Sequence-based features that are determinant for tail-anchored membrane protein sorting in eukaryotes

Sequence-based features that are determinant for tail-anchored

membrane protein sorting in eukaryotes

Michelle Y. Fry

Shyam M. Saladi

Alexandre Cunha

William M. Clemons Jr

Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena,

California, USA

Division of Biology and Biological Engineering, Center for Advanced Methods in Biological Image

Analysis, Beckman Institute, Pasadena, California, USA

These authors contributed equally to this work.

Abstract

The correct targeting and insertion of tail-anchored (TA) integral membrane proteins is critical for

cellular homeostasis. TA proteins are defined by a hydrophobic transmembrane domain (TMD)

at their C-terminus and are targeted to either the ER or mitochondria. Derived from experimental

measurements of a few TA proteins, there has been little examination of the TMD features that

determine localization. As a result, the localization of many TA proteins are misclassified by

the simple heuristic of overall hydrophobicity. Because ER-directed TMDs favor arrangement

of hydrophobic residues to one side, we sought to explore the role of geometric hydrophobic

properties. By curating TA proteins with experimentally determined localizations and assessing

hypotheses for recognition, we bioinformatically and experimentally verify that a hydrophobic

face is the most accurate singular metric for separating ER and mitochondria-destined yeast TA

proteins. A metric focusing on an 11 residue segment of the TMD performs well when classifying

human TA proteins. The most inclusive predictor uses both hydrophobicity and C-terminal charge

in tandem. This work provides context for previous observations and opens the door for more

detailed mechanistic experiments to determine the molecular factors driving this recognition.

Keywords

co-chaperones; EMC; GET pathway; protein targeting; SND pathway; tail-anchored proteins

Correspondence:

William M. Clemons, Jr, Division of Chemistry and Chemical Engineering, California Institute of Technology,

Pasadena, CA 91125, USA. clemons@caltech.edu.

AUTHOR CONTRIBUTIONS

Michelle Y. Fry, Shyam M. Saladi and William M. Clemons designed the experiments. Michelle Y. Fry, Shyam M. Saladi, Alexandre

Cunha and William M. Clemons wrote the manuscript. Michelle Y. Fry and Shyam M. Saladi compiled the lists of putative TA

proteins in yeast and humans. Shyam M. Saladi created the code to determine the best hydrophobicity metric for classifying TA

proteins. Michelle Y. Fry performed the live yeast cell imaging experiments. Michelle Y. Fry and Alexandre Cunha wrote the code to

process the images and determine localization. All the authors have reviewed and approved the article.

CONFLICT OF INTEREST

The authors declare that they have no conflicts of interest with the contents of this article.

PEER REVIEW

The peer review history for this article is available at

https://publons.com/publon/10.1111/tra.12809

SUPPORTING INFORMATION

Additional supporting information may be found online in the Supporting Information section at the end of this article.

HHS Public Access

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1 | INTRODUCTION

Biogenesis of membrane proteins is an essential yet complicated process necessary for

maintaining cellular homeostasis. Synthesized by ribosomes in the cytosol, membrane

proteins account for approximately a third of the proteome and must be targeted to

specified membranes (reviewed in References

–

). A hydrophobic alpha-helical stretch,

often a transmembrane domain (TMD), encodes this information and its position within

an open reading frame dictates the cellular machinery responsible for its recognition and

targeting.

While computational methods have refined the ability to detect and predict

cellular localization of these integral membrane proteins over time,

the precise molecular

signals continue to be elusive. Historically, decoding known signals into detailed rules has

proven difficult given their great variation and the lack of sequence motifs–thus these signals

are often discussed at a high level, for example, hydrophobic alpha-helical stretches. Despite

the inability to define these rules, cellular chaperones accurately recognize the various

signals to sort substrates into their distinct cellular destinations.

Here, we attempt to address one class of membrane proteins, tail-anchored (TA) proteins,

found across cellular compartments and involved in a variety of roles including vesicle

trafficking, protein translocation, quality control and apoptosis (reviewed in References

–

). TA proteins are marked by a single TMD near their C-terminus and account

for approximately 2% of the genome.

–

Because of the position of their signals, TA

proteins are translated by the ribosome and then post-translationally targeted primarily to the

endoplasmic reticulum (ER) or outer mitochondrial membrane. The TMD and C-terminal

residues following have been demonstrated to be necessary and sufficient for correct

targeting in many experimental contexts.

Thus, it is suggested that the information

recognized by TA protein targeting pathways is contained within the TMD and neighboring

residues.

The recent identification of a new route for TA proteins to the ER membrane has challenged

how we previously differentiated between mitochondria and ER-bound TA proteins.

To date, while the cellular components involved in mitochondrial TA protein targeting

remain unclear, multiple overlapping pathways have been identified for TA protein targeting

to the ER membrane.

–

The first identified and most studied pathway is the

Guided

Entry of

TA protein (GET) pathway.

Consisting of six proteins, Sgt2 and Get1–5, the

GET pathway is responsible for targeting ER-bound TA proteins (“ER TA proteins” for

simplicity) with more hydrophobic TMDs. In yeast, the co-chaperone Sgt2 first captures TA

proteins from Ssa1 and, with the aid of Get4 and Get5, transfers the client to the ATPase

Get3 that acts as the central targeting factor of the pathway.

–

An ER membrane bound

Get1/2 complex facilitates disassociation of the Get3/TA complex and insertion of the TA

protein into the membrane. Recently, Guna and colleagues demonstrated that human Get3

(

Get3) fails to bind to TA proteins with relatively low hydrophobicity within their TMDs.

These proteins instead are inserted into the ER membrane by the ER Membrane Complex

(EMC).

A 10-subunit complex, the EMC inserts TA proteins delivered by calmodulin.

For TA proteins with moderately hydrophobic TMDs, both the GET pathway and EMC can

facilitate insertion. A third dedicated pathway capable of targeting TA proteins into the ER

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membrane is the SRP-independent (SND) pathway.

Snd1, the first component of the SND

pathway, interacts with the ribosome and possibly the nascent chain while the membrane

bound Snd2 and Snd3 interact with the translocon complex. In the absence of the GET

pathway, the SND pathway is capable of targeting ER TA proteins with TMDs further away

from their C-termini. These overlapping pathways, dependent on either hydrophobicity or

signal positions, highlight the diversity in these proteins and the difficulty in identifying a

common characteristic of ER-destined TMDs.

General patterns have been observed based on exploration of targeting information within

the TMD and the C-terminal residues of TA proteins. ER TA proteins tend to have more

hydrophobic TMDs

while some mitochondria TA proteins are amphipathic.

modifying the positive charge following their TMDs with an example TMD, studies have

shown how insertion by the GET pathway into the ER membrane can be impaired.

Distinction between peroxisomal and mitochondria TA proteins have been made based on

the charge of their C-terminal tails, whereas mitochondria and ER TA proteins in mammals

are differentiated by a combination of TMD hydrophobicity and C-terminal charge.

charged tail was overcome by increasing the hydrophobicity of the TMD, directing the

mitochondrial TA protein to the ER. Guna and colleagues determine a threshold in total

hydrophobicity by modifying a model TMD to delineate substrates that are inserted either

via the GET or EMC pathways.

Throughout these previous works, the ability of these rules

to separate ER vs mitochondrial TA proteins at-large has not been systematically assessed,

so their broader applicability is still unclear.

With multiple pathways with overlapping substrates, understanding the factors within

substrates recognized for targeting is critical. Here we show that formalizing previously

suggested criteria, while adequate, are not sufficient for classifying ER TA proteins with

moderately hydrophobic TMDs suggested to be substrates of the EMC insertase. We

demonstrate through computational and experimental methods that classifying TA proteins

by the presence of a hydrophobic face in their TMD is more inclusive, properly capturing

both ER TMDs with low hydrophobicity and mitochondrial TA proteins in both yeast and

humans.

2 | RESULTS

2.1 | Curating TA proteins with experimentally determined localizations

In order to screen TA proteins to identify a concise criterium for localization, we first

curated a comprehensive set of TA proteins from the yeast proteome pulling together

localizations across public repositories and publication-associated datasets. We screened

the reference yeast genome from UniProt

for putative TA proteins and filtered for unique

genes longer than 50 residues (Figure 1A). Uniprot and TOPCONS2

were used to identify

proteins with a single TMD within 30 amino acids of the C-terminus

that lacked a predicted

signal peptide (as determined by SignalP 4.1

). While this set encompasses proteins

previously predicted as TA proteins,

it is larger (95 vs 55 or 56) and we believe a more

accurate representation of the repertoire of TA proteins (Figure 1B). Based on their UniProt

annotated and Gene Ontology Cellular Components (GO CC) localizations,

TA proteins

were subcategorized as ER-bound (encompassing labels including cell membrane, Golgi

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apparatus, nucleus, lysosome and vacuole membrane and referred to as ER TA proteins),

mitochondrial (inner and outer mitochondria membrane [IMM & OMM]), peroxisomal,

and unknown (Figure 1C). This set is readily available for future analyses (Table S1). The

majority of proteins have no annotated cellular localization. Several previously identified TA

proteins are not identified by our pipeline and excluded from this new set. These proteins

include OTOA (otoancorin) that contains a predicted signal peptide, FDFT1 (squalene

synthase or SQS) with two predicted hydrophobic helices by this method, and YDL012C

which has a TMD with very low hydrophobicity.

This analysis was also applied to the

human genome and a list of 573 putative TA proteins was compiled and annotated based

on published localizations (Figure 1A-C). Like with the yeast list, the human list is larger

than previous reports (573 vs 411), and the majority of the proteins have no annotated

localization.

2.2 | Assessing current metrics for TA classification

To identify factors encoded within TA proteins that ensure correct localization, we began

by considering several posited properties including the charge following the TMD, TMD

length and TMD hydrophobicity. Previous reports suggest that the presence of positively

charged residues following the TMD of mitochondria-bound TA proteins prevents insertion

into the ER membrane.

The number of positively charged C-terminal residues for all 95

yeast proteins was calculated, avoiding issues associated with defining the extent of TMDs

by counting any charge from the center of the predicted TMD to the C-terminus. No clear

separation is observed when plotting TA proteins with known localizations by number of

positively charged residues (Figure 2A). As a metric this does a poor job distinguishing

between the two; six ER-annotated proteins have a C-terminal positive charge of three or

more and one out of the eight mitochondria-annotated proteins has no C-terminal positive

charge. Furthermore, neither negative nor net charge of the C-terminal loop separates

ER from mitochondrial TA proteins (Figure 2B,C). While modulating the C-terminal

positive charge affects localization,

cells do not solely use this signal to specify protein

localization. Considering the difference in lipid compositions of the ER and mitochondrial

membranes, a signal might be encoded in the TMD lengths, but this metric also fails to

separate the two sets (Figure 2D).

TMD hydrophobicity is the proposed localization-determining feature of TA proteins in

studies thus far.

The TM tendency scale, used here and in past studies with TA

targeting,

is a statistical hydrophobicity scale that incorporates both hydrophobicity and

helical propensity into a single value assigned to each of the 23 amino acids by using

amino acid propensities in TMDs known at the time of its creation

(Figure 2E). The

total hydrophobicity (sum of each residue’s hydrophobicity value) of a TMD sufficiently

splits ER and mitochondrial proteins but places a significant number of ER TA proteins

among mitochondrial TA proteins. In other words, the total hydrophobicity can classify GET

pathway substrates as ER-bound but fails to identify substrates of the EMC insertase that

are also ER TA proteins.

For example, the TMD of squalene synthase, a bona fide EMC

substrate,

has a lower hydrophobicity than that of model mitochondrial TA protein, Fis1

(Total TM Tendency = 12.5 vs 18.78, respectively). Limiting the hydrophobicity to a single

helix stretch, that is, 18aa, sees no improvement in classification (Figure 2F).

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To examine this inability to correctly classify lower hydrophobic ER TA proteins,

we comprehensively assess hydrophobicity across a variety of established scales

–

(Figure 2G) and then quantitatively assess predictive power using the receiver operating

characteristic (ROC) framework (for a primer, see Reference

). An ROC curve captures

how well a numerical score separates two categories, here ER vs mitochondria, and whose

figure of merit is the area under the curve (AUROC). This is a more accurate representation

of prediction than simpler numbers like accuracy and precision, which require setting a

specific threshold in a numerical score because it accounts for sensitivity and selectivity.

A perfect separation gives an AUROC of 100 whereas a random separation results in an

AUROC of 50. No matter the hydrophobicity scale used, the total hydrophobicity captures

the ER vs mitochondria split to varying extents. In each case, the mean hydrophobicity

performs more poorly, yet considering the most hydrophobic 18-residue single-helix stretch

results in a slight improvement in predictive ability suggesting that a subset of the helix can

explain recognition (Figure 2G).

2.3 | TMD residue organization better classifies TA protein localization

We wondered if TA protein classification could be improved by carefully assessing the

hydrophobicity of the TMDs. Data showing that Sgt2 (a co-chaperone in the GET pathway)

binds a TMD of a minimal length of 11 residues suggests only a subset of each helix may

be necessary to classify localization.

Indeed, the maximum hydrophobicity of segments,

specified by the number residues selected, better classifies ER vs mitochondrial TA proteins

across hydrophobicity scales (Figure 3A,B).

Furthermore, it was also reported that TMDs where the most hydrophobic residues cluster

to one side of a helical wheel plot,

a 2D representation of an alpha-helix, bind more

efficiently to Sgt2.

We sought to examine if this clustering is a feature of ER TA proteins

and absent in mitochondria TA proteins. This clustering we define as a helical wheel face

(Wheel Face) and specify a length by the number of residues selected (Figure 3A,B). We

also extend the face along the sides of the helix, defining a Patch, selecting three of the four

residues in a single turn of a helix. Patch geometries are specified by length of the segment

considered, that is, Patch 11 is confined in a 11 segment residues with 9 residues selected

(Figure 3A,B). Improvements in classification over the total hydrophobicity metric are seen

in several cases (Figure 3B, green, Figure S1B, green, Table S2). The metrics with the best

classification capability are Patch 15 (Kyte & Doolittle and TM Tendency), Wheel Face 5

(TM Tendency scale) and Patch 11 (Kyte & Doolittle scale; Figure 3B, dashed red box).

These metrics have an improved AUROC value of 96, 96, 95 and 95, respectively, compared

to the TMD hydrophobicity score of 90 (Kyte & Doolittle) and 88 (TM Tendency; Figure

3B). At the best threshold of the ROC curve, these metrics correspond to five, seven, six

and eight miscategorized proteins, respectively. A scatter plot illustrates how these metrics

translate to improved separation of ER and mitochondrial TA proteins (Figure 3C).

Other hydrophobic geometries were also explored as potential competing hypotheses:

residues in a line (every fourth residue), rectangle (one residue plus two residues two away

on either side) or star (two adjacent residues and one residue two away on either side; Figure

S1A,B). As with the Patch geometries, these geometries are specified by the length of the

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TMD considered. Again, improvements are seen in geometries that present hydrophobic

patches, that is, Rectangle 9 and Star 8, where line geometries rarely improved classification

regardless of scale used (Figure S1B). Given the relative dearth of experimental data and

the substantial number of hypotheses being tested (geometries and hydrophobicity scales),

it is difficult to definitively say if one geometry is the sole deciding factor for localization

based only on bioinformatics. Regardless of the hydrophobicity scale used, it is clear that the

organization of hydrophobic residues within a TMD is important for targeting TA proteins to

their intended membranes.

2.4 | Testing the localization of unknown TA proteins

We then tested if either face (Wheel Face or Patch), Segment, or TMD hydrophobicity

metrics enabled us to predict the localization of unknown TA proteins. To do this we

selected a subset of unknown TA proteins, whose localization would be predicted differently

by TMD and Wheel Face 5 metrics using the TM Tendency scale (Figure 3D, numbered

gray points, Table S3). This selection was made because of the strong AUROC and

biochemical data suggesting TA protein containing a helical wheel face bind more efficiently

to Sgt2. Several in this group have a hydrophobicity less than the previously suggested

cut-off for EMC substrates

(Figure 3D, lower right quadrant). Our experimental setup

based on that from Rao et al–GFP is fused N-terminally to the TMD and C-terminal residues

of the unknown TA protein (Figure 4A yellow panel). Localization is determined by overlap

with either a BFP-tagged mitochondria presequence that marks the mitochondria (Figure

4A cyan panel) and a tdTomato-tagged Sec63 acting as an ER marker (Figure 4A magenta

panel).

Overlap was determined computationally using two algorithms we developed: one

to segment individual cells in brightfield and another to determine which fluorescence probe

the GFP overlapped with on a per cell basis (Table S3).

This experimental setup and computational analysis were first applied to the known

mitochondria proteins Fis1 and Cox26.

The analysis correctly determines these

proteins to colocalize with BFP, thus correctly classifying them as mitochondria TA proteins

(Figure 4A). We then experimentally tested the 15 Unknown TA proteins where 11 localize

to the ER, three to the mitochondria, and one to another cellular compartment (Figure 4B,

Table 1). The localization of this latter TA protein cannot be determined by our experimental

setup except to say it does not clearly colocalize with the ER or mitochondria markers

visually or through our computational analysis (Table S3). The shape of the organelle is

consistent with localization to the ER-derived vacuole (Figure 4B, #17).

In total, we report

the first localization of 10 previously Unknown TA proteins.

Several datasets report protein localizations in yeast but are not yet, or partially,

integrated into bioinformatics databases like Uniprot. One in particular was of use for

this study, reporting the localizations assigned by qualitatively accessing the pattern of

protein expression in images of 17 TA proteins in the Unknown category

(Table S4).

Coincidentally, a few of these proteins were included in our experimental test set, for a

combined 27 new TA proteins with previously unknown localizations (Tables S3 and S4). Of

the TA proteins identified by Weill and colleagues, all but one, YKL044W, was confirmed

(Table S3). Given the ability to mark ER and mitochondria and quantitate colocalization on

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a per-cell basis, we use the localization determined here throughout our analysis, that is,

YKL044W localizes to the ER. Collectively, we have compiled a list of 27 TA proteins and

their localizations that have yet to be integrated into protein databases or reported: 20 ER,

six mitochondrial, and one peroxisomal.

2.5 | Reassessing classification metrics using newly determined localizations

The newly determined localizations were compared to the predicted localizations of the

best performing hydrophobicity metrics. Total hydrophobicity metrics across all scales

only correctly predict 9 or 14 of the 26 ER and mitochondria TA proteins. Experimental

localizations from this work and the Schuldiner Lab

result in a putative yeast TA protein

list with 88% having known localizations (Figure 5A). With most localizations known,

comparing metrics based on AUROC values is a good representation of the overall dataset

(Table S5). The best performing metrics were Wheel Face 7 (TM Tendency) and Wheel

Face 5 (TM Tendency), with scores of 89 and 88, respectively vs the TMD hydrophobicity

AUROC score of 76 (Table S5). These metrics correctly predicted the localization of 19

out of 26 and 17 out of 26, respectively, of the subset of our test set that localized to the

ER or mitochondria (Figure 5A,B). A Patch geometry using the Fauchere & Pliska scale

performs well when predicting new localizations–correctly predicting 18 of 26 localizations

(Figure 5A). Segment metrics performed similarly when predicting new localizations and

their AUROC values improved with the inclusion of the new localizations (Figure 5A). In

all, metrics focused on the organization of hydrophobic residues within the TMD of TA

proteins better predict TA protein localization–the best consider just a five or seven residue

face or a fraction of the TMD.

2.6 | Expanding this metric to human TA proteins

We next applied this analysis to the human genome. Using our compiled list of 573 putative

human TA proteins, we sought to identify a more inclusive set of criteria for ER- vs

mitochondria-bound TA proteins. The best performing hydrophobicity scales in the yeast

dataset were TM tendency and Kyte & Doolittle, so the other scales were not further

considered with the human dataset. While TMD hydrophobicity metrics correctly capture

mitochondria TA proteins, they fail to capture many ER TA proteins (Figure 6A, Table

S6). Quantitatively assessing all metrics, we see slight improvements in classification with

metrics using patches or segments compared to total hydrophobicity (Figure 6A,B, Table

S6). The metric with the highest AUROC score is Patch 11 (Kyte and Doolittle). Many

proteins in our dataset have a single report of their localizations in databases. There is

potential for changes to these localizations as seen with many Bcl-2 family members (Figure

6B filled blue points) where there exist multiple reports of these proteins localizing to the

ER and/or to the mitochondria. While this may be unique to these TA proteins, as their

function to regulating apoptosis is tied in with their transport between the two membranes,

some reported localizations may be the product of over-expression. Future work verifying

and determining localizations of human TA proteins will likely result in improvements in

classification by a metric derived from hydrophobic geometries.

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2.7 | Determining a two-step criterion for localization determination

We then tested if combining a hydrophobicity geometry with a C-terminal charge

metric resulted in more accurate classification of TA proteins. Costello and colleagues

demonstrated in mammals, distinctions between ER, mitochondria and peroxisomal TA

proteins can be made using a combination of charge and TMD hydrophobicity cut-offs.

They suggest mitochondria TA proteins have tails that are less charged than peroxisomal

TA proteins, but more charged than ER TA proteins, which are generally more hydrophobic

than mitochondria TA proteins. Previous reports demonstrated the GET pathway fails to

insert TA proteins with a sufficiently charged C-terminus.

This selectivity filter was seen

at the membrane and cytosolic components were unaffected by the presence of a charge.

Perhaps this rejection of TA proteins with a C-terminal charge is seen across all ER targeting

pathways in both yeast and humans. To further explore this, we determined anything to be

above the hydrophobicity cut-off to be classified as ER-bound and anything below the cut

off to be passed through a charge filter. When analyzing the number of C-terminal positive

residues following the TMD of TA proteins that fall below the hydrophobicity cut-off, we

find that a benchmark of three positive residues best separates ER and mitochondria TA

proteins–mitochondria TA proteins generally contain at least three charged residues. We

applied this secondary filter to our best performing yeast metrics (Wheel Face 5 and Wheel

Face 7 residues) and the TMD hydrophobicity (Table 1). In these cases, the three metrics

perform the same, misclassifying 10 TA proteins. Intriguingly, a Patch 15 metric does best,

correctly classifying 88% of all yeast TA proteins. A metric utilizing both a helical wheel

face and C-terminal charge does slightly better than that using TMD hydrophobicity and

charge, but the significance of that improvement is difficult to determine based on this small

dataset.

The human dataset is larger, and we sought to apply this tandem metric application to our

list of putative TA proteins (Figure 7). Similar to what was observed in the yeast dataset,

improvements in classification are seen (Table 1). Interestingly, applying a C-terminal

charge sequentially to hydrophobic metrics constrained to a fragment of ~11 residues,

either a Patch (TM tendency) or the entire segment (Kyte & Doolittle), and the TMD

hydrophobicity metric (Kyte & Doolittle), perform equally well, each misclassifying 38 TA

proteins. Most hydrophobicity metrics performed similarly with either scale, suggesting a

subset of the TMD is required for correct targeting (Table 1). It is clear that in both human

and yeast, a combination of hydrophobicity and C-terminal charge filters are necessary

for correct classification as was demonstrated in the context of the GET pathway. The

hydrophobicity window can be limited to a fraction of the TMD and still perform as well as

the entire TMD.

3 | DISCUSSION

Decoding the signaling information in membrane proteins responsible for their correct

targeting to cellular membranes is still a mystery. For the class of membrane proteins with

a single TMD and no signal peptide, TA proteins, some observations have been made to

distinguish between those destined for the ER and those destined for the mitochondria. This

report provides an extensive analysis of yeast and human TA proteins to identify a set of

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criteria to distinguish between ER- and mitochondria-bound TA proteins. This study also

includes an expansion of putative TA proteins in both humans and yeast as well as newly

determined experimental localization of several yeast TA proteins.

An initial separation by hydrophobicity can be applied to TA proteins, relegating TMDs with

high hydrophobicities as ER proteins. A secondary filter can be applied to those below the

cut-off classifying TA proteins with at least three charged residues following their TMDs

as mitochondria-bound and the rest as ER-bound (Figure 7). This sequential selectivity was

noted in the yeast GET pathway.

In this case, it was demonstrated that the cytosolic

targeting factors Sgt2 and Get3 bind to optimal TMDs based on a combination of high

hydrophobicity and helical propensity. Regardless of hydrophobicity, TA proteins containing

a charged C-termini were not inserted into ER microsomes. The analysis here demonstrates

that generally ER TA proteins, not just GET substrates, lack charges in their C-terminus.

When determining the effectiveness of a hydrophobicity metric alone, metrics that focus on

a hydrophobic geometry, a hydrophobic face in yeast and a hydrophobic segment restricted

to 11 to 19 residues in humans, perform better than the hydrophobicity of the entire TMD.

Applying the charge filter reveals that total hydrophobicity is as effective as hydrophobic

face or segment metrics. Differences in the best performing hydrophobicity metrics between

the yeast and human dataset could be explained by the observation that SGTA is more

permissive to client binding than Sgt2.

Collectively, these datasets demonstrate that a

fraction of the TMD is necessary and sufficient for correct localization. Interestingly, in the

human dataset, some of the best performing metrics are limited to an 11-residue window,

concurring with reports that SGTA recognizes TMDs of at least 11 amino acids.

While biochemical data suggested that clustering hydrophobic residues to one side of a helix

increased binding to Sgt2, a co-chaperone in an ER TA protein targeting pathway, a cellular

role of this hydrophobic face remained unclear.

From the bioinformatic analysis and

experimental localization data presented here, we demonstrate most yeast ER TA proteins

contain a hydrophobic face–made of five to seven adjacent residues along a helical wheel

plot. The two components of the GET pathways that directly bind to TA proteins, Sgt2 and

Get3, both have binding sites composed of a hydrophobic groove. One could imagine the

hydrophobic face in clients buried in the hydrophobic groove of Sgt2 and Get3, enhancing

the hydrophobic binding interactions. Perhaps cellular factors involved in targeting TA

proteins to the ER recognize this face and future identified ER TA protein binding partners

will also feature a helical hand for client binding.

In this work, we provide a comprehensive bioinformatics analysis of naturally occurring

TA proteins in the yeast and human genomes. While comprehensive, subtle differences in

each metric’s geometries and hydrophobic scales cannot easily be differentiated analyzing

just wild-type proteins. Similar work has helped disentangle the positional dependence of

hydrophobicity in the insertion of integral membrane proteins.

Likewise, future work

could better define the geometry and hydrophobic scale needed for TA targeting by larger

scale mutational analyses, perhaps even transforming the question of TA targeting into that

of sequence selection/enrichment.

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The targeting of TA proteins presents an intriguing and enigmatic problem for understanding

the biogenesis of this important class of proteins. How subtle differences in clients modulate

the interplay of hand-offs that direct these proteins to the correct membrane remains to

be understood. Through in vivo imaging of yeast cells and computational analysis, we

provide more clarity to client discrimination. A major outcome of this is the clear preference

for a hydrophobic face on ER TA proteins of low hydrophobicity. In yeast, this alone

is sufficient to predict the destination of a TA protein. In mammals, and likely more

broadly in metazoans, while clearly an important component, alone the hydrophobic face

cannot fully discriminate targets. For a full understanding, we expect other factors to

contribute, reflective of the increased complexity of higher eukaryotes, perhaps involving

more players.

4 | METHODS

4.1 | Assembling a database of putative TA proteins and their TMDs

Proteins identified from UniProt

containing a single TMD within 30 residues of the

C-terminus were separated into groups based on their localization reported in UniProt. The

topology of all proteins with 3 TMs or fewer was further analyzed using TOPCONS

to avoid missed single-pass TM proteins. Proteins with a predicted signal peptide,

annotated transit peptide, problematic cautions, or with a length less than 50 or greater

than 1000 residues were excluded. Proteins localized to the ER, golgi apparatus, nucleus,

endosome, lysosome and cell membrane were classified as ER-bound, those localized to the

outer mitochondrial membrane were classified as mitochondria-bound, those localized to the

peroxisome were classified as peroxisomal proteins, and those with unknown localization

were classified as unknown. Proteins with a compositional bias overlapping with the

predicted TMD were also excluded. A handful of proteins and their inferred localizations

were manually corrected or removed (see notebook and Table S1).

4.2 | Assessing the predictive power of various hydrophobicity metrics

We thoroughly examined the metrics relating hydrophobicity, both published and by our

own exploration, to better understand their relationship to protein localization. Notably, we

recognized that a TMD’s hydrophobic moment (μ

)

was a poor predictor of localization,

for example, although a Leu

helix is extremely hydrophobic, it has (μ

) = 0 since

opposing hydrophobic residues are penalized in this metric. To address this, we define

a metric that capture the presence of a hydrophobic face of the TMD: the maximally

hydrophobic cluster on the face. For this metric, we sum the hydrophobicity of residues

that orient sequentially on one side of a helix when visualized in a helical wheel diagram.

While a range of hydrophobicity scales were predictive using this metric, we selected the

TM Tendency scale

to characterize the TMDs of putative TA proteins and determined the

most predictive window by assessing a range of lengths from 4 to 12 (this would vary from

three turns of a helix to six).

By considering sequences with inferred ER or mitochondrial localizations, we calculated the

Area Under the Curve of a Receiver Operating Characteristic (AUROC) to assess predictive

power. As we are comparing a real-valued metric (hydrophobicity) to a 2-class prediction,

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the AUROC is better suited for this analysis over others like accuracy or precision (a

primer

). Because of many fewer mitochondrial proteins (ie, a class imbalance), we also

confirmed that ordering hydrophobicity metrics by AUROC was consistent with the ordering

produced by the more robust, but less common, Average Precision (see notebook).

4.3 | Constructing plasmids for live cell imaging

A p416ADH-GFP-Fis1 plasmid and a mt-TagBFP described in Rao et al were gifted to us

from the Walter lab, UCSF

and a Sec63-tdtomato was a gift from Sebastian Schuck,

ZMVH, Universitat Heidelberg. TMDs sequences were ordered from Twist Biosciences (San

Francisco, CA) with flanking HindIII and XhoI sites. GFP-TMD constructs were made

by restriction enzyme digestion (New England Biolabs, USA) of the p416ADH-GFP-Fis1

plasmid and the genes ordered from Twist Biosciences followed by T4 DNA (New England

Biolabs, USA) ligation of the template and TMD fragments.

4.4 | Live cell imaging

The yeast strain used are those described in Rao et al, also a gift from the Walter Lab,

UCSF. Strains containing each GFP fused TMD were grown in appropriate selection media.

Coverslips were prepped by coating with 0.1 mg/mL concavalin A (Sigma, USA) in 0.9%

NaCl solution. Cells were immobilized on coverslips at a concentration of 5000 cells/mm

(plates at 1.8 cm

, thus 9 × 10

cells/well) and imaged using a Nikon LSM800 (Nikon,

Japan). Images were collected at wavelengths 488, 514 and 581 nm and were processed with

ImageJ

and two in-house image processing algorithms.

4.5 | Image processing to determine localization

Yeast cells were segmented using deep learning-based tools. The variable pattern of DIC

images with mixed low and high contrasts for back-grounds and cell bodies (signal variance

of each whole image ranging from 67.4 to 2706.3, a ×40 difference–average, median and SD

of signal variance for all images were, respectively 645.6, 563.8 and 419.1) prevented using

classical gradient based methods to successfully segment cells. We adopted and compared

two contemporary tools, YeastSpotter, a Mask-RCNN method dedicated to yeast cells,

and

Cellpose, a generalist method trained on a large pool of cell images.

Note that, the former

was not trained on yeast cell images but used a model pretrained on a larger set of other cell

images to build a friendly tool for yeast cell segmentation. Cellpose is a more sophisticated

tool whose pretrained models have learned to segment well based on a myriad of intensity

gradient values and image styles. It has shown to achieve high quality segmentation on an

extended variety of cell images, including in our yeast cells images, producing superior

results when compared to YeastSpotter with the advantage of running faster on GPUs (tested

on Nvidia RTX 2080 Ti). We thus exclusively used Cellpose with its

cyto

pretrained model

to segment yeast cells in all our DIC images. We used maximum intensity projections of

up to two or three slices per image stack but mostly a single slice was sufficient to create a

single representative image for segmentation. Spurious, tiny, segmented regions whose size

were shown to be outliers were automatically removed using an area opening morphological

operation.

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Individual cells were isolated by applying the mask to the corresponding florescent images

of each of the three wavelengths. Masks less than 7.5 μm

corresponded to incorrectly

identified, incomplete, or out-of-plane cells and were omitted from analysis. Masks were

applied to each florescence channel. An empirical threshold was applied to each channel to

identify true florescence from background, and the percentage of each cell with co-localized

GFP and BFP or GFP and tdTomato was then calculated. Localization was then determined

identifying which pair of channels (GFP&BFP vs GFP&tdTomato) had greater overlap,

that is, Overlap

GFP&BFP

> Overlap

GFP&tdTomato

resulted in a mitochondria annotation. The

number of individual cells in each category were counted. Outputs from this algorithm were

verified by manually inspecting individual images.

Supplementary Material

Refer to Web version on PubMed Central for supplementary material.

ACKNOWLEDGMENTS

The authors thank members of the Clemons lab for support and discussion. The authors would also like to thank

the Caltech Biological Imaging center for the use of their microscopes for our live cell imaging as well as the

Caltech Center for Advanced Methods in Biological Image Analysis for aiding in the isolation of individual cells

in our images. This work was supported by National Institutes of Health (NIH) Grant R01GM097572 (to William

M. Clemons), NIH/National Research Service Award Training Grant 5T32GM07616 (to Shyam M. Saladi and

Michelle Y. Fry) and a National Science Foundation Graduate Research fellowship under Grant 1144469 (to Shyam

M. Saladi).

Funding information

National Science Foundation Graduate Research, Grant/Award Number: 1144469; NIH/National Research Service

Award Training, Grant/Award Number: 5T32GM07616; National Institutes of Health, Grant/Award Number:

R01GM097572

DATA AVAILABILITY STATEMENT

All code employed is available openly at

github.com/clemlab/sgt2a-modeling

with analysis

done in Jupyter Lab/Notebooks using Python 3.6 enabled by Numpy, Pandas, Scikit-Learn,

BioPython, bebi103,

and Bokeh as well as in Rstudio/Rmarkdown Notebooks enabled by

packages within the Tidyverse ecosystem.

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FIGURE 1.

Compiling a list of TA proteins from the human and yeast genomes. (A) A schematic of

the pipeline used to gather TA proteins by filtering the Human and Yeast proteomes for TA

proteins. (B) A comparison of the TA proteins collected for the analyses here vs previous

datasets. (C) Localizations gathered from Uniprot entry Subcellular Localizations (CC) and

Gene Ontology Cellular Compartment (GO) annotations. Those with conflicts were resolved

by manually parsing the literature to build the final set

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FIGURE 2.

Investigating properties encoded in the C-terminal residues of TA proteins. For A-F,

Jitter plots of property distribution for predicted TA proteins identified as ER (green) or

mitochondria (purple) with the best predictive threshold indicated by a dashed red line.

Properties visualized are for the C-terminal number of (A) positive residues, (B) negative

residues, and (C) net charge and then for (D) TMD length, (E) TMD hydrophobicity, and

(F) maximum hydrophobicity of an 18-residue stretch. (G) The AUROC across various

hydrophobicity scales for the mean, total, and 18-residue windows of the predicted TMDs

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FIGURE 3.

Analyzing different geometries of hydrophobic residues in TMDs to improve classification.

(A) Alpha-helices and helical wheel plots illustrating the residues selected (orange) for each

metric tested, patch, wheel face and segment, showing residues selected and not selected

(blue) in each analysis. (B) AUROC values for the metrics illustrated in (A) and total

hydrophobicity. (C) Jitter plots as in Figure 2 for the top four hydrophobic metrics: Patch

15 (Kyte & Doolittle scale), Patch 15 (TM Tendency scale), Wheel Face 5 (TM Tendency

scale) and Patch 11 (Kyte & Doolittle scale). Red dashed line indicates the best predictive

threshold. (D) 2D comparison plot of total hydrophobicity (

y-axis

) and a Wheel Face 5 (TM

Tendency scale) (

-axis). TA proteins are colored by localization, ER (

green

), mitochondria

(

purple

), Unknown (

gray

), both mitochondria and ER (

blue

), and peroxisome (

orange

TA proteins selected for experimental determination of localizations are marked squares.

Dashed lines indicate best predictive threshold

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