Systematic survey of local amino acid composition

Fundamentally, both sequence complexity and statistical amino acid bias are indirect measures of local amino acid composition. Since composition is a more direct indication of overall protein domain properties, we sought to examine whether composition alone could be used to infer residue-specific relationships between local amino acid composition and protein regulation and function. We first developed an algorithm to partition the yeast proteome on the basis of maximum local composition for each amino acid using a series of scanning window sizes (Fig 1; see Methods). For all amino acids, the majority of proteins are partitioned into composition bins of ≤ 25% (Fig 2 and S1 Table). However, the number of proteins achieving higher local compositions, indicated by a right-hand shoulder or tail in the distribution, were strongly residue-dependent. For example, proteins containing local enrichment of highly hydrophobic residues (I, L, M, and V), aromatic residues (F, W, and Y), or cysteine are almost exclusively limited to composition bins of ≤ 45% for the smallest window size, whereas alanine and proline distributions extend to slightly higher composition ranges (up to 60–65%). Proteins containing local enrichment of polar (G, N, Q, S, and T) or charged (D, E, and K) residues in composition bins of ≥ 40% are relatively common even among larger window sizes (albeit to differing degrees), whereas histidine and arginine rich regions are relatively rare. These data indicate that relatively high local enrichment is tolerated for some amino acids, while compositional enrichment for other amino acids appears to be restricted in yeast.

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Fig 1. Depiction of proteome sorting on the basis of maximum local composition.

(A) For each amino acid and window size combination, each yeast protein is sorted into percent composition bins based on the maximum local composition of the amino acid within the given sliding window size. This effectively sorts the yeast proteome 200 distinct ways (20 amino acids x 10 different sliding window sizes). (B) Visual representation of proteins sorted based on maximum local aspartic acid composition with a 20 amino acid sliding window.

https://doi.org/10.1371/journal.pcbi.1006256.g001

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Fig 2. Distribution of the yeast proteome based on maximum local amino acid composition.

The number of proteins partitioned into each window size/percent composition bin for each of the 20 canonical amino acids are plotted as a function of maximum local composition for each window size. Scatter points are connected by line segments for visual clarity only.

https://doi.org/10.1371/journal.pcbi.1006256.g002

Residue-specific relationships between local compositional enrichment and protein metabolism

While the origins and evolution of LCDs have been extensively explored [3,4,14,38,43,44], the regulation and metabolism of LCD-containing proteins remain poorly-understood. Proteins with intrinsically disordered segments, which often qualify as LCDs [45,46], have been associated with lower protein half-lives [47]. However, not all intrinsically disordered regions lead to short protein half-lives, and not all LCDs are intrinsically disordered [15]. Additionally, proteins with homopolymeric repeats, when considered as a single class, are associated with lower translation efficiency, lower protein abundance, and lower protein half-life compared to proteins lacking homopolymeric repeats [37]. However, the regulation and structural properties of proteins with LCDs or homopolymeric repeats is likely strongly dependent on the predominant amino acids within the domain of interest [42].

To explore relationships between local compositional enrichment and protein metabolism, we first examined possible links between local compositional enrichment and protein abundance. Recent advances in proteomic methods have facilitated remarkable proteome coverage for both protein abundance [48] and protein half-life [49] measurements in yeast. At each window size/percent composition bin, the distribution of protein abundance values for all proteins partitioned into that bin was compared to the protein abundance distribution for all other yeast proteins (Mann-Whitney U test). Transitions from significantly lower median abundance to significantly higher median abundance or vice versa are observed upon enrichment for many amino acids individually (Fig 3). However, the direction of the trends upon progressive compositional enrichment are dependent on amino acid type. For the majority of amino acids (C, D, F, H, I, L, M, N, P, Q, R, S, T, W, or Y) compositional enrichment is associated with lower median protein abundance. However, compositional enrichment of A, G, or V is associated with higher median protein abundance. Two very similar transitions are observed for both E-rich and K-rich sequences: as compositional enrichment increases, the relative median protein abundance transitions from high to low, then back to high. Collectively, these trends are consistent with, yet much stronger than, previously observed correlations between protein abundance and whole-protein composition [50,51]. This suggests that the trends observed previously may actually reflect the effects of local compositional enrichment, which would increase apparent whole-protein composition for the enriched amino acid yet be dampened by confounding effects from the remainder of the protein sequence.

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Fig 3. Maximum local amino acid composition is associated with residue-specific differences in protein abundance.

For each amino acid, protein abundance values corresponding to proteins partitioned into a given window size and percent composition bin were compared to values for all proteins of length ≥ the corresponding window size that were excluded from the bin. For this figure and related subsequent figures, red and blue points indicate bins for which the distribution of protein abundance values differ significantly (Bonferroni-corrected p ≤ 0.05) from those of excluded proteins: red points indicate bins with a lower median value relative to that of excluded proteins, whereas blue points indicate bins with a higher relative median value. Grey points indicate comparisons lacking statistical significance. Individual points are scaled within each subplot to reflect the sample sizes of proteins contained within each bin.

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Similar trends are observed when compositional enrichment is compared to protein half-lives (Fig 4). Compositional enrichment for the majority of amino acids (C, H, K, M, N, P, S, or T) is associated with lower protein half-life, whereas enrichment for A, G, I, or V is associated with higher protein half-life. Enrichment for F leads to an initial transition from lower to higher half-lives, while further enrichment leads to a transition back to lower half-lives. It is worth noting that similar trends were observed in an independent protein half-life dataset when the proteins were analyzed based on whole-protein amino acid composition [52], suggesting that maximum local composition is sufficient to detect associations between amino acid composition and half-life. Although for many amino acids the trends are readily apparent, the strength of the association between compositional enrichment and protein half-life appears to be slightly weaker than the association between compositional enrichment and protein abundance. This is likely due, at least in part, to limited proteome coverage (relative to the protein abundance dataset). However, a recent study also suggested that protein half-life is strongly affected by factors other than sequence characteristics [53], which would likely further dampen relationships between compositional enrichment and protein half-life. Finally, protein half-life is generally less-conserved than protein abundance [54], perhaps suggesting that specific relationships between conserved sequence features and protein half-life may not be particularly strong. Therefore, it is rather surprising that we observe the indicated trends in spite of these limitations, and could suggest that half-life is more strongly influenced by local composition than particular primary sequence motifs.

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Fig 4. Maximum local amino acid composition is associated with residue-specific differences in protein half-life.

For each amino acid, protein half-life values corresponding to proteins partitioned into a given window size and percent composition bin were compared to half-life values for all proteins of length ≥ the corresponding window size that were excluded from the bin.

https://doi.org/10.1371/journal.pcbi.1006256.g004

Direct measurement of protein synthesis rates is more experimentally challenging. Consequently, proteome-wide coverage for experimentally-derived translation efficiency remains substantially lower than coverage for protein abundance and half-life. The normalized translation efficiency (nTE), a reported metric of translation elongation efficiency [55], is based on codon usage frequencies and tRNA gene copy numbers, allowing for calculation of translation efficiency for the entire proteome. Therefore, we first examined relationships between local compositional enrichment and calculated translation elongation efficiency. nTEs were calculated for whole-protein sequences using the corresponding coding region on mRNA transcripts (see Methods). Translation efficiency is strongly dependent on the locally-enriched amino acid (Fig 5). For the majority of amino acids (C, D, E, F, H, I, K, L, M, N, P, Q, R, or Y), local enrichment is associated with significantly lower median nTEs suggesting that, as a single class, proteins with local compositional enrichment tend to be translated relatively inefficiently. Proteins with domains enriched in S, T, or W are generally associated with significantly lower median nTEs, although proteins with very high S, T, or W enrichment are associated with significantly higher median nTEs. However, proteins with domains enriched in A, G, or V residues are consistently associated with significantly higher median nTEs, suggesting that these proteins may be translated relatively efficiently. Remarkably, nearly identical trends are observed between local compositional enrichment and the experimentally-derived protein synthesis rates reported for a limited proteome (S1 Fig) despite a substantial reduction in sample size (n = 1115; [56]), suggesting that nTE can serve as a good surrogate for overall protein synthesis efficiency. Collectively, these results indicate that local amino acid enrichment is associated with differences in protein production rates in a composition-dependent manner.

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Fig 5. Maximum local amino acid composition is associated with residue-specific differences in nTE.

For each amino acid, nTE values corresponding to proteins partitioned into a given window size and percent composition bin were compared to nTE values for all proteins of length ≥ the corresponding window size that were excluded from the bin.

https://doi.org/10.1371/journal.pcbi.1006256.g005

For most amino acids, we noticed a remarkable correspondence in the trends for translation efficiency, protein abundance, and protein half-life, despite the fact that these values are derived from entirely different methods and experiments. For example, local enrichment for many amino acid types is associated with low nTE values, low protein abundance, and low protein half-life (Table 1). While translation efficiency and protein degradation rate are largely functionally independent in cells, protein abundance depends, at least in part, on both translation efficiency and protein half-life [49]. This may suggest that protein abundance for these proteins is limited in cells by a combination of poor translation efficiency and rapid degradation rate. In contrast, local enrichment for some amino acids is associated with high protein abundance also tended to have higher nTE values and higher half-lives, perhaps suggesting that high protein abundance for these proteins is achieved by a combination of efficient translation and poor degradation.

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Table 1. The life-cycle of proteins with high local composition of individual amino acids involves the coordinated regulation of translation efficiency, protein abundance, and protein half-life.

For each amino acid, trends in median values for nTE, protein abundance, and half-life upon enrichment (i.e. approaching higher percent compositions) of the given amino acid are indicated. “Higher” indicates that proteins in larger percent composition bins tend to have a larger median value compared to all other proteins, while “Lower” indicates that proteins in larger percent composition bins tend to have a larger median value compared to all other proteins. “Mixed” indicates amino acids for which multiple transitions are observed upon progressive compositional enrichment. Datasets without clear, statistically significant transition thresholds are also indicated (“n.s.”). Colors correspond to the colors used in Figs 3–5.

https://doi.org/10.1371/journal.pcbi.1006256.t001

Nearly-identical, residue-specific relationships between local compositional enrichment and protein abundance are observed in C. elegans

As a model eukaryotic organism, S. cerevisiae provides a number of important advantages in proteome-scale studies relating protein sequence to protein metabolism and function. In addition to the unmatched proteome coverage in protein abundance and protein half-life datasets, and the availability of yeast-specific tools such as nTE, sequence-function analyses in yeast are further simplified by the absence of tissue-specific effects and limited alternative splicing (only ~4% of yeast genes contain introns and, of those genes, only a small fraction is capable of producing alternative protein products [57,58]).

With these caveats in mind, we sought to examine whether similar relationships between local amino acid composition and protein abundance could be detected in a model multicellular eukaryotic organism. We decided to focus on whole-organism protein abundance measurements in C. elegans [59] for four main reasons: 1) due to technical experimental challenges, protein abundance measurements in C. elegans are substantially more robust than protein half-life measurements; 2) on a proteome-wide scale, protein abundance is more strongly conserved across yeast species than protein half-life [49], suggesting that final protein levels tend to be constrained across organisms, while regulation of the metabolic pathways that contribute to protein abundance may vary; 3) protein abundance is, at least partially, a function of translation efficiency and protein half-life; and 4) the parameters underlying the translation efficiency method (namely the “s-vector”, or the efficiency of wobble base pairing between tRNA isoacceptors) were optimized for yeast [60]. Therefore, the nTE method may not be amenable to application in other organisms.

In order to examine relationships between maximum local composition and protein abundance, we first determined the proteome distribution of C. elegans proteins as a function of maximum local composition for each amino acid. The C. elegans-specific proteome distributions (S2 Fig and S2 Table) were overall quite similar to the yeast proteome distributions (Fig 2). However, the maximum local composition for S and N appear to be slightly more constrained in C. elegans (indicated by contraction of the shoulder to lower maximum compositions), while G, P, and T achieve slightly higher maximum local compositions, indicating relaxed constraints on local enrichment of these residues. These results are consistent with previous observations noting both shared and organism-specific homopolymeric repeat signatures or bulk proteome compositions across proteomes from different organisms [4,38,40,41,44–46,61].

As observed in yeast, progressive compositional enrichment results in a transition from higher to lower median abundance for the majority of amino acids with a clear trend (C, F, I, M, N, P, S, W, and Y; Fig 6). Furthermore, all three amino acids (A, G, and V) that exhibit a transition from lower to higher median abundance upon progressive enrichment in yeast exhibit the same trend in C. elegans as well. Indeed only one amino acid with a clear transition in protein abundance upon local enrichment differs between C. elegans and S. cerevisiae: in yeast, local K enrichment is associated with mixed effects on protein abundance (depending on the degree of K enrichment), whereas in C. elegans local K enrichment is weakly (yet consistently) associated with higher protein abundance. Therefore, nearly identical residue-specific relationships are observed between local amino acid enrichment and protein abundance in a more complex eukaryote.

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Fig 6. Relationships between maximum local composition and protein abundance in C. elegans.

For each amino acid, protein abundance values corresponding to proteins partitioned into a given window size and percent composition bin were compared to values for all proteins of length ≥ the corresponding window size that were excluded from the bin. Trends in protein abundance as a function of maximum local composition for many amino acids are remarkably similar between yeast and C. elegans (compare with Fig 3).

https://doi.org/10.1371/journal.pcbi.1006256.g006

Compositional enrichment is linked to effects on protein metabolism in the absence of classical low-complexity, statistically-biased, and homopolymeric domains

An important advantage of approaching LCDs from a composition-centric perspective is the ability to examine relationships between amino acid composition and protein outcomes without appealing to pre-defined thresholds of statistical amino acid bias [11] or sequence complexity [1,10], which may not reflect biologically-relevant thresholds. Indeed, the transitions observed in the median translation efficiencies, protein abundances, and protein half-lives often occur at surprisingly mild levels of compositional enrichment, suggesting that these trends may be observed even in the absence of classically-defined statistically-biased or low-complexity domains.

Statistical amino acid bias conceptually parallels our investigation of compositional enrichment, and has been used to investigate the functions of proteins with statistically-biased domains [11,12]. To examine whether compositional enrichment may be linked to biologically-relevant effects on protein metabolism independently of statistically-biased domains, a conservative bias threshold was employed to define statistically-biased domains using previously developed methodology [12] (also, see Methods). Proteins with statistically-biased domains were then filtered from the yeast proteome (n = 866 statistically-biased proteins for the yeast translated proteome of sequences ≥ 30 residues in length). However, even in the absence of statistically-biased domains, compositional enrichment resulted in robust trends in translational efficiency, protein abundance, and protein half-life that re-capitulated those originally observed (S3–S5 Figs). This suggests that compositional enrichment affects protein metabolism at thresholds preceding those required for classification as statistically-biased by alternative methods.

The SEG algorithm, by default, employs substantially more relaxed criteria when classifying protein domains as low-complexity [1]. Indeed, of the 5,901 proteins of length ≥30 amino acids in the translated ORF proteome, 4,147 proteins contain at least one LCD, which is consistent with previous estimates [3]. Nevertheless, despite a large reduction in proteome size, many of the trends in protein metabolism are discernible even when all proteins with a SEG-positive sequence are filtered from the proteome (S6–S8 Figs). This suggests that compositional enrichment exerts biologically relevant effects even among non-LCD-containing proteins.

Proteins containing homopolymeric amino acid repeats (often defined as five or more identical amino acids in succession), were recently reported to have lower translation efficiency, lower protein abundance, and lower protein half-life when compared to proteins without homopolymeric repeats [37]. Homopolymeric repeats are effectively short sequences of maximum possible single-amino acid density. Therefore, proteins with homopolymeric repeats are expected to be disproportionately common among compositionally enriched domains, raising the possibility that the trends observed in the present study have been mis-attributed to compositional enrichment alone. To examine this possibility directly, the relationship between compositional enrichment and nTE, abundance, and half-life was re-evaluated for a filtered proteome that excludes all proteins containing at least one homopolymeric repeat (n = 755 proteins excluded). While exclusion of these proteins preferentially reduces the sample sizes at higher compositional enrichment percentages, the absence of homopolymeric repeat proteins has little effect on the trends in nTE, abundance, and half-life as a function of compositional enrichment (S9–S11 Figs). This does not definitively rule out the possibility that homopolymeric repeats may, in some way, specifically affect translation efficiency, abundance, and half-life. However, since homopolymeric repeats per se are not absolutely required, the effects of homopolymeric repeats may instead be explained simply by local compositional enrichment.

Collectively, these results suggest that compositional enrichment affects translation efficiency, protein abundance, and protein half-life at thresholds preceding those required for classification as low-complexity or statistically-biased by traditional methods. It is worth noting that in the course of eliminating proteins with classically-defined low-complexity, statistically-biased, or homopolymeric domains, proteins with multiple distinct domains strongly enriched in different amino acid types, or with single domains strongly enriched in more than one amino acid, are eliminated from the proteome before re-evaluation. Therefore, the trends in protein metabolism observed upon enrichment of a given amino acid are not due to confounding effects of domains strongly enriched in other amino acids occurring within the same protein sequences.

Local compositional enrichment influences protein-protein interaction promiscuity in a residue-specific manner

Local enrichment of a single amino acid can dramatically influence the physicochemical properties of a given protein domain [24]. In a cellular context, these physicochemical properties likely influence interactions between proteins and surrounding molecules, including other proteins.

To examine whether local compositional enrichment affects protein-protein interactions, we explored relationships between enrichment for each of the amino acids and protein-protein interaction promiscuity (defined as the number of unique interacting partners per protein). Proteins found in a range of high-percent composition-bins for most amino acids (A, D, E, G, K, N, P, Q, R, and V) are associated with significantly more interacting partners relative to all other proteins (Fig 7), suggesting that these domains are relatively promiscuous. Additionally, proteins with mild enrichment for select hydrophobic residues (I, L, and M) are generally associated with more interacting partners, although fewer comparisons reach statistical significance (blue or red dots). These results are consistent with previous reports that, as a single class, proteins with LCDs or homopolymeric repeats tend to have more protein-protein interaction partners [16,37]. However, proteins in a range of high-percent composition-bins for each of the aromatic residues (F, W, and Y) are associated with significantly fewer interacting partners relative to other proteins, suggesting that aromatic residues tend to lack the interaction promiscuity observed at higher percent compositions for other amino acids. Furthermore, proteins with moderate to high local C content and proteins with extremely high maximum local S or T content are also associated with significantly fewer interacting partners relative to other proteins, suggesting that these domains are relatively non-promiscuous as well. This is particularly interesting, given that these trends were not observed upon enrichment for other polar residues. Again, this highlights the potential pitfall of grouping amino acids with related physicochemical properties into a single category. Collectively, these results indicate that protein-protein interaction promiscuity varies for proteins with high compositional enrichment in a residue-specific manner.

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Fig 7. Maximum local amino acid composition corresponds to protein-protein interaction promiscuity in a residue-specific manner.

Local enrichment for individual amino acids corresponds to composition-dependent changes in the number of unique protein-protein interaction partners.

https://doi.org/10.1371/journal.pcbi.1006256.g007

Proteins with high compositional enrichment can fulfill overlapping or specialized molecular roles in the cell

Previous studies have attempted to associate proteins containing LCDs, statistically-biased domains, and homopolymeric repeats with particular cellular functions [12,16–18,34,37]. However, one important consideration when inferring relationships between proteins with LCDs and cellular functions, for example, is the prevalence of proteins with multiple LCDs [3], and of LCDs strongly enriched in more than one amino acid type [11,14,18,36]. Therefore, attempts to associate cellular functions to specific LCD types, without controlling for other LCDs within the same protein sequences, risk mis-attributing functions to unrelated protein features [12,14,34,36]. While multiple LCDs within the same protein (or multiple amino acid types enriched within the same LCD) may cooperate to generate novel structures or functions, this complicates interpretation of the role of each individual amino acid type within LCDs. Furthermore, because some types of LCDs are more common than others, general attempts to associate cellular functions with LCDs, statistically-biased domains, or homopolymeric repeats likely reflect the functions associated with only the most common types when considered as a single, unified class [16,37]. Therefore, definitive assignment of cellular functions to each individual class of LCD necessitates exclusion of proteins with other types of LCDs.

In order to minimize possible confounding effects introduced by proteins with multiple regions enriched in different amino acid types, a modified version of the initial calculation performed by the SEG algorithm (namely, the Shannon entropy; see Methods) was employed to define proteins with only a single type of compositionally-enriched domain (CED). In an effort to incorporate our results (which indicate that compositional enrichment may exert biologically-relevant effects at compositions preceding the SEG algorithm threshold) into our definition of single-CED proteins, percent composition bins for which at least 75% of the residing proteins contained a SEG-positive sequence (as defined above) were pooled to generate a single list of CED-containing proteins for each amino acid. Proteins that contain multiple types of CEDs were then removed from the dataset, resulting in a non-redundant set of proteins with only one type of CED. Importantly, this method captures the exclusion of proteins containing more than one type of CED, as well as proteins with CEDs strongly enriched in more than one amino acid type.

Gene Ontology (GO) term analysis was performed separately for each window size within each single-CED category. For each type of CED, there is strong overlap in the enriched GO terms across the range of window sizes, suggesting that the associations between functions and residue-specific CEDs are not strongly length-dependent at this scale. Therefore, for simplicity of interpretation, significantly enriched GO terms for each window size were pooled to generate a single non-redundant list of enriched GO terms for each CED type.

Removal of proteins with multiple types of CEDs reveals a remarkable degree of specialization for CEDs of different types (Fig 8, and S3 Table), which is often not observed for CEDs when considered as a single category or when multi-CED proteins are not excluded. For example, L-rich proteins are predominantly associated with functions at the ER and vacuole membranes, whereas I-rich proteins are more strongly associated with carbohydrate transport at the plasma membrane. A-rich proteins are associated with a variety of processes or cellular components, including translation, protein kinase activity, the cell wall, and carbohydrate/alcohol catabolism. N-rich proteins are strongly (and perhaps exclusively) associated with functions related to transcription, whereas Q-rich proteins appear to be more weakly associated with transcription and, instead, are associated with a larger variety of functions including endocytosis, mating projection of the membrane, and response to glucose. Finally, although yeast cell wall proteins are often radically S/T-rich, after controlling for co-enrichment of S and T in the same proteins, S-rich proteins are more strongly associated with membrane-related processes (cell wall, cellular bud tip, cellular bud neck, mating tip projection, etc.), protein kinase activity, and transcription, whereas T-rich proteins tend to be associated with nucleic acid binding and helicase activity, with fewer associations with membrane-related processes. Therefore, after controlling for the presence of multiple CEDs within the same proteins, specialized functions emerge even among commonly grouped amino acids.

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Fig 8. Cell model depicting predominant functions of CEDs.

Residue-specific CEDs are associated with both overlapping and distinct functions. Main cellular/molecular processes associated for each type of CED are derived from significantly enriched (Bonferroni-corrected p ≤ 0.05) GO-terms in S3 Table.

https://doi.org/10.1371/journal.pcbi.1006256.g008

Furthermore, CEDs enriched in some amino acids share functions despite the removal of multi-CED proteins, suggesting some degree of co-specialization. For example, D-, E-, and K-rich CEDs were each associated with functions in the nucleus/nucleolus, including ribosomal RNA processing, nucleic acid binding, transcription, and histone/chromatin binding. Intriguingly, intrinsically disordered domains with opposite net charges (along with other charged macromolecules such as nucleic acids and polyADP-ribose) can drive phase separation or complex coacervation in the nucleus [62–64]. It is possible that these domains, along with nucleic acids and other polyionic molecules, may participate in nuclear processes via dynamic electrostatic association with these or other membraneless assemblies. By contrast, H-rich CEDs are associated with processes related to zinc ion transport and regulation. There were no GO terms significantly associated with R-rich CEDs. However, compositional enrichment for R appears to be constrained, as evidenced by the sharp decline in the number of proteins with R-rich domains toward higher maximum local percent compositions (see Fig 2), which may be further impacted by the removal of proteins with other types of CEDs.

In summary, when examined as separate classes, different types of CEDs can have overlapping or specialized roles in the cell.

Compositional enrichment corresponds to preferential localization to specific subcellular compartments

The molecular specialization observed for CEDs indicates that proteins with enrichment of particular residues may localize to particular subcellular compartments in order to execute their specialized functions. Furthermore, protein quality control factors can differ between subcellular compartments (for review, see [65]), which may contribute to composition-dependent differences in protein metabolism. Therefore, we applied a bottom-up approach to infer the composition profiles associated with the major subcellular compartments (see Methods).

Largely aqueous subcellular compartments are almost exclusively associated with proteins containing domains enriched in charged residues, polar residues, and proline (Fig 9; see also S12 Fig). However, differences in compositional enrichment profiles are apparent even among related aqueous compartments. For example, significant associations with charged, Q, or N residues reach more extreme percent compositions in the nucleus, whereas as significant associations with P enrichment reach higher percent compositions in the cytoplasm. By contrast, the highly membraneous internal organelles (e.g. the endoplasmic reticulum and Golgi apparatus) are predominantly associated with enrichment of hydrophobic or aromatic residues (Figs 9 and S13). The yeast vacuole is also associated with composition profiles resembling those of membraneous compartments, with additional weaker associations with S and C enrichment. Few weak associations are observed for mitochondria. The yeast cell wall is strongly associated with S enrichment (likely related to its ability to be glycosylated), with additional moderate associations with T and A enrichment, and a weak association with mild V enrichment (Figs 9 and S14). As expected, the plasma membrane is associated with enrichment for a variety of hydrophobic and aromatic residues. However, the plasma membrane is also significantly associated with enrichment of a select subset of polar residues (namely C, G, S, and T), further corroborating the specialized roles observed for these CEDs at the outer membrane. Indeed, G-rich CEDs are significantly associated with amino acid transport (see S3 Table), and S- or T-rich CEDs of the plasma membrane could have overlapping functions or interactions with S- and T-rich CEDs of the cell wall. Together, these observations indicate that subcellular compartments may tolerate or prefer proteins with specific types of CEDs.

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Fig 9. Compositional enrichment profiles associated with major subcellular compartments.

All plotted points indicate protein sets for which association with the indicated subcellular compartment is statistically significant (Fisher’s exact test, with Bonferonni-corrected p < 0.05). Warm colors (reds, oranges, and yellows) correspond to charged residues. Green colors indicate polar residues. Cool colors (purples and blues) correspond to hydrophobic and aromatic residues respectively.

https://doi.org/10.1371/journal.pcbi.1006256.g009

Components of stress granules and processing bodies possess shared and unique compositional features

Recent observations indicate that a variety of Q/N-rich and G-rich domains can form highly dynamic protein-rich droplets in aqueous environments [25–30], a process referred to as liquid-liquid phase separation. These types of LCDs are prevalent among components of membraneless organelles such as stress granules and P-bodies [23]. Furthermore, stress granules and P-bodies share many properties with protein-rich liquid droplets formed in vitro, suggesting that the fundamental biophysical properties of these domains are related to the formation of membraneless organelles in vivo. However, while amino acid composition is acknowledged as a critical determinant of this behavior, the precise compositional requirements associated with membraneless organelles remain largely undefined.

Therefore, we also applied our bottom-up approach to infer the compositional enrichment profiles associated with protein components of stress granules and P-bodies (as defined in [66]). Stress granules and P-bodies have overlapping protein constituents and can exchange protein components [67,68], suggesting that they are closely related yet distinct organelles. Accordingly, we observe both shared and unique features in the composition profiles associated with stress granule and P-body proteins (Fig 10). As expected, both stress granules and P-bodies are strongly associated with proteins containing Q-rich or N-rich domains. For example, minimum Q or N compositions significantly associated with stress granules range from ~15–100% at small window sizes (≤30 amino acids) and ~10–30% at large window sizes (≥80 amino acids), although these values vary slightly depending on window size and residue. Similarly, minimum Q or N compositions significantly associated with P-bodies range from ~15–100% at small window sizes and ~10–40% at larger window sizes.

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Fig 10. Composition profiles associated with membraneless organelles.

All colored points indicate minimum percent composition thresholds for which components of stress granules (A) or P-bodies (B) are significantly enriched (p < 0.05). Only amino acids for which significant enrichment of stress granule or P-body proteins was observed in at least two composition bins are shown. For greater sensitivity, plots were generated using uncorrected p-values. Therefore, any individual point should be viewed with some skepticism: however, the presence of multiple consecutive significant points within each window size suggest that the observed trend is likely not an artifact of multiple hypothesis testing.

https://doi.org/10.1371/journal.pcbi.1006256.g010

In addition to the commonly appreciated link between stress granule/P-body components and Q/N-rich domains, we identify and define a variety of currently underappreciated compositional features common to stress granule and P-body components. Components of both stress granules and P-bodies are strongly associated with P-rich domains, weakly associated with K-rich domains, and very weakly (yet significantly) associated with Y-rich domains. Furthermore, while both stress granules and P-bodies are associated with proteins containing G-rich domains, stress granule components are associated with a much broader range of G enrichment, suggesting that G enrichment may be a more characteristic feature of stress granules than P-bodies. This is particularly striking in light of recent observations indicating that high glycine content helps maintain the liquid-like characteristics of phase-separated droplets and prevents droplet hardening in vitro [69].

Additionally, some compositional features are unique to either stress granules or P-bodies. For example, stress granule constituents are significantly associated with A-rich, M-rich, E-rich, and R-rich domains, whereas P-body constituents exhibit little or no preference for these compositional features (a key role for arginine in the phase separation of stress granule-associated proteins was also recently reported [69]). By contrast, P-body components are weakly associated with H-rich domains, whereas stress granule components are not enriched among proteins containing H-rich domains.

To our knowledge, this represents the first attempt to systematically define the range of amino acid compositions associated with membraneless organelles such as stress granules and P-bodies. These observations suggest that components of related, membraneless organelles have overlapping yet distinct compositional preferences. It is possible that shared compositional features facilitate the physical interactions between stress granules and P-bodies and allow for the exchange of components, while differences in compositional features facilitate their ability to function as independent organelles.

Composition-based proteome scanning algorithm

Protein sequences were parsed using FASTA sequence parsing module from the Biopython package [70]. For each amino acid in the set of 20 canonical amino acids, each protein in the translated ORF proteome (latest release from the Saccharomyces Genome Database website, last modified 13-Jan-2015) or the ORF coding sequences (organismID:UP000001940_6239, release date 23-May-2018 downloaded from the UniProt website) was scanned using a sliding window of defined size (ranging from 10 to 100 amino acids, in increments of 10). The percent composition of the amino acid of interest (AAoI) is calculated for each window, and the protein is sorted into bins based on the maximum percent composition achieved for the AAoI (ranging from 0 to 100 percent composition in 5 percent increments). Analyses were performed for all possible AAoI, window size, and percent composition combinations.

Normalized translation efficiency (nTE)

Translation efficiency for each gene was estimated using the normalized translation efficiency (nTE) scale [55], which is based on tRNA gene copy number, codon-anticodon wobble base-pairing efficiency, and transcriptome-wide codon usage. However, the original nTE algorithm plots all nTE values for each codon to generate a separate translation efficiency profile for each gene. In order to condense translation efficiency information to a single value for each gene (in a manner analogous to the tRNA adaptation index; [60]), the geometric mean of nTE values across the transcript was calculated as (1) where nTE_iks represents the translation efficiency value of the ith codon defined by the kth triplet in nucleotide sequence s, and l_s represents the length of the nucleotide sequence excluding stop codons. Therefore, nTE values reported in the current study represent whole-gene nTE values. nTE analyses were performed using an in-house Python script.

Defining Shannon entropy, statistical amino acid bias, and homopolymeric repeats

The Shannon entropy of each sequence was calculated as (2) where N represents the size of the residue alphabet (N = 20, for the canonical amino acids), n_i represents the number of occurrences of the ith residue within the given sequence window of length L. For comparison with established measures of sequence complexity, we defined low-complexity domains by using the default window size (12 amino acids) and Shannon entropy threshold (SE ≤ 2.2bits) used in the first pass of the SEG algorithm to initially identify LCDs [1,10].

In the SEG algorithm, the complexity state vector used to calculate the Shannon entropy is blind to the amino acid composition (i.e. the n_i values in Eq 2 are not attributed their respective amino acids). Therefore, when indicated, in order to distinguish LCDs on the basis of the predominant amino acid, sequences for which the SE ≤ 2.2bits and n_AAoI ≥ n_max within the complexity state (indicating that the AAoI is a major contributor to the sequence’s classification as an LCD) were assigned to the corresponding amino acid category (e.g. A-rich LCDs, C-rich LCDs, etc.). Single-LCD/CED proteins are proteins classified as LCDs or CEDs that do not appear on multiple amino acid-specific LCD/CED lists.

Statistical amino acid bias was calculated as described in [12]. Briefly, the lowest probability subsequence for each protein was determined by exhaustively scanning proteins with window sizes ranging from 25 to 2500 amino acids. For each window, the subsequence bias probability (P_bias) was defined as (3) where w denotes the window size, n denotes the number of occurrences of the amino acid of interest within the subsequence, and f_x denotes the fraction of the amino acid of interest in the yeast translated proteome. The lowest probability subsequence for each protein is the subsequence with the lowest P_bias.

A suitable threshold to define statistically-biased proteins within the yeast protein was determined as previously described [12], except that more relaxed criteria were used in order to include additional proteins with less extreme biases. Briefly, the P_bias corresponding to the lowest probability subsequence (P_min) for each protein was plotted on a log-log plot against whole-protein sequence length. A line was fitted, then the y-intercept was decreased until only 15% of the proteome had P_min values below the line (previous analyses used a more stringent cutoff of 10% to define statistically-biased proteins [12]). Additionally, a length-independent threshold was designated as the P_min value at which 15% of the proteome had smaller absolute P_min values. This threshold was used when it was less than the P_min threshold given by the length-dependent method to avoid unreasonably relaxed bias criteria for small protein sequences. Amino acid bias was calculated using values from the translated orf proteome only, and implemented via an in-house Python script with pre-computed look-up tables for computational efficiency.

Proteins containing homopolymeric sequences were defined simply as any protein with a subsequence of five or more contiguous residues of the same amino acid, as previously described [37].

Protein abundance and protein half-life data

Yeast protein abundance values (in average number of molecules per cell per protein) were obtained from [48] (n = 5,391). Protein abundance values for C. elegans were obtained from [59]. Yeast protein half-life data were obtained from [49]. For simplicity of interpretation, only proteins with unambiguous, non-zero half-life or abundance values were included in the datasets. Proteins listed on separate lines with identical half-life or abundance values were retained, whereas protein half-life or abundance measurements assigned to more than one protein on the same line were excluded (these were often highly homologous genes, suggesting that the measurement could not be unambiguously assigned to one of the proteins). Furthermore, all proteins corresponding to “low-confidence” measurements in the half-life dataset were excluded (see [49] for criteria). n = 3,525 for the filtered yeast half-life dataset, and n = 5,952 for the filtered C. elegans protein abundance dataset.

Statistics and plotting

For all AAoI/window size/percent composition bins, the distribution of nTE, abundance, or half-life values for proteins included in the given bin was compared to the distribution of the respective values of all proteins excluded from the given bin. Statistical significance was estimated using a two-sided Mann-Whitney U test (also referred to as the Wilcoxon rank-sum test; refer to Supplemental Experimental Procedures from [47] for a detailed description and rationale). Where indicated, p-values were adjusted within each window using the Bonferroni correction method for multiple hypothesis testing. All statistical tests were performed using modules available in the SciPy package with default settings, unless otherwise specified. All plots were generated using Matplotlib or Seaborn modules.

Gene ontology (GO) term enrichment analyses

GO term enrichment tests were performed using the GOATOOLS package (version 0.7.9) [71] for each set of proteins contained in a given amino acid/window size/percent composition bin. For each test, the set of background proteins was defined as all proteins from the translated ORF proteome of sequence length greater than or equal to the given window size. All reported p-values were adjusted using the Bonferroni correction during GO term association. To evaluate the compositional enrichment profiles associated with GO terms related to subcellular compartments, we applied a minimum-threshold-scanning approach to all partitioned proteomes. For each AAoI, window size, and percent composition bin, all proteins with maximum local compositions greater than or equal to the current percent composition under consideration are pooled and evaluated for possible enriched GO terms. This effectively evaluates possible GO term enrichment iteratively with increasing maximum local composition criteria. GO term results were subsequently evaluated for significant enrichment of a single GO term describing each subcellular compartment (or two related GO terms, “outer membrane” and “plasma membrane”, in the case of the plasma membrane). p-values were further adjusted within each window size using the Bonferroni correction method.

Similar analyses were performed for the sets of experimentally-defined stress granule (n = 83) and P-body (n = 52) proteins [66]. Specifically, a minimum-threshold-scanning approach was applied to all partitioned proteomes. For each AAoI, window size, and percent composition bin, all proteins with maximum local compositions greater than or equal to the current percent composition under consideration are pooled. Significant enrichment of experimentally-defined stress granule or P-body proteins within each pool of proteins was evaluated using Fisher’s exact test (p < 0.05).