb of Science
  66. ↵
    Molliex A, Temirov J, Lee J, Coughlin M, Kanagaraj AP, Kim HJ, Mittag T, Taylor JP. 2015. Phase Separation by Low Complexity Domains Promotes Stress Granule Assembly and Drives Pathological Fibrillization. Cell 163:123–133. doi:10.1016/j.cell.2015.09.015
    OpenUrlCrossRefPubMed
  67. ↵
    Nguyen Ba AN, Strome B, Hua JJ, Desmond J, Gagnon-Arsenault I, Weiss EL, Landry CR, Moses AM. 2014. Detecting Functional Divergence after Gene Duplication through Evolutionary Changes in Posttranslational Regulatory Sequences. PLoS Comput Biol 10:e1003977. doi:10.1371/journal.pcbi.1003977
    OpenUrlCrossRefPubMed
  68. ↵
    Nguyen Ba AN, Yeh BJ, van Dyk D, Davidson AR, Andrews BJ, Weiss EL, Moses AM. 2012. Proteome-wide discovery of evolutionary conserved sequences in disordered regions. Sci Signal 5:rs1. doi:10.1126/scisignal.2002515
    OpenUrlAbstract/FREE Full Text
  69. ↵
    Nott TJ, Petsalaki E, Farber P, Jervis D, Fussner E, Plochowietz A, Craggs TD, Bazett-Jones DP, Pawson T, Forman-Kay JD, Baldwin AJ. 2015. Phase Transition of a Disordered Nuage Protein Generates Environmentally Responsive Membraneless Organelles. Mol Cell 57:936–947. doi:10.1016/j.molcel.2015.01.013
    OpenUrlCrossRefPubMed
  70. ↵
    Oldfield CJ, Cheng Y, Cortese MS, Romero P, Uversky VN, Dunker AK. 2005. Coupled folding and binding with α-helix-forming molecular recognition elements. Biochemistry 44:12454–12470. doi:10.1021/bi050736e
    OpenUrlCrossRefPubMed
  71. ↵
    Ondrechen MJ, Clifton JG, Ringe D. 2001. THEMATICS: A simple computational predictor of enzyme function from structure. Proc Natl Acad Sci 98:12473–12478. doi:10.1073/pnas.211436698
    OpenUrlAbstract/FREE Full Text
  72. ↵
    Pagès H, Aboyoun P, Gentleman R, DebRoy S. 2018. Biostrings: Efficient manipulation of biological strings.
  73. ↵
    Paradis E, Schliep K. 2018. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in {R}. Bioinformatics xx:xxx–xxx.
    OpenUrl
  74. ↵
    Patel A, Lee HO, Jawerth L, Maharana S, Jahnel M, Hein MY, Stoynov S, Mahamid J, Saha S, Franzmann TM, Pozniakovski A, Poser I, Maghelli N, Royer LA, Weigert M, Myers EW, Grill S, Drechsel D, Hyman AA, Alberti S. 2015. A Liquid-to-Solid Phase Transition of the ALS Protein FUS Accelerated by Disease Mutation. Cell 162:1066–77. doi:10.1016/j.cell.2015.07.047
    OpenUrlCrossRefPubMed
  75. ↵
    Pau G, Fuchs F, Sklyar O, Boutros M, Huber W. 2010. EBImage-an R package for image processing with applications to cellular phenotypes. Bioinformatics 26:979–981. doi:10.1093/bioinformatics/btq046
    OpenUrlCrossRefPubMedWeb of Science
  76. ↵
    Peng Z, Mizianty MJ, Kurgan L. 2013. Genome-scale prediction of proteins with long intrinsically disordered regions. Proteins 1–14. doi:10.1002/prot.24348
    OpenUrlCrossRef
  77. ↵
    Protter DSW, Rao BS, Van Treeck B, Lin Y, Mizoue L, Rosen MK, Parker R. 2018. Intrinsically Disordered Regions Can Contribute Promiscuous Interactions to RNP Granule Assembly. Cell Rep 22:1401–1412. doi:10.1016/j.celrep.2018.01.036
    OpenUrlCrossRef
  78. ↵
    Ravarani CNJ, Erkina TY, Baets G De, Dudman DC, Erkine AM, Babu MM. 2018. High-throughput discovery of functional disordered regions : investigation of transactivation domains 1–14. doi:10.15252/msb.20188190
    OpenUrlAbstract/FREE Full Text
  79. ↵
    Riback JA, Katanski CD, Kear-Scott JL, Pilipenko E V, Rojek AE, Sosnick TR, Drummond DA. 2017. Stress-Triggered Phase Separation Is an Adaptive, Evolutionarily Tuned Response. Cell 168:1028–1040.e19. doi:10.1016/j.cell.2017.02.027
    OpenUrlCrossRefPubMed
  80. ↵
    Saitoh T, Igura M, Miyazaki Y, Ose T, Maita N, Kohda D. 2011. Crystallographic snapshots of Tom20-mitochondrial presequence interactions with disulfide-stabilized peptides. Biochemistry 50:5487–5496. doi:10.1021/bi200470x
    OpenUrlCrossRefPubMedWeb of Science
  81. ↵
    Saitoh T, Igura M, Obita T, Ose T, Kojima R, Maenaka K, Endo T, Kohda D. 2007. Tom20 recognizes mitochondrial presequences through dynamic equilibrium among multiple bound states. EMBO J 26:4777–4787. doi:10.1038/sj.emboj.7601888
    OpenUrlAbstract/FREE Full Text
  82. ↵
    Sawle L, Ghosh K. 2015. A theoretical method to compute sequence dependent configurational properties in charged polymers and proteins. J Chem Phys 143. doi:10.1063/1.4929391
    OpenUrlCrossRef
  83. ↵
    Schlessinger A, Schaefer C, Vicedo E, Schmidberger M, Punta M, Rost B. 2011. Protein disorder-a breakthrough invention of evolution? Curr Opin Struct Biol 21:412–418. doi:10.1016/j.sbi.2011.03.014
    OpenUrlCrossRefPubMed
  84. ↵
    Sondheimer N, Lindquist S. 2000. Rnq1: an epigenetic modifier of protein function in yeast. Mol Cell 5:163–72. doi:10.1016/S1097-2765(00)80412-8
    OpenUrlCrossRefPubMedWeb of Science
  85. ↵
    Storici F, Lewis LK, Resnick M a. 2001. In vivo site-directed mutagenesis using oligonucleotides. Nat Biotechnol 19:773–6. doi:10.1038/90837
    OpenUrlCrossRefPubMedWeb of Science
  86. ↵
    Strickfaden SC, Winters MJ, Ben-Ari G, Lamson RE, Tyers M, Pryciak PM. 2007. A mechanism for cell-cycle regulation of MAP kinase signaling in a yeast differentiation pathway. Cell 128:519–31. doi:10.1016/j.cell.2006.12.032
    OpenUrlCrossRefPubMedWeb of Science
  87. ↵
    Tang X, Orlicky S, Mittag T, Csizmok V, Pawson T, Forman-Kay JD, Sicheri F, Tyers M. 2012. Composite low affinity interactions dictate recognition of the cyclin-dependent kinase inhibitor Sic1 by the SCFCdc4 ubiquitin ligase. Proc Natl Acad Sci 109:3287–3292. doi:10.1073/pnas.1116455109
    OpenUrlAbstract/FREE Full Text
  88. ↵
    Tatebayashi K, Tanaka K, Yang H-Y, Yamamoto K, Matsushita Y, Tomida T, Imai M, Saito H. 2007. Transmembrane mucins Hkr1 and Msb2 are putative osmosensors in the SHO1 branch of yeast HOG pathway. EMBO J 26:3521–33. doi:10.1038/sj.emboj.7601796
    OpenUrlCrossRefPubMedWeb of Science
  89. ↵
    Taylor JS, Raes J. 2004. Duplication and Divergence: The Evolution of New Genes and Old Ideas. Annu Rev Genet 38:615–643. doi:10.1146/annurev.genet.38.072902.092831
    OpenUrlCrossRefPubMedWeb of Science
  90. ↵
    Teixeira D, Parker R. 2007. Analysis of P-body assembly in Saccharomyces cerevisiae. Mol Biol Cell 18:2274–87. doi:10.1091/mbc.e07-03-0199
    OpenUrlAbstract/FREE Full Text
  91. ↵
    Terry LJ, Wente SR. 2009. Flexible gates: Dynamic topologies and functions for FG nucleoporins in nucleocytoplasmic transport. Eukaryot Cell 8:1814–1827. doi:10.1128/EC.00225-09
    OpenUrlAbstract/FREE Full Text
  92. ↵
    Tompa P. 2014. Multisteric regulation by structural disorder in modular signaling proteins: An extension of the concept of allostery. Chem Rev 114:6715–6732. doi:10.1021/cr4005082
    OpenUrlCrossRef
  93. ↵
    Tompa P, Davey NE, Gibson TJ, Babu MM. 2014. A Million peptide motifs for the molecular biologist. Mol Cell 55:161–169. doi:10.1016/j.molcel.2014.05.032
    OpenUrlCrossRefPubMedWeb of Science
  94. ↵
    Tompa P, Schad E, Tantos A, Kalmar L. 2015. Intrinsically disordered proteins: Emerging interaction specialists. Curr Opin Struct Biol 35:49–59. doi:10.1016/j.sbi.2015.08.009
    OpenUrlCrossRefPubMed
  95. ↵
    Tóth-Petróczy A, Tawfik DS. 2013. Protein insertions and deletions enabled by neutral roaming in sequence space. Mol Biol Evol 30:761–71. doi:10.1093/molbev/mst003
    OpenUrlCrossRefPubMedWeb of Science
  96. ↵
    Treusch S, Lindquist S. 2012. An intrinsically disordered yeast prion arrests the cell cycle by sequestering a spindle pole body component. J Cell Biol 197:369–379. doi:10.1083/jcb.201108146
    OpenUrlAbstract/FREE Full Text
  97. ↵
    Truckses DM, Bloomekatz JE, Thorner J. 2006. The RA domain of Ste50 adaptor protein is required for delivery of Ste11 to the plasma membrane in the filamentous growth signaling pathway of the yeast Saccharomyces cerevisiae. Mol Cell Biol 26:912–28. doi:10.1128/MCB.26.3.912-928.2006
    OpenUrlAbstract/FREE Full Text
  98. ↵
    Uversky VN. 2011. Intrinsically disordered proteins from A to Z. Int J Biochem Cell Biol 43:1090–103. doi:10.1016/j.biocel.2011.04.001
    OpenUrlCrossRefPubMed
  99. ↵
    Uversky VN. 2002. Natively unfolded proteins: A point where biology waits for physics. Protein Sci 11:739–756. doi:10.1110/ps.4210102
    OpenUrlCrossRefPubMedWeb of Science
  100. ↵
    Vacic V, Oldfield CJ, Mohan A, Radivojac P, Cortese MS, Uversky VN, Dunker AK. 2007. Characterization of molecular recognition features, MoRFs, and their binding partners. J Proteome Res 6:2351–2366. doi:10.1021/pr0701411
    OpenUrlCrossRefPubMedWeb of Science
  101. ↵
    Van Der Lee R, Buljan M, Lang B, Weatheritt RJ, Daughdrill GW, Dunker AK, Fuxreiter M, Gough J, Gsponer J, Jones DT, Kim PM, Kriwacki RW, Oldfield CJ, Pappu R V., Tompa P, Uversky VN, Wright PE, Babu MM. 2014. Classification of intrinsically disordered regions and proteins. Chem Rev 114:6589–6631. doi:10.1021/cr400525m
    OpenUrlCrossRefPubMedWeb of Science
  102. ↵
    Vernon RM, Chong PA, Tsang B, Kim TH, Bah A, Farber P, Lin H, Forman-Kay JD. 2018. Pi-Pi contacts are an overlooked protein feature relevant to phase separation. Elife 7:1–48. doi:10.7554/eLife.31486
    OpenUrlCrossRefPubMed
  103. ↵
    Vögtle FN, Wortelkamp S, Zahedi RP, Becker D, Leidhold C, Gevaert K, Kellermann J, Voos W, Sickmann A, Pfanner N, Meisinger C. 2009. Global Analysis of the Mitochondrial N-Proteome Identifies a Processing Peptidase Critical for Protein Stability. Cell 139:428–439. doi:10.1016/j.cell.2009.07.045
    OpenUrlCrossRefPubMedWeb of Science
  104. ↵
    Ward JJ, Sodhi JS, McGuffin LJ, Buxton BF, Jones DT. 2004. Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. J Mol Biol 337:635–45. doi:10.1016/j.jmb.2004.02.002
    OpenUrlCrossRefPubMedWeb of Science
  105. ↵
    Warren C, Shechter D. 2017. Fly Fishing for Histones: Catch and Release by Histone Chaperone Intrinsically Disordered Regions and Acidic Stretches. J Mol Biol 429:2401–2426. doi:10.1016/j.jmb.2017.06.005
    OpenUrlCrossRef
  106. ↵
    Waterhouse AM, Procter JB, Martin DMA, Clamp M, Barton GJ. 2009. Jalview Version 2-A multiple sequence alignment editor and analysis workbench. Bioinformatics 25:1189–1191. doi:10.1093/bioinformatics/btp033
    OpenUrlCrossRefPubMedWeb of Science
  107. ↵
    Wickham H. 2010. Stringr: Modern, Consistent String Processing. R J 2:38–40.
    OpenUrl
  108. ↵
    Wootton JC, Federhen S. 1993. Statistics of local complexity in amino acid sequences and sequence databases. Comput Chem 17:149–163. doi:10.1016/0097-8485(93)85006-X
    OpenUrlCrossRefWeb of Science
  109. ↵
    Wright PE, Dyson HJ. 2014. Intrinsically disordered proteins in cellular signalling and regulation. Nat Rev Mol Cell Biol 16:18–29. doi:10.1038/nrm3920
    OpenUrlCrossRef
  110. ↵
    Yamamoto K, Tatebayashi K, Tanaka K, Saito H. 2010. Dynamic control of yeast MAP kinase network by induced association and dissociation between the Ste50 scaffold and the Opy2 membrane anchor. Mol Cell 40:87–98. doi:10.1016/j.molcel.2010.09.011
    OpenUrlCrossRefPubMedWeb of Science
  111. ↵
    Yang Z. 2007. PAML 4: Phylogenetic analysis by maximum likelihood. Mol Biol Evol 24:1586–1591. doi:10.1093/molbev/msm088
    OpenUrlCrossRefPubMedWeb of Science
  112. ↵
    Yeung M, Durocher D. 2011. Srs2 enables checkpoint recovery by promoting disassembly of DNA damage foci from chromatin. DNA Repair (Amst) 10:1213–1222. doi:10.1016/j.dnarep.2011.09.005
    OpenUrlCrossRefPubMed
  113. ↵
    Zarin T, Tsai CN, Nguyen Ba AN, Moses AM. 2017. Selection maintains signaling function of a highly diverged intrinsically disordered region. Proc Natl Acad Sci 114:E1450–E1459. doi:10.1073/pnas.1614787114
    OpenUrlAbstract/FREE Full Text
  114. ↵
    Zheng J, Yang J, Choe YJ, Hao X, Cao X, Zhao Q, Zhang Y, Franssens V, Hartl FU, Nyström T, Winderickx J, Liu B. 2017. Role of the ribosomal quality control machinery in nucleocytoplasmic translocation of polyQ-expanded huntingtin exon-1. Biochem Biophys Res Commun 493:708–717. doi:10.1016/j.bbrc.2017.08.126
    OpenUrlCrossRef

Supplementary references

  1. Alberti S, Halfmann R, King O, Kapila A, Lindquist S. 2009. A Systematic Survey Identifies Prions and Illuminates Sequence Features of Prionogenic Proteins. Cell 137:146–158. doi:10.1016/j.cell.2009.02.044
    OpenUrlCrossRefPubMedWeb of Science
  2. Budovskaya Y V., Stephan JS, Deminoff SJ, Herman PK. 2005. An evolutionary proteomics approach identifies substrates of the cAMP-dependent protein kinase. Proc Natl Acad Sci 102:13933–13938. doi:10.1073/pnas.0501046102
    OpenUrlAbstract/FREE Full Text
  3. Chavali S, Chavali PL, Chalancon G, De Groot NS, Gemayel R, Latysheva NS, Ing-Simmons E, Verstrepen KJ, Balaji S, Babu MM. 2017. Constraints and consequences of the emergence of amino acid repeats in eukaryotic proteins. Nat Struct Mol Biol 24:765–777. doi:10.1038/nsmb.3441
    OpenUrlCrossRef
  4. Cheeseman IM, Anderson S, Jwa M, Green EM, Kang J-S, Yates lii JR, Chan CSM, Drubin DG, Barnes G. 2002. Phospho-Regulation of Kinetochore-Microtubule Attachments by the Aurora Kinase Ipl1p will require the identification of any remaining kineto-chore proteins. Given the central role that kinetochore-microtubule. Cell 111:163–172.
    OpenUrlCrossRefPubMedWeb of Science
  5. Chong PA, Vernon RM, Forman-Kay JD. 2018. RGG/RG Motif Regions in RNA Binding and Phase Separation. J Mol Biol 430:4650–4665. doi:10.1016/j.jmb.2018.06.014
    OpenUrlCrossRefPubMed
  6. Das RK, Pappu R V. 2013. Conformations of intrinsically disordered proteins are influenced by linear sequence distributions of oppositely charged residues. Proc Natl Acad Sci U S A 110:13392–7. doi:10.1073/pnas.1304749110
    OpenUrlAbstract/FREE Full Text
  7. Daughdrill GW, Narayanaswami P, Gilmore SH, Belczyk A, Brown CJ. 2007. Dynamic behavior of an intrinsically unstructured linker domain is conserved in the face of negligible amino acid sequence conservation. J Mol Evol 65:277–288. doi:10.1007/s00239-007-9011-2
    OpenUrlCrossRefPubMedWeb of Science
  8. ↵
    Dinkel H, Van Roey K, Michael S, Kumar M, Uyar B, Altenberg B, Milchevskaya V, Schneider M, Kühn H, Behrendt A, Dahl SL, Damerell V, Diebel S, Kalman S, Klein S, Knudsen AC, Mäder C, Merrill S, Staudt A, Thiel V, Welti L, Davey NE, Diella F, Gibson TJ. 2016. ELM 2016—data update and new functionality of the eukaryotic linear motif resource. Nucleic Acids Res 44:D294–D300. doi:10.1093/nar/gkv1291
    OpenUrlCrossRefPubMed
  9. Elam WA, Schrank TP, Campagnolo AJ, Hilser VJ. 2013. Evolutionary conservation of the polyproline II conformation surrounding intrinsically disordered phosphorylation sites. Protein Sci 22:405–417. doi:10.1002/pro.2217
    OpenUrlCrossRefPubMed
  10. Elbaum-Garfinkle S, Kim Y, Szczepaniak K, Chen CC-H, Eckmann CR, Myong S, Brangwynne CP. 2015. The disordered P granule protein LAF-1 drives phase separation into droplets with tunable viscosity and dynamics. Proc Natl Acad Sci 112:7189–7194. doi:10.1073/pnas.1504822112
    OpenUrlAbstract/FREE Full Text
  11. Frey S, Görlich D. 2009. FG/FxFG as well as GLFG repeats form a selective permeability barrier with selfhealing properties. EMBO J 28:2554–2567. doi:10.1038/emboj.2009.199
    OpenUrlCrossRefPubMedWeb of Science
  12. Fukasawa Y, Tsuji J, Fu S-C, Tomii K, Horton P, Imai K. 2015. MitoFates: Improved Prediction of Mitochondrial Targeting Sequences and Their Cleavage Sites. Mol Cell Proteomics 14:1113–1126. doi:10.1074/mcp.M114.043083
    OpenUrlAbstract/FREE Full Text
  13. Halfmann R, Alberti S, Krishnan R, Lyle N, O’Donnell CW, King OD, Berger B, Pappu R V., Lindquist S. 2011. Opposing Effects of Glutamine and Asparagine Govern Prion Formation by Intrinsically Disordered Proteins. Mol Cell 43:72–84. doi:10.1016/j.molcel.2011.05.013
    OpenUrlCrossRefPubMedWeb of Science
  14. Haynes C, Oldfield CJ, Ji F, Klitgord N, Cusick ME, Radivojac P, Uversky VN, Vidal M, Iakoucheva LM. 2006. Intrinsic disorder is a common feature of hub proteins from four eukaryotic interactomes. PLoS Comput Biol 2:0890–0901. doi:10.1371/journal.pcbi.0020100
    OpenUrlCrossRef
  15. Holehouse AS, Das RK, Ahad JN, Richardson MOG, Pappu R V. 2017. CIDER: Resources to Analyze Sequence-Ensemble Relationships of Intrinsically Disordered Proteins. Biophys J 112:16–21. doi:10.1016/j.bpj.2016.11.3200
    OpenUrlCrossRefPubMed
  16. Holt LJ, Hutti JE, Cantley LC, Morgan DO. 2007. Evolution of Ime2 phosphorylation sites on Cdk1 substrates provides a mechanism to limit the effects of the phosphatase Cdc14 in meiosis. Mol Cell 25:689–702. doi:10.1016/j.molcel.2007.02.012
    OpenUrlCrossRefPubMedWeb of Science
  17. Holt LJ, Tuch BB, Villén J, Johnson AD, Gygi SP, Morgan DO. 2009. Global analysis of Cdk1 substrate phosphorylation sites provides insights into evolution. Science 325:1682–6. doi:10.1126/science.1172867
    OpenUrlAbstract/FREE Full Text
  18. Huang B, Zeng G, Ng AYJ, Cai M. 2003. Identification of novel recognition motifs and regulatory targets for the yeast actin-regulating kinase Prk1p. Mol Biol Cell 14:4871–84. doi:10.1091/mbc.e03-06-0362
    OpenUrlAbstract/FREE Full Text
  19. Kemp BE, Pearson RB. 1990. Protein kinase recognition sequence motifs. Trends Biochem Sci 15:342–346. doi:10.1016/0968-0004(90)90073-K
    OpenUrlCrossRefPubMedWeb of Science
  20. Kyte J, Doolittle RF. 1982. A simple method for displaying the hydropathic character of a protein. J Mol Biol 157:105–132. doi:10.1016/0022-2836(82)90515-0
    OpenUrlCrossRefPubMedWeb of Science
  21. Lai ACW, Nguyen Ba AN, Moses AM. 2012. Predicting kinase substrates using conservation of local motif density. Bioinformatics 28:962–9. doi:10.1093/bioinformatics/bts060
    OpenUrlCrossRefPubMed
  22. Lai J, Koh CH, Tjota M, Pieuchot L, Raman V, Chandrababu KB, Yang D, Wong L, Jedd G. 2012. Intrinsically disordered proteins aggregate at fungal cell-to-cell channels and regulate intercellular connectivity. Proc Natl Acad Sci 109:15781–15786. doi:10.1073/pnas.1207467109
    OpenUrlAbstract/FREE Full Text
  23. Mao AH, Crick SL, Vitalis A, Chicoine CL, Pappu R V. 2010. Net charge per residue modulates conformational ensembles of intrinsically disordered proteins. Proc Natl Acad Sci U S A 107:8183–8. doi:10.1073/pnas.0911107107
    OpenUrlAbstract/FREE Full Text
  24. Mao AH, Lyle N, Pappu R V. 2013. Describing sequence–ensemble relationships for intrinsically disordered proteins. Biochem J 449:307–318. doi:10.1042/BJ20121346
    OpenUrlAbstract/FREE Full Text
  25. Marsh JA, Forman-Kay JD. 2010. Sequence determinants of compaction in intrinsically disordered proteins. Biophys J 98:2374–2382. doi:10.1016/j.bpj.2010.02.012
    OpenUrlCrossRefPubMed
  26. Martin EW, Holehouse AS, Grace CR, Hughes A, Pappu R V, Mittag T. 2016. Sequence determinants of the conformational properties of an intrinsically disordered protein prior to and upon multisite phosphorylation. J Am Chem Soc jacs.6b10272. doi:10.1021/jacs.6b10272
    OpenUrlCrossRefPubMed
  27. Matsushima N, Tanaka T, Kretsinger R. 2009. Non-Globular Structures of Tandem Repeats in Proteins. Protein Pept Lett 16:1297–1322. doi:10.2174/092986609789353745
    OpenUrlCrossRefPubMed
  28. Meggio F, Pinna LA. 2003. One-thousand-and-one substrates of protein kinase CK2? FASEB J 17:349–68. doi:10.1096/fj.02-0473rev
    OpenUrlCrossRefPubMedWeb of Science
  29. Neduva V, Russell RB. 2005. Linear motifs: Evolutionary interaction switches. FEBS Lett 579:3342–3345. doi:10.1016/j.febslet.2005.04.005
    OpenUrlCrossRefPubMedWeb of Science
  30. Nguyen Ba AN, Yeh BJ, van Dyk D, Davidson AR, Andrews BJ, Weiss EL, Moses AM. 2012. Proteome-wide discovery of evolutionary conserved sequences in disordered regions. Sci Signal 5:rs1. doi:10.1126/scisignal.2002515
    OpenUrlAbstract/FREE Full Text
  31. Niefind K, Yde CW, Ermakova I, Issinger OG. 2007. Evolved to Be Active: Sulfate Ions Define Substrate Recognition Sites of CK2α and Emphasise its Exceptional Role within the CMGC Family of Eukaryotic Protein Kinases. J Mol Biol 370:427–438. doi:10.1016/j.jmb.2007.04.068
    OpenUrlCrossRefPubMed
  32. Perez RB, Tischer A, Auton M, Whitten ST. 2014. Alanine and proline content modulate global sensitivity to discrete perturbations in disordered proteins. Proteins Struct Funct Bioinforma 82:3373–3384. doi:10.1002/prot.24692
    OpenUrlCrossRef
  33. Sawle L, Ghosh K. 2015. A theoretical method to compute sequence dependent configurational properties in charged polymers and proteins. J Chem Phys 143. doi:10.1063/1.4929391
    OpenUrlCrossRef
  34. Schwartz MF, Duong JK, Sun Z, Morrow JS, Pradhan D, Stern DF. 2002. Rad9 Phosphorylation Sites Couple Rad53 to the Saccharomyces cerevisiae DNA Damage Checkpoint. Mol Cell 9:1055–1065. doi:10.1016/S1097-2765(02)00532-4
    OpenUrlCrossRefPubMedWeb of Science
  35. Simon M, Hancock JM. 2009. Tandem and cryptic amino acid repeats accumulate in disordered regions of proteins. Genome Biol 10:1–16. doi:10.1186/gb-2009-10-6-r59
    OpenUrlCrossRef
  36. Strickfaden SC, Winters MJ, Ben-Ari G, Lamson RE, Tyers M, Pryciak PM. 2007. A mechanism for cell-cycle regulation of MAP kinase signaling in a yeast differentiation pathway. Cell 128:519–31. doi:10.1016/j.cell.2006.12.032
    OpenUrlCrossRefPubMedWeb of Science
  37. Tomasso ME, Tarver MJ, Devarajan D, Whitten ST. 2016. Hydrodynamic Radii of Intrinsically Disordered Proteins Determined from Experimental Polyproline II Propensities. PLoS Comput Biol 12:1–22. doi:10.1371/journal.pcbi.1004686
    OpenUrlCrossRef
  38. Townsend RR, Lipniunas PH, Tulk BM, Verkman AS. 1996. Identification of protein kinase a phosphorylation sites on NBD1 and R domains of CFTR using electrospray mass spectrometry with selective phosphate ion monitoring. Protein Sci 5:1865–1873. doi:10.1002/pro.5560050912
    OpenUrlCrossRefPubMedWeb of Science
  39. Van Der Lee R, Buljan M, Lang B, Weatheritt RJ, Daughdrill GW, Dunker AK, Fuxreiter M, Gough J, Gsponer J, Jones DT, Kim PM, Kriwacki RW, Oldfield CJ, Pappu R V., Tompa P, Uversky VN, Wright PE, Babu MM. 2014. Classification of intrinsically disordered regions and proteins. Chem Rev 114:6589–6631. doi:10.1021/cr400525m
    OpenUrlCrossRefPubMedWeb of Science
  40. Vernon RM, Chong PA, Tsang B, Kim TH, Bah A, Farber P, Lin H, Forman-Kay JD. 2018. Pi-Pi contacts are an overlooked protein feature relevant to phase separation. Elife 7:1–48. doi:10.7554/eLife.31486
    OpenUrlCrossRefPubMed
  41. Warren C, Shechter D. 2017. Fly Fishing for Histones: Catch and Release by Histone Chaperone Intrinsically Disordered Regions and Acidic Stretches. J Mol Biol 429:2401–2426. doi:10.1016/j.jmb.2017.06.005
    OpenUrlCrossRef
  42. Wootton JC, Federhen S. 1993. Statistics of local complexity in amino acid sequences and sequence databases. Comput Chem 17:149–163. doi:10.1016/0097-8485(93)85006-X
    OpenUrlCrossRefWeb of Science
  43. Zarin T, Tsai CN, Nguyen Ba AN, Moses AM. 2017. Selection maintains signaling function of a highly diverged intrinsically disordered region. Proc Natl Acad Sci 114:E1450–E1459. doi:10.1073/pnas.1614787114
    OpenUrlAbstract/FREE Full Text
Back to top
Posted March 16, 2019.
Download PDF
Email
Proteome-wide signatures of function in highly diverged intrinsically disordered regions
Taraneh Zarin, Bob Strome, Alex N Nguyen Ba, Simon Alberti, Julie D Forman-Kay, Alan M Moses
bioRxiv 578716; doi: https://doi.org/10.1101/578716
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools

Subject Area