BioMed Research International

Volume 2014 (2014), Article ID 921218, 9 pages

http://dx.doi.org/10.1155/2014/921218

Research Article

Topography Prediction of Helical Transmembrane Proteins by a New Modification of the Sliding Window Method

Maria N. Simakova¹ and Nikolai N. Simakov²

¹A.F. Mozhaisky Military Space Academy, Yaroslavl 150001, Russia

²Yaroslavl State Technical University, Yaroslavl 150023, Russia

Received 18 February 2014; Revised 25 March 2014; Accepted 16 April 2014; Published 11 May 2014

Academic Editor: Hesham H. Ali

Copyright © 2014 Maria N. Simakova and Nikolai N. Simakov. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Protein functions are specified by its three-dimensional structure, which is usually obtained by X-ray crystallography. Due to difficulty of handling membrane proteins experimentally to date the structure has only been determined for a very limited part of membrane proteins (<4%). Nevertheless, investigation of structure and functions of membrane proteins is important for medicine and pharmacology and, therefore, is of significant interest. Methods of computer modeling based on the data on the primary protein structure or the symbolic amino acid sequence have become an actual alternative to the experimental method of X-ray crystallography for investigating the structure of membrane proteins. Here we presented the results of the study of 35 transmembrane proteins, mainly GPCRs, using the novel method of cascade averaging of hydrophobicity function within the limits of a sliding window. The proposed method allowed revealing 139 transmembrane domains out of 140 (or 99.3%) identified by other methods. Also 236 transmembrane domain boundary positions out of 280 (or 84%) were predicted correctly by the proposed method with deviation from the predictions made by other methods that does not exceed the detection error of this method.

1. Introduction

Problem and relevance of the study of membrane proteins, including GPCRs, are as follows. Membrane proteins are responsible for many cellular functions and processes, in particular ensuring the selective exchange of substances between the cell and its environment, maintaining the electric potential inside and outside the cell, and providing the transfer of electric signals into and out of the cell. They participate in nearly all energy transduction processes in the organism.

Protein functions are specified by its three-dimensional structure, which is usually obtained by X-ray crystallography [1, 2]. This method is directly applied to protein crystals, which must be produced beforehand using a very complex and laborious technique. The difficulty of handling membrane proteins during their production, purification, and crystallization due to protein instability, unfolding, aggregation, and heterogeneity has made it hard to solve their structures experimentally and to date the structure has only been determined for a very limited part of membrane proteins (<4%).

It is supposed that all information about the ultimate structure of a protein is contained in its amino acid sequence. Therefore, methods of computer modeling based on the data on the primary protein structure or the symbolic amino acid sequence have become an actual alternative to the experimental method of X-ray crystallography for studying the structure of membrane proteins [3].

From the variety of membrane proteins, the group of integral polytopic proteins (transmembrane proteins, TMPs) with multiple hydrophobic sites, domains permeating the membrane, is of considerable interest. Many of these proteins function as gateways or “loading docks” to transport specific substances and relay signals across the biological membrane.

The apparent feature and the inherent property of -helical membrane proteins are the (possibly periodical) repetition of transmembrane domains consisting of hydrophobic amino acids (15–30 aa in length) [4]. If the mentioned repetition is periodic, it can be detected using the known method of Fourier transform, applied to a digital image of a symbolic sequence of amino acids in a protein, as was done in our previous works [4, 5].

If the repetition of transmembrane regions is aperiodic, it can be revealed by another method, that is, the method of the reiterated (four to five times) averaging of the protein hydrophobicity function in a window within the limits of 9–11 amino acids that moves along the sequence. This method is a novel advanced version of the known method of sliding window, which has been proposed and used in our previous work [4] to investigate the secondary structure of different membrane proteins.

The aim of the present work is to apply this method for the prediction of the characteristics of unknown secondary structures of TMPs, mainly of GPCRs; these characteristics specify the functional properties of the proteins.

G protein-coupled receptors (GPCRs), also known as seven-transmembrane domain receptors, comprise the largest family of membrane proteins in the human genome and the richest source of targets for the pharmaceutical industry [6].

Over 800 unique GPCRs have been revealed from human genome sequence analysis, approximately 460 of which are predicted to be olfactory receptors [7, 8]. The physiologic function of a large fraction of these 800 GPCRs is unknown. There are many obstacles to obtaining structures of GPCRs by X-ray crystallography; the major difficulties include poor protein stability and absence of homogeneity during crystallization due to inherent properties of these receptors [6, 9, 10].

Therefore, it is necessary to develop novel approaches in structurally resolving aspects of their biology [11–13]. One of such useful approaches is to screen these proteins with help of structural bioinformatics and methods of computer modeling to identify those of them with the best characteristics for structural studies and for crystallography trials.

2. Materials and Methods

We used the method of reiterated averaging hydrophobicity function within a sliding window over the amino acid sequence. Since TM domains (TMDs) consist predominantly of hydrophobic amino acids, it is evident that the average hydrophobicity for this region, as specified in the protein sequence by a function of amino acid number in the sequence, must be higher than that for both hydrophilic topological domains (TPDs) adjacent to it. Furthermore, this local property does not depend on the periodicity of the arrangement of characteristic TMDs and TPDs in the amino acid sequence. Here, is the number of amino acids of the 20 known (Table 1), which is located at position in the protein sequence.

Table 1: Hydrophobicity scales .

Code
Abbreviation
Name
, [14]
, [19]
, [19]
, [17, 19]
, [18]
, [16]
[20]

1
A
Ala
Alanine
1.8
0
0
0.74
0.62
1.60
−0.17

2
C
Cys
Cysteine
2.5
1
1
0.91
0.29
2.00
0.24

3
D
Asp
Aspartic acid
−3.5
0
−1
0.62
−0.90
−9.20
−1.23

4
E
Glu
Glutamic acid
−3.5
0
−1
0.62
−0.74
−8.20
−2.02

5
F
Phe
Phenylalanine
2.8
1
1
0.88
1.19
3.70
1.13

6
G
Gly
Glycine
−0.4
0
−1
0.72
0.48
1.00
−0.01

7
H
His
Histidine
−3.2
0
0
0.78
−0.40
−3.00
−0.96

8
I
Ile
Isoleucine
4.5
1
1
0.88
1.38
3.10
0.31

9
K
Lys
Lysine
−3.9
0
−1
0.52
−1.50
−8.80
−0.99

10
L
Leu
Leucine
3.8
1
1
0.85
1.06
2.80
0.56

11
M
Met
Methionine
1.9
1
1
0.85
0.64
3.40
0.23

12
N
Asp
Asparagine
−3.5
0
−1
0.63
−0.78
−4.80
−1.23

13
P
Pro
Proline
−1.6
0
−1
0.64
0.12
−0.20
−0.45

14
Q
Gln
Glutamine
−3.5
0
−1
0.62
−0.85
−4.10
−0.58

15
R
Arg
Arginine
−4.5
0
−1
0.64
−2.53
−12.3
−0.81

16
S
Ser
Serine
−0.8
0
−1
0.66
−0.18
0.60
−0.13

17
T
Thr
Threonine
−0.7
0
−1
0.70
−0.05
1.20
−0.14

18
V
Val
Valine
4.2
1
1
0.86
1.08
2.60
−0.07

19
W
Trp
Tryptophan
−0.9
1
1
0.85
0.81
1.90
1.85

20
Y
Tyr
Tyrosine
−1.3
0
0
0.76
0.26
−0.70
0.94

For the first time, this idea was realized in [14], where averaging of the function within the limits of a segment, or window of width , or 13 amino acids, moving along the amino acid sequence, was used. The result of averaging was assigned to a member of a new numerical sequence with number corresponding to the current position of the average segment point.

The scale of hydrophobicity used in this method can be specified in different ways (Table 1) depending on the physically measured value that characterizes this property [14–20]. In [14–16], the change of value of free energy of amino acid side groups upon their transfer into water from a hydrophobic medium was used as a measure of hydrophobicity. In [17, 19], the measure (scale) of amino acid hydrophobicity was defined as the function (Table 1) based on the values of the amino acid surface area , which is available to solvent in the standard state, and the mean solvent accessible surface area in a folded protein conformation. In [17], the correlation between the free energy value and the surface area available to solvent was established.

The set of 20 amino acids can be divided into a few characteristic groups based on their degree of hydrophobicity by different ways. Thus, according to [19], we used the division of 20 amino acids into three groups by the degree of hydrophobicity, including hydrophobic (C, F, I, L, M, V, and W, seven in total), hydrophilic (D, E, G, K, N, P, Q, R, S, and T, ten in total), and neutral (A, H, and Y, three in total). The hydrophobic amino acids were assigned a value of +1, the hydrophilic amino acids were assigned a value of −1, and the neutral amino acids were assigned a value of 0. Thus, we obtained the crude scale in Table 1. On another crude scale the hydrophobic amino acids were assigned a value of +1, and the remaining amino acids were assigned a value of 0.

In our previous work [4], we proposed the procedure, different from that used in [14], for averaging the function on the scale . The averaging was carried out not once, but repeatedly, using the algorithm

where every new averaging was performed on the previous function over a window with a greater width ; thus, the first averaging was over three elements, the second one was over five elements, and so on. In our opinion, the best result was obtained at and the averaging over the window of width amino acids (sometimes at and amino acids).

It is interesting to compare the values ​​of the functions with the characteristic value of the initial hydrophobicity function , its arithmetic mean, calculated for the entire length of the protein chain

For the major part of each hydrophobic region, in particular TMD, the correlation must be performed, and in the hydrophilic region (TPD), a different correlation must be performed.

The scale and function of hydrophobicity can be specified in different ways (there are more than 30 known ones). A comparison of different scales and functions of hydrophobicity carried out in our previous work [4] showed that the numbers and arrangements of transmembrane regions obtained upon their usage were often almost identical, even for very simple (rough) scales, for example, and (see Table 1). However, sometimes a particular scale can be preferable for a given protein due to the better resolution of closely spaced TMDs.

3. Results and Discussion

3.1. Testing of the Improved Method of a Sliding Window on Proteins with Known Structure

The improved method of a sliding window proposed in [4] by algorithm (1) was applied in this work to the group of membrane proteins, such as GPCRs, and to some other transmembrane -helical proteins.

To further test the predictions of our method, first it was used to examine 5 proteins with already known structure (Table 2).

Table 2: Comparison of TMD boundaries calculated upon processing of hydrophobicity functions at on ( and 5) scales for GPCRs with known data from [21].

Protein name, code, length
Data source
Number and boundaries of transmembrane domains

Scale level
1
2
3
4
5
6
7

GLR_

HUMAN P47871

477 aa
[21, 22]
137–161
174–198
226–249
264–285
304–326
351–369
382–402

143–166
180–192
218–257
261–288
303–327
353–368
384–401

CRFR1_

HUMAN P34998

444 aa
[21, 23]
112–142
179–203
219–247
255–282
299–324
336–360
368–397

116–146
178–204
217–247
255–280
302–325
344–362
370–397

ADRB1_

MELGA P07700

483 aa
[21, 24]
39–67
77–103
116–137
156–179
206–231
286–315
321–343

,

44–64
81–99
108–138
160–181
214–229
293–314
320–331

5HT1B_

HUMAN P28222

390 aa
[21, 25]
50–75
85–110
124–145
166–187
206–228
316–336
350–371

46–72
86–109
119–145
168–185
205–230
316–340
343–369

45–73
85–110
118–145
168–185
205–229
316–340
344–370

5HT2B_

HUMAN P41595

481 aa
[21, 26]
57–79
91–113
130–151
172–192
217–239
325–345
361–382

54–81
89–

89–109
−151

131–151
173–194
215–243
325–352
356–381

Figure 1 shows the results of averaging the hydrophobicity function for the protein sequence P47871 on the scale in Table 1. Obviously, a hydrophobic segment in the form of a narrow peak relating to the signal peptide (SP) is present on the left edge of the graph of the function . If this peak is excluded, the remaining seven wide peaks that exceed the mean level = const = 0.27 will just correspond to 7 TMDs in the resolved structure of this protein [21, 22]. In the graph of the function the 2nd, the 3rd, the 5th, and the 7th TMDs have not been resolved yet, and there are several narrow peaks in their places.

Figure 1: Hydrophobicity functions for the protein P47871 in Table 2 after averaging at and on the scale in Table 1; dotted line shows the level = const = 0.266.

Figure 2 shows the results obtained for the protein sequence P34998 using the relatively rough hydrophobicity scale in Table 1. Apparently, a hydrophobic segment relating to the SP is revealed on the left edge of the graph of the function above the mean level , and also, in contrast to the function , all 7 TMDs known for the protein structure P34998 [21, 23] are resolved.

Figure 2: Hydrophobicity functions for the protein P34998 in Table 2 after averaging at and on the scale in Table 1; dotted line shows the level = const = .

The boundaries of TMDs of different proteins were determined by the intersection of the graph of the function with the straight line of some level = const (e.g., the mean level for the whole protein sequence). They are summarized in Table 2 for 5 known proteins.

The TMD boundaries from [21] are also shown for comparison in Table 2.

Taking into account the errors of the TMD boundary detection, good agreement of the results of the TMD boundary position calculations with the data from [21] can be obtained. Indeed, according to Table 2, 34 TMDs out of 35 were resolved (or 97%); the obtained TMD boundary positions do not exceed the detection errors for 62 out of 70 boundaries (or 89%).

Remark 1. In the protein with a code P41595, the 2nd and the 3rd domains not resolved in calculating can be resolved using the outer boundaries of the combined segment of 89–151 aa by adding to the left border and subtracting from the right border the estimated average length of a domain 20 aa, as shown in Table 2 in a bold font.

In [21], a signal peptide (SP) consisting of 1–25 aa of a protein sequence is indicated in the structure of the protein P47871. In this part of the protein chain, the hydrophobic region of 11–23 aa was detected by the proposed method. Similarly, the sequence of the protein P34998 [21] contains a signal peptide consisting of 1–23 amino acid residues. The proposed method was helpful to reveal here the hydrophobic region of 9–19 aa.

It is worth noting that processing with reiterated (four to five times) averaging of the hydrophobicity function on different scales (the rough scales and or the more precise scales ) produces different values for the TMD boundaries. Sometimes these differences are minor, but sometimes they are significant [4].

3.2. Comparison of Protein Secondary Structure Predictions Made by the Proposed Method and Other Techniques

Secondary structure predictions of a set of 20 membrane proteins belonging to a class of GPCRs performed using the new proposed method were compared with the predictions made ​​by other methods (Table 3).

Table 3: Comparison of TMD boundaries calculated upon processing of hydrophobicity functions at on () scales for GPCRs with known data from [21].

Protein name, code, length
Data source
Number and boundaries of transmembrane domains

Scale level
1
2
3
4
5
6
7

S1PR1_

HUMAN P21453

382 aa
[21], by similarity
47–71
79–107
122–140
160–185
202–222
256–277
294–314

48–69
83–107
122–142
160–195
199–223
255–281
293–310

ACM2_

HUMAN P08172

466 aa
[21], by similarity
23–45
60–80
98–119
140–162
185–207
389–409
424–443

21–48
60–85
90–122
142–167
192–208
389–415
422–429

ACM3_

RAT P08483

589 aa
[21], by similarity
67–90
104–124
142–163
184–206
229–251
492–512
527–546

62–92
105–128
137–161
187–208
221–249
492–515
526–541

CXCR1_

HUMAN P25024

350 aa
[21], potential
40–66
76–96
112–133
155–174
200–220
243–264
286–308

39–67
76–96
102–141
152–175
199–230
241–267
291–308

CCR5_

HUMAN P51681

352 aa
[21], potential
31–58
69–89
103–124
142–166
199–218
236–260
278–301

33–56
68–93
100–136
141–164
196–218
238–264
288–299

HRH1_

HUMAN P35367

487 aa
[21], potential
30–49
64–83
102–123
146–165
190–210
419–438
451–470

25–50
63–93
96–122
147–167
188–212
418–442
449–469

OPRK_

HUMAN P41145

380 aa
[21], potential
59–85
96–117
133–154
174–196
223–247
276–299
312–333

56–83
99–122
143–151
180–195
227–248
277–300
302–320

OPRM_

MOUSE P42866

398 aa
[21], potential
65–94
104–121
144–163
194–209
235–257
281–303
312–328

68–95
105–114
136–162
187–205
229–262
280–306
317–325

OPRD_

MOUSE P32300

372 aa
[21], potential
46–75
85–102
125–144
175–190
216–238
262–284
294–310

44–74
85–102
112–142
167–187
211–236
263–286
296–319

OPRX_

HUMAN P41146

370 aa
[21], potential
51–77
88–109
125–146
166–188
212–236
265–288
301–322

42–79
90–107
112–130
172–186
212–241
263–284
301–335

NTR1_

RAT P20789

424 aa
[21], potential
65–87
97–121
144–165
189–210
236–260
309–330
349–372

63–86
103–139
154–172
191–208
220–268
306–324
338–374

PAR1_

HUMAN P25116

425 aa
[21], potential
103–128
138–157
177–198
219–239
269–288
312–334
351–374

101–133
136–158
175–208
221–238
270–296
313–338
350–371

O51E1_

HUMAN Q8TCB6

317 aa
[21], potential
28–48
57–77
102–122
142–162
199–219
239–259
275–295

12–49
60–77
80–120
146–166
198–227
243–260
276–292

SMO_

HUMAN Q99835

787 aa
[21], potential
234–254
263–283
315–335
359–379
403–423
452–472
525–545

236–251
264–283
313–340
362–380
403–425
451–473
519–545

GP160_

HUMAN Q9UJ42

338 aa
[21], potential
24–44
59–79
94–114
137–157
178–198
245–265
269–289

26–40
59–81
97–118
139–157
182–202
244–271
274–292

HRH3_

HUMAN Q9Y5N1

445 aa
[21], potential
40–60
71–91
109–129
157–177
197–217
360–380
396–416

33–61
72–95
105–132
155–173
191–222
360–388
395–416

HRH4_

HUMAN Q9H3N8

390 aa
[21], potential
20–40
53–73
88–108
132–152
173–193
305–325
342–362

16–41
55–79
83–107
130–153
169–198
305–331
341–357

RAI3_

HUMAN Q8NFJ5

357 aa
[21], potential
34–54
69–89
98–118
130–150
177–197
213–233
248–268

26–53
68–92
96–118
130–155
178–202
213–233
246–265

VN1R1_

HUMAN Q9GZP7

353 aa
[21], potential
57–77
85–105
133–153
170–190
227–247
275–295
304–324

53–77
90–103
122–145
165–188
222–245
274–301
306–338

APJ_

HUMAN P35414

380 aa
[21], potential
27–51
67–91
101–125
145–166
201–221
245–271
285–308

30–52
67–85
98–135
147–167
208–228
246–

246–266
−312

292–312

As can be seen from Table 3, the proposed method allowed revealing 139 TMDs out of 140 (or 99.3%) identified by other methods. In the protein P35414 (the last one in Table 3) the 6th and the 7th domains “merged” into one long stretch of 246–312 aa. However, taking into account Remark 1, the boundaries of these two domains can be easily recovered using the outer boundaries of the combined segment by adding to the left border and subtracting from the right border the estimated average length of a domain 20 aa, as shown in Table 3 in a bold font.

236 TMD boundary positions out of 280 (or 84%) were predicted correctly by the proposed method with deviation from the predictions made by other methods that does not exceed the detection error of this method .

In [21], a signal peptide (SP) consisting of 1–21 aa of a protein sequence is indicated in the structure of the protein P25116. In this part of the protein chain the hydrophobic region of 6–17 aa was detected by the proposed method. Similarly, the sequence of the protein Q99835 [21] contains a signal peptide consisting of 1–27 amino acid residues. The proposed method was helpful to reveal here the hydrophobic region of 13–23 aa.

3.3. Predictions of Unknown Secondary Structure of GPCRs and Other Membrane Proteins

Then the proposed method of multiple averaging of hydrophobicity function was used to predict the location of hydrophobic regions, including TMDs, in several GPCRs with unknown structure. The results are shown in Table 4.

Table 4: Prediction of TMD boundaries calculated upon processing of hydrophobicity functions at on () scales for GPCRs.

Protein name, code, length
Scale level
Number and boundaries of hydrophobic regions, including TMDs

1
2
3
4
5
6
7

A4D1U0_

HUMAN A4D1U0

299 aa

7–28
45–70
82–102
127–147
173–194
222–240
253–274

7–29
46–71
77–103
124–144
179–194
222–237
258–275

A5Z1T7_

HUMAN A5Z1T7

300 aa

7–27
43–57
75–100
121–146
185–210
225–240
263–274

7–26
41–64
75–97
123–144
185–209
225–238
264–274

B5B0C2_

HUMAN B5B0C2

337 aa

14–40
49–72
85–122
132–155
189–201
227–255
275–293

14–39
51–71
89–

89–109
−154

134–154
193–205
226–256
277–292

M9TID6_

9BETA M9TID6

347 aa

43–57
69–88
97–123
149–161
188–219
232–262
265–288

33–56
66–87
100–123
148–164
186–217
233–

233–253
−295

275–295

Q76L88_

HUMAN Q76L88

321 aa

11–40
54–78
93–116
156–178
196–223
251–270

13–37
55–78
90–117
153–174
198–225
248–282

At least two hydrophobicity scales were applied to make predictions for each of the 5 proteins. Obviously, these predictions are consistent with each other for most of the domain boundaries considering the detection errors .

For the protein B5D0C2 the calculation on the scale resolved the 3rd and the 4th domains, but the application of the scale did not resolve these domains; they merged into a single domain. And it was vice versa for the protein M9TID6 with the 6th and the 7th TMDs. Taking into account Remark 1, the boundaries of unresolved domains can be restored, as shown in Table 4 in a bold font.

Surprisingly, for the protein Q76L88 given that is higher than the mean level , only 6 domains were surely detected instead of 7 as for other proteins in Table 4.

The results of prediction of TMDs using the proposed method are shown in Table 5 for 4 -helical membrane proteins of unknown structure. The first two proteins (P71044 and P49785) belong to the group of channels: intercellular, the third one Q8TMG0 to the group of methyltransferases, and the fourth one P77335 to the group of adventitious membrane proteins: alpha-helical pore-forming toxins.

Table 5: Prediction of hydrophobic regions and TMDs calculated upon processing of hydrophobicity functions at on () scales for -helical membrane proteins.

Protein name, code, length
Data source
Number and boundaries of hydrophobic regions, including TMDs

Scale level
1
2
3
4
5
6
7

SP2Q_

BACSU P71044

283 aa
[21], potential
22–42

20–47
70–94
107–124
130–175
207–229

16–48
70–94
109–121
132–174
197–225

SP3AH_

BACSU P49785

218 aa
[21], potential
7–26

3–31
92–106
146–179
193–211

3–30
95–113
146–179
193–211

Q8TMG0_METAC

Q8TMG0

194 aa

7–20
49–67
76–93
130–162

0–22
45–62
77–91
127–163

HLYE_

ECOLI P77335

303 aa
[21], potential

183–203

0–17
24–38
82–103
114–123
180–209
242–247
264–280

5–26
32–40
81–102
115–123
179–208
242–253
267–275

Here, as well as in Table 4, the predictions were made on at least two hydrophobicity scales . Evidently, these predictions are consistent with each other for all domain boundaries considering the detection errors . Individual single domains predicted earlier by other methods [21] were also identified by the proposed method.

Table 6 shows data comparison from [21] with prediction of TMDs made by the proposed method for the long ( aa) -helical membrane protein from the group of adventitious membrane proteins: alpha-helical pore-forming toxins. Obviously, compliance between the predictions takes place for most of TMDs considering errors in determining their boundaries .

Table 6: Prediction of TMDs calculated upon processing of hydrophobicity functions at on the scale for the long -helical membrane protein.

Protein name, code, length
Data source
Number and boundaries of transmembrane domains

Scale
1
2
3
4
5
6

CAC1A_

RABIT P27884

2424 aa
[21], potential
99–117
136–155
168–185
191–209
229–248
336–360

101–116
141–158
172–185
210–249
302–317
336–358

Scale
7
8
9
10
11
12

[21], potential
488–506
522–541
550–568
579–597
617–636
690–714

491–507
518–537
554–577
609–638
654–665
685–714

Scale
13
14
15
16
17
18

[21], potential
1254–1272
1289–1308
1321–1339
1351–1369
1389–1408
1496–1520

1255–1270
1293–1312
1323–1339
1384–1408
1456–1467
1497–1522

Scale
19
20
21
22
23
24

[21], potential
1576–1604
1610–1629
1638–1656
1666–1684
1704–1723
1796–1820

1575–1599
1607–1633
1641–1660
1691–1725
—
1794–1820

In the calculation using the proposed method of multiple averaging of hydrophobicity function over a sliding window, besides those domains indicated in Table 6, a hydrophobic region of 16–28 aa was identified, which may belong to a signal peptide (SP) or may be the 1st one out of 24 TMDs of the present protein. Moreover, it is obvious that TMDs numbered in [21] as 5, 11, 17, and 23 and highlighted in Table 6 by a bold font in our prediction have the numbers, which are one less than in [21], but other domains that are not specified in [21] have the numbers, which are one more. Thus, two varied predictions in Table 6 have great similarities as well as notable differences.

4. Conclusions

The first membrane protein topology prediction algorithms were based solely on the hydrophobicity plots, for example, [14, 16, 18], and it seemed that the performance of these early methods was rather poor in practice. Hence, they soon were supplied by novel statistical, machine-learning methods, which use hundreds of free parameters extracted from databases of experimentally mapped topologies [13, 27]. However, as it is stated in [27], the translocons (cellular machineries) responsible for membrane-protein biogenesis do not have access to statistical data but rather exploit molecular interactions to ensure that membrane proteins attain their correct topology. Therefore, as it is concluded in [13], those methods which are based on the same physical properties that determine translocon-mediated membrane insertion, by using properly scaled hydrophobicity values, may access the same level of prediction accuracy as the best statistical methods.

Thereby, here we presented the results of the study of 35 transmembrane proteins using cascade averaging of hydrophobicity function within the limits of a sliding window, as expressed in formula (1).

In the work [4], the proposed method was successfully applied to predict the location of TMDs, secondary structure elements of a number of membrane proteins, in particular, bacteriorhodopsin, halorhodopsin, sensory rhodopsin 2, some connexins, and others.

In the current work, this method was used to analyze the arrangement of the hydrophobic regions, including the transmembrane domains of another protein class, primarily GPCRs. At first, the method was tested on 5 known proteins of this class. Then an additional comparison of TMDs location predictions made by the proposed method and some other methods [21] was carried out on 20 proteins of the same class. These verifications confirmed the applicability of the proposed method for the stated purposes.

Whereupon, this method was used to predict the TMDs in proteins with unknown structure, namely, 5 GPCRs and 5 -helical transmembrane proteins of other classes. For 9 out of 10 of these proteins (Tables 4 and 5) concordant predictions were made using at least two different hydrophobicity scales. The prediction made by the proposed method for a very long protein (Table 6) is consistent largely with the prediction made by another method [21].

These facts indicate the applicability and usefulness of the new method presented in our work [4] and proposed here.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

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