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

Adapted from Fry MY*, Saladi SM*, Cunha A, Clemons WM. 2021b. Sequence-based features that are determinant for tail-anchored membrane protein sorting in eukaryotes. Traffic 22:306–318. doi:10.1111/tra.12809

4.1 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

4.2 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 (Fry and Clemons, 2018; Guna and Hegde, 2018; Krogh et al., 2001)). 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 (Guna and Hegde, 2018). While computational methods have refined the ability to detect and predict cellular localization of these integral membrane proteins over time (Almagro Armenteros et al., 2017), 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 (Borgese et al., 2003; Chartron et al., 2012; Fry and Clemons, 2018; Rabu et al., 2009)). TA proteins are marked by a single TMD near their C-terminus and account for approximately 2% of the genome (Chartron et al., 2012; Denic, 2012; Kutay et al., 1993; Wattenberg and Lithgow, 2001). 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 (Lin et al., 2021; Wang et al., 2010). 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 (Guna and Hegde, 2018; Rao et al., 2016; Wattenberg and Lithgow, 2001). 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 (Aviram et al., 2016; Chartron et al., 2012; Fry and Clemons, 2018; Guna et al., 2018; Schuldiner et al., 2008; Stefanovic and Hegde, 2007). The first identified and most studied pathway is the Guided Entry of TA protein (GET) pathway (Schuldiner et al., 2008; Stefanovic and Hegde, 2007). 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 (Chio et al., 2017; Cho and Shan, 2018; Guna and Hegde, 2018; Shao and Hegde, 2011a, 2011b). 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 (HsGet3) 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) (Guna et al., 2018). 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 membrane is the SRP-independent (SND) pathway (Aviram et al., 2016). 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 (Aviram et al., 2016).

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 (Chitwood et al., 2018; Guna et al., 2018; Rao et al., 2016; Wattenberg and Lithgow, 2001) while some mitochondria TA proteins are amphipathic (Wattenberg and Lithgow, 2001). By 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 (Figueiredo Costa et al., 2018; Rao et al., 2016). 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 (Costello et al., 2017). A 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 (Guna et al., 2018). 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.

4.3 Results

4.3.1 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 (UniProt Consortium, 2021) for putative TA proteins and filtered for unique genes longer than 50 residues (Figure 4.1A). Uniprot and TOPCONS2 (Tsirigos et al., 2015) were used to identify proteins with a single TMD within 30 amino acids of the C-terminus (Borgese et al., 2003) that lacked a predicted signal peptide (as determined by SignalP 4.1 (Nielsen, 2017)). While this set encompasses proteins previously predicted as TA proteins (Beilharz et al., 2003; Kalbfleisch et al., 2007), it is larger (95 vs 55 or 56) and we believe a more accurate representation of the repertoire of TA proteins (Figure 4.1A). Based on their UniProt-annotated and Gene Ontology Cellular Components (GO CC) localizations (Ashburner et al., 2000; Gene Ontology Consortium, 2021), TA proteins were subcategorized as ER-bound (encompassing labels including cell membrane, Golgi 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 4.1C). This set is readily available for future analyses (Table 4.2). 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 (Beilharz et al., 2003; Guna et al., 2018). 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 4.1A-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.

Figure 4.1: 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.

4.3.2 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 (Figueiredo Costa et al., 2018; Rao et al., 2016). 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 4.2A). 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 4.2B,C). While modulating the C-terminal positive charge affects localization (Rao et al., 2016), 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 4.2D).

Figure 4.2: 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.

TMD hydrophobicity is the proposed localization-determining feature of TA proteins in studies thus far (Guna and Hegde, 2018; Rao et al., 2016; Wattenberg and Lithgow, 2001). The TM tendency scale, used here and in past studies with TA targeting (Guna et al., 2018; Guna and Hegde, 2018), 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 (Zhao and London, 2006) (Figure 4.2E). 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 (Guna et al., 2018). For example, the TMD of squalene synthase, a bona fide EMC substrate (Guna et al., 2018), 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 4.2F).

To examine this inability to correctly classify lower hydrophobic ER TA proteins, we comprehensively assess hydrophobicity across a variety of established scales (Eisenberg et al., 1984; Fauchere and Pliska, 1983; Roseman, 1988; Wimley et al., 1996; Zhao and London, 2006) (Figure 4.2G) and then quantitatively assess predictive power using the receiver operating characteristic (ROC) framework (for a primer, see Reference (Swets et al., 2000)). 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 4.2G).

4.3.3 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 (Lin et al., 2021). Indeed, the maximum hydrophobicity of segments, specified by the number residues selected, better classifies ER vs mitochondrial TA proteins across hydrophobicity scales (Figure 4.3A,B).

Furthermore, it was also reported that TMDs where the most hydrophobic residues cluster to one side of a helical wheel plot (Schiffer and Edmundson, 1967), a 2D representation of an alpha-helix, bind more efficiently to Sgt2 (Lin et al., 2021). 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 4.3A,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 4.3A,B). Improvements in classification over the total hydrophobicity metric are seen in several cases (Figure 4.3B, green, Supplementary Figure 4.1B, green, Table 4.3). 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 4.3B, 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 4.3B). 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 4.3C).

Figure 4.3: 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 4.2 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) (x-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.

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; Supplementary Figure 4.1A,B). As with the Patch geometries, these geometries are specified by the length of the 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 (Supplementary Figure 4.1B). 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.

4.3.4 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 4.3D, numbered gray points, Table 4.4). 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 (Guna et al., 2018) (Figure 4.3D, 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 4.4A yellow panel). Localization is determined by overlap with either a BFP-tagged mitochondria presequence that marks the mitochondria (Figure 4.4A cyan panel) and a tdTomato-tagged Sec63 acting as an ER marker (Figure 4.4A magenta panel).(Rao et al., 2016) 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 4.4).

Figure 4.4: Localization of unknown yeast TA proteins. The ER (magenta panel) and mitochondria (cyan panel) were labeled with tdTomato and BFP, respectively. TA protein localization was visualized by GFP (yellow panel) and colocalization was determined by overlap (merge panel). The ratio of the number of cells with the TA protein localizing to the ER vs the mitochondria are noted in the merge image. Numbered as in Figure 4.3D with labels colored based on their determined localizations: ER (green) and mitochondria (purple). TA proteins include (A) two mitochondrial TA proteins with known localizations and (B) 15 with unknown localizations.

This experimental setup and computational analysis were first applied to the known mitochondria proteins Fis1 and Cox26 (Hartley et al., 2019; Levchenko et al., 2016; Rao et al., 2016). The analysis correctly determines these proteins to colocalize with BFP, thus correctly classifying them as mitochondria TA proteins (Figure 4.4A). 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 4.4B, Table 4.1). 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 4.4). The shape of the organelle is consistent with localization to the ER-derived vacuole (Figure 4.4B, #17) (Vida and Emr, 1995). 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 (Weill et al., 2018) (Table 4.5). Coincidentally, a few of these proteins were included in our experimental test set, for a combined 27 new TA proteins with previously unknown localizations (Table 4.4 and Table 4.5). Of the TA proteins identified by Weill et al. (2018), all but one, YKL044W, was confirmed (Table 4.4). Given the ability to mark ER and mitochondria and quantitate colocalization on 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.

4.3.5 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 (Weill et al., 2018) result in a putative yeast TA protein list with 88% having known localizations (Figure 4.5A). With most localizations known, comparing metrics based on AUROC values is a good representation of the overall dataset (Table 4.6). 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 4.6). 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 4.5A,B). A Patch geometry using the Fauchere & Pliska scale performs well when predicting new localizations–correctly predicting 18 of 26 localizations (Figure 4.5A). Segment metrics performed similarly when predicting new localizations and their AUROC values improved with the inclusion of the new localizations (Figure 4.5A). 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.

Figure 4.5: A hydrophobic Wheel Face metric of 5 or 7 residues best separates ER and mitochondria TA proteins. (A) A ranking of the five best performing hydrophobicity metrics compared to the TMD hydrophobicity metrics of the appropriate hydrophobicity scales (TM Tendency, Fauchere & Pliska and Kyte and Doolittle). The number of correctly predicted localizations as well as the final AUROC scores are used to assess the effectiveness of each metric. The total number of correctly classified yeast TA proteins is also noted. The two metrics directly compared in the 2D comparison plot in (B) are highlighted in blue (TM Tendency, Wheel Face 5, x-axis) and red (TM Tendency, TMD, y-axis). Hydrophobicities are plotted and TA proteins are colored as they were in Figure 4.3D. Newly determined localizations from Figure 4.4 (black outlined) and Weill et al. (2018) (squares) are filled in with the appropriate colors, ER (green), mitochondria (purple) and peroxisome (orange)

4.3.6 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 4.6A, Table 4.7). Quantitatively assessing all metrics, we see slight improvements in classification with metrics using patches or segments compared to total hydrophobicity (Figure 4.6A,B; Table 4.7). 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 4.6B 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.

Figure 4.6: Human ER and mitochondrial TA proteins can be separated by the most hydrophobic 11 residues segment. (A) A table of the with the AUROC values of the best performing hydrophobicity metrics and the overall TMD hydrophobicity, along with their ranking. The number of total misclassified proteins are separated by ER and mitochondria TA proteins. (B) 2D comparison for the human dataset of TMD hydrophobicity and Patch 11 metrics using the Kyte and Doolittle scale. Hydrophobicities are plotted and TA proteins are colored as in Figure 4.3D. Unknown TA proteins are not plotted.

4.3.7 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 (Costello et al., 2017). 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 (Rao et al., 2016). 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 4.1). 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.

i	Metric	Scale	Organism	Correct (%)	Misclassified mito	Misclassified ER	Miclassified total
1	Patch 11	TM Tendency	H. sapiens	82	26	12	38
2	TMD	Kyte & Doolittle	H. sapiens	82	33	5	38
3	Segment 19	Kyte & Doolittle	H. sapiens	81	29	9	38
4	Segment 11	TM Tendency	H. sapiens	81	27	12	39
5	Segment 15	TM Tendency	H. sapiens	81	25	14	39
6	Wheel face 9	TM Tendency	H. sapiens	81	26	13	39
7	Wheel face 7	TM Tendency	H. sapiens	81	27	13	40
8	Segment 11	Kyte & Doolittle	H. sapiens	81	28	12	40
9	Segment 15	Kyte & Doolittle	H. sapiens	81	30	10	40
10	Patch 19	TM Tendency	H. sapiens	80	27	14	41
11	Patch 15	TM Tendency	H. sapiens	80	30	11	41
12	Segment 19	TM Tendency	H. sapiens	80	26	15	41
13	Patch 19	Kyte & Doolittle	H. sapiens	80	30	12	42
14	Patch 11	Kyte & Doolittle	H. sapiens	79	28	15	43
15	Patch 15	Kyte & Doolittle	H. sapiens	78	28	17	45
16	TMD	TM Tendency	H. sapiens	78	31	14	45
17	Wheel face 5	TM Tendency	H. sapiens	77	28	19	47
18	Patch 15	TM Tendency	S. cerevisiae	88	7	2	9
19	Wheel face 5	TM Tendency	S. cerevisiae	87	6	4	10
20	Wheel face 7	TM Tendency	S. cerevisiae	87	6	4	10
21	Patch 11	TM Tendency	S. cerevisiae	87	6	4	10
22	TMD	TM Tendency	S. cerevisiae	87	6	4	10
23	Segment 15	TM Tendency	S. cerevisiae	86	6	5	11
24	Segment 19	TM Tendency	S. cerevisiae	86	6	5	11
25	Patch 19	TM Tendency	S. cerevisiae	84	7	5	12
26	Segment 11	TM Tendency	S. cerevisiae	83	6	7	13

Table 4.1: Best performing hydrophobicity metrics when combined with charge are those restricted to shorter segments of a helix in humans. A ranked comparison of the best performing hydrophobicity metric when combined with a C-terminal charge cut-off for both human and yeast TA proteins.

The human dataset is larger, and we sought to apply this tandem metric application to our list of putative TA proteins (Figure 4.7). Similar to what was observed in the yeast dataset, improvements in classification are seen (Table 4.1). 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 4.1). 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.

Figure 4.7: Combining a hydrophobicity and C-terminal charge metric results in a more effective predictor. The most hydrophobic 11 amino acid segment of all human TA protein TMDs with known localizations to either the ER (green) or mitochondria (purple) was calculated using the Kyte and Doolittle scale and plotted along the x-axis. The number of positive charge residues was counted and plotted along the y-axis. The best fit cut-off for the hydrophobicity metric (blue dotted line) and charge metric (red dotted line) are marked. The number of ER and mitochondria TA proteins captured in each step is denoted in the corresponding quadrant.

4.4 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 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 4.7). This sequential selectivity was noted in the yeast GET pathway (Rao et al., 2016). 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 (Lin et al., 2021). 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 (Lin et al., 2021).

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 (Lin et al., 2021). 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 (Hessa et al., 2007). 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 (Fowler and Fields, 2014).

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 (Aviram et al., 2016).

4.5 Material and Methods

4.5.1 Assembling a database of putative TA proteins and their TMDs

Proteins identified from UniProt (UniProt Consortium, 2021) 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 (Tsirigos et al., 2015) to avoid missed single-pass TM proteins. Proteins with a predicted signal peptide (Nielsen, 2017), an 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 4.2).

4.5.2 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 (\(\mu\text{H}\)) (Eisenberg et al., 1984) was a poor predictor of localization, for example, although a Leu₁₈ helix is extremely hydrophobic, it has (\(\mu\text{H}\)) = 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 (Zhao and London, 2006) 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, the AUROC is better suited for this analysis over others like accuracy or precision (a primer (Swets et al., 2000)). 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).

4.5.3 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 (Okreglak and Walter, 2014; Rao et al., 2016) 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.

4.5.4 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/mm² (plates at 1.8 cm², thus 9 × 10⁸ 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 (Schneider et al., 2012) and two in-house image processing algorithms.

4.5.5 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 (Lu et al., 2019), and Cellpose, a generalist method trained on a large pool of cell images (Stringer et al., 2021). 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.

Individual cells were isolated by applying the mask to the corresponding florescent images of each of the three wavelengths. Masks less than \(7.5 \mu\text{m}^2\) 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.

4.5.6 Data Availability

All code employed is available openly at http://github.com/clemlab/sgt2a-modeling with analysis done in Jupyter Lab/Notebooks using Python 3.6 enabled by Numpy, Pandas, Scikit-Learn, BioPython, bebi103 (Bois, 2020), and Bokeh as well as in Rstudio/Rmarkdown Notebooks enabled by packages within the Tidyverse ecosystem.

4.6 Supplementary Figure

Supplementary Figure 4.1: Alternative geometries of hydrophobic residues in TMDs tested for improved classification. (A) Alpha-helices and helical wheel plots to illustrate the residues used for each metric tested, patch, wheel face, and segment, showing residues selected (orange) and not selected (blue) in each analysis. (B) AUROC values for the metrics illustrated in (A) and total hydrophobicity.

4.7 Supplementary Tables

i	Organism	Entry	Gene names	Localization
1	H. sapiens	Q16611	BAK1 BAK BCL2L7 CDN1	Both
2	H. sapiens	P10415	BCL2	Both
3	H. sapiens	O60238	BNIP3L BNIP3A BNIP3H NIX	Both
4	H. sapiens	P26678	PLN PLB	Both
5	H. sapiens	Q12981	BNIP1 NIP1 SEC20L TRG8	Both
6	H. sapiens	Q09013	DMPK DM1PK MDPK	Both
7	H. sapiens	Q969F0	FATE1 FATE	Both
8	H. sapiens	Q07812	BAX BCL2L4	Both
9	H. sapiens	Q07817	BCL2L1 BCL2L BCLX	Both
10	H. sapiens	Q9HD36	BCL2L10 BCLB	Both
11	H. sapiens	Q12983	BNIP3 NIP3	Both
12	H. sapiens	Q9UMX3	BOK BCL2L9	Both
13	H. sapiens	Q9BWH2	FUNDC2 HCBP6 DC44 HCC3 PD03104	Both
14	H. sapiens	Q07820	MCL1 BCL2L3	Both
15	H. sapiens	Q86Y07	VRK2	Both
16	H. sapiens	P23763	VAMP1 SYB1	Both
17	H. sapiens	A0A0A0MTJ1	FKBP8	Both
18	H. sapiens	I3L3X5	PLSCR3	Both
19	H. sapiens	P18031	PTPN1 PTP1B	ER
20	H. sapiens	Q13323	BIK NBK	ER
21	H. sapiens	Q9H305	CDIP1 C16orf5 CDIP LITAFL	ER
22	H. sapiens	Q5VV42	CDKAL1	ER
23	H. sapiens	A4D256	CDC14C CDC14B2 CDC14Bretro	ER
24	H. sapiens	Q96JN2	CCDC136 KIAA1793 NAG6	ER
25	H. sapiens	Q9NXE4	SMPD4 KIAA1418 SKNY	ER
26	H. sapiens	Q9H0X9	OSBPL5 KIAA1534 OBPH1 ORP5	ER
27	H. sapiens	Q9BZF1	OSBPL8 KIAA1451 ORP8 OSBP10	ER
28	H. sapiens	Q9NZM1	MYOF FER1L3 KIAA1207	ER
29	H. sapiens	Q9HC10	OTOF FER1L2	ER
30	H. sapiens	Q9HCU5	PREB SEC12	ER
31	H. sapiens	O15162	PLSCR1	ER
32	H. sapiens	Q9NRQ2	PLSCR4 GIG43	ER
33	H. sapiens	A0PG75	PLSCR5	ER
34	H. sapiens	Q9NRY7	PLSCR2	ER
35	H. sapiens	P50876	RNF144A KIAA0161 RNF144 UBCE7IP4	ER
36	H. sapiens	Q9NZ42	PSENEN PEN2 MDS033	ER
37	H. sapiens	Q96CS7	PLEKHB2 EVT2	ER
38	H. sapiens	Q6ZNB6	NFXL1 OZFP	ER
39	H. sapiens	P17706	PTPN2 PTPT	ER
40	H. sapiens	Q7Z6L0	PRRT2	ER
41	H. sapiens	Q9Y6X1	SERP1 RAMP4	ER
42	H. sapiens	P61266	STX1B STX1B1 STX1B2	ER
43	H. sapiens	Q9UNK0	STX8	ER
44	H. sapiens	Q16623	STX1A STX1	ER
45	H. sapiens	Q8WXE9	STON2 STN2 STNB	ER
46	H. sapiens	Q86Y82	STX12	ER
47	H. sapiens	Q7Z699	SPRED1	ER
48	H. sapiens	Q9P2W9	STX18 GIG9	ER
49	H. sapiens	Q13190	STX5 STX5A	ER
50	H. sapiens	O15400	STX7	ER
51	H. sapiens	P32856	STX2 EPIM STX2A STX2B STX2C	ER
52	H. sapiens	O60499	STX10 SYN10	ER
53	H. sapiens	Q13277	STX3 STX3A	ER
54	H. sapiens	O14662	STX16	ER
55	H. sapiens	Q5QGT7	RTP2 Z3CXXC2	ER
56	H. sapiens	P59025	RTP1 Z3CXXC1	ER
57	H. sapiens	Q9BQQ7	RTP3 TMEM7 Z3CXXC3	ER
58	H. sapiens	Q8N205	SYNE4 C19orf46	ER
59	H. sapiens	Q6ZMZ3	SYNE3 C14orf139 C14orf49 LINC00341	ER
60	H. sapiens	B2RUZ4	SMIM1	ER
61	H. sapiens	Q7Z698	SPRED2	ER
62	H. sapiens	Q96DX8	RTP4 IFRG28 Z3CXXC4	ER
63	H. sapiens	Q96QK8	SMIM14 C4orf34	ER
64	H. sapiens	Q14BN4	SLMAP KIAA1601 SLAP UNQ1847/PRO3577	ER
65	H. sapiens	Q86T96	RNF180	ER
66	H. sapiens	Q8N8N0	RNF152	ER
67	H. sapiens	Q12846	STX4 STX4A	ER
68	H. sapiens	O43752	STX6	ER
69	H. sapiens	P60059	SEC61G	ER
70	H. sapiens	Q14D33	RTP5 C2orf85 CXXC11 Z3CXXC5	ER
71	H. sapiens	P60468	SEC61B	ER
72	H. sapiens	Q8N6R1	SERP2 C13orf21	ER
73	H. sapiens	P01850	TRBC1	ER
74	H. sapiens	P03986	TRGC2 TCRGC2	ER
75	H. sapiens	Q629K1	TRIQK C8orf83	ER
76	H. sapiens	A0A5B9	TRBC2 TCRBC2	ER
77	H. sapiens	Q9NSU2	TREX1	ER
78	H. sapiens	P01848	TRAC TCRA	ER
79	H. sapiens	B7Z8K6	TRDC	ER
80	H. sapiens	Q96D59	RNF183	ER
81	H. sapiens	P00167	CYB5A CYB5	ER
82	H. sapiens	O75923	DYSF FER1L1	ER
83	H. sapiens	P50402	EMD EDMD STA	ER
84	H. sapiens	O42043	ERVK-18	ER
85	H. sapiens	Q52LJ0	FAM98B	ER
86	H. sapiens	Q9NYM9	BET1L GS15	ER
87	H. sapiens	P54710	FXYD2 ATP1C ATP1G1	ER
88	H. sapiens	O95415	BRI3	ER
89	H. sapiens	Q9BXU9	CALN1 CABP8	ER
90	H. sapiens	O15155	BET1	ER
91	H. sapiens	Q86V35	CABP7 CALN2	ER
92	H. sapiens	Q01740	FMO1	ER
93	H. sapiens	P49326	FMO5	ER
94	H. sapiens	P31513	FMO3	ER
95	H. sapiens	Q8N8J7	FAM241A C4orf32	ER
96	H. sapiens	P31512	FMO4 FMO2	ER
97	H. sapiens	Q9P0K9	FRRS1L C9orf4	ER
98	H. sapiens	Q9Y2H6	FNDC3A FNDC3 HUGO KIAA0970	ER
99	H. sapiens	P13164	IFITM1 CD225 IFI17	ER
100	H. sapiens	Q01629	IFITM2	ER
101	H. sapiens	Q01628	IFITM3	ER
102	H. sapiens	Q8TBA6	GOLGA5 RETII RFG5 PIG31	ER
103	H. sapiens	O95249	GOSR1 GS28	ER
104	H. sapiens	Q14789	GOLGB1	ER
105	H. sapiens	Q96JJ6	JPH4 JPHL1 KIAA1831	ER
106	H. sapiens	Q9HDC5	JPH1 JP1	ER
107	H. sapiens	Q9BR39	JPH2 JP2	ER
108	H. sapiens	Q8WXH2	JPH3 JP3 TNRC22	ER
109	H. sapiens	Q99732	LITAF PIG7 SIMPLE	ER
110	H. sapiens	O75427	LRCH4 LRN LRRN1 LRRN4	ER
111	H. sapiens	Q9Y2L9	LRCH1 CHDC1 KIAA1016	ER
112	H. sapiens	Q3KP22	MAJIN C11orf85	ER
113	H. sapiens	Q86Z14	KLB	ER
114	H. sapiens	Q9Y6H6	KCNE3	ER
115	H. sapiens	P42167	TMPO LAP2	ER
116	H. sapiens	P30519	HMOX2 HO2	ER
117	H. sapiens	Q8WWP7	GIMAP1 IMAP1	ER
118	H. sapiens	Q96F15	GIMAP5 IAN4L1 IAN5 IMAP3	ER
119	H. sapiens	P09601	HMOX1 HO HO1	ER
120	H. sapiens	Q9UPX6	MINAR1 KIAA1024	ER
121	H. sapiens	Q8NHP6	MOSPD2	ER
122	H. sapiens	P51648	ALDH3A2 ALDH10 FALDH	ER
123	H. sapiens	Q8N2K1	UBE2J2 NCUBE2	ER
124	H. sapiens	O94966	USP19 KIAA0891 ZMYND9	ER
125	H. sapiens	Q9NZ43	USE1 USE1L MDS032	ER
126	H. sapiens	Q9P0L0	VAPA VAP33	ER
127	H. sapiens	O95159	ZFPL1	ER
128	H. sapiens	Q5T7W0	ZNF618 KIAA1952	ER
129	H. sapiens	O14653	GOSR2 GS27	ER
130	H. sapiens	P51809	VAMP7 SYBL1	ER
131	H. sapiens	Q9BV40	VAMP8	ER
132	H. sapiens	Q9UEU0	VTI1B VTI1 VTI1L VTI1L1 VTI2	ER
133	H. sapiens	Q96AJ9	VTI1A	ER
134	H. sapiens	O95292	VAPB UNQ484/PRO983	ER
135	H. sapiens	O95183	VAMP5 HSPC191	ER
136	H. sapiens	Q15836	VAMP3 SYB3	ER
137	H. sapiens	O75379	VAMP4	ER
138	H. sapiens	Q9Y385	UBE2J1 NCUBE1 CGI-76 HSPC153 HSPC205	ER
139	H. sapiens	P63027	VAMP2 SYB2	ER
140	H. sapiens	A0A1W2PPG1	GOSR2	ER
141	H. sapiens	A0A087WWT2	NRN1	ER
142	H. sapiens	E9PLT1	CD36	ER
143	H. sapiens	E7ENI6	ICA1	ER
144	H. sapiens	E9PN33	STX3	ER
145	H. sapiens	X6R383	SETDB2	ER
146	H. sapiens	K7EJC8	GOSR1	ER
147	H. sapiens	A0A1W2PRH6	PAX6	ER
148	H. sapiens	D6RE10	ELOVL7	ER
149	H. sapiens	F5H2S3	P2RX4	ER
150	H. sapiens	F8WAT4	PAPOLG	ER
151	H. sapiens	F5H3K6	SPI1	ER
152	H. sapiens	E9PE96	PCLO	ER
153	H. sapiens	D6RF86	CDH6	ER
154	H. sapiens	E7ETP9	LAMP3	ER
155	H. sapiens	A0A1W2PRF6	SCARB2	ER
156	H. sapiens	B4DSN5	PTPN1	ER
157	H. sapiens	D6RBD7	EEF1E1 hCG_15559	ER
158	H. sapiens	A0A087WTJ2	GIMAP1-GIMAP5	ER
159	H. sapiens	A0A1W2PS81	GOSR2	ER
160	H. sapiens	A0A087WWT0	JPH4	ER
161	H. sapiens	A0A0J9YW33	STX3	ER
162	H. sapiens	C9JUH5	SERP1	ER
163	H. sapiens	H7C410	GPC1	ER
164	H. sapiens	U3KQS5	TATDN1	ER
165	H. sapiens	G5EA09	SDCBP hCG_1787561	ER
166	H. sapiens	B1AL79	PKN2 hCG_23733	ER
167	H. sapiens	B7Z5N5	SMAD2	ER
168	H. sapiens	I3L3H3	P2RX1	ER
169	H. sapiens	A0A087WT82	GPC6	ER
170	H. sapiens	F5H895	DAD1	ER
171	H. sapiens	F2Z2S5	SERP2	ER
172	H. sapiens	A0A1B0GTF8	EPB41L3	ER
173	H. sapiens	E9PCT3	CAV2	ER
174	H. sapiens	F8WBE5	TFRC	ER
175	H. sapiens	K7EQG9	PTPN2	ER
176	H. sapiens	A0A0U1RQC9	TP53	ER
177	H. sapiens	F8WCE5	LMLN	ER
178	H. sapiens	A0SDD8	CLDN16	ER
179	H. sapiens	B5MCA4	EPCAM	ER
180	H. sapiens	Q5HY57	EMD hCG_41343	ER
181	H. sapiens	B4DJ94	ATP9B	ER
182	H. sapiens	Q86XC5	TMEM97	ER
183	H. sapiens	Q9NRY6	PLSCR3	Mit
184	H. sapiens	Q7Z419	RNF144B IBRDC2 P53RFP	Mit
185	H. sapiens	P56378	MP68 C14orf2 PRO1574	Mit
186	H. sapiens	P57105	SYNJ2BP OMP25	Mit
187	H. sapiens	Q9P0U1	TOMM7 TOM7 TOMM07 AD-014	Mit
188	H. sapiens	Q8N4H5	TOMM5 C9orf105 TOM5	Mit
189	H. sapiens	Q8WWH4	ASZ1 ALP1 ANKL1 C7orf7 GASZ	Mit
190	H. sapiens	P27338	MAOB	Mit
191	H. sapiens	Q9BXK5	BCL2L13 MIL1 CD003	Mit
192	H. sapiens	O43169	CYB5B CYB5M OMB5	Mit
193	H. sapiens	Q14318	FKBP8 FKBP38	Mit
194	H. sapiens	Q9Y3D6	FIS1 TTC11 CGI-135	Mit
195	H. sapiens	Q96I36	COX14 C12orf62	Mit
196	H. sapiens	O00198	HRK BID3	Mit
197	H. sapiens	Q14410	GK2 GKP2 GKTA	Mit
198	H. sapiens	Q9GZY8	MFF C2orf33 AD030 AD033 GL004	Mit
199	H. sapiens	Q7Z434	MAVS IPS1 KIAA1271 VISA	Mit
200	H. sapiens	Q8IXI1	RHOT2 ARHT2 C16orf39	Mit
201	H. sapiens	Q13505	MTX1 MTX MTXN	Mit
202	H. sapiens	Q8IXI2	RHOT1 ARHT1	Mit
203	H. sapiens	P21397	MAOA	Mit
204	H. sapiens	Q14409	GK3P GKP3 GKTB	Mit
205	H. sapiens	Q96IX5	USMG5 DAPIT HCVFTP2 PD04912	Mit
206	H. sapiens	A0A087WT64	MCL1	Mit
207	H. sapiens	E9PH05	FAM162A	Mit
208	H. sapiens	A0A0C4DFQ1	MTX1	Mit
209	H. sapiens	A0A087WZY2	TOMM7	Mit
210	H. sapiens	C9JU26	ATP5MF	Mit
211	H. sapiens	S4R2X2	SFXN1	Mit
212	H. sapiens	H7BXZ6	RHOT1 hCG_1991479	Mit
213	H. sapiens	A0A0A0MS29	MFF	Mit
214	H. sapiens	J3KNF8	CYB5B hCG_28205	Mit
215	H. sapiens	P56134	ATP5J2 ATP5JL	MitIn
216	H. sapiens	O43676	NDUFB3	MitIn
217	H. sapiens	O14957	UQCR11 UQCR	MitIn
218	H. sapiens	Q9UDW1	UQCR10 UCRC HSPC119	MitIn
219	H. sapiens	O95168	NDUFB4	MitIn
220	H. sapiens	P09669	COX6C	MitIn
221	H. sapiens	Q9Y2R0	COA3 CCDC56 MITRAC12 HSPC009	MitIn
222	H. sapiens	Q96AQ8	MCUR1 C6orf79 CCDC90A	MitIn
223	H. sapiens	F8WAR4	CHCHD3	MitIn
224	H. sapiens	D6R9C3	COX7A2	MitIn
225	H. sapiens	A0A087WU07	MINOS1	MitIn
226	H. sapiens	A0A087WYS9	SURF1	MitIn
227	H. sapiens	C9IZW8	NDUFB2	MitIn
228	H. sapiens	O96011	PEX11B	Pex
229	H. sapiens	Q8NFP0	PXT1 STEPP	Pex
230	H. sapiens	P53816	PLA2G16 HRASLS3 HREV107	Pex
231	H. sapiens	Q5T8D3	ACBD5 KIAA1996	Pex
232	H. sapiens	B7Z2R7	ACBD5	Pex
233	H. sapiens	Q9P0B6	CCDC167 C6orf129 HSPC265	Unknown
234	H. sapiens	Q8N111	CEND1 BM88	Unknown
235	H. sapiens	Q8WVX3	C4orf3	Unknown
236	H. sapiens	Q9H7X2	C1orf115	Unknown
237	H. sapiens	Q6ZSY5	PPP1R3F	Unknown
238	H. sapiens	Q9UF11	PLEKHB1 EVT1 KPL1 PHR1 PHRET1	Unknown
239	H. sapiens	Q6ZS82	RGS9BP R9AP	Unknown
240	H. sapiens	Q16821	PPP1R3A PP1G	Unknown
241	H. sapiens	Q9NS64	RPRM	Unknown
242	H. sapiens	Q6IEE8	SLFN12L SLFN5	Unknown
243	H. sapiens	Q9NRQ5	SMCO4 C11orf75 FN5	Unknown
244	H. sapiens	Q8NCU8	SMIM37 LINC00116 NCRNA00116	Unknown
245	H. sapiens	Q96HG1	SMIM10 CXorf69	Unknown
246	H. sapiens	Q71RC9	SMIM5 C17orf109	Unknown
247	H. sapiens	P0DL12	SMIM17	Unknown
248	H. sapiens	Q8WVI0	SMIM4 C3orf78	Unknown
249	H. sapiens	A0A1B0GUA5	SMIM32	Unknown
250	H. sapiens	Q96KF7	SMIM8 C6orf162 DC18	Unknown
251	H. sapiens	Q8TC41	RNF217 C6orf172 IBRDC1	Unknown
252	H. sapiens	Q9Y228	TRAF3IP3 T3JAM	Unknown
253	H. sapiens	Q5JXX7	TMEM31	Unknown
254	H. sapiens	Q2MJR0	SPRED3 EVE-3	Unknown
255	H. sapiens	L0R6Q1	SLC35A4	Unknown
256	H. sapiens	O75920	SERF1A FAM2A SERF1 SMAM1; SERF1B FAM2B SERF1 SMAM1	Unknown
257	H. sapiens	Q9H4I3	TRABD TTG2 PP2447	Unknown
258	H. sapiens	A6NCQ9	RNF222	Unknown
259	H. sapiens	Q5SWX8	ODR4 C1orf27 TTG1 TTG1A	Unknown
260	H. sapiens	Q8N326	C10orf111	Unknown
261	H. sapiens	Q96LL3	C16orf92	Unknown
262	H. sapiens	Q6P4D5	FAM122C	Unknown
263	H. sapiens	Q96D05	FAM241B C10orf35	Unknown
264	H. sapiens	Q8N7S6	ARIH2OS C3orf71	Unknown
265	H. sapiens	Q8IVJ8	APRG1 C3orf35	Unknown
266	H. sapiens	Q86W74	ANKRD46	Unknown
267	H. sapiens	Q8WVC6	DCAKD	Unknown
268	H. sapiens	Q2WGJ9	FER1L6 C8orfK23	Unknown
269	H. sapiens	Q5RGS3	FAM74A1	Unknown
270	H. sapiens	A9Z1Z3	FER1L4 C20orf124	Unknown
271	H. sapiens	Q53EP0	FNDC3B FAD104 NS5ABP37 UNQ2421/PRO4979/PRO34274	Unknown
272	H. sapiens	Q96JQ2	CLMN KIAA1188	Unknown
273	H. sapiens	Q9NPU4	C14orf132 C14orf88	Unknown
274	H. sapiens	Q6ZS62	COLCA1 C11orf92	Unknown
275	H. sapiens	A1L1A6	IGSF23	Unknown
276	H. sapiens	Q8IUY3	GRAMD2A GRAMD2	Unknown
277	H. sapiens	Q9NWW9	HRASLS2	Unknown
278	H. sapiens	Q9UL19	RARRES3 RIG1 TIG3	Unknown
279	H. sapiens	Q68G75	LEMD1	Unknown
280	H. sapiens	P59773	KIAA1024L	Unknown
281	H. sapiens	Q9HDD0	HRASLS	Unknown
282	H. sapiens	Q96EZ4	MYEOV OCIM	Unknown
283	H. sapiens	A0A024RCL3	MICA hCG_2001511	Unknown
284	H. sapiens	A0A087WXU0	RMND1	Unknown
285	H. sapiens	I3L1J9	TNFRSF12A	Unknown
286	H. sapiens	E9PLR7	RNF121	Unknown
287	H. sapiens	H0YIU3	RNASEK	Unknown
288	H. sapiens	E7EX18	MPV17	Unknown
289	H. sapiens	H0YNW0	SLC12A1	Unknown
290	H. sapiens	A0A087WWM7	MME	Unknown
291	H. sapiens	F5GZV7	VAMP1	Unknown
292	H. sapiens	A0A0D9SGD9	SLFN12L	Unknown
293	H. sapiens	J3KR13	FOLR2	Unknown
294	H. sapiens	A0A087X240	EFNA5	Unknown
295	H. sapiens	A0A1W2PRR9	EGFR	Unknown
296	H. sapiens	C9JD05	FSD1L	Unknown
297	H. sapiens	C9JXZ5	VAMP8	Unknown
298	H. sapiens	E9PQR3	FTH1	Unknown
299	H. sapiens	J3KNC7	CYB5A	Unknown
300	H. sapiens	J3KPI8	GPR139	Unknown
301	H. sapiens	E9PQY3	ACP2	Unknown
302	H. sapiens	E7EPM7	COQ2	Unknown
303	H. sapiens	A0A087X286	CKLF-CMTM1	Unknown
304	H. sapiens	V9GYT2	ANKRD29	Unknown
305	H. sapiens	K7EQB1	STX8	Unknown
306	H. sapiens	E9PAR0	FKBP11	Unknown
307	H. sapiens	J3QS48	MPDU1	Unknown
308	H. sapiens	H7BXF4	SMPD4	Unknown
309	H. sapiens	F8WDY4	TMBIM1	Unknown
310	H. sapiens	J3KN43	TMEM33	Unknown
311	H. sapiens	A8MTT8	ZNF286A	Unknown
312	H. sapiens	C9JYK0	LRCH4	Unknown
313	H. sapiens	E5RFY6	RNF217	Unknown
314	H. sapiens	E9PFI9	ZP3	Unknown
315	H. sapiens	A0A0J9YWK4	HBB	Unknown
316	H. sapiens	A0A0D9SFF9	MELK	Unknown
317	H. sapiens	A0A1W2PPL1	SPIN3	Unknown
318	H. sapiens	F8WF90	ARL6IP5	Unknown
319	H. sapiens	F5H0W1	DPY19L2	Unknown
320	H. sapiens	F5H543	IYD	Unknown
321	H. sapiens	A0A1W2PRW4	PLA2G16	Unknown
322	H. sapiens	E9PKL4	C11orf96	Unknown
323	H. sapiens	E9PNH0	OSBPL5	Unknown
324	H. sapiens	E9PJ90	HBS1L	Unknown
325	H. sapiens	E9PPZ2	NPEPPS	Unknown
326	H. sapiens	C9IZ55	MALL	Unknown
327	H. sapiens	A8MPV4	MPV17	Unknown
328	H. sapiens	H0YNT6	FES	Unknown
329	H. sapiens	F5H1L9	JPH4	Unknown
330	H. sapiens	A0A087X175	SLC38A3	Unknown
331	H. sapiens	A0A286YEN9	C5orf60	Unknown
332	H. sapiens	A0A1W2PP90	ST3GAL5	Unknown
333	H. sapiens	H3BUG9	TMEM202	Unknown
334	H. sapiens	A0A1W2PQZ3	HLA-B	Unknown
335	H. sapiens	D6R9K1	CLDND1	Unknown
336	H. sapiens	A0A2R8Y7N0	EPB41	Unknown
337	H. sapiens	A0A087WWT8	GDAP1L1	Unknown
338	H. sapiens	K7EJ34	RETREG3	Unknown
339	H. sapiens	A0A0A0MRG8	BAK1	Unknown
340	H. sapiens	I3L376	TVP23B	Unknown
341	H. sapiens	F5GYX3	SEMA7A	Unknown
342	H. sapiens	Q5VTX9	IFNLR1	Unknown
343	H. sapiens	J3KP61	PLD5	Unknown
344	H. sapiens	A0A087X0T8	CADM1	Unknown
345	H. sapiens	J3KSN8	SMIM21	Unknown
346	H. sapiens	F8WAW2	KIAA0319L	Unknown
347	H. sapiens	F8W1Z3	CERS5	Unknown
348	H. sapiens	A0A1B0GW78	RASGEF1B	Unknown
349	H. sapiens	H7C593	MFSD1	Unknown
350	H. sapiens	A0A0G2JM16	MUC4	Unknown
351	H. sapiens	E9PM16	ZNF7	Unknown
352	H. sapiens	E5RFT6	LYPLA1	Unknown
353	H. sapiens	E9PM70	CYB561D1	Unknown
354	H. sapiens	C9JQU6	ARL6IP5	Unknown
355	H. sapiens	J3QLU8	PEMT	Unknown
356	H. sapiens	E9PQQ2	MYB	Unknown
357	H. sapiens	A0A0A0MRG3	ZNF138	Unknown
358	H. sapiens	A0A0D9SF04	CLN3	Unknown
359	H. sapiens	F8W782	ADIPOR1	Unknown
360	H. sapiens	F8WEP4	CHL1	Unknown
361	H. sapiens	J3KRT1	DHX38	Unknown
362	H. sapiens	B5MEG5	USP19	Unknown
363	H. sapiens	D6RHV8	TMEM175	Unknown
364	H. sapiens	I3L1G0	SLC5A11	Unknown
365	H. sapiens	F8WCS3	POLR1B	Unknown
366	H. sapiens	A0A140TA65	CES5A	Unknown
367	H. sapiens	F8WCU3	SLC30A6	Unknown
368	H. sapiens	D6R9B4	CD164	Unknown
369	H. sapiens	F8VXV4	SLC48A1	Unknown
370	H. sapiens	E9PIV8	CKLF-CMTM1	Unknown
371	H. sapiens	F8VWE0	TSPAN31	Unknown
372	H. sapiens	A0A1B0GUE0	JAKMIP1	Unknown
373	H. sapiens	F5H7K7	LPCAT3	Unknown
374	H. sapiens	D6RB93	ZNF451	Unknown
375	H. sapiens	E9PM26	MS4A7	Unknown
376	H. sapiens	F8WF83	SLC9A9	Unknown
377	H. sapiens	F5H7G2	RGMA	Unknown
378	H. sapiens	E5RI04	ANKRD46	Unknown
379	H. sapiens	D6RJC0	SLC41A3	Unknown
380	H. sapiens	E9PJF1	MYB	Unknown
381	H. sapiens	F8WEW7	PORCN	Unknown
382	H. sapiens	F8WF33	ARL6IP5	Unknown
383	H. sapiens	J3KPT4	TRABD	Unknown
384	H. sapiens	H0YL57	RPLP1	Unknown
385	H. sapiens	E9PMW8	COP1	Unknown
386	H. sapiens	A0A1B0GU12	ATP6AP2	Unknown
387	H. sapiens	E9PKL6	OR51E1	Unknown
388	H. sapiens	K7EPN3	RAMP2	Unknown
389	H. sapiens	A0A0G2JQ71	ZNF66	Unknown
390	H. sapiens	A0A0A0MTQ3	CFAP54	Unknown
391	H. sapiens	A0A0A0MSB7	CALN1	Unknown
392	H. sapiens	F8WDW0	LMBR1	Unknown
393	H. sapiens	A0A0U1RQZ5	ENTPD1	Unknown
394	H. sapiens	D6RI03	TSPAN17	Unknown
395	H. sapiens	V9GYR6	ADPRM	Unknown
396	H. sapiens	J3QKR4	ICAM2	Unknown
397	H. sapiens	D6RC55	OCIAD1	Unknown
398	H. sapiens	C9JE17	CCDC136	Unknown
399	H. sapiens	C9JU31	CCDC136	Unknown
400	H. sapiens	F6VI00	ACOT2	Unknown
401	H. sapiens	H3BTX6	ARL6IP1	Unknown
402	H. sapiens	F8WCI3	CDK5RAP2	Unknown
403	H. sapiens	F8WB21	SYS1	Unknown
404	H. sapiens	A6NG31	RARRES3	Unknown
405	H. sapiens	F8WCL9	ECE2	Unknown
406	H. sapiens	A0A1B0GU51	C14orf132	Unknown
407	H. sapiens	J3KTR2	PEMT	Unknown
408	H. sapiens	F8WDI1	C3orf33	Unknown
409	H. sapiens	F8WDN0	URGCP	Unknown
410	H. sapiens	E9PN09	SLC36A4	Unknown
411	H. sapiens	F8VQZ6	CERS5	Unknown
412	H. sapiens	F8W0W6	SNRPF	Unknown
413	H. sapiens	F5GX39	TMED2	Unknown
414	H. sapiens	F8WEN8	LMBR1	Unknown
415	H. sapiens	A0A0G2JN91	NCR1	Unknown
416	H. sapiens	F8WAW3	GPR156	Unknown
417	H. sapiens	D6RDM3	SLC41A3	Unknown
418	H. sapiens	D6RBY2	TMEM33	Unknown
419	H. sapiens	A0A0A6YYJ0	MSANTD3-TMEFF1	Unknown
420	H. sapiens	D6RBP2	GYPB	Unknown
421	H. sapiens	A0A0G2JMZ5	UGT2B15	Unknown
422	H. sapiens	A0A075B785	RELCH	Unknown
423	H. sapiens	A0A087WZR4	FCGR3B	Unknown
424	H. sapiens	F8VSK7	CERS5	Unknown
425	H. sapiens	F5H0T7	SLC22A6	Unknown
426	H. sapiens	B5MC89	THADA	Unknown
427	H. sapiens	E9PRZ6	CDC27	Unknown
428	H. sapiens	F5H5G1	LSAMP	Unknown
429	H. sapiens	F8W1K4	CERS5	Unknown
430	H. sapiens	F6WFR7	NTM	Unknown
431	H. sapiens	H3BP21	NFAT5	Unknown
432	H. sapiens	A0A2R8YF92	SEL1L2	Unknown
433	H. sapiens	E9PR36	MTNR1B	Unknown
434	H. sapiens	F8WB98	GGCX	Unknown
435	H. sapiens	C9JAX8	SMIM4	Unknown
436	H. sapiens	H3BS23	MOSMO	Unknown
437	H. sapiens	A0A0D9SFD8	CCDC163	Unknown
438	H. sapiens	E9PFA2	WDR17	Unknown
439	H. sapiens	E7EQN9	INPP4B	Unknown
440	H. sapiens	A0A087WX97	BCL2L13	Unknown
441	H. sapiens	E9PHR9	PLSCR4	Unknown
442	H. sapiens	F8WE64	ELP6	Unknown
443	H. sapiens	X6RLY7	CACNA2D4	Unknown
444	H. sapiens	F8WCA0	VAMP2	Unknown
445	H. sapiens	G3V5F3	SCFD1	Unknown
446	H. sapiens	H3BQA3	PDPK1	Unknown
447	H. sapiens	E9PM87	PTPN22	Unknown
448	H. sapiens	F8WDI5	STIMATE	Unknown
449	H. sapiens	F5H4H7	CLEC12B	Unknown
450	H. sapiens	K7ENK9	VAMP2	Unknown
451	H. sapiens	A0A087WT28	CD200R1L	Unknown
452	H. sapiens	G3V232	ADSSL1	Unknown
453	H. sapiens	F8W1N7	CERS5	Unknown
454	H. sapiens	E9PRZ2	PGAP2	Unknown
455	H. sapiens	A0A182DWE8	CFAP47 RP13-11B7.1 hCG_1982542	Unknown
456	H. sapiens	Q8TDQ4	TMEM222	Unknown
457	H. sapiens	I3L1D2	MPDU1	Unknown
458	H. sapiens	A0A0G2JJ55	MICA	Unknown
459	H. sapiens	E9PC20	RAMP1	Unknown
460	H. sapiens	H0YNL7	PIGH	Unknown
461	H. sapiens	E9PKT4	TMEM123	Unknown
462	H. sapiens	G8JLJ3	SMIM29	Unknown
463	H. sapiens	G3V1A8	LY6G6C hCG_43718	Unknown
464	H. sapiens	A6NGS0	UBE2J2 hCG_20420	Unknown
465	H. sapiens	F5H3M3	MANSC1	Unknown
466	H. sapiens	K4JQN1	BAX	Unknown
467	H. sapiens	A0A075B778	ABCA5	Unknown
468	H. sapiens	A0A1W2PR24	ST3GAL5	Unknown
469	H. sapiens	A0A0G2JP96	LILRA1	Unknown
470	H. sapiens	M9MML0	FCGR3A	Unknown
471	H. sapiens	A0A2R8Y694	SLC19A3	Unknown
472	H. sapiens	F8WDB3	ARF4	Unknown
473	H. sapiens	F8WE00	MFSD9	Unknown
474	H. sapiens	J3QS78	CD7	Unknown
475	H. sapiens	D6RCD9	TMEM175	Unknown
476	H. sapiens	F2Z397	TMEM184B	Unknown
477	H. sapiens	M0R1X3	CEACAM8	Unknown
478	H. sapiens	S4R453	KCNMA1	Unknown
479	H. sapiens	Q0P6N6	NRG4	Unknown
480	H. sapiens	F2Z2J3	COA1	Unknown
481	H. sapiens	I3L1Z6	ABCC6	Unknown
482	H. sapiens	F8WCB8	FTO	Unknown
483	H. sapiens	K7ENB6	SLC7A10	Unknown
484	H. sapiens	F5H326	LDHC	Unknown
485	H. sapiens	E9PKZ1	SLC16A4	Unknown
486	H. sapiens	M0R2F1	KCNN4	Unknown
487	H. sapiens	G3V5W3	SOS2	Unknown
488	H. sapiens	Q4KN23	KIR3DS1	Unknown
489	H. sapiens	G3V1I3	FAM9C hCG_19162	Unknown
490	H. sapiens	F8VRN7	TMEM116	Unknown
491	H. sapiens	A0A0G2JJ84	BTNL2	Unknown
492	H. sapiens	Q8WZ67	KLRK1	Unknown
493	H. sapiens	F5GWC9	TMEM91	Unknown
494	H. sapiens	B7Z596	TPM1	Unknown
495	H. sapiens	C9JKN6	THSD7B	Unknown
496	H. sapiens	G3V248	IFI27L1	Unknown
497	H. sapiens	G3XAK3	CLIP4 RSNL2 hCG_1783765	Unknown
498	H. sapiens	A1A4Z5	TRPC7	Unknown
499	H. sapiens	C9J7K9	PLSCR1 hCG_17108	Unknown
500	H. sapiens	H3BUX2	CYB5B	Unknown
501	H. sapiens	S4R3Y8	TMEM91	Unknown
502	H. sapiens	F5H5K1	LRRC37B	Unknown
503	H. sapiens	A0A286YFJ5	MFSD8	Unknown
504	H. sapiens	F8W7G1	CD200	Unknown
505	H. sapiens	F8VVR0	MRPL42	Unknown
506	H. sapiens	A0A087X1Q6	TARM1	Unknown
507	H. sapiens	F8WCC4	C3orf18	Unknown
508	H. sapiens	K7EQ13	G6PC3	Unknown
509	H. sapiens	F8WEV1	MAATS1	Unknown
510	H. sapiens	A0A0A0MT53	CD200R1L	Unknown
511	H. sapiens	E9PHY6	LRRC8C	Unknown
512	H. sapiens	H7BXH0	KCTD20	Unknown
513	H. sapiens	E9PIJ2	CYB561D1	Unknown
514	H. sapiens	V9GYC5	COMMD7	Unknown
515	H. sapiens	B5MCI6	MEMO1	Unknown
516	H. sapiens	E9PQX4	TDRKH	Unknown
517	H. sapiens	K7ELD9	SYNGR2	Unknown
518	H. sapiens	F5H038	CLEC1A	Unknown
519	H. sapiens	K7EIN4	TMED1	Unknown
520	H. sapiens	Q5SNW4	CLCN6	Unknown
521	H. sapiens	E9PQJ6	BET1L	Unknown
522	H. sapiens	F2Z2P5	ERGIC1	Unknown
523	H. sapiens	F8WDT4	SUN3	Unknown
524	H. sapiens	D6RE04	PLRG1	Unknown
525	H. sapiens	J3KST8	CRLF3	Unknown
526	H. sapiens	J3KRW3	CEP95	Unknown
527	H. sapiens	H3BNZ7	C16orf95	Unknown
528	H. sapiens	A2A2E0	MANBAL	Unknown
529	H. sapiens	E7ETC6	PDPN	Unknown
530	H. sapiens	A0A0H2UH41	POTEM	Unknown
531	H. sapiens	I3L380	ABHD12	Unknown
532	H. sapiens	H0Y870	TMEM222	Unknown
533	H. sapiens	F8WCD4	TMEM184B	Unknown
534	H. sapiens	A0A0A0MS18	RAD51B	Unknown
535	H. sapiens	E5RK16	FAXDC2	Unknown
536	H. sapiens	A0A087WXA9	KIZ	Unknown
537	H. sapiens	I3L072	C17orf80	Unknown
538	H. sapiens	K7EPU5	SPRED3	Unknown
539	H. sapiens	F8W1G5	RNASEK	Unknown
540	H. sapiens	Q5T4Q8	CD72	Unknown
541	H. sapiens	A0A1W2PRT0	ST3GAL5	Unknown
542	H. sapiens	H3BU94	SNAP23	Unknown
543	H. sapiens	A0A2R8YEW2	CYSTM1 ORF1-FL49 hCG_45310	Unknown
544	H. sapiens	B3KT51	TM2D3 hCG_26600	Unknown
545	H. sapiens	E9PI46	ABCD4	Unknown
546	H. sapiens	B7Z863	SLMAP	Unknown
547	H. sapiens	F8WEN7	MTFP1	Unknown
548	H. sapiens	A0A0C4DFN5	TCTN3 C10orf61 hCG_39491	Unknown
549	H. sapiens	M0QZX7	ZNF816	Unknown
550	H. sapiens	Q8N329	EOGT C3orf64	Unknown
551	H. sapiens	F2Z2A2	MFSD9	Unknown
552	H. sapiens	A0A1W2PQE2	HLA-B	Unknown
553	H. sapiens	E5RG25	UBE2W	Unknown
554	H. sapiens	A0A0J9YWY1	LLCFC1 C7orf34 hCG_20688	Unknown
555	H. sapiens	M0R0R3	SMIM7	Unknown
556	H. sapiens	A0AVG3	TSNARE1	Unknown
557	H. sapiens	B1ANB7	MCOLN3 hCG_1775160	Unknown
558	H. sapiens	I3L288	TMEM159 LOC57146 hCG_38247	Unknown
559	H. sapiens	B7Z964	SLMAP	Unknown
560	H. sapiens	X6R3D1	HRASLS hCG_16315	Unknown
561	H. sapiens	E7EM61	SLC19A3	Unknown
562	H. sapiens	Q3KQS6	MME	Unknown
563	H. sapiens	B9TX75	MED24	Unknown
564	H. sapiens	G5E972	TMPO hCG_2015322	Unknown
565	H. sapiens	F8VV56	CD63	Unknown
566	H. sapiens	D6RCL9	SERF1B SERF1A	Unknown
567	H. sapiens	B3KT28	FAF1	Unknown
568	H. sapiens	G3V1R8	TMBIM4 hCG_16706	Unknown
569	H. sapiens	G5E9Q6	PFN2 hCG_21343	Unknown
570	H. sapiens	A0A0C4DGX8	ATP6AP1 hCG_2008012	Unknown
571	H. sapiens	K7EMW4	NCLN hCG_23630	Unknown
572	H. sapiens	B4DKD2	ADAM11	Unknown
573	H. sapiens	E5RGC5	TVP23C-CDRT4 TVP23C	Unknown
574	S. cerevisiae	Q03941	CAB5 YDR196C YD9346.07C	Both
575	S. cerevisiae	Q08215	PEX15 PAS21 YOL044W	Both
576	S. cerevisiae	P25580	PBN1 YCL052C YCL52C	ER
577	S. cerevisiae	P32854	PEP12 VPS6 VPT13 YOR036W OR26.29	ER
578	S. cerevisiae	Q05637	PHM6 YDR281C D9954.14	ER
579	S. cerevisiae	Q08931	PRM3 YPL192C	ER
580	S. cerevisiae	P39926	SSO2 YMR183C YM8010.13C	ER
581	S. cerevisiae	P31377	SYN8 UIP2 YAL014C FUN34	ER
582	S. cerevisiae	P32867	SSO1 YPL232W P1405	ER
583	S. cerevisiae	P31109	SNC1 YAL030W	ER
584	S. cerevisiae	P33328	SNC2 YOR327C	ER
585	S. cerevisiae	P38247	SLM4 EGO3 GSE1 YBR077C YBR0723	ER
586	S. cerevisiae	P43682	SFT1 YKL006C-A YKL006BC	ER
587	S. cerevisiae	Q03322	TLG1 YDR468C D8035.11	ER
588	S. cerevisiae	P52870	SBH1 SEB1 YER087C-B YER087BC	ER
589	S. cerevisiae	Q6Q595	SCS22 YBL091C-A	ER
590	S. cerevisiae	P35179	SSS1 YDR086C D4475	ER
591	S. cerevisiae	P40075	SCS2 YER120W	ER
592	S. cerevisiae	P22214	SEC22 SLY2 TSL26 YLR268W L8479.3	ER
593	S. cerevisiae	P52871	SBH2 SEB2 YER019C-A YER019BC	ER
594	S. cerevisiae	Q01590	SED5 YLR026C	ER
595	S. cerevisiae	P38342	TSC10 YBR265W YBR1734	ER
596	S. cerevisiae	Q12255	NYV1 MAM2 YLR093C	ER
597	S. cerevisiae	P14020	DPM1 SED3 YPR183W P9705.3	ER
598	S. cerevisiae	Q08955	CSM4 YPL200W	ER
599	S. cerevisiae	Q06001	FAR10 YLR238W	ER
600	S. cerevisiae	P22804	BET1 SLY12 YIL004C YIA4C	ER
601	S. cerevisiae	P25385	BOS1 YLR078C L9449.9	ER
602	S. cerevisiae	P40312	CYB5 YNL111C N1949	ER
603	S. cerevisiae	P38736	GOS1 YHL031C	ER
604	S. cerevisiae	P32363	SPT14 CWH6 GPI3 YPL175W P2269	ER
605	S. cerevisiae	P43560	LAM5 LTC2 YFL042C	ER
606	S. cerevisiae	P48353	HLJ1 YMR161W YM8520.10	ER
607	S. cerevisiae	Q99332	FRT1 HPH1 YOR324C O6159	ER
608	S. cerevisiae	P32339	HMX1 YLR205C L8167.18	ER
609	S. cerevisiae	Q3E790	TSC3 YBR058C-A	ER
610	S. cerevisiae	Q04338	VTI1 YMR197C YM9646.10C	ER
611	S. cerevisiae	Q3E842	YMR122W-A	ER
612	S. cerevisiae	P38216	YBR016W YBR0222	ER
613	S. cerevisiae	P53146	USE1 SLT1 YGL098W	ER
614	S. cerevisiae	P38374	YSY6 YBR162W-A YBR162BW	ER
615	S. cerevisiae	Q05899	YLR297W	ER
616	S. cerevisiae	Q03944	VPS64 FAR9 YDR200C YD9346.10C	ER
617	S. cerevisiae	P33296	UBC6 DOA2 YER100W	ER
618	S. cerevisiae	P41834	UFE1 YOR075W YOR29-26	ER
619	S. cerevisiae	Q08959	PGC1 YPL206C	Mit
620	S. cerevisiae	P80967	TOM5 MOM8A YPR133W-A	Mit
621	S. cerevisiae	P53507	TOM7 MOM7 YNL070W N2378	Mit
622	S. cerevisiae	P33448	TOM6 ISP6 YOR045W	Mit
623	S. cerevisiae	P40515	FIS1 MDV2 YIL065C	Mit
624	S. cerevisiae	P39722	GEM1 YAL048C	Mit
625	S. cerevisiae	P22289	QCR9 UCR9 YGR183C	MitIn
626	S. cerevisiae	P07255	COX9 YDL067C D2520	MitIn
627	S. cerevisiae	P10174	COX7 YMR256C YM9920.10C	MitIn
628	S. cerevisiae	Q2V2P9	YDR119W-A	MitIn
629	S. cerevisiae	Q02969	PEX25 YPL112C	Pex
630	S. cerevisiae	P38335	MTC4 YBR255W YBR1723	Pex
631	S. cerevisiae	Q02820	NCE101 NCE1 YJL205C YJL205BC YJL205C-A	Unknown
632	S. cerevisiae	Q03441	RMD1 YDL001W	Unknown
633	S. cerevisiae	P43620	RMD8 YFR048W	Unknown
634	S. cerevisiae	Q08559	FYV12 YOR183W	Unknown
635	S. cerevisiae	P11927	KAR1 YNL188W N1611	Unknown
636	S. cerevisiae	Q08630	IRC13 YOR235W	Unknown
637	S. cerevisiae	P0CD97	YER039C-A	Unknown
638	S. cerevisiae	Q3E828	YJL127C-B	Unknown
639	S. cerevisiae	Q8TGS8	YMR105W-A	Unknown
640	S. cerevisiae	Q3E760	YMR030W-A	Unknown
641	S. cerevisiae	O13511	YAL065C	Unknown
642	S. cerevisiae	P39563	YAR064W	Unknown
643	S. cerevisiae	Q2V2Q3	YBR201C-A	Unknown
644	S. cerevisiae	Q3E743	YJR112W-A	Unknown
645	S. cerevisiae	P47080	YJL007C J1379	Unknown
646	S. cerevisiae	P36092	YKL044W YKL257	Unknown
647	S. cerevisiae	Q2V2P2	YKL065W-A	Unknown
648	S. cerevisiae	Q07738	YDL241W	Unknown
649	S. cerevisiae	Q05612	YDR278C	Unknown
650	S. cerevisiae	Q03480	YDR209C YD8142A.06c	Unknown
651	S. cerevisiae	Q3E750	YGL041C-B	Unknown
652	S. cerevisiae	Q8TGK1	YHR213W-B	Unknown
653	S. cerevisiae	Q07074	YHR007C-A	Unknown
654	S. cerevisiae	A5Z2X5	YPR010C-A	Unknown
655	S. cerevisiae	P53229	YGR045C	Unknown
656	S. cerevisiae	Q2V2P3	YKL023C-A	Unknown
657	S. cerevisiae	Q3E814	YLL006W-A	Unknown
658	S. cerevisiae	Q12506	YOR314W 06123	Unknown
659	S. cerevisiae	Q8TGU7	YBR126W-A	Unknown
660	S. cerevisiae	Q04597	YDR114C	Unknown
661	S. cerevisiae	P0C268	YBL039W-B	Unknown
662	S. cerevisiae	Q96VH3	YCL021W-A	Unknown
663	S. cerevisiae	Q8TGT9	YGR146C-A	Unknown
664	S. cerevisiae	Q08110	YOL014W	Unknown
665	S. cerevisiae	Q08734	YOR268C	Unknown
666	S. cerevisiae	P53156	YGL081W	Unknown
667	S. cerevisiae	Q05898	YLR296W L8003.6	Unknown
668	S. cerevisiae	P38185	YBL071C YBL0615	Unknown

Table 4.2: Putative TA proteins in yeast and humans. A combined list of all identified TA proteins in both the human and yeast genomes with their known localization marked as ER (which includes ER, Golgi apparatus, nucleus, cell membrane, vacuole, endosomes, and lysosomes), mitochondria, both (ER and mitochondria), peroxisome, and unknown.

i	Metric	Scale	AUROC	Best Threshold	TP	FP	TN	FN	Correct	Incorrect
1	Patch 15	Kyte & Doolittle	96.28	29.40	38	0	10	5	48	5
2	Patch 15	TM Tendency	95.81	13.42	36	0	10	7	46	7
3	Patch 11	Kyte & Doolittle	94.88	25.50	35	0	10	8	45	8
4	Wheel Face 5	TM Tendency	94.65	7.97	38	1	9	5	47	6
5	Wheel Face 9	TM Tendency	93.95	12.31	37	0	10	6	47	6
6	Patch 15	GES	93.95	5.09	32	0	10	11	42	11
7	Patch 19	Kyte & Doolittle	93.49	28.30	34	0	10	9	44	9
8	Wheel Face 7	TM Tendency	93.02	10.57	34	0	10	9	44	9
9	Wheel Face 8	TM Tendency	92.91	11.04	36	0	10	7	46	7
10	Segment 15	Kyte & Doolittle	92.56	35.20	31	0	10	12	41	12
11	Segment 15	TM Tendency	92.33	16.28	33	0	10	10	43	10
12	Patch 19	TM Tendency	91.86	13.32	35	0	10	8	45	8
13	Segment 15	GES	91.63	5.43	35	0	10	8	45	8
14	Segment 11	Kyte & Doolittle	91.63	27.10	33	0	10	10	43	10
15	Rectangle 9	Kyte & Doolittle	91.28	18.10	31	0	10	12	41	12
16	Wheel Face 5	Kyte & Doolittle	91.05	18.10	35	1	9	8	44	9
17	Wheel Face 7	Kyte & Doolittle	91.05	21.00	38	2	8	5	46	7
18	Rectangle 9	Fauchere Pliska	90.93	7.55	35	0	10	8	45	8
19	Patch 19	Octanol	90.93	6.86	34	0	10	9	44	9
20	Rectangle 9	TM Tendency	90.93	8.32	33	0	10	10	43	10
21	Patch 19	GES	90.70	4.60	33	0	10	10	43	10
22	Line 13	Kyte & Doolittle	90.70	13.90	37	2	8	6	45	8
23	Patch 11	GES	90.35	4.32	31	0	10	12	41	12
24	Star 8	Kyte & Doolittle	90.12	14.60	33	1	9	10	42	11
25	TMD (18 aa)	Kyte & Doolittle	90.00	42.90	27	0	10	16	37	16
26	TMD	Kyte & Doolittle	90.00	42.90	27	0	10	16	37	16
27	Wheel Face 9	GES	89.88	4.46	32	0	10	11	42	11
28	Patch 19	Fauchere Pliska	89.77	14.67	36	1	9	7	45	8
29	Segment 19	Kyte & Doolittle	89.77	35.40	35	1	9	8	44	9
30	Patch 11	TM Tendency	89.77	13.17	31	0	10	12	41	12
31	Star 8	TM Tendency	89.77	6.94	35	1	9	8	44	9
32	Wheel Face 8	Kyte & Doolittle	89.53	26.60	28	0	10	15	38	15
33	Segment 19	TM Tendency	89.53	16.48	32	0	10	11	42	11
34	Rectangle 9	GES	89.30	2.86	33	0	10	10	43	10
35	Wheel Face 8	GES	89.19	4.10	31	0	10	12	41	12
36	Line 13	GES	89.19	2.13	35	1	9	8	44	9
37	Wheel Face 6	TM Tendency	89.07	9.79	32	0	10	11	42	11
38	Segment 19	Fauchere Pliska	89.07	19.91	36	1	9	7	45	8
39	Segment 11	TM Tendency	89.07	12.43	34	1	9	9	43	10
40	TMD (18 aa)	TM Tendency	88.84	16.45	34	0	10	9	44	9
41	Line 13	TM Tendency	88.72	6.71	34	1	9	9	43	10
42	Wheel Face 7	GES	88.37	3.87	27	0	10	16	37	16
43	Wheel Face 9	Roseman	88.37	12.65	37	2	8	6	45	8
44	Rectangle 9	Roseman	88.37	8.89	29	0	10	14	39	14
45	TMD average	Kyte & Doolittle	88.37	1.69	36	2	8	7	44	9
46	Star 8	Roseman	88.26	6.75	36	2	8	7	44	9
47	Wheel Face 9	Kyte & Doolittle	88.14	27.20	35	2	8	8	43	10
48	Patch 15	Fauchere Pliska	88.14	15.50	31	0	10	12	41	12
49	Patch 19	Roseman	88.14	15.30	31	0	10	12	41	12
50	Patch 15	Octanol	88.14	7.89	32	0	10	11	42	11
51	Wheel Face 7	Roseman	88.02	10.75	35	1	9	8	44	9
52	Wheel Face 8	Roseman	87.91	12.57	30	0	10	13	40	13
53	Star 8	Fauchere Pliska	87.91	6.97	25	0	10	18	35	18
54	TMD	TM Tendency	87.67	16.80	33	0	10	10	43	10
55	Segment 11	GES	87.44	4.60	30	0	10	13	40	13
56	Segment 15	Fauchere Pliska	87.44	17.09	32	1	9	11	41	12
57	Star 8	GES	87.21	2.24	35	1	9	8	44	9
58	Wheel Face 5	GES	87.09	2.64	38	3	7	5	45	8
59	Wheel Face 4	Kyte & Doolittle	87.09	14.40	38	3	7	5	45	8
60	Rectangle 9	Octanol	86.63	5.73	29	0	10	14	39	14
61	Wheel Face 9	Fauchere Pliska	86.51	12.24	37	2	8	6	45	8
62	Wheel Face 9	Octanol	86.28	7.10	36	2	8	7	44	9
63	Segment 19	Octanol	86.28	9.65	32	0	10	11	42	11
64	TMD average	TM Tendency	86.28	0.88	31	0	10	12	41	12
65	Wheel Face 6	Kyte & Doolittle	85.93	19.60	33	1	9	10	42	11
66	TMD (18 aa)	GES	85.93	5.78	34	1	9	9	43	10
67	Patch 11	Fauchere Pliska	85.93	13.22	27	0	10	16	37	16
68	Segment 11	Fauchere Pliska	85.81	13.72	31	1	9	12	40	13
69	Star 8	Octanol	85.81	4.54	34	1	9	9	43	10
70	TMD	GES	85.81	5.78	34	1	9	9	43	10
71	Twist 8	Kyte & Doolittle	85.70	18.50	35	2	8	8	43	10
72	Patch 15	Roseman	85.35	15.46	31	1	9	12	40	13
73	Segment 15	Octanol	85.35	8.78	32	1	9	11	41	12
74	Wheel Face 8	Octanol	85.23	7.44	31	1	9	12	40	13
75	Wheel Face 6	GES	84.88	3.39	28	1	9	15	37	16
76	Segment 19	GES	84.65	6.61	27	0	10	16	37	16
77	Twist 8	Fauchere Pliska	84.65	8.74	32	1	9	11	41	12
78	Patch 11	Roseman	84.65	13.51	28	0	10	15	38	15
79	Line 9	Kyte & Doolittle	84.30	11.10	36	2	8	7	44	9
80	Twist 8	TM Tendency	84.19	9.59	29	0	10	14	39	14
81	Wheel Face 8	Fauchere Pliska	84.07	10.93	35	2	8	8	43	10
82	Twist 8	GES	83.95	2.93	35	2	8	8	43	10
83	Segment 15	Roseman	83.95	18.21	27	0	10	16	37	16
84	TMD average	Fauchere Pliska	83.95	1.02	34	1	9	9	43	10
85	TMD	Fauchere Pliska	83.95	23.74	28	0	10	15	38	15
86	Line 13	Roseman	83.72	6.76	33	1	9	10	42	11
87	TMD average	GES	83.72	0.29	33	1	9	10	42	11
88	TMD (18 aa)	Fauchere Pliska	83.49	23.74	27	0	10	16	37	16
89	Wheel Face 7	Fauchere Pliska	83.26	10.17	35	2	8	8	43	10
90	Segment 11	Roseman	83.02	12.94	33	2	8	10	41	12
91	TMD (18 aa)	Octanol	83.02	9.25	31	0	10	12	41	12
92	TMD	Octanol	83.02	9.25	31	0	10	12	41	12
93	Wheel Face 7	Octanol	82.79	7.29	26	0	10	17	36	17
94	Line 17	Kyte & Doolittle	82.79	14.60	30	1	9	13	39	14
95	Patch 11	Octanol	82.56	7.67	32	1	9	11	41	12
96	TMD average	Octanol	82.56	0.41	32	1	9	11	41	12
97	Segment 19	Roseman	82.21	19.04	29	1	9	14	38	15
98	Wheel Face 5	Fauchere Pliska	81.40	7.57	37	3	7	6	44	9
99	Wheel Face 6	Roseman	81.28	10.33	29	1	9	14	38	15
100	Wheel Face 5	Octanol	80.93	5.84	27	1	9	16	36	17
101	Wheel Face 5	Roseman	80.70	8.68	32	2	8	11	40	13
102	Twist 8	Roseman	80.70	10.05	30	1	9	13	39	14
103	Segment 11	Octanol	80.70	6.58	35	3	7	8	42	11
104	Line 17	TM Tendency	80.70	7.06	30	1	9	13	39	14
105	Wheel Face 6	Fauchere Pliska	80.47	8.73	37	2	8	6	45	8
106	Wheel Face 4	Octanol	80.35	4.74	33	2	8	10	41	12
107	TMD average	Roseman	79.88	1.06	27	0	10	16	37	16
108	TMD	Roseman	79.88	22.76	25	0	10	18	35	18
109	TMD (18 aa)	Roseman	79.65	22.76	25	0	10	18	35	18
110	Wheel Face 6	Octanol	79.42	6.25	32	2	8	11	40	13
111	Wheel Face 4	Fauchere Pliska	79.30	6.50	37	3	7	6	44	9
112	Line 17	GES	79.19	2.42	28	0	10	15	38	15
113	Line 9	GES	78.60	1.79	34	3	7	9	41	12
114	Twist 8	Octanol	76.86	6.32	28	1	9	15	37	16
115	Wheel Face 3	Kyte & Doolittle	76.63	12.20	31	2	8	12	39	14
116	Line 13	Fauchere Pliska	75.81	5.93	37	4	6	6	43	10
117	Line 13	Octanol	74.88	4.08	31	3	7	12	38	15
118	Wheel Face 4	GES	74.30	2.24	36	3	7	7	43	10
119	Wheel Face 4	TM Tendency	74.07	6.65	40	3	7	3	47	6
120	Line 9	TM Tendency	74.07	5.19	38	4	6	5	44	9
121	Line 17	Fauchere Pliska	73.60	7.45	23	1	9	20	32	21
122	Wheel Face 4	Roseman	72.44	7.21	36	4	6	7	42	11
123	Wheel Face 3	Fauchere Pliska	70.58	5.27	32	3	7	11	39	14
124	Line 17	Octanol	70.35	3.68	35	4	6	8	41	12
125	Line 17	Roseman	68.37	7.39	27	3	7	16	34	19
126	Wheel Face 3	TM Tendency	66.98	5.62	31	3	7	12	38	15
127	Wheel Face 3	GES	66.05	1.80	36	5	5	7	41	12
128	Wheel Face 3	Roseman	64.77	5.77	27	3	7	16	34	19
129	Line 9	Roseman	64.19	5.87	19	1	9	24	28	25
130	Wheel Face 3	Octanol	63.60	4.08	23	4	6	20	29	24
131	Line 9	Fauchere Pliska	55.23	5.19	26	4	6	17	32	21
132	TMD average	Roseman	53.02	0.29	11	1	9	32	20	33
133	TMD average	TM Tendency	51.16	0.17	31	6	4	12	35	18
134	TMD average	GES	-51.16	0.09	33	9	1	10	34	19
135	TMD length	NA	-51.28	20.00	8	4	6	35	14	39
136	Line 9	Octanol	-51.40	4.08	22	7	3	21	25	28
137	TMD average	Kyte & Doolittle	-53.26	0.68	34	10	0	9	34	19
138	TMD average	Fauchere Pliska	-57.21	0.15	26	7	3	17	29	24
139	TMD average	Octanol	-61.63	0.33	40	10	0	3	40	13

Table 4.3: Hydrophobicity geometry metrics perform the best when classifying yeast TA proteins. A list of all the metrics tested against the yeast genome ranked from highest to lowest AUROC score.

Fig. 4 Ref #	Entry	TA Name	ER	Mito	Total # Of Cells	ER (%)	Mito (%)	Localization
1	P40515	Fis1	0	33	33	0.00	1.00	Mitochondria
2	Q2V2P9	Cox26	2	18	20	0.10	0.90	Mitochondria
3	Q2V2P3	YKL023C	131	3	134	0.98	0.02	ER
4	P38185	YBL071C	122	3	125	0.98	0.02	ER
5	Q05612	YDR278C	221	0	221	1.00	0.00	ER
6	P0CD97	YER039C	161	0	161	1.00	0.00	ER
7	P53156	YGL081W	135	0	135	1.00	0.00	ER
8	Q12506	YOR314W	176	2	178	0.99	0.01	ER
9	P53229	YGR045C	169	2	171	0.99	0.01	ER
10	P36092	YKL044W	89	3	92	0.97	0.03	ER
11	Q04597	YDR114C	1	141	142	0.01	0.99	Mitochondria
12	Q03480	YDR209C	91	7	98	0.93	0.07	ER
13	Q3E743	YJR112W	39	2	41	0.95	0.05	ER
14	Q3E750	YGL041C	69	1	70	0.99	0.01	ER
15	Q08110	YOL014W	91	7	98	0.93	0.07	ER
16	Q05898	YLR296W	0	64	64	0.00	1.00	Mitochondria
17	P0C268	YBL039W	38	8	46	0.83	0.17	Other

Table 4.4: Determined localization of unknown TA proteins in yeast cells. A list of the experimentally determined localization of 2 known (control) and 15 unknown TA proteins. Localization is split between mitochondria and ER on a per cell basis and TA proteins are referenced based on the number in Figure 4.4.

i	Organism	Entry	Name	Localization
1	S. cerevisiae	Q08110	YOL014W	ER
2	S. cerevisiae	P11927	KAR1	ER
3	S. cerevisiae	Q07738	YDL241W	ER
4	S. cerevisiae	Q3E750	YGL041C-B	ER
5	S. cerevisiae	Q8TGU7	YBR126W-A	ER
6	S. cerevisiae	P43620	RMD8	ER
7	S. cerevisiae	Q07074	YHR007C-A	ER
8	S. cerevisiae	Q08734	YOR268C	ER
9	S. cerevisiae	Q3E743	YJR112W-A	ER
10	S. cerevisiae	Q3E828	YJL127C-B	Mit
11	S. cerevisiae	P36092	YKL044W	Mit
12	S. cerevisiae	Q2V2P2	YKL065W-A	Pex
13	S. cerevisiae	P0C268	YBL039W-B	ER
14	S. cerevisiae	Q96VH3	YCL021W-A	ER
15	S. cerevisiae	Q03441	RMD1	ER

Table 4.5: Localization of unknown TA proteins identified in Weill et al. (2018). The reported localization for 17 TA proteins identified in the high-throughput screen performed in (Weill et al., 2018).

i	Metric	Scale	AUROC	Best Threshold	TP	FP	TN	FN	Correct	Incorrect
1	Wheel Face 7	TM Tendency	89.06	10.71	44	4	10	16	54	20
2	Wheel Face 5	TM Tendency	87.64	8.38	45	1	13	20	58	21
3	Patch 19	Fauchere Pliska	86.85	14.67	48	2	12	17	60	19
4	Segment 11	Kyte & Doolittle	86.79	27.90	43	1	13	17	56	18
5	Wheel Face 9	TM Tendency	86.51	12.68	43	1	13	21	56	22
6	Wheel Face 6	TM Tendency	86.39	9.79	40	2	12	17	52	19
7	Segment 15	Kyte & Doolittle	86.34	31.80	50	1	13	17	63	18
8	Wheel Face 8	TM Tendency	86.34	12.16	40	0	14	29	54	29
9	Patch 19	Octanol	86.05	7.02	44	2	12	20	56	22
10	Wheel Face 5	Kyte & Doolittle	85.94	17.70	47	2	12	18	59	20
11	Wheel Face 7	Kyte & Doolittle	85.94	21.20	51	3	11	15	62	18
12	Patch 15	Kyte & Doolittle	85.71	31.00	46	3	11	12	57	15
13	Wheel Face 9	Fauchere Pliska	85.60	12.78	44	0	14	25	58	25
14	Patch 15	Fauchere Pliska	85.26	14.69	47	2	12	14	59	16
15	Patch 15	TM Tendency	85.09	14.16	45	0	14	19	59	19
16	Rectangle 9	Fauchere Pliska	84.86	7.96	46	1	13	19	59	20
17	Patch 11	Kyte & Doolittle	84.75	25.50	46	1	13	15	59	16
18	Segment 15	TM Tendency	84.47	16.28	41	1	13	18	54	19
19	Patch 15	Octanol	84.13	7.78	45	1	13	23	58	24
20	Segment 19	Fauchere Pliska	84.01	19.91	49	1	13	15	62	16
21	Star 8	Kyte & Doolittle	84.01	14.60	48	2	12	26	60	28
22	Wheel Face 5	GES	83.73	2.78	44	0	14	32	58	32
23	Wheel Face 6	Kyte & Doolittle	83.67	19.40	48	3	11	15	59	18
24	Rectangle 9	TM Tendency	83.50	8.23	42	0	14	20	56	20
25	Star 8	Fauchere Pliska	83.50	6.99	30	0	14	26	44	26
26	Wheel Face 7	Fauchere Pliska	83.45	10.17	49	3	11	16	60	19
27	Wheel Face 7	GES	83.45	3.30	53	3	11	18	64	21
28	Segment 11	Fauchere Pliska	83.11	13.97	39	0	14	29	53	29
29	Rectangle 9	Kyte & Doolittle	82.99	18.10	40	4	10	15	50	19
30	Segment 15	Fauchere Pliska	82.99	16.10	48	2	12	16	60	18
31	Wheel Face 5	Fauchere Pliska	82.77	7.84	47	2	12	14	59	16
32	Rectangle 9	Octanol	82.65	5.33	43	0	14	29	57	29
33	Line 13	GES	82.65	2.30	40	6	8	15	48	21
34	Patch 15	GES	82.65	5.09	38	4	10	10	48	14
35	Rectangle 9	Roseman	82.60	8.43	43	3	11	14	54	17
36	Wheel Face 7	Octanol	82.54	7.18	40	3	11	21	51	24
37	Wheel Face 9	Octanol	82.54	8.82	34	0	14	23	48	23
38	Wheel Face 8	Fauchere Pliska	82.48	11.57	46	1	13	17	59	18
39	Patch 19	Roseman	82.43	13.34	43	1	13	25	56	26
40	Wheel Face 7	Roseman	82.43	11.46	40	3	11	25	51	28
41	Segment 11	TM Tendency	82.31	13.75	36	0	14	24	50	24
42	Segment 11	GES	82.20	4.58	37	1	13	20	50	21
43	Wheel Face 9	Roseman	82.20	12.65	49	1	13	27	62	28
44	Wheel Face 6	Fauchere Pliska	82.14	9.00	48	0	14	33	62	33
45	Segment 15	GES	81.97	5.43	43	0	14	25	57	25
46	Line 13	TM Tendency	81.80	6.71	45	4	10	15	55	19
47	Wheel Face 4	Fauchere Pliska	81.80	6.64	45	3	11	15	56	18
48	Patch 11	Fauchere Pliska	81.75	13.22	34	2	12	15	46	17
49	TMD average	Fauchere Pliska	81.75	1.01	46	2	12	23	58	25
50	Wheel Face 8	Roseman	81.63	12.67	38	0	14	31	52	31
51	Wheel Face 8	Octanol	81.58	7.44	43	1	13	21	56	22
52	Line 13	Kyte & Doolittle	81.41	14.90	40	2	12	38	52	40
53	Patch 11	TM Tendency	81.41	13.17	39	0	14	23	53	23
54	Rectangle 9	GES	81.35	2.86	40	0	14	24	54	24
55	Wheel Face 9	GES	81.24	4.46	39	2	12	21	51	23
56	Star 8	TM Tendency	81.18	6.92	46	1	13	20	59	21
57	Wheel Face 6	GES	81.18	3.02	47	2	12	17	59	19
58	Wheel Face 8	Kyte & Doolittle	81.18	22.80	53	2	12	26	65	28
59	Star 8	GES	81.12	2.23	46	2	12	21	58	23
60	Wheel Face 9	Kyte & Doolittle	81.12	24.80	48	2	12	26	60	28
61	Wheel Face 8	GES	81.01	4.10	36	2	12	23	48	25
62	Segment 19	Kyte & Doolittle	80.95	35.40	45	2	12	21	57	23
63	Twist8	Kyte & Doolittle	80.95	18.50	48	0	14	23	62	23
64	Twist8	Fauchere Pliska	80.90	8.74	47	0	14	17	61	17
65	Patch 11	GES	80.78	4.03	43	3	11	16	54	19
66	Patch 19	TM Tendency	80.61	13.32	44	0	14	23	58	23
67	Segment 19	TM Tendency	80.50	16.48	43	1	13	19	56	20
68	Segment 19	Octanol	80.33	9.65	43	0	14	19	57	19
69	Star 8	Roseman	80.33	6.75	51	1	13	27	64	28
70	Star 8	Octanol	80.27	4.17	52	0	14	23	66	23
71	TMD	Fauchere Pliska	80.16	22.95	38	4	10	25	48	29
72	Wheel Face 4	Octanol	80.10	4.74	48	2	12	20	60	22
73	TMD (18aa)	Fauchere Pliska	79.93	22.95	37	5	9	15	46	20
74	Line 13	Roseman	79.88	6.76	46	3	11	23	57	26
75	Segment 15	Octanol	79.71	8.78	43	1	13	18	56	19
76	Wheel Face 6	Octanol	79.31	6.25	43	3	11	11	54	14
77	Patch 19	Kyte & Doolittle	79.20	28.30	42	5	9	10	51	15
78	Patch 15	Roseman	79.14	15.16	41	1	13	23	54	24
79	Wheel Face 4	Kyte & Doolittle	79.08	16.30	27	2	12	13	39	15
80	Patch 11	Octanol	79.02	6.18	51	2	12	20	63	22
81	Wheel Face 6	Roseman	78.63	10.33	38	4	10	12	48	16
82	Segment 11	Octanol	78.57	9.52	30	4	10	12	40	16
83	Patch 19	GES	78.46	4.92	33	0	14	27	47	27
84	TMD	Kyte & Doolittle	78.23	38.30	37	6	8	13	45	19
85	TMD (18aa)	Kyte & Doolittle	78.17	38.30	37	1	13	23	50	24
86	Patch 11	Roseman	78.12	13.51	36	1	13	25	49	26
87	TMD average	Kyte & Doolittle	77.83	1.82	37	0	14	33	51	33
88	Wheel Face 5	Octanol	77.83	5.79	37	1	13	20	50	21
89	TMD average	Octanol	77.55	0.40	42	5	9	13	51	18
90	Segment 15	Roseman	77.32	18.21	35	2	12	22	47	24
91	Line 9	GES	77.10	1.79	49	2	12	19	61	21
92	Wheel Face 5	Roseman	76.87	9.03	30	1	13	30	43	31
93	Line 17	Fauchere Pliska	76.76	7.26	40	2	12	18	52	20
94	TMD	Octanol	76.76	9.25	40	3	11	39	51	42
95	TMD (18aa)	Octanol	76.76	9.25	40	4	10	17	50	21
96	Segment 11	Roseman	76.64	12.91	44	1	13	27	57	28
97	TMD (18aa)	TM Tendency	76.53	16.45	43	2	12	18	55	20
98	Line 17	TM Tendency	76.13	7.06	40	5	9	8	49	13
99	Segment 19	GES	75.85	6.61	31	0	14	30	45	30
100	Line 17	Kyte & Doolittle	75.74	16.70	30	0	14	36	44	36
101	TMD	TM Tendency	75.74	16.80	42	3	11	21	53	24
102	TMD average	TM Tendency	75.74	0.94	34	2	12	23	46	25
103	Wheel Face 4	TM Tendency	75.62	6.81	55	0	14	22	69	22
104	Line 17	GES	75.57	2.49	32	4	10	11	42	15
105	Line 9	Kyte & Doolittle	75.51	11.10	50	2	12	16	62	18
106	Line 13	Fauchere Pliska	75.28	6.25	48	4	10	17	58	21
107	Segment 19	Roseman	75.00	20.18	x	1	13	20	13	21
108	Line 9	TM Tendency	74.72	5.40	42	1	13	18	55	19
109	Twist8	TM Tendency	74.66	8.72	47	0	14	21	61	21
110	Line 13	Octanol	74.60	4.08	46	5	9	30	55	35
111	Twist8	GES	74.38	2.93	45	0	14	23	59	23
112	Wheel Face 4	Roseman	74.26	7.38	37	1	13	28	50	29
113	Wheel Face 3	Fauchere Pliska	74.21	5.20	46	1	13	24	59	25
114	TMD average	Roseman	74.09	1.06	32	7	7	11	39	18
115	Wheel Face 4	GES	74.04	2.24	52	0	14	20	66	20
116	TMD	Roseman	73.64	19.92	36	1	13	34	49	35
117	TMD (18aa)	Roseman	73.53	19.92	36	2	12	17	48	19
118	Line 17	Octanol	73.36	3.68	50	3	11	15	61	18
119	TMD (18aa)	GES	73.19	5.78	42	0	14	23	56	23
120	TMD	GES	73.02	5.78	42	4	10	14	52	18
121	TMD average	GES	72.56	0.29	40	0	14	31	54	31
122	Wheel Face 3	TM Tendency	72.05	5.42	48	0	14	27	62	27
123	Wheel Face 3	Kyte & Doolittle	70.52	12.50	25	0	14	20	39	20
124	Twist8	Octanol	70.29	6.25	42	1	13	20	55	21
125	Line 9	Roseman	70.07	5.87	29	0	14	33	43	33
126	Wheel Face 3	Roseman	69.78	5.77	40	3	11	19	51	22
127	Line 17	Roseman	69.50	6.14	52	2	12	26	64	28
128	Twist8	Roseman	68.48	10.05	38	1	13	20	51	21
129	Wheel Face 3	GES	66.89	1.80	48	0	14	26	62	26
130	Wheel Face 3	Octanol	66.67	4.08	33	0	14	33	47	33
131	Line 9	Fauchere Pliska	64.34	5.19	38	3	11	16	49	19
132	Line 9	Octanol	59.24	4.08	24	2	12	26	36	28
133	TMD len	NA	-53.23	20.00	11	5	9	52	20	57

Table 4.6: Metrics using a helical wheel geometry are the best predictors for localization of unknown TA proteins. A list of the metrics used ranked by performance over the entire yeast dataset (old and new localizations included) with number of correctly predicted TA proteins listed.

i	Metric	Scale	AUROC	Best Threshold	TP	FP	TN	FN	Correct	Incorrect
1	Patch 11	Kyte & Doolittle	82.63	28.00	94	3	42	70	136	73
2	Segment 19	Kyte & Doolittle	82.61	38.20	114	8	37	50	151	58
3	Segment 11	Kyte & Doolittle	82.06	27.80	111	6	39	53	150	59
4	Patch 11	TM Tendency	81.74	12.66	107	4	41	57	148	61
5	Patch 15	TM Tendency	81.51	14.71	107	2	43	57	150	59
6	Patch 15	Kyte & Doolittle	81.23	33.40	93	3	42	71	135	74
7	Segment 11	TM Tendency	80.75	12.90	101	3	42	63	143	66
8	TMD	Kyte & Doolittle	80.72	32.10	142	15	30	22	172	37
9	Patch 19	TM Tendency	80.59	13.13	125	9	36	39	161	48
10	TMD (18 aa)	Kyte & Doolittle	80.53	32.10	143	15	30	21	173	36
11	Segment 15	Kyte & Doolittle	79.78	34.90	112	7	38	52	150	59
12	Patch 19	Kyte & Doolittle	79.55	30.10	113	10	35	51	148	61
13	Segment 15	TM Tendency	79.24	17.16	86	1	44	78	130	79
14	TMD	TM Tendency	79.17	18.86	106	9	36	58	142	67
15	TMD (18 aa)	TM Tendency	78.91	18.86	106	9	36	58	142	67
16	Wheel Face 8	Kyte & Doolittle	78.90	22.70	131	16	29	33	160	49
17	TMD (avg)	Kyte & Doolittle	78.89	1.71	129	14	31	35	160	49
18	Wheel Face 9	Kyte & Doolittle	78.88	26.60	113	11	34	51	147	62
19	Segment 19	TM Tendency	78.40	19.68	85	1	44	79	129	80
20	Wheel Face 9	TM Tendency	77.47	13.05	94	4	41	70	135	74
21	Wheel Face 8	TM Tendency	77.23	11.32	116	13	32	48	148	61
22	Wheel Face 7	Kyte & Doolittle	77.19	23.30	99	10	35	65	134	75
23	Wheel Face 7	TM Tendency	76.99	11.10	88	5	40	76	128	81
24	Rectangle 9	TM Tendency	76.76	7.98	110	9	36	54	146	63
25	Wheel Face 6	Kyte & Doolittle	76.52	21.20	91	8	37	73	128	81
26	Wheel Face 6	TM Tendency	76.44	9.28	109	10	35	55	144	65
27	Twist 8	Kyte & Doolittle	75.75	20.90	95	5	40	69	135	74
28	Star 8	TM Tendency	75.71	7.08	99	9	36	65	135	74
29	Wheel Face 5	Kyte & Doolittle	75.55	19.20	82	4	41	82	123	86
30	Twist 8	TM Tendency	75.37	9.58	89	5	40	75	129	80
31	TMD (avg)	TM Tendency	75.06	0.92	96	11	34	68	130	79
32	Rectangle 9	Kyte & Doolittle	74.84	18.00	100	9	36	64	136	73
33	Wheel Face 4	Kyte & Doolittle	72.90	15.60	88	11	34	76	122	87
34	Line 9	TM Tendency	72.76	5.35	123	17	28	41	151	58
35	Wheel Face 5	TM Tendency	70.75	9.10	71	5	40	93	111	98
36	Wheel Face 4	TM Tendency	70.65	7.08	111	17	28	53	139	70
37	Line 13	Kyte & Doolittle	70.51	12.70	130	19	26	34	156	53
38	Line 9	Kyte & Doolittle	69.50	11.10	126	21	24	38	150	59
39	Star 8	Kyte & Doolittle	69.32	14.90	106	15	30	58	136	73
40	Line 17	Kyte & Doolittle	69.28	14.00	122	19	26	42	148	61
41	Line 13	TM Tendency	69.00	7.18	78	7	38	86	116	93
42	Line 17	TM Tendency	67.77	6.91	117	17	28	47	145	64
43	Wheel Face 3	TM Tendency	65.30	5.40	134	25	20	30	154	55
44	Wheel Face 3	Kyte & Doolittle	64.40	12.60	66	6	39	98	105	104
45	TMD length	NA	59.14	21.00	27	8	37	137	64	145
46	CTE negative charge	NA	-61.06	11.00	164	45	0	0	164	45
47	CTE net charge	NA	-62.47	-2.00	3	3	42	161	45	164
48	CTE positive charge	NA	-72.68	8.00	162	45	0	2	162	47

Table 4.7: Hydrophobic geometry metrics better classify human TA proteins than total TMD hydrophobicity metrics. A list of all the metrics tested against the human genome ranked from highest to lowest AUROC score.

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