Degradation of Structurally Characterized Proteins … · THE JOURNAL OF BIOLOGICAL CHEMISTRY 0...

THE JOURNAL OF BIOLOGICAL CHEMISTRY 0 1988 by The American Society for Biochemistry and Molecular Biology, Inc.

Vol. 263, No. 36, Issue of December 25, pp. 19850-19862,1988 Printed in U. S. A.

Degradation of Structurally Characterized Proteins Injected into HeLa Cells TESTS OF HYPOTHESES*

(Received for publication, June 21, 1988)

Scott W. Rogers$$ and Martin RechsteinerSY From the Departments of $Biology and TBiochemistry, University of Utah, Salt Lake City, Utah 84132

We have compared sequence and structural features of 35 proteins to their metabolic stabilities in HeLa cells. No relationship was observed between the half- life of an injected protein and its subunit molecular weight, isoelectric point, hydrophobicity, thermostability, surface charge density, or N-terminal residue. Other properties, including susceptibility to oxidation, specific combinations of amino acids, secondary structure composition, and solvent exposed residues, also failed to correlate with protein stability. Although a weak inverse correlation was obtained when stability was compared to asparagine and glutamine content, we conclude that the degradation of an injected protein is unlikely to be related to any single structural parameter. Rather, we hypothesize that it results from an interplay between subcellular location and still poorly defined surface features of the injected proteins.

In the preceding papers (1, 2), we reported the half-lives, intracellular distributions, and the extent to which lysosomes participate in the degradation of 35 proteins injected into HeLa cells. We now use these data to evaluate a number of proposed correlations between protein structure and intracellular stability. We also examine potential relationships between susceptibility to proteolysis within the cytosol and previously unavailable features of proteins, including amino acid composition, secondary structure, and solvent accessibility of the amino terminus.

MATERIALS AND METHODS

Basic Measurements Radiolabeling, red blood cell-mediated microinjection, half-life

measurements, cellular distribution, and the effect of lysosomotropic agents on degradation were reported in the preceding papers (1, 2). Table I lists the injected proteins, their half-lives, and their extract- ability upon treating injected HeLa cells with buffers containing Triton X-100.

Determination of Isoelectric Point Isoelectric points of radioiodinated proteins were determined by

tube gel isoelectric focusing as described for the first phase of the two-phase system of O’Farrell (3). Gels run to equilibrium were expelled from glass tubes by hydrostatic force, sliced into 5-mm segments, and the amount of isotope in each piece was measured. Tube gels prepared in parallel were similarly sectioned, and the

* This work was supported by Grant GM 27159 from the National Institutes of Health. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Present address: The Salk Institute, Molecular Neurobiology Laboratory, La Jolla, CA 92037.

segments were incubated for 8 h in 1 ml of glass-distilled water in a sealed tube before determining the pH. Representative results from such analyses are shown in Fig. 1, and a compilation of isoelectric points is presented in Table 11.

Calculation of Protein Physical Parameters Hydrophobicity-We have used several empirical techniques to

estimate the hydrophobicity of the 35 injected proteins. First we computed simple ratios of polar to nonpolar and hydrophilic to hydrophobic amino acids (33). Hydrophobicity was also estimated from the average hydrophobicity scale of Bigelow (34). In this method average hydrophobicity (H) is determined by solving the equation: H = Zi A G A where AG,i is the transfer free energy for the ith residue and Xi is the percent of that residue in the protein. Values for the term G,i are controversial, as are most estimates of hydrophobicity (see Ref. 35 for discussion). Therefore we used values from Kyte and Doolittle (36), Dayhoff (37), or the consensus scale of Sweet and Eisenberg (38). None of the scales produced significant correlations.

Although useful, these estimates of hydrophobicity fail to account for the asymmetric distribution of amino acids within proteins (39). In addition, since the surface of the protein may contain the signals for degradation (40), it is important to consider only those amino acids which compose the protein surface. For 22 of the proteins in this study the solvent exposure index of each residue has been reported by Kabsch and Sander (41), who used computer aided algorithms to “roll” a water molecule over calculated x-ray structures of proteins. Their calculations allowed us to estimate “surface hydrophobicity.” To do this, proteins were divided into- two layers where residues with a solvent exposure index of four (40 A*) or greater were termed “solvent exposed” and those with a solvent exposure index of less than four as “buried.” The total number and the percent of residues in each layer was then computed for the 22 proteins, and hydrophobicity values were again calculated using only values for solvent exposed residues. The 22 proteins used in these calculations were ABP, ADK, ADH, CAB, CAT, CCC, CHY, CPA, DHF, ELA, GPD, HEM, LCD, LYS, MYO, PAR, PLA, RNA, SOD, SUB, TRI, and TRY.

Susceptibility to Oxidation-The susceptibility of proteins to oxidation was estimated as follows. First, the number and percent of oxidizable residues (Le. Cys, His, Met, Trp, and Tyr) were determined for each protein. Second, because the susceptibility to oxidation varies with pH (42), the relative susceptibility of a protein to oxidative damage was calculated for pH 7 by multiplying the fraction of each oxidizable residue by its correction value (see Table 111). These values were also recalculated using only solvent-exposed residues as defined under hydrophobicity.

Surface Negative Charge Density-This parameter was calculated in the following manner. First, surface negative charge density was calculated for each protein as described by Momany et al. (43). At the time of their report most available protein sequences failed to distinguish between Asp and Asn or Glu and Gln. Consequently, the total number of Asx and Glx residues was calculated; the total number of Arg, His, and Lys residues was subtracted, and this value was then divided by their estimate of protein surface area (Sa) calculated for either subunit or total molecular weight (MJ using the formula: Sa

With the availability of complete sequence data for the proteins in this study, we recalculated these values to include only Asp and Glu and estimated protein surface area from molecular dimensions given in the references in Table I of (1). In addition, the surface area for

= 0.1 x M:’3.

19850

Protein Structure versu 19851 s Intracellular Stability

1.4 0

TABLE I Half-lives and extractabilities of 35 proteins injected into HeLa cells

Extracted Classifi- in buffers cation” containing

Triton XlOW

- 8

...’ - 7

- 6

- 5

- 4

.m- 3 2 4 6 8 10 12 14

1.2 Protein

? 0 x 0.8

1 .o T-

h % I 133 14 MPI 139 12 MPI 214 7

Adenylate kinase (ADK) Alcohol dehydrogenase (ADH) Alcohol dehydrogenase

Aldolase (ALD) D-Amino acid oxidase (AAO) Arabinose binding protein (ABP) Aspartic amino transferase (AAT) Carbonic anhydrase B (CAB) Carboxypeptidase A (CPA) Catalase (CAT) Chymotrypsinogen (CHY) Citrate synthase (CIS) Cytochrome c (CCC) Dihydrofolate reductase (DHF) Elastase (ELA) Ferritin (FER) Glyceraldehyde-3-phosphate

Hemoglobin (HEM) Lactate dehydrogenase (LDH) Light chain dimer McG (LCD) Lysozyme (LYS) Myoglobin (MYO) Parvalbumin (PAR) Phosphoglycerate kinase (PGK) Phosphoglycerate kinase

Phospholipase A2 (PLA) Phosphorylase A (PPA) Pyruvate kinase (PYK) Ribonuclease A (RNA) Subtilisin BPN’ (SUB) Superoxide dismutase (SOD) Thioredoxin (THI) Triose phosphate isomerase (TPI) Trypsin inhibitor, soybean (STI) Tmsinogen (TRY)

(YDH - yeast)

dehydrogenase (GPD)

(YGK, yeast)

2 5 0.6 I MPI

77 118 65 66 72

137 80 50

128 26 97 46 61 84

209 171 48 16

127 41

210 207

25 12

E I I MPI I E MPI MPI I E I I

MPI I E E MPI MPI I MPI

27 29 86 18 58 36 14 74 20 38 38 12

89 14 53 66 71 53 14 19

0.4

0.2

cm from bottom FIG. 1. Isoelectric focusing of arabinose-binding protein

and glyceraldehyde-3-POa dehydrogenase. ABP and GPD were radioiodinated by the chloramine-T method and subjected to isoelectric focusing as described under “Materials and Methods.” Similar analyses were performed on 11 other proteins, and the results are summarized in Table 11.

TABLE I1 General DroDerties of Droteins injected into HeLa cells

subunits No. of Melting

temperature‘ Subunit

molecular weight”

22 40

Isoelectric point

Literature Measured*

6.1 (4)d 6.8 (5) 5.4 (5) 6.1 (6) 6.8 7.2 (7) 7.7 5.8 (8) 5.5 5.6 (9) 6.4 5.7 (10) 6.0 (11) 5.8, 6.2 5.8 (12) 5.1, 5.8 9.1 (13) 9.5 (8) 9.2 (14) 8.3 (8) 8.5 (13) 5.0 (15) 6.6 (16) 6.3 7.5 (17) 7.5 6.6 (18) 7.1 7.0 (8)

11.0 (19) 7.6 (20) 7.6, 7.8 4.5 (21) 7.2 (22) 7.2 (22) 7.3 (23)

6.0 (24) 9.5 (25) 7.8 (26) 5.0 (27) 4.5 (28) 6.4 (29) 6.0

7.5 (22) 6.0-6.5

4.5 (20) 4.0-4.8

Protein

ADK ADH YDH ALD AAO ABP AAT CAB CPA CAT

MPI 78 31 I 136 16 I 181 10 E 61 73 E 84 24 I 186 75 MPI 117 80 I 122 67 E 40 47 E 44 35

1 2 4 4 2 1 2 1 1 4

62 64 64 58

37 39 40 33 46 29 34 62 25 50 12 12 26 19 35 15

57 63 61 56

83

57

CHY CIS

1 2

~ ~ ~

The proteins are classified on the basis of their original location as follows: I = intracellular, E = extracellular and MPI = misplaced intracellular. The latter category encompasses procaryotic intracellular proteins (e.g. THI), eucaryotic organellar proteins that have been reintroduced into the cytosol (CPA, CIS, CCC, PLA), tissue specific proteins (ADH, HEM, MYO, PAR), and yeast intracellular proteins (YDH, YGK). Although one could argue that almost all proteins should be classified MPI since only two are of human origin, we consider mammalian cytosolic proteins to be interchangeable.

* See the first paper in this series (1) for details on the measurement of protein stability.

E See the second paper in this series (2) for methods used to extract proteins from injected HeLa cells.

ccc DHF ELA FER GPD HEM LDH LCD LYS MY0 PAR PGK YGK PLA PPA PYK RNA SUB SOD THI TPI ST1

1 1 1

-24 4 4 4 2 1 1 1 1 1 1 2 4 1 1 2 1 2 1

-39 67

-56

72 76

36 23 15 16 12 46 46 14 97 58 13 28 16 12 27 22

56

48 62 63 65 83

22 proteins in this study and solvent accessibility of Asp and Glu contained within these proteins has been reported as noted above (41). This allowed us to calculate the surface negative charge density with greater precision.

Statistical Analyses-Statistical analyses were performed on an IBM-personal computer using the “Number-Cruncher” statistical package (version 4.0). In addition to the correlation analyses, the scatter-plot for each test was inspected on screen, and the correlation coefficient verified using an original program written in IBM-BASIC. In this manner, some 12,000 attempted correlations were scanned.

On average, extracellular proteins were degraded more rapidly than intracellular proteins after injection into HeLa cells ( 2 ) . Hence, for all analyses, proteins were grouped as follows: 1) the entire set of proteins, 2) intracellular versus extracellular, and 3) Triton X soluble (270%) uersus insoluble proteins (520%, see Table I). The results were similar regardless of these groupings, so we usually present just those correlations based on the entire set of proteins.

61 77 55 TRY 25 1 9.3 (30)

Taken from sources cited in Table I of Ref. 1. Isoelectric points of chloramine T radioiodinated proteins were

measured as described under “Materials and Methods.” Multiple entries are values from replicate measurements.

Values taken from Refs. 31 and 32. References to values are provided; some values were estimated as

described in Ref. 8.

19852 Protein Structure versus Intracellular Stability

RESULTS AND DISCUSSION

T h e Effect of Size and Charge on the Intracellular Stability of Proteins-Size was one of the first proposed correlations between protein structure and half-life. In 1970, Dehlinger and Schimke (44) used the double-isotope method (45) to examine the turnover of soluble rat liver proteins, and they found larger polypeptides were degraded faster than smaller. These studies were soon extended to rat liver membrane proteins (46), ribosomes (47, 481, and chromosomal proteins (49). During the past 15 years, the relationship between size and turnover has been re-examined in other tissues. Hendil (50) measured the turnover of total cellular proteins in mouse fibroblasts under different growth conditions and observed isotope ratios consistent with the preferential degradation of larger proteins. Likewise, Acton and Gupta (51) examined the stability of five plant enzymes by a density-labeling procedure and found a significant inverse relationship between subunit size and stability. Neff et al. (52) injected a mixture of iodinated rat liver proteins into IMR 90 human fibroblasts and obtained results supporting the proposition that larger proteins are more labile.

On the other hand, two-dimensional sodium dodecyl sul- fate- polyacrylamide gel electrophoresis analysis of Esche- richia coli (53) or mouse blastocyst proteins (54) failed to reveal a significant correlation between size or charge and half-life, although in the latter study, trends in the data did agree with the proposition that larger proteins are degraded more rapidly than smaller. Several reports by Mayer and his colleagues (55,56) also provided no evidence for a relationship between the size of a polypeptide chain and its rate of turnover. In experiments similar to those described by Neff et al. (50), we injected a mixture of iodinated proteins into HeLa cells finding bias against larger proteins but no relationship of intracellular stability to isoelectric point (57). Moreover, recent experiments from Hendil’s laboratory do not confirm that larger cytosolic proteins are degraded faster than smaller in diploid human fibroblasts (58). Thus, there has been inconsistent support for the idea the larger proteins are degraded faster than smaller.

In view of this controversy, we re-examined the hypothesis in the present study. The 35 proteins surveyed by microinjection vary from 12,000 to 97,000 daltons in subunit molecular mass and from 12,000 to 500,000 daltons in total molecular mass (see Table 11). Despite this substantial range in protein size, we found no evidence for inverse relationship between molecular weight and stability (see Fig. 2). In fact, larger proteins were slightly, although not significantly, longer-lived than smaller proteins. An obvious explanation for our results involves the source of the proteins under study. Most of the smaller proteins are extracellular and shorter-lived. The larger kinases and glycolytic enzymes normally reside in the cytosol, which may account for their relatively longer half- lives. In addition, extracellular proteins are more easily extracted from injected HeLa cells than are reintroduced cellular proteins, and we have suggested that protein location may be an important determinant of stability (2). Still, molecular weight failed to correlate with stability even when analyses were restricted to intracellular proteins. Although the present results align with those failing to demonstrate a relationship between size and stability (53-58), it should be noted that only 18 intracellular proteins were analyzed by injection, a small number compared to the total pool of cellular proteins.

Like size, the charge on a protein was an early focus of attention with regard to metabolic stability. In 1975, Dice and Goldberg (59) compared the isoelectric points of 22 proteins to their half-lives and concluded that negatively charged proteins are degraded more rapidly than positively charged

proteins. But like size, this proposed relationship has also failed to receive consistent support (51, 52, 59 uersus 53, 58, 60). In addition to the studies just cited, microinjection of IgG molecules into HeLa cells revealed that positively charged IgGs were degraded more rapidly than negatively charged members of the population (60). This result, contrary to the charge hypothesis, is perhaps more compelling than some since IgG molecules should have equivalent conformations irrespective of their isoelectric points, which are largely dic- tated by heavy and light chain variable regions. However, the isoelectric points of iodinated immunoglobulin G molecules did not extend below pH 6 or above pH 7.5, so these serum proteins did not provide a stringent test of the isoelectric hypothesis. By contrast, the proteins examined in this series of experiments are evenly distributed at pH values between 4.5 and 11 (see Table 11). Thus, if isoelectric point per se were a strong determinant of half-life, the points in Fig. 3 would form a line with positive slope. The expected correlation is

100 e

l e e e

e

e e . e.

e . 0 . e 1 e

* e . e . e e

e 0 . *

40 80 120 160 200 241

Half-life (hours)

FIG. 2. Protein stability and subunit molecular weight. Half-life values taken from Table I are plotted against subunit molecular weights as reported in Table 11. The coefficient of correlation between these entries is r = 0.32.

0 0

e o 0 % e

e

a e

J - 40 ao I 20 160 200 240

Half-life (hours) FIG. 3. Protein stability and isoelectric point. Protein half-

lives from Table I are plotted against isoelectric points as measured in this study (0) or obtained from the literature (0). The correlation coefficient between these variables is r = -0.30.

Protein Structure versus Intracellular Stability 19853

clearly absent. These results constitute the third failure by us to confirm the proposed isoelectric point correlation (see 57 and 60). Possible explanations for these failures are presented in the general discussion below.

Hydrophobicity-Segal et al. (61) and Bohley et al. (62) have reported that hydrophobic proteins are degraded more rapidly than hydrophilic. They labeled whole cell proteins in vivo and then separated them using water-organic phase partitioning (61) or chromatography on octyl-Sepharose (62). To examine each of the 35 surveyed proteins by phase partitioning or chromatography would have required months of extra effort. Consequently, we used several empirical techniques to estimate the hydrophobicity of the injected proteins (see “Mate- rials and Methods” and Table 111). When these estimates of hydrophobicity were compared to half-life, there was no correlation (see Fig. 4A). Half-lives of the injected proteins did not correlate with surface hydrophobicity either (Fig. 4B).

Thermostability-Protein thermostability, as reflected by the unfolding temperature (T,,,), was reported by McLendon and Radany (63) to correlate with metabolic stability. T,,, values are available for 23 of the proteins in this study (Table 11), and a test of this relationship fails to support their finding (see Fig. 5). The discrepancy between the two studies may be explained by the half-life values used in their analysis; the stability of ribonuclease was overestimated almost 5-fold, and the stability of pyruvate kinase was underestimated by a similar amount. Correcting these two values changes their

I ”

l -

l -

l -

l -

I - l -

l -

l -

I ”

-

I -

-

e e* e .

e

e e

e

e e

e 5.

‘ B

e

e

40 80 120 160 200

Half-life (hours)

FIG. 4. Protein stability and hydrophobicity. Each entry represents the half-life of an injected protein compared with its average (A) or surface average ( B ) hydrophobicity. The estimates of hydrophobicity were calculated as described in Table I11 and under “Ma- terials and Methods.” The coefficient of correlation for entries in panelA is r = 0.25; for those in panel B, it is r = 0.17.

t

I 50

ea e e

e

e

* 40 80 120 160 200 240

Half-life (hours)

FIG. 5. Protein stability and thermostability. This plot shows the relationship between half-lives of 24 injected proteins and their reported melting temperature (Table 11). The coefficient of correlation between these parameters is t = -0.03.

correlation from r = 0.94 to r = 0.02, a value similar to that reported here for 23 proteins ( r = -0.03).

Whether T,,, should correlate with the half-life of intracellular protein is unclear since it does not reflect localized, transient unfolding, but only the net stability of the protein (64). In this context protein-protein interactions, which can increase thermostability, do not necessarily increase half- lives. For example, some trypsin-trypsin inhibitor complexes are degraded at the same rate as free trypsin or free inhibitors (40) despite the greater thermostability of the complexes (65). Also, iodination can introduce an intramolecular cross-link in lysozyme, thereby increasing thermostability (66), yet chloramine T lysozyme is degraded as fast as Bolton-Hunter- labeled lysozyme within HeLa cells. Finally, the half-lives of RNase A and S protein are equivalent (40, 67) even though their melting temperatures differ by almost 30 “C (68). Pri- valov (69) further reports the thermal stability of “very different proteins does not differ greatly.” Notably, he illustrated this point with cytochrome c, lysozyme, RNase A, and myoglobin, proteins shown in this study to differ in half-life more than 6-fold.

Oxidation-There is abundant evidence that oxidation of proteins can enhance their rates of degradation in vivo and in vitro. Stadtman (70) has proposed that mixed-function oxidase systems first inactivate certain enzymes, and the modi- fied proteins are then degraded by specific enzymes. The recent identification and purification of an E. coli protease that cleaves oxidized glutamine synthetase, but spares the native enzyme (71), as well as the report of similar activities in rat and mouse liver (72, 73) provide support for this hypothesis. Goldberg and his colleagues (74) have shown that oxidative damage of red blood cells leads to enhanced degradation of damaged proteins. Earlier studies using rat liver extracts led to the suggestion that oxidation of cysteine residues may increase the susceptibility of specific proteins to degradation, possibly by mediating membrane attachment (75).

The changes in protein structure that accompany oxidative damage are largely unknown. Indeed, such conformational adjustments will vary from one protein to another and to depend upon the exact site of oxidation. It is clear, however, that the five amino acids, Cys, His, Met, Trp, and Tyr are likely targets for most oxidative damage (42). Therefore, we


I

140 t . h h . .

40 00 120 160 200 240

Half-life (hours)

FIG. 6. Protein stability and oxidation. Half-lives for the proteins in Table I were compared to their susceptibility to oxidation (see Table 111). As described under “Materials and Methods,” relative oxidation values were determined by summing the subunit susceptibility of Cys, His, Met, Trp, and Tyr to oxidation at pH 7 (42) and then dividing this value by the subunit molecular weight. The correlation coefficient is r = -0.25.

asked whether the intracellular stability of the proteins in our data set was related to their content of these residues. All oxidizable amino acid combinations (ie. Cys, Cys + His, Cys + His + Met, etc.) were tested, and representative results are reported in Fig. 6, and Table 111. Although by some statistical criteria, marginally significant relationships between potential oxidation and stability were obtained, visual inspection of the graphed data did not convince the authors that the correlations were biologically significant.

The content of oxidizable amino acids may not be a good measure of the susceptibility of a protein to oxidative damage. For example, a number of the examined proteins contain disulfide bonds, and cystines are not susceptible to oxidation. Therefore, we repeated the analysis after excluding proteins with cystine cross-links. This correction did not significantly change any conclusions. The analysis just described does not address the impact of the oxidizing specific residues, particularly those thought to be more susceptible to oxidation (e.g. those in an active site (70)). Similarly, microenvironments of specific residues, the presence of metals and other ligands could all influence the extent of damage to the polypeptide chain. However, we consider it significant that the oxidative conditions required for chloramine T iodination did not mark-

TABLE I11 Estimated features of proteins injected into HeLa cells

hydrophobicitf Average Susceptibility to oxidation at pH 7‘

Subunit Surface* Subunit

CYS HIS MET TYR TRP Total

Surface

CYS HIS MET TYR TRP Total

ADK ADH YDH ALD AAO ABP AAT CAB CPA CAT CHY CIT ccc DHF ELA FER GPD HEM LDH LCD LYS MY0 PAR PGK YGK PLA PPA PYK RNA SUB SOD THI TPI ST1 TRY

1079 1138 1089 1075 1189 1112 1158 1083 1147 1147 1053 1170 1105 1227 1015 1039 1090 1117 1145 977 998

1133 1050 1091 1095 1031 1178 1063 873 982 892

1225 1076 1137 1009

866 1080

987

862 1001 650 911

964 1085 932

874 904

78 1 798 830 809

919

706 931 792

775

765

4.7 10.7 17.2 19.5 10.6 29.9 10.2 31.7 6.4 26.2 1.5 10.2 5.6 20.2 1.8 44.0 3.0 27.1 3.7 44.4

19.1 8.6 4.2 33.3 8.8 30.0 2.4 22.0

15.3 26.0 5.3 35.9 5.5 34.5 3.3 73.8 6.9 21.9

10.6 14.4 28.5 8.1 0.0 81.6 0.0 19.1 7.7 15.0 1.1 20.0

44.9 25.4 4.9 27.2 7.2 26.4

29.7 33.5 0.0 22.7 9.1 55.1 8.5 9.6 9.3 16.8

10.2 11.5 24.1 13.6

10.2 7.9 5.7 2.7 4.6

10.8 4.8 2.5 3.2 6.7 2.7

11.3 6.3 8.7 2.8 5.7 8.9 4.7 8.9 0.0 5.1 4.3 9.1

10.3 2.4 5.4 8.2

11.0 10.6 4.8 2.2 3.1 2.7 3.6 2.9

32.0 9.8

35.6 29.4 34.7 17.8 25.8 27.6 55.2 36.5 15.1 39.2 34.7 37.4 40.9 31.1 24.0 18.7 21.4 45.4 20.5 17.8 0.0 6.2

15.1 57.8 38.3 16.0 42.7 39.2 6.2

16.9 14.2 19.6 39.2

0.0 57.6 ~ ~~

4.1 58.5 11.3 93.1 6.5 80.5 6.5 78.4

13.0 53.3 17.8 74.2 18.6 94.5 18.6 107.1 9.7 101.1

26.7 72.2 17.0 105.0 8.1 87.9

13.0 83.5 23.5 108.5 4.9 82.9 7.3 80.2 5.7 106.2

14.6 73.7 11.3 81.7 38.1 100.3 10.5 114.2 0.0 28.2 9.7 48.9 4.1 42.7 6.5 140.0

11.3 89.9 4.9 65.5 0.0 116.5 8.9 75.6 0.0 72.6

15.4 53.5 16.2 59.2 8.9 53.8

2.4 10.7 2.5 11.1

0.0 3.4

0.0 20.0 3.0 10.2 0.2 27.5 1.9 4.3

4.4 30.0 0.0 22.0 0.0 4.3

1.4 25.1 0.0 118.0

2.1 14.4 0.0 72.6 0.0 54.4 0.0 9.5

22.4 25.4

0.0 16.8 0.0 3.8 0.0 6.9

0.0 4.2

14.6 94.4 2.0 9.1

5.1 3.5

1.1

0.0 0.0 2.0 1.4

6.3 5.2 0.0

2.0 2.3

0.0 0.0 0.0 9.1

2.7

0.0 1.2 0.0

0.0

0.0

16.7 6.5

15.9

15.6 31.7 19.8 10.1

7.8 21.4 33.8

7.3 17.7

22.5 12.6 10.6 0.0

26.3

32.7 29.5 5.4

0.0

0.0 34.9 4.9 28.4

5.8 26.2

3.4 39.0 5.8 50.7 0.0 49.5

11.1 28.8

0.0 48.5 0.0 48.6

11.1 49.2

2.7 38.5 6.3 144.3

4.1 43.1 13.8 99.0 0.0 65.0 0.0 18.6

101.3 178.1

0.0 49.5 42.0 76.5 0.0 12.3

3.6 7.8

21.2 7.8 40.1

Average hydrophobicity was calculated according to the method of Bigelow (34; see “Materials and Methods”) using the Kyte and Doolittle (36) amino acid hydrophobicity scale.

Surface hydrophobicity was calculated by including only amino acids with a solvent exposure index of 4 or greater as defined under “Materials and Methods.”

This parameter was calculated by multiplying the mol % of an oxidizable residue by the residue’s relative susceptibility to oxidation at pH 7.0 (see “Materials and Methods”). Susceptibility values, which are Cys = 4.6, His = 10.4, Met = 3.3, Tyr = 8.1, and Trp = 8.9, were taken from Ref. 42.


TABLE IV ASNIGLN content and surface negative charge density

7% ASN + GLN content Surface negative charge density

(density function, X 10-*)0

Subunit total Subunit surface Subunit total Subunit surface

1 ADK 2 ADH 3 YDH 4 ALD 5 AAO 6 ABP 7 AAT 8 CAB 9 CPA

10 CAT 11 CHY 12 CIT 13 CCC 14 DHF 15 ELA 16 FER 17 GPD 18 HEM 19 LDH 20 LCD 21 LYS 22 MY0 23 PAR 24 PGK 25 YGK 26 PLA 27 PPA 28 PYK 29 RNA 30 SUB 31 SOD 32 THI 33 TPI 34 ST1 35 TRY

1.0 2.1 3.4 3.9 8.1 2.9 4.9 6.5 5.5 4.7 5.4 4.1 4.8 4.2 7.5 3.4 3.9 2.8 6.0 3.7

10.1 0.7 0.9 5.3 3.4

13.8 5.4 3.1 8.1 5.5 4.0 3.7 3.6 5.0 7.4

3.1 4.1 2.7 2.1 4.3 1.7 2.3 5.7 6.1 10.0 4.2 12.3 3.6 6.5 5.3 3.9 8.7 3.5 10.0 4.7 3.6 9.1 7.4 4.3 8.9 4.1 9.5 6.8 3.9 8.0 2.9 7.7 0.0 3.2 7.4 2.4 6.3 13.8 7.8 6.3 9.8 1.5 5.4 5.1 0.7 3.5 3.7 4.5 10.5 4.6 8.3 5.4 2.3 12.4 8.5 3.9 4.6 3.9 0.9 1.8 0.0 1.4 6.7 1.9 5.3 0.8 14.6 15.3 3.7 9.0 3.1 6.3 5.6 13.7 9.2 3.6 9.1 8.2 2.0 6.0 12.0 2.8 6.5 4.0 7.7 5.7 2.8 7.7 5.2 12.7 10.5

ASN GLN ASN+GLN ASN GLN ASN+GLN ASX+GLX ASP+GLU ASX+GLX ASP+GLU 9.2 11.9 5.1 -5.1 0.8 -5.8 0.0 1.7

3.9 9.2

5.3 10.0 6.3 13.7

5.0 11.8

0.0 0.0 0.0 2.4 2.9 10.7

3.8 8.9 2.1 5.8

4.1 9.5 0.0 8.5 0.0 3.9 0.0 0.0

0.0 15.3

10.4 19.6 2.3 10.5 4.5 16.5

0.0 5.7

4.3 12.6 13.9 31.1 21.3 23.7 18.1 18.9 20.3 16.4 16.2

-1.3 21.0 24.0 25.3 8.4

-11.5 27.3 17.3 9.9

-12.6 7.7

15.0 10.3 23.9 27.4 14.0 14.2 24.1 9.4

21.0 16.8 23.3

-9.4 -5.4

-17.4 -6.9

1.9 -4.7 -9.6 -7.6 -5.1 -8.2 -9.6

-26.0 -9.5

-12.6 1.4

-8.4 -19.6 -4.5 -3.7

-16.6 -23.6

3.8 -7.1 -7.1 -6.8 -8.5 -7.3

-16.0 -3.2 -4.6

7.6 -4.5

5.2

-2.5

5.8

11.4 10.3

3.0

-11.7 -1.1 14.3

12.1 1.1

7.4 1.6

-3.7 1.7

11.6

7.3 23.5 16.6

8.1

-8.8

-6.5

-3.2 -6.0

-11.0

-21.7 -11.0 -8.6

1.5 -18.5

-5.3 -17.1 -11.1

0.0

-7.3

-13.1 2.0 3.0

-3.0

3.0 3.5 23.4 -10.5 8.9 -13.4 Values are rounded to nearest 0.1.

edly reduce protein half-lives relative to the milder Bolton- Hunter-labeling procedure (1).

Asparagine and Glutamine-Deamidation of Asn and Gln has been postulated to enhance degradation through local disruption of protein structure (76, 77). This proposal was tested by comparing total and solvent exposed Asn, Gln, and Asn + Gln to stability. As shown in Tables IV and V and Fig. 7, the content of Asn + Gln was inversely correlated with stability, in agreement with the results of Robinson et al. (77). A significant, inverse relationship was also found between the release of injected proteins (see (1) for discussion) and their content of Asn + Gln (r = -0.36, p < 0.05). This relationship was improved to r = -0.49 0, < 0.01) when only solvent exposed residues were included. It is possible, then, that deamidation is related to degradation and release. For example, transglutaminase-mediated cross-linking of proteins may result in aggregation, an event previously suggested to be important for endocytosis and subsequent degradation of extracellular proteins (78, 79). However, the inverse correlation with stability is not necessarily associated with either Asn or Gln separately, whereas release is clearly related to only solvent exposed Asn (Table V). It should be noted that deamidated Asn residues are particularly susceptible to meth- ylation (80), a post-translational modification that has also been suggested to signal degradation (81).

Surface Negative Charge Density-Almost 10 years ago, Momany et al. (43) reported that surface negative charge

density correlated with increased stability of globular proteins. As shown in Table VI and Fig. 8A, the earlier reported correlation was not confirmed. In a separate analysis, surface negative charge density was recalculated for 22 proteins using only solvent-exposed Asp and Glu residues, and again no correlation was found (see Fig. 8B).

The N Terminus and Intracellular Stability-Acylation of the a-amino terminus is a widespread cotranslational event (82). The blocking group is usually acetate, but when processing leaves a terminal glycine, myristic acid may be added (83). Occasionally, N-terminal glutamic acid residues may cyclize to produce pyroglutamic acid such as that found in the light chain dimer used in the present survey. In 1975, Jornvall(84) proposed that N-acylation served to protect proteins from degradation. Although Brown (85) found no differences in the rates of degradation of N-acylated and nonacylated proteins in mouse 1 cells, his experimental protocol did not distinguish cytosolic proteins from those residing in membranes or organ- elles. Today, there are ample reasons to believe that many membrane and organellar proteins have unblocked amino termini due to proteolytic processing (86). Thus, N-acylation may be an important stabilizer of cytosolic proteins.

In 1984, Hershko, et al. (87) provided direct experimental support for the idea that acylation may protect against proteolysis. Using reagents that differentially block a- or t-amino groups, they demonstrated that an unblocked a-amino terminus was an important feature of substrates for ubiquitin-


TABLE V Asparagine and glutamine content uersus the fate of injected proteins

Asparagine, glutamine Correlation coefficients with

Half-life Release Solubility mol %

Entire protein ASN -0.32 -0.35" GLN

0.01 -0.38" -0.14

ASN + GLN -0.04

-0.46b -0.36" -0.01

Solvent exposed ASN -0.27 -0.55" GLN

0.00 -0.08 -0.09

ASN + GLN -0.15

-0.25 -0.49" -0.07 Significant (p < 0.05). Significant (p < 0.01).

gl 6

0. . . 3t .

40 80 120 160 200 240 Half-life (hours)

FIG. 7. Protein stability versus the content of asparagine and glutamine. Stabilities of the injected proteins (Table I) are compared with their content of asparagine and glutamine shown in Table IV. As reported in Table V the correlation coefficient between these parameters is r = -0.46 (p < 0.01).

dependent proteolysis. Recent studies have shown that component E3, required for the transfer of ubiquitin to such substrates, preferentially binds proteins with unblocked a- amino groups (88).

Genetic approaches also emphasize the importance of the amino terminus. Bachmair et al. (89) constructed ubiquitin- p-galactosidase fusion proteins, and upon expression of the plasmid vector in yeast, they found that ubiquitin was rapidly cleaved from the amino terminus of P-galactosidase. Site- directed mutagenesis was then used to produce enzymes with different amino acids at their amino termini after ubiquitin removal. The resulting proteins varied considerably in stability: those with amino-terminal Met, Ser, Ala, Thr, Val, or Gly were stable; those with Ile, Glu, Tyr, or Gln were degraded with half-lives between 10 and 30 min; those with amino- terminal Phe, Leu, Asp, Lys, or Arg were degraded with half- lives of less than 3 min. Bachmair et al. (89) proposed the "N- end" rule: the specific amino acid at the amino terminus determines the rate at which proteins are targeted for destruc- tion.

About half of the proteins in the present survey have unblocked a-amino groups, and in many cases, the N terminus is readily accessible to solvent (see Table VII). Yet, inspection of the data in Table VI1 reveals little correlation of N- acylation to metabolic stability. Whereas there is a tendency

40

30

20

10

.- >,

v) c

U al

g o

$ -10 c 0

.- g -20 c 10 ID 0 Q C

Q 0 a 0

v)

r

5

-10

-20

-30

- A

. . .

- B . . - 0

. I I I I I 1

40 80 120 160 200 24 Half-life (hours)

FIG. 8. Protein stability and surface negative charge density. Half-lives of injected proteins (Table I) are compared to the surface negative charge density as determined empirically (panel A , see Table IV and "Materials and Methods") or as determined for the 22 proteins where solvent accessible aspartic and glutamic residues and protein surface area have been calculated from x-ray structures. The correlation coefficient between entries in panel A is r = -0.03 and for entries in panel B is r = 0.05. See Table VI for additional correlations.

TABLE VI Surface negative charge density compared to the fate of injected

nroteins

Surface negative charge Correlation coefficients with

Half-life Release Solubility

Empirical" Subunit M,

Asp, Glu, Asn, Gln -0.22 -0.02 -0.39 Asp and Glu only 0.03 0.12 -0.21

Asp, Glu, Asn, Gln 0.01 0.02 -0.37' Asp and Glu only 0.18 -0.01 0.05

Total M,

X-ray structure Subunit M,

Asp, Glu, Asn, Gln 0.18 -0.30 -0.10 Asp and Glu only 0.05 0.22 -0.25

Asp, Glu, Asn, Gln 0.13 -0.32 -0.02 Asp and Glu only 0.08 0.19 -0.26

Indices were calculated as described under "Materials and Meth-

Significant (p < 0.05).

Total M,

ods."

for longer-lived proteins to have blocked N termini, these more stable proteins are mainly enzymes that normally reside in the cytosol, and as a group, they exhibit increased associ-


TABLE VI1 Status of the N terminus compared to metabolic stability

Proteins are listed in order of increasing stability. Acylation was taken from either sequence or structural data (see Table I of (1) for references). Question marks indicate the state of acylation was unavailable or uncertain. LCD (*) is not acylated, but its N terminus is blocked by the presence of pyroglutamic acid. Relative solvent exposure is reported for the N-terminal residue and for the average of the 4 N-terminal residues as reported by Kabsch and Sander (41) where each unit represents -10A2 exposure to solvent. Those residues with a rating of 10 usually lack electron density and are therefore presumed to be completely exposed and flexible. Values marked by an (*) were estimated based on the absence of these residues from electron density maps (see Table I of Ref. 1). ND = not determined.

Blocked Protein Half-life N-Terminal or

Relative solvent exposure

Residue 1 Residues 1-4 residue

LY s ccc ST1 PAR TRY ELA LCD CHY FER RNA ABP AAT CAB PLA ALD CAT SUB GPD DHF THI AAO TPI MY0 CIS ADK PPA CPA ADH LDH PYK SOD YGK HEM PGK YDH

h 16 26 40 41 44 46 48 50 61 61 65 66 72 78 77 80 84 84 97

117 118 122 127 128 133 136 137 139 171 181 186 207 209 210 214

LYS GLY ASP ALA VAL VAL pGLU CY s SER LY s GLU ALA ALA ALA PRO ALA ALA VAL VAL SER MET ALA VAL ALA MET SER ALA SER ALA SER ALA SER VAL SER SER

No 9 Yes 10 No ND Yes 4 No ND No 0 Yes 9 No 10 No ND No 10 No 10 No? ND Yes 10 No 1 Yes ND No? 10* No 10 No 5 No 3 No ND Yes ND Yes 10 No 10 No 10* Yes 9 Yes 10 No 10 Yes 10 Yes 10 Yes 10* Yes 8 Yes ND No 10 Yes ND Yes ND

mean

5.8

ND 7.3

ND 7.3

2.0 5.8

ND 5.0

6.8

ND 7.8

5.0 5.3 ND

10.0* 8.3 2.8 1.3 ND ND 9.3 6.5

10.0* 9.8

10.0 5.8 7.3 9.5

10.0* 5.8 ND 6.8 ND ND

ation with the “cyboskeleton” (see Table I). We consider the latter property more important than their possession of blocked a-amino groups. This position is bolstered by consid- ering just those proteins readily extracted in buffer containing Triton X-100 (group C proteins as defined in Ref. 2). In such a survey (see Table VII), one finds no correlation between increasing metabolic stability and masking of the a-amino group (i.e. CCC, blocked; RNA, unblocked; CAB, blocked; THI, unblocked; SOD, blocked; and HEM, unblocked).

The data in Table VI1 further indicate that the N-end rule does not apply to folded proteins. The amino termini of LYS, ABP, and RNA consist of well-exposed lysines or glutamic acid, and according to the N-end rule, the three proteins should have been rapidly degraded following their injection into HeLa cells. In fact, their half-lives are 200 to 1000 times longer than those predicted by the rule. As we have previously speculated, the N-end pathway may be restricted to unfolded, nascent, or highly flexible polypeptides (90). Dice (91) has

presented a reasonable alternate suggestion; namely, N-end may operate on cleavage products of intracellular proteins.

The Relationship between Secondary Structure and Intra- cellular Stability-Our desire to examine the relationship of defined structural features of proteins, e.g. a-helix, p-sheet, turns, to their intracellular stabilities was the major impetus for microinjecting 35 x-rayed proteins into HeLa cells. The subsequent analyses were complicated by a lack of agreement in assigning secondary structure, particularly reverse turns. Although assignments presented in the original x-ray study or deposited into the Brookhaven Data Bank were used, those designations are often either incomplete or inconsistent among closely related proteins. Therefore, secondary structures as defined by the computer algorithms of Levitt and Greer (92) and Kabsch and Sander (41) were also used. Assignments for carboxypeptidase A using each technique, shown in Fig. 9, illustrate the differences encountered when quantitating secondary structure. Table VI11 presents complications of the amounts of a-helix, &sheet, and @-turns in the proteins surveyed. Since proteases preferentially cleave polypeptide chains in disordered regions, turns, hinges, or flexible loops (93, 94), one might have expected that the half- lives of injected proteins would be positively related to their content of “ordered structure. However, as shown in Fig. 1OA-D, there is no correlation between intracellular stability of the injected proteins and their content of a-helix, p-sheet, turns, or the sum of these secondary structures.

Other Correlations-Regression analyses on other parameters were also performed. These tests included comparisons of metabolic stability to individual amino acids, pairwise combinations of amino acids, and grouping of amino acids based upon common characteristics (e.g. aliphatic, aromatic, charged, etc.). We also limited some analyses to solvent exposed residues (see “Materials and Methods”). Regardless of classification, correlations between these parameters and stability were not identified.

The results of these statistical searches may not be surpris- ing, however, since we have shown previously (2) that degradation rates are related to both the structure of a protein and its location within the cell. This observation suggests that multiple correlative statistical tests are the next logical step in analysis of these data. Although computer-aided methods allow such calculations, we believe that random analyses hold little hope of identifying correlations of biological relevance. Therefore, we attempted a limited multiple correlative analyses based on the following assumptions. First, as discussed previously, we believe that the x-ray structure of the protein mirrors its structure within the cell. Second, we assume that the degradative machinery recognizes the surfaces of injected proteins (40). Finally, our results indicate that interaction of proteins with the Triton-insoluble fraction protects against proteolysis (2). Using these assumptions we have devised a parameter, termed “reactive index,” that may be useful in predicting the stability of injected proteins (95). Briefly, reactive index (RI) is described for Triton-soluble proteins (>70% soluble) by the equation: RI’ = Us. Ra where Us is the percent of residues found in disordered or nonassigned secondary structure (see Table VIII) and Ra is the percent solvent-accessible “reactive” amino acids. Reactive amino acids are those that are susceptible to deamidation (Asn and Gln) or oxidation (Cys, His, Met, Tyr, and Trp) and hydrophilic amino acids directly recognized by the E3 component of the ubiquitin system (Lys, Ref. 96, and Arg, Refs. 89, 97). RI’ values for Triton-soluble proteins range from 1020 for cytochrome to 28 for hemoglobin (Table IX). When the log of RI’ for soluble proteins was plotted against half-life (Fig. l l A ) , the correlation coefficient was -0.99.


FIGURE 9 . SBCONDARY STRDcl"R.E ASSIG-S FOR CARBOXYPEPTIDASE A.

1 A R S T N T F N Y A T Y H T L D E I Y D F M D L L V A Q H P ( A ) t t t t t t t a-aaaaaa-aaaa t ( B ) t t t t t t t t t t t t t t acaaaca" t ( C ) t t t t t t tt Paa1lXQMYXaCKlEl t t t t t

3 1 E L V S K L Q I G R S Y E G R P I Y V L K F S T G G S N R P t t t t BBBBBBBBBBBBBBB t t t t t t t BBBBBBBBBBBBB t t t t t t t B t BBBBBBBBBBBBBBBBBBBB t t t BBBBBBBBBBBBBBBBBBB t t t t t t t t t BBB t t t BBBBBBBBBBBBBBB t t t t t BBBBBBBBBBBBB t t t t

61

91

FIG. 9. Comparison of secondary structure assignments for carboxypeptidase A. The individual amino acids of carboxypeptidase A, presented in the one-letter code, are assigned as a- helix (a), &sheet @), or turn and loops ( t ) according to the original authors (see Table I of Ref. l), Levitt and Greer (92) 1 5 1 or Kabsch and Sander (41). The remaining proteins have been analyzed in the same manner, and their contents of helix, sheet, or turn are compiled in Table VIII. Specific assignments for other pro- 81 teins in the data set can be obtained from the authors.

2 11

2 41

2 71

301

A I W I D L G I H S R E W I T Q A T G V W F A K K F T E N Y BBBBBBBBBBBBB t t t t t t t aaaaoaaaaaaaaaaaa awaaacumaa t t t BBBBBBBBBBBBBBBBBBBBB aaaauuaaamaa"Laaa(UYTacrcoaaaaaa BBBBBBBBB t t t t t t t t a a a a a a c w a a a a a a ~ a a a a a a t

G Q N P S F T A I L D S M D I F L E I V T N P N G F A F T H t t t a a a ~ ~ a a a a BBBBBBBBBBB tttttt- t t t aaa-- BBBBBBBBBBBBBBBBBBB aa~~aoaaoco~aaaa t t t t t aaaaraaaraauxaaa t t t t t BBBBBBBBB t t t t C L Q O ~ ~ O O M L O ~ ~ ~ Q ~

S E N R L W R K T R S V T S S S L C V G V D A N R N W D A G aaa t t t t t t t t t t t t t t t t t t t a t t t t t t t BBBBBBBBBBB t t t t t t t BBBBBBB CUYXaElOCaa t t t a t t t t t t t t t aaaaa t t t t t t t t t

F G K A G A S S S P C S E T Y H G K Y A N S E V E V K S I V t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t ClaaMKlMYXCOaQMYX t t t t t t t t t BBBBB t t t BBBBB t t t t t t t t t oaaa~o~~"l t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t t aaaaaaauaaaaa

Q S I P D K T E L N Q V A K S A V A A L K S L Y G T S Y K Y ttttttaOlLuurnrrnnnrrrrnnannnnnnrura("YYJOO t t t t t t t BBB (XMOOlmaa t BBBBB

t t t aauuaaamaaaOLaaMOOaaCICIQaQO t t t t t t t t t BBB

G S I I T T I Y Q A S G G S I D W S Y N Q G I K Y S F T F E B t t t t t t t t t t t t t t t t t t aaaODLQOMaaOOCIUEl BBBBBBBBBBB BBB t t t t t t t t t t t t t t t t a a m a a a ~ a a BBBBBBBBBBBBB BBB aaaaaaa t t - t t BBBBBBBBB

L R D T G R Y G F L L P A S Q I I P T A Q E T W L G V L T I B t t t t t t t t t t t t t t t t t t t t a a a a a C a a a a BBBBBBB t t t t t t t t t t t t t t awaa"W

t t t t t t t t t t t t a 0 1 Y u m n n r r r a c u m a a a a

M E H T V N N -COOH aaaaaaaaaaaaa awaaaaaa t aaOaMxaaOt

To describe the stability of less soluble proteins, we included Triton insolubility in the RI calculation. This produced the formula: RI = RI' . (TXs)' where RI' is the reactive index as defined above and the TXs is the percent Triton solubility of the injected protein. Since solubility and structure are consid- ered equally important in determining proteolytic rate, TXs is squared to scale this parameter over the same range as RI'. When the log of RI was plotted against half-life (Fig. 11B) the coefficient of correlation for insoluble proteins was -0.86. These encouraging results suggest that RI may serve as a

useful predictor of intracellular protein stability, and we plan further tests of the measure using proteins whose x-ray structure has only recently been solved.

GENERAL DISCUSSION

Here and in the two preceding papers (1, 2) we have presented the methods used to assess the intracellular stabilities of proteins with known x-ray structure, and we have attempted to correlate the half-lives of the injected proteins to their structures. Although we obtained a weak inverse


TABLE VI11 Assignment of secondary structure .- - x loo,

% of the polypeptide chain in . I

Protein a-

helix" sheet Turns B-

helixb sheet Turns helix' sheet Turns a- /3- a- /3-

ADK 53 12 20 62 19 14 56 22 14 ADH 28 32 18 29 45 19 23 21 17 YDH ALD 40 AAO ABP 41 23 14 35 4 10 AAT 52 23 9 CAB 15 41 24 16 45 25 8 26 16 CPA 36 15 27 40 30 21 34 15 11 CAT 37 19 18 CHY 8 33 38 11 49 21 13 32 16 CIS 70 2 15 CCC 37 0 28 53 12 23 43 0 16 DHF 21 28 33 19 23 9 ELA 6 40 38 8 45 28 7 34 18 FER 60 0 24 GPD 34 30 21 32 38 22 26 20 15 HEM 86 0 12 86 0 8 78 0 9 LDH 40 28 20 42 26 19 34 11 14 LCD 11 65 5 7 69 17 1 42 10 LYS 42 20 22 46 19 23 51 9 15 MY0 80 0 15 88 0 7 77 0 10 PAR 57 6 15 58 5 16 52 6 14 PGK 39 24 16 42 33 14 YGK 38 12 17 29 22 21 PLA 50 8 28 44 0 22 PPA 42 25 15 PYK 36 26 12 RNA 28 37 15 23 46 19 18 35 7 SUB 29 16 34 28 32 37 30 18 15 SOD 0 45 52 0 52 35 0 38 17 THI 35 30 22 41 31 19 TPI 44 17 21 49 24 12 43 16 9 ST1 0 55 10 TRY 8 42 25 6 32 16

1 of (1). Author assignments are reported in references provided in Table

As reported by Levitt and Greer (92). 'As reported by Kabsch and Sander (41). Secondary structure

assignments of some proteins were estimated from homology with closely related proteins. They are: CAB from carbonic anhydrase C, CHY from y-chymotrypsin, LDH from LDH M4, LCD (McG) from LCD (NEW), and TRY from trypsin.

correlation of Asn/Gln content to stability and derived a promising, but untested empirical predictor, the results of our analyses are largely negative. That is, a number of hypotheses have not been confirmed. These hypotheses may be incorrect or our experiments may not provide adequate tests.

There are several reasons why a valid structure/stability correlation might not receive support by microinjection studies. Foremost among the limitations of our survey is bias in the set of proteins used. Compared to the several thousand polypeptides present in HeLa cells, the 35 examined in our studies are but a mere handful. Moreover, we have chosen molecules of known x-ray structure, and it is clear that proteins vary markedly in crystallizability. Consequently, any correlation between metabolic stability and protein structure based on substantial contributions from cytoskeletal, hydrophobic membrane proteins, or rapidly degraded regulatory proteins might not be confirmed by our analysis.

The likely existence of multiple proteolytic pathways presents an additional complication. Three distinct proteases have been identified in the cytosol/nuclear compartment of animal cells. A large 26 S ubiquitin/ATP-dependent protease has recently been purified from rabbit reticulocyte lysate (98); a smaller 20 S protease discovered over a decade ago (99), has

1 i " i 7 60 I

1 I . I I I I I I " I I I I I I I 1 40 SO 120 160 200 240

60 40 80 120 180 200 240

Half-life (hours)

FIG. 10. Protein stability and secondary structure. Half-lives for 32 injected proteins are compared to their content of secondary structures (see author assignments in Table VIII). Stability is plotted against a-helix in panel A ( r = 0.28), @ structure in panel B ( r = -0.251, turns and loops in panel C ( r = -0.17), and combined secondary structure in panel D ( r = 0.30).

TABLE IX Calculation of reactive index

Reactive index prime (RI') and reactive index (RI) were calculated from the amount of nonordered secondary structure (Us, see Table VIII) and from the number of surface accessible "reactive" amino acids as described in the methods. Triton solubility is from Table I. Proteins are grouped according to their solubility (>70 or <70% Triton extractable) as described in the preceding paper (2).

Protein Us Ra RI' LOG RI' TX-SOL RI Log RI

>70% Triton

CAB Soluble

20 20 400 ccc 35 32 1120 HEM 2 14 28 MY0 5 16 80 RNA 20 35 700 SOD 3 15 45

<70% Triton Soluble ADK 15 38 570 ADH 22 16 352 ABP 22 17 374 CPA CAT

22 16 352 26 23 598

CHY ELA

21 17 357 16 23 368

GPD 15 17 255 LDH 12 15 180 LCD (Mcg) 19 18 342 LY s 16 26 416 PAR 22 19 418 PLA 14 26 364 PPA 18 34 612 SUB 21 16 336 TPI 18 15 270 TRY 25 21 525

2.60 3.05 1.45 1.91 2.85 1.66

2.76 2.55 2.58 2.55 2.78 2.56 2.57 2.41 2.26 2.54 2.62 2.62 2.56 2.79 2.53 2.43 2.72

86 74 89 71 72 77

14 12 27 18 53 36 38 12 14 53 66 53 31 16 24 67 35

2,958,400 6.48 6,133,120 6.80

221,788 5.35 403,280 5.61

3,628,800 6.57 266,805 5.43

111,720 5.05 50,688 4.71

272,646 5.44 114,048 5.06

1,679,782 6.23 462,672 5.67 531,392 5.73 36,720 4.57 35,280 4.55

960,678 5.99 1,812,096 6.27 1,174,162 6.08

349,804 5.55 156,672 5.20 193,536 5.29

1,212,030 6.09 643,125 5.81

now been purified from a variety of tissues (100-104). In addition, several calcium-activated proteases are found in the cytosol (105). If each protease were responsible for degrading a subset of the injected proteins, then pooling data from all


4.2 1 I I I 1 40 80 120 160 200 240

Half-life (hours)

FIG. 11. Protein stability and reactive index. Each entry represents the half-life of a protein graphed against the log of its reactive index. See the text and Table IX for methods of calculating reactive index. Panel A shows the relation between stability and RI’; entries distinguished by filled circles (0) were greater than 70% Triton soluble. The correlation coefficient for these parameters is r = -0.99 (p c 0.001). In panel B reactive index, which includes a correction for Triton solubility, is plotted against the half-life for 23 proteins reported in Table IX. Filled circles represent proteins greater than 70% Triton soluble whereas the open circles represent the remaining less soluble proteins. Two lines are evident. The correlation coefficient between half-life and insoluble proteins is r = -0.86 ( P < 0.001). In the upper line, composed mostly of soluble proteins, two “insoluble” proteins are present (CAT and TPZ). The insoluble proteins were not included in the calculation of the upper line. The correlation value for these proteins is -0.93 (p < 0.001).

35 proteins might obscure correlations that would be evident for substrates of a specific protease. Unfortunately, at present, we know almost nothing about the natural substrates of the three known cytosolic proteases. Nor do we know how many different cytosolic pathways will eventually be discovered.

It is also an implicit assumption of our experimental strat- egy that injected proteins are selected for degradation in conformations closely resembling their crystal structures. If intracellular proteolytic pathways preferentially recognize unfolded conformations, our failure to correlate structure and stability would be readily explained. In fact, such a possibility invalidates the use of structurally characterized proteins as probes. Fortunately, several studies support the idea that polypeptide unfolding or chain flexibility are not major determinants of protein turnover rate.

RNases and trypsin/trypsin inhibitor complexes provide two sets of proteins exhibiting large flexibility differences and little structural variation within each series. We previously injected proteins from each set into HeLa cells to assess the

influence of protein flexibility on stability in vivo (40). RNase- A, RNase-S, and S-protein had equal stabilities within the cytosol even though the latter two proteins were rapidly degraded by trypsin, pepsin, and papain in vitro. Equivalent results were obtained by Dice and his colleagues (67) using IMR 90 fibroblasts as recipients. Consistent with results using RNases, the intracellular half-lives of anhydrotrypsin and various proteinaceous trypsin inhibitors were unaffected by forming complexes between protease and inhibitors, a process that stabilizes both proteins against unfolding (40). It would appear, then, that degradation of RNases, trypsin, and trypsin inhibitors in HeLa cells is controlled by proteolytic systems that recognize surface features of the folded proteins.

Two other injection studies suggest that unfolding does not necessarily enhance degradation. Hough and Rechsteiner (106) measured the degradation of several injected proteins in HeLa cells maintained at temperatures between 6 and 37 “C. They observed Arrhenius activation energies (E,s) of 27 f 5 kcal/mol for all proteins tested. Since both local protein unfolding and peptide hydrolysis by defined proteases proceed with E,s between 5 and 15 kcal/mol, the 2-fold higher values indicate that protein unfolding is not rate limiting in the degradation of injected proteins. Katznelson and Kulka (107) compared the degradation of native and denatured forms of bovine serum albumin, @-lactoglobulin, and cytochrome c in cultured rat hepatoma cells. Whereas denatured P-lactoglobulin was degraded three times faster than native @-lactoglobulin, degradation of cytochrome c did not change following denaturation, and denatured albumin was hydrolyzed 5-fold slower than native bovine serum albumin.

In light of these results, we believe that injected proteins are degraded by features inherent in their folded conforma- tion. However, we do not exclude the possibility that other pathways ( e g . N-end, 89) may preferentially degrade unfolded or nascent polypeptide chains. Again, by injecting folded proteins, we may have failed to confirm valid correlations based on contributions from newly synthesized, unfolded polypeptides.

Given these reservations, one must be cautious when ex- trapolating from data obtained by microinjection. This is especially true with regard to the size, charge, and hydrophobicity hypotheses. Also, the empirical methods to estimate values for protein hydrophobicity and sensitivity to oxidation are limited without further experimental verification. On the other hand, certain conclusions are warranted by our results. Thermal stability does not correlate with metabolic stability. An exposed N-terminal lysine or glutamic acid residue does not necessarily result in rapid proteolysis. The amount of ordered structure alone does not determine the stability of injected proteins. Deamidation may play an important role in selective degradation. Whether a protein is “bound“ or “dif- fusible’’ appears to affect stability.

Clearly, however, the major conclusion from our survey is that no single feature of protein structure has been identified that will allow one to predict susceptibility to intracellular proteolysis. Rather, our findings indicate that relative stability results from complex interactions between structural parameters and degradative pathways. The existence of multiple cytosolic pathways makes the search for structural determinants a difficult one. Moreover, some determinants appear to be conditional. That is, a protein’s metabolic stability can be influenced by interactions with other cytosolic proteins or can depend upon the physiological state of the cell. For example, certain proteins that contain PEST sequences are only rapidly degraded upon exposure to light or when glucose is present in the medium (108). Similarly, the degradative signal present in S peptide of RNase A is generated (or enhanced) upon


serum starvation (109). Because of these two complications, multiple pathways and conditionality, we believe that the eventual identification of structural features that determine the intracellular stability of proteins with either require in vitro systems containing a single proteolytic pathway or specific inhibitors of individual pathways.

Acknowledgments-We wish to thank A. Edmundson, K. Ely, R. Fletterick, and H. Muirhead for helpful discussions and providing us with materials prior to publication. A special thanks is given to Noel Carlson, Ronald Hough, Tom McGarry, and Kevin Rote for the time and effort they contributed to these studies. We also thank Kristen Ballantyne for her excellent word processing skills and considerable patience during numerous drafts of this and the preceding manu- scripts.

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