Structural Biology

Volume 2013 (2013), Article ID 249234, 10 pages

http://dx.doi.org/10.1155/2013/249234

Research Article

Structure Topology Prediction of Discriminative Sequence Motifs in Membrane Proteins with Domains of Unknown Functions

Steffen Grunert, Florian Heinke, and Dirk Labudde

Hochschule Mittweida, University of Applied Sciences, Technikumplatz 17, 09648 Mittweida, Germany

Received 31 October 2012; Accepted 15 January 2013

Academic Editor: Shandar Ahmad

Copyright © 2013 Steffen Grunert et al. 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.

Motivation. Membrane proteins play essential roles in cellular processes of organisms. Photosynthesis, transport of ions and small molecules, signal transduction, and light harvesting are examples of processes which are realised by membrane proteins and contribute to a cell's specificity and functionality. The analysis of membrane proteins has shown to be an important part in the understanding of complex biological processes. Genome-wide investigations of membrane proteins have revealed a large number of short, distinct sequence motifs. Results. The in silico analysis of 32 membrane protein families with domains of unknown functions discussed in this study led to a novel approach which describes the separation of motifs by residue-specific distributions. Based on these distributions, the topology structure of the majority of motifs in hypothesised membrane proteins with unknown topology can be predicted. Conclusion. We hypothesise that short sequence motifs can be separated into structure-forming motifs on the one hand, as such motifs show high prediction accuracy in all investigated protein families. This points to their general importance in α-helical membrane protein structure formation and interaction mediation. On the other hand, motifs which show high prediction accuracies only in certain families can be classified as functionally important and relevant for family-specific functional characteristics.

1. Introduction

Membrane proteins are essential for many fundamental biological processes within organisms. Active nutrient transport, signal and energy transduction, and ion flow are only a few of the numerous functions enabled by membrane proteins [1]. Membrane proteins obtain their specific functionality by individual folding and interactions with the hydrophobic membrane environment as well as, in many cases, by oligomeric complex formation and protein-protein interactions [1, 2]. The identification of such complexes and interactions is valuable, since, on the one hand, detailed information of the function of an unknown membrane protein can be obtained by analysing its interactions with proteins of known function. On the other hand, biological processes can be comprehended as a dynamically fluctuating system, whereby the biological role of the unknown membrane protein can be defined more precisely [3, 4]. Accordingly, destabilisation of the three-dimensional structure of a membrane protein caused by mutations or ligand interactions are triggers for numerous diseases, for example, diabetes insipidus, cystic fibrosis, hereditary deafness and retinitis pigmentosa [5–7].

Although 20%–30% of all open reading frames of a typical genome are encoding membrane proteins [5, 8, 9] and 60% of all drug targets are membrane proteins [2], membrane proteomics is still an experimentally challenging field due to poor protein solubility, wide intracellular concentration range, and thus, inaccessibility to many proteomics methodologies [10]. Hence, the number of known three-dimensional structures is relatively small, with 394 nonredundant membrane protein chains currently available [11–13]. Therefore, there is a necessity for approaches that allow to predict structural and functional features of unknown membrane proteins. A variety of methods have been developed to predict structural features from sequence, such as -helical membrane-spanning helices and extra/intracellular domains (i.e., TMHMM [14], PHDhtm [15], MEMSAT3 [16]) as well as membrane-spanning beta-strands of transmembrane -barrel proteins (i.e., BOCTOPUS [17]). Furthermore, in genome-wide membrane protein sequence analyses, numerous short conserved sequence motifs were identified [18]. As an example, the most widely discussed GxxxG motif has been shown to be significantly present in transmembrane -helices. With both glycines resting on one side of the helix as spatially neighbouring residues and by that forming a smooth helix membrane surface, structural studies confirmed that the GxxxG motif plays an important part in mediating helix-helix interactions [18–22]. In general, short conserved membrane protein motifs are considered to be significantly relevant for membrane protein folding and structural stability as well as being involved in defining a protein’s function. Hence, sequence motif analyses and resulting insights can support the understanding of protein dynamics. Information can be derived which may contribute to study the dynamics of mutant proteins and the effects of mutagens [23–25]. Additionally, as addressed in [26], the analysis of sequence motifs in proteins with similar function or structure might help to identify essential functional sites and locations which contribute to structural stability.

In this work, we focused on previous studies and results that have been reported by Liu and colleagues [18]. In the process, various integral membrane protein families with polytopic membrane domains had been obtained from Pfam database [27]. As part of their studies, locations of the least conserved residues (glycine, proline, and tyrosine) in -helical transmembrane regions had been investigated. As a result, short motifs consisting of pairs of small residues (glycine, alanine, and serine) surrounding single or multiple variable positions had been identified in conserved sequences and Pfam-classified families. Based on these results, we have developed a prediction approach to allocate the topological state of a sequence motif in the protein structure based on sequence information. We have used cross-validation to verify the prediction accuracy. However, prediction accuracy has been found to be variable for certain motifs with regard to the investigated protein families. According to this, we hypothesise that short sequence motifs can be separated into structure-forming motifs on the one hand, as such motifs show high prediction accuracy in all investigated protein families. This points to their general importance in -helical membrane protein structure formation and interaction mediation. On the other hand, motifs which show high prediction accuracies only in certain families can be classified as functionally important and relevant for family-specific functional characteristics.

2. Materials and Methods

2.1. Used Membrane Protein Families

As the first step of our analysis, 32 membrane protein families with domains of unknown functions (DUF) were obtained from the Pfam database [27] using extended keyword searching. All 7051 sequences were retrieved for statistical analysis. The full list of employed membrane protein families is given in Table 1. Subsequently, 50 sequence motifs, identified by Liu and colleagues [18], were localised in the obtained set of families.

Table 1: Thirty-two membrane protein families were derived from Pfam database [28] and employed for statistical analysis.

Accession
Family

PF09767
DUF2053

PF09834
DUF2061

PF09842
DUF2069

PF09843
DUF2070

PF09852
DUF2079

PF09858
DUF2085

PF09874
DUF2101

PF09877
DUF2104

PF09878
DUF2105

PF09879
DUF2106

PF09880
DUF2107

PF09881
DUF2108

PF09882
DUF2109

PF09900
DUF2127

PF09913
DUF2142

PF09925
DUF2157

PF09945
DUF2177

PF09946
DUF2178

PF09971
DUF2206

PF09972
DUF2207

PF09973
DUF2208

PF09980
DUF2214

PF09990
DUF2231

PF09991
DUF2232

PF09997
DUF2238

PF10002
DUF2243

PF10011
DUF2254

PF10067
DUF2306

PF10080
DUF2318

PF10081
DUF2319

PF10097
DUF2335

PF10101
DUF2339

2.2. Programs and Tools

To avoid generating misguiding statistics by including identical or highly similar sequences, a set of nonredundant sequences was generated. Here, we defined the sequence redundancy threshold at 25% sequence identity. In the first step of sequence processing, CD-HIT [29] was applied for first clustering. However, CD-HIT accepts only nonredundancy thresholds of 40%. This limitation is caused by the internal word-length filtering approach and statistical presets. Hence, to ensure clustering sensitivity, a 60% nonredundancy threshold, which corresponds to tetra-peptide word filtering used by the program, was applied. In the second step, sequence clustering using the 25% redundancy threshold was obtained by means of utilising BLASTClust [30]. The representative sequences of all clusters were extracted, leading to a set of 2511 nonredundant sequences.

Subsequently, the determination of membrane and nonmembrane associated sequence regions was derived using by the TMHMM Server v. 2.0 [14]. Basically, TMHMM performs a prediction of intra/extracellular regions and integral membrane helices based on sequence. Additionally, the probability of the prediction is given for each residue as well. According to the obtained results from TMHMM, a topological state was assigned to each residue. A residue was assigned as “TM” if the posterior prediction probability of this residue being a part of a membrane helix has been found to be greater than 90%. If the posterior prediction probability of the residue has been found to be greater 90% for extra/intracellular prediction, the residue was assigned as “nTM.”

2.3. Used Motifs

The short sequence motifs analysed in our work have been reported in [18]. In this study, Liu et al. analysed consensus sequences of 168 Pfam-A families to identify significant amino acid pair motifs. By the comparison of their results in earlier published findings (see [20]), a list of 50 significant motifs has been derived which we used in our work (for original data see [18], Table 1, List 3): GG4, GL3, GG7, GL1, AG7, GA7, AG4, PL2, AS4, AL6, LP1, PG9, GA4, FG1, SL1, SG4, PL1, AA7, AG5, LF8, IA1, GV1, AI1, AA2, GL2, AA3, SL2, PG5, PG6, IL4, GS5, VL4, GV2, IG1, PG10, LY6, LF10, SA6, LG5, SA3, PF1, GS4, IV4, LS1, GY8, IG2, LF9, VF8, VG6, GN4.

Intuitively, the reported short sequence motifs can be written in a generalised, regular expression-like form of XYn, where X and Y correspond to amino acids separated by highly variable positions. However, in the process of analysis we found that short motifs with a relatively small number of variable positions (more precisely, if is found to be 3) do not contain enough information to be investigated by our approach. Thus, these motifs have been discarded in the process, which resulted in a final set of 33 sequence motifs. In our nonredundant sequence set, almost 250,000 single motif occurrences were identified. As an example of motifs located in a membrane protein structure, Figure 1 illustrates seven motifs which can be found in the structure of the bacteriorhodopsin (PDB-Id: 1brr).

Figure 1: In the bacteriorhodopsin trimer (PDB-Id: 1brr), seven of 33 sequence motifs which were analysed in this study are present. Each motif can be written in a regular expression like XYn, where X and Y are amino acids separated by highly variable positions. For example, the LG5 motif occurrence (highlighted in red) corresponds to a pair of leucine and glycine residues which are separated by four amino acids.

2.4. Information Extraction and Clustering

In this work, a novel approach is elucidated which predicts the topology state of a short sequence motif in membrane proteins. The following steps were completed to realise this approach.

At first, all single motif occurrences were identified in the nonredundant sequence set. Including TMHMM predictions, each motif occurrence was assigned to a topology state as elucidated in Section 2.2. Additional to the defined topology states “TM” and “nTM,” a further state has been defined for this study. Each motif, where the beginning and the end has been located in the different topology states “TM” and “nTM,” has been assigned with the “trans” state. Subsequently, all variable positions within each motif occurrence were examined more closely. Ultimately for each variable position, the relative occurrence of each amino acid at the specified position of each motif was calculated.

To define a separation rule for the investigated motifs, an information-based approach was applied. Formally, a motif , for instance LG5, can be interpreted as a set of variable strings with a length of . Intuitively, in case of LG5 equals 4. To include the membership information of the three topology states, we separated into three motif subsets , and according to the topology state in which each single motif occurrence is located. Furthermore, in each motif each position with can be investigated concerning its amino acid distribution. To this end, interpreting as a set of strings (all identified motif occurrences found in topology state ) allows formulating the relative probability :

with

where corresponds to one of the 20 canonical amino acids. To weight the significance of each probability , the probability is applied in a log-odd formula:

The amino acid distribution used to test the significance of the observed relative probability at each motif position was computed from the NCBI nonredundant protein sequence set [32] (ftp://ftp.ncbi.nih.gov/blast/db/FASTA/nr.gz).

Using these log-odd values, visualisation, clustering, and information extraction can be performed. To this end, we transformed each position into a vector consisting of log-odd values which we refer to as log-odd profile and which is defined as

Clustering all resulting was finally ensured by implementing the following distance formula:

where corresponds to the Spearman’s rank correlation coefficient. Clustering methods were applied to the LOPs to derive characteristics in motifs which determine the protein’s structural and functional features.

Furthermore, with these values at hand, the algorithm for predicting the topology state based on a single motif occurrence was implemented. At this, the precalculated LOPs of the corresponding motif are employed as look-up values to compute a straight-forward winner-takes-it-all formula:

The assessment of topology state prediction was performed by means of cross-validating and F-measure calculation.

By utilising clustering methods, differences and similarities of all LOPs can be visualised and analysed in detail.

For dimensionality reduction and finally data clustering of the 20-dimensional LOP data, we used the unweighted pair group method with arithmetic mean (UPGMA) [33] and the exploratory observation machine (XOM) [34]. This analysis is helpful to understand the correspondences of physicochemical properties observed in LOPs and topology states. Furthermore, this analysis enforces the found predictability of topology states. We chose the UPGMA as visualisation approach, since it is a widely used bottom-up clustering method that can be understood intuitively.

The XOM algorithm is relatively new for dimensionality reduction. A great advantage lies in its visualisation capabilities, since it can transform neighbourhood or distance relations embedded in multidimensional data into human-intelligible spaces, such as into . In the literature, this property is referred to as topology-preserving mapping. However, the degree of topology-preserving mapping achieved by the XOM depends on the given problem (mainly influenced by the structure of data and applied distance measure), and thus the XOM output can be insufficient for analysis. In application to LOP data, however, it has shown to perform more than satisfying. Further, visualisations were obtained by generating heat maps.

3. Results and Discussion

3.1. Identification of Topology-Discriminative Positions

The identification of topology-discriminative positions in motifs is crucial for drawing meaningful correlations between physicochemical properties plus structural and functional features. A straight-forward approach to address this task is the utilisation of a method to determine the residue conservation at each variable motif position. WebLogo [31], for instance, is a widely used method to address such problems. However, WebLogo does not include any amino-acid-specific background information in deriving residue conservation, since natural amino acid frequencies are not taken into account. To circumvent this problem, we used LOPs for visualisation instead, which, as shown in (4), include natural amino-acid-specific background probabilities. Essentially, this approach is quite similar to the methods recently described in [36]. Single LOPs can be visualised as heat maps [37] (see Figure 3), and amino-acid-specific propensities at each variable position in each motif can be extracted and thus information can be gained.

3.2. LOP Visualisation and Classification

The LOP heat map depicted in Figure 3 exemplary shows the apparent amino-acid-specific propensities according to the three topology states. Here, increasing amino acid propensities defined in (3) are illustrated by increasing red colour content. In comparison to the WebLogos (Figure 2), distinct amino acid propensities become obvious. For instance, glycine is observed more frequently in all LG5 motifs which are located in transmembrane regions. In nontransmembrane regions, the propensity of glycine is found to be reduced distinctly. As a second example, the LG5 motif found in transmembrane regions, leucine is observed more frequently at the third variable position as at other positions. This sequence constellation results into two spatially adjacent leucine residues that form a bulky helix surface. In general, relations of topology states and the amino-acid-specific propensities can be derived. This emphasises the predictability of topology states based on single motif occurrences. The full LOP heat map generated by this approach consists of 471 motif positions. To visualise LOP-wide correspondences, we applied UPGMA hierarchical clustering as well as the XOM algorithm. Distance measurement between LOPs was realised by utilising (5). Since 471 variable motif positions were investigated, the UPGMA-tree generated by the first approach consists of 471 nodes. To ease the analysis of the tree, the nodes were coloured according to the topological state in which the corresponding motif is located. Due to the huge number of nodes, we depicted the tree only as a schematically representation which represents the observed general tree topology and identified memberships (see Figure 4). As shown, a distinct clustering, more precisely a formation of three distinct subtrees, according to the topology states is obvious. The cluster arrangement correlates to the physicochemical properties found in membrane and nonmembrane located regions, since greater LOP distances are mainly dictated by the propensities of hydrophobic, hydrophilic, and polar amino acids. The sub-tree mainly consisting of motifs located in “trans” regions is arranged in between, which points to intermediate physicochemical motif compositions and equally distributed amino acid compositions. Similar to these findings, the XOM output (see Figure 5) shows three main clusters which correspond to the topology states too. Additionally, the cluster arrangement is found to be equal to the arrangement observed in the UPGMA-tree, where the causes of cluster formation are analogue as well. The distinct cluster formation observed by the output of both methods points to a good separability of the variable motif positions.

(a)

(b)

(c)

Figure 2: WebLogos [31] of the LG5 motif in the order of three topology states “TM” (a), “nTM” (b), and “trans” (c). However, the symbol height in each logo reflect only the relative occurrence of the corresponding amino acid. Additionally, background amino-acid-specific frequencies are not taken into account which decreases the sensitivity of this method. Compared to the heat map generated from LOPs (see Figure 3), less information can be gained. By applying WebLogo, residue propensities, with regard to the topology states, cannot be derived or identified. For instance, the leucine amino acid in “TM” (WebLogo A) cannot be observed as more frequently at the third variable position as at other positions.

Figure 3: LOP heat maps of the LG5 and LY6 motif. LOP heat maps reflect the propensities of each amino acid relative to natural amino-acid-specific frequencies. Increasing amino acid propensities are illustrated by an increased red colour content. The below listed colour scale represents the colour assigning to each amino acid propensities. This visualisation allows a sensitive approach to analyse amino acid propensities of each variable position of a motif according to topology states. Here, the LOP heat map is separated by topology states, so that amino acid propensities become obvious. For example, cysteine can be observed more frequently at the second variable position of transmembrane-located LY6 motif. This results in two spatially adjacent cysteine residues which form a bulky surface in transmembrane helices. Such a bulky helix surface might be important in mediating helix-helix interactions, as knob-to-hole helix packing has been reported as a key folding process in many studies (e.g., [1, 35]).

Figure 4: Schematic UPGMA-tree derived from LOP clustering: the 471 LOPs of all variable motif positions were clustered using UPGMA hierarchical clustering [33] by utilising the LOP distance measure defined in (5). Due to the original size, the resulting UPGMA-tree is only depicted schematically. However, the tree shows three separated, distinct subtrees which correlate to the topology states in which the corresponding motifs are located. The cluster arrangement corresponds to amino acid propensities and thus to physicochemical properties observed in motifs. This tree proves that the topological location of short sequence motifs are well separable and especially predictable from their amino acid sequence in the variable positions.

Figure 5: Output of the XOM clustering: XOM [34] is a relatively new approach for dimensionality reduction and clustering of multidimensional data. We used this approach to visualise the distance relations of the 471 investigated variable motif positions by employing the distance measure defined in (5). Here, XOM delivers a two-dimensional mapping of the distance relations of all LOPs. Coloured according to the topology state in which the corresponding motif is located, three well separable clusters can be seen. The LOP distances which contribute to the cluster formation are mainly dictated by the propensities of hydrophilic, hydrophobic, and polar residues. Thus, the XOM output reflects physicochemical correspondences which also applies for the general cluster arrangement, with the cluster of LOPs mainly observed in “trans” topology states (which corresponds basically to helix caps) located between the other two clusters. Similar to the UPGMA-tree depicted in Figure 4, the XOM output points to a good separability and predictability of topology states of short sequence motifs from their amino acid sequence in variable motif positions.

A possible approach to predict the topology state of a motif from the amino acid sequence alone was implemented as elucidated in Section 2.4. In this calculation, for each motif, the three log-odd sums of all variable positions are computed with respect to the three topology states. The highest log-odd sum leads to the topology state winner (see (6)). Cross-validation was performed by excluding the evaluation set of motifs from the training motif set, which was used to generate the look-up log-odd values. In the process, each topology state winner has been assessed by F-measure. The corresponding F-measures for each investigated sequence motif are listed in the given result Tables 1, 2, and 3. It is apparent from these tables that there are motifs with high and rather small F-measures. Each representative F-measure value indicates how good or bad a motif can be separated and assigned to the respective topology state. For example, the LY6 motif with an F-measure 0.8 in all result tables says that this motif is well assignable (by (6)) to each topology state.

Table 2: Statistical analyses of the motifs in the protein families with domains of unknown functions (EDS1). The results are split into three subtables. The “TMHMM prediction,” the “Prediction on log-odds,” and the “F-measures”-table. Thereby the “TMHMM prediction”-table represents the absolute occurrences of a motif in all investigated protein families with domains of unknown functions. The “Prediction on log-odds”-table represents the topology state winners (see (6)) followed by the “F-measures”-table which indicates how good or bad a motif can be separated and assigned to a topology state.

Motif
TMHMM prediction
Prediction on log-odds
F-measures

PG10
430
1556
900
429
1556
901
0.997
1.0
0.998

LF10
2838
1535
2860
2840
1536
2857
0.998
0.998
0.999

PG9
572
1596
896
577
1590
897
0.99
0.998
0.995

LF9
3271
1392
2425
3272
1392
2424
0.998
0.997
0.999

VF8
1936
1065
1116
1933
1065
1119
0.998
0.999
0.996

LF8
3589
1446
2185
3583
1447
2190
0.998
0.998
0.997

GY8
775
863
685
771
860
692
0.995
0.998
0.995

GA7
3035
2907
1943
3047
2889
1949
0.996
0.996
0.993

AG7
3009
2939
2104
3016
2926
2110
0.995
0.997
0.992

AA7
5100
4623
2883
5124
4592
2890
0.993
0.995
0.99

GG7
2380
3171
1463
2373
3175
1466
0.99
0.997
0.987

LY6
1861
1315
1263
1873
1305
1261
0.993
0.995
0.989

VG6
2518
2331
1317
2536
2324
1306
0.987
0.993
0.981

SA6
1747
2683
1269
1757
2674
1268
0.987
0.997
0.985

PG6
566
1756
681
583
1745
675
0.983
0.997
0.982

AL6
6974
3789
2931
7155
3680
2859
0.981
0.98
0.969

PG5
640
1576
696
682
1542
688
0.955
0.989
0.957

GS5
1041
2161
763
1115
2097
753
0.951
0.98
0.959

LG5
4775
3050
1959
5071
2879
1834
0.951
0.952
0.919

AG5
3464
3092
1433
3761
2895
1333
0.942
0.958
0.908

GN4
228
952
271
276
891
284
0.869
0.96
0.919

IV4
3285
1562
723
3568
1339
663
0.905
0.861
0.765

IL4
5700
2244
1282
6209
1889
1128
0.879
0.773
0.699

GS4
1080
2356
651
1381
2063
643
0.807
0.905
0.791

GG4
2302
3822
893
2758
3387
872
0.814
0.89
0.714

SG4
1125
2542
723
1457
2228
705
0.796
0.908
0.789

VL4
6680
3046
1592
7202
2498
1618
0.847
0.737
0.634

AS4
1903
2807
946
2423
2311
922
0.795
0.854
0.769

GA4
3769
3300
1253
4463
2664
1195
0.823
0.807
0.698

AG4
3594
3456
1214
4381
2661
1222
0.813
0.795
0.695

SA3
2005
2965
728
2603
1901
1194
0.65
0.674
0.452

AA3
6719
5358
1327
7855
3199
2350
0.747
0.596
0.386

GL3
5758
3252
1343
6026
2066
2261
0.767
0.597
0.452

Table 3: Statistical analyses of the motifs in the bacteriorhodopsin-like protein families (EDS2). The results are split into three subtables. The “TMHMM prediction,” the “Prediction on log-odds,” and the “F-measures”-table. Thereby the “TMHMM prediction”-table represents the absolute occurrences of a motif in all investigated bacteriorhodopsin-like protein families. The “Prediction on log-odds”-table represents the topology state winners (see (6)) followed by the “F-measures”-table witch indicates how good or bad a motif can be separated and assigned to a topology state.

Motif
TMHMM prediction
Prediction on log-odds

F-measures

PG10
105
17
464
103
17
466
0.942
1.0
0.987

LF10
1900
131
1165
2147
214
835
0.864
0.655
0.782

PG9
187
61
395
223
63
357
0.893
0.952
0.944

LF9
1278
164
565
1170
307
530
0.852
0.586
0.842

VF8
739
118
623
796
104
580
0.945
0.928
0.943

LF8
654
168
362
625
209
350
0.916
0.78
0.966

GY8
715
185
1450
881
186
1283
0.876
0.981
0.928

GA7
1581
2013
1963
1618
1877
2062
0.889
0.95
0.935

AG7
1737
722
1347
1653
782
1371
0.919
0.92
0.924

AA7
1887
1618
1455
1936
1530
1494
0.922
0.923
0.907

GG7
1837
562
1939
1760
506
2072
0.946
0.944
0.955

LY6
1868
189
639
1579
333
784
0.823
0.456
0.704

VG6
1642
199
1011
1562
175
1115
0.956
0.898
0.938

SA6
503
1030
579
614
925
573
0.843
0.926
0.92

PG6
316
56
242
301
54
259
0.94
0.982
0.93

AL6
1969
975
1525
1954
982
1533
0.909
0.908
0.917

PG5
247
39
78
284
37
43
0.904
0.974
0.595

GS5
208
302
574
248
272
564
0.899
0.944
0.965

LG5
2287
949
854
2254
805
1031
0.913
0.796
0.858

AG5
1228
766
746
1222
656
862
0.934
0.878
0.889

GN4
34
222
108
33
179
152
0.925
0.878
0.815

IV4
2612
484
532
2066
821
741
0.811
0.651
0.654

IL4
3586
648
611
2735
1153
957
0.817
0.499
0.681

GS4
136
643
497
193
612
471
0.76
0.969
0.915

GG4
2057
768
945
1972
568
1230
0.95
0.814
0.818

SG4
397
836
365
405
783
410
0.895
0.956
0.88

VL4
2621
619
775
1840
1264
911
0.759
0.579
0.81

AS4
378
1584
943
447
1441
1017
0.815
0.95
0.884

GA4
911
1411
1298
869
1372
1379
0.828
0.934
0.9

AG4
1418
1013
1258
1413
833
1443
0.875
0.813
0.826

SA3
522
1301
359
625
1125
432
0.806
0.893
0.781

AA3
2125
3004
1010
2652
1874
1613
0.695
0.687
0.455

GL3
851
528
435
829
339
646
0.751
0.646
0.614

3.3. Evaluation of the Prediction Accuracy

To evaluate the prediction accuracy, our new approach has been applied to three datasets. The first dataset (EDS1) consists of DUF-families sequence information described in previous Section 2.1. The second dataset (EDS2) consists of 2254 membrane protein sequences with 55 known structures of the bacteriorhodopsin-like protein (PF01036) family. EDS2 was also obtained from Pfam database [27]. EDS1 and EDS2 include the topology specific recorded statistically occurrence for each motif generated from TMHMM information. These statistics are listed under the “TMHMM prediction”-table heading and the right of it followed by our predicted (see (6)) information. The prediction quality is determined by the respective F-values. The comparison evidence of the number of statistical determined motifs with the predicted ones shows how well our approach for the most motifs works. For all proteins from DUF families and for the bacteriorhodopsin-like protein families, our approach works well and can be stated for the majority motifs. Deviations can be traced back to motifs with different functions. Furthermore, our approach has been transferred to all common known structures. EDS3 as third evaluation dataset consists of all known alpha helical membrane proteins with structures obtained from PDBTM [13]. It is important to note that results from EDS3 only include PDBTM protein information. That means, each found motif has been annotated with one of three given topology states “H,” “Side1,” and “Side2,” in which “H” stands for alpha-helix structure and both Side states refer to the outside or inside of the membrane. Here, “H” can be equated with “TM” because “H” includes only alpha-helical information referring to the interior of the cell membrane. Both Side states can be equated with “nTM.” The “trans” state is not included at this point by less membrane information. This means that we have separated a motif into three motif subsets , , and according to the topology state in which each single motif occurrence was located. Further calculations are described in Section 2.4 based on these motif subsets. All results from Table 4 show that our approach can be applied on known structures. The topology specific recorded statistically motif occurrence is listed in the “PDBTM prediction”-table heading and the right of it followed by our predicted information.

Table 4: Statistical analyses of the motifs in all known PDBTM protein structures (EDS3). The results are split into three subtables. The “PDBTM prediction,” the “Prediction on log-odds,” and the “F-measures”-table. Thereby the “PDBTM prediction”-table represents the absolute occurrences of a motif in all investigated PDBTM protein structures. The “Prediction on log-odds”-table represents the topology state winners (see (6)) followed by the “F-measures”-table witch indicates how good or bad a motif can be separated and assigned to a topology state.

Motif
PDBTM prediction
Prediction on log-odds
F-measures

α-helical
Side1
Side2
α-helical
Side1
Side2
α-helical
Side1
Side2

PG10
382
1719
1780
382
1297
2202
1.0
0.86
0.894

LF10
2084
1248
1381
2092
1007
1614
0.998
0.893
0.918

PG9
473
1559
1583
474
1158
1983
0.999
0.852
0.887

LF9
2206
1103
1202
2207
962
1342
0.999
0.93
0.945

VF8
1891
1006
1120
1907
787
1323
0.996
0.878
0.905

LF8
3638
1450
1346
3637
1067
1730
0.998
0.845
0.873

GY8
393
1228
1186
392
930
1485
0.999
0.862
0.888

GA7
2614
2516
2914
2607
1775
3662
0.993
0.817
0.881

AG7
3443
2411
2937
3469
1739
3583
0.995
0.836
0.895

AA7
2870
3288
3650
2870
2280
4658
0.991
0.811
0.873

GG7
2899
2982
3285
2917
2132
4117
0.997
0.834
0.883

LY6
1326
1127
1066
1345
901
1273
0.992
0.888
0.904

VG6
2962
2230
2588
2961
1723
3096
0.996
0.869
0.907

SA6
1499
1984
1947
1497
1551
2382
0.998
0.875
0.899

PG6
347
1697
1558
348
1356
1898
0.999
0.888
0.901

AL6
6110
2672
2947
6140
1951
3638
0.996
0.844
0.889

PG5
971
1651
2095
991
1334
2392
0.985
0.893
0.923

GS5
1101
1609
1708
1154
1131
2133
0.976
0.826
0.874

LG5
5049
3013
3411
5083
2124
4266
0.993
0.826
0.879

AG5
3601
3012
3278
3623
2177
4091
0.986
0.833
0.879

GN4
427
1700
1898
453
1281
2291
0.964
0.857
0.894

IV4
4596
1717
1855
4914
1317
1937
0.947
0.838
0.822

IL4
6972
1827
2344
6956
1299
2888
0.964
0.752
0.842

GS4
1298
1773
1858
1425
1331
2173
0.936
0.854
0.879

GG4
3656
2463
2738
3897
1653
3307
0.93
0.784
0.84

SG4
1493
2141
2419
1629
1349
3075
0.948
0.771
0.86

VL4
6840
2363
3081
7067
1757
3460
0.963
0.821
0.873

AS4
2172
2066
2498
2267
1399
3070
0.934
0.807
0.859

GA4
4397
2954
3845
4685
1883
4628
0.933
0.756
0.85

AG4
3668
3402
3838
3950
2376
4582
0.937
0.807
0.856

SA3
2204
2198
2292
2936
1357
2401
0.773
0.678
0.758

AA3
5085
3463
4144
6342
1865
4485
0.798
0.646
0.733

GL3
5730
3075
3552
6026
2147
4184
0.789
0.591
0.723

4. Conclusion

In this work, 33 short sequence motifs reported in [18] were investigated in 32 polytopic membrane protein families with domains of unknown functions. Transmembrane and nontransmembrane sequence regions were predicted using the TMHMM method [38] and topology states were annotated to all detected sequence motif occurrences. These amino acid propensities were derived and employed to define log-odd profiles (LOP) of all variable sequence positions in the investigated motifs. Propensity tendencies according to the topology states were identified using UPGMA and XOM clustering. Both methods pointed to good separability and predictability of the topology state of a motif from its amino acid sequence. An information-based prediction algorithm was implemented and assessed using cross-validation and F-measure evaluation. Motifs showing high F-measures over all or only in certain investigated protein families were identified. From this insight, we postulate that short sequence motifs can be divided in general, structure-forming elements, which are present in numerous protein families and highly specific to their topology location. But they are probably less important for functional properties. Finally, motifs showing high F-measures only in certain membrane protein families may be important elements in establishing the individual properties which are necessary for the function of an entire protein family.

Also, the information of the spatial structure and the folding of proteins to be explored can be evaluated by affinities, because the spatial structure of proteins has been stronger conserved in evolution than the sequential composition of the folded protein chains. These are individual motifs or characteristic sequence parts which expose a certain biochemical function of proteins. Why does the nature pursue the principle of structure and function separation? Residues, which support a stable domain folding, are separated from those that induce a specific function. This procedure is a very efficient strategy of evolution. Two areas were simultaneously optimised [39]:

(i)the stability of the protein backbone in a given folding pattern,(ii)the design of the amino acid sequence according to a specific function.

Based on this information, further work will discuss and deal with how the evolution has spawned motifs in their function as structure building blocks. In addition, motifs originated by evolution and spatially interacting with other should be determined as structure stabilizing.

Acknowledgment

The authors would like to thank the Free State of Saxony and the European Social Fond (ESF) for the financial support.

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