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Open Access

Peer-reviewed

Research Article

Prediction of Multi-Type Membrane Proteins in Human by an Integrated Approach

Abstract

Membrane proteins were found to be involved in various cellular processes performing various important functions, which are mainly associated to their types. However, it is very time-consuming and expensive for traditional biophysical methods to identify membrane protein types. Although some computational tools predicting membrane protein types have been developed, most of them can only recognize one kind of type. Therefore, they are not as effective as one membrane protein can have several types at the same time. To our knowledge, few methods handling multiple types of membrane proteins were reported. In this study, we proposed an integrated approach to predict multiple types of membrane proteins by employing sequence homology and protein-protein interaction network. As a result, the prediction accuracies reached 87.65%, 81.39% and 70.79%, respectively, by the leave-one-out test on three datasets. It outperformed the nearest neighbor algorithm adopting pseudo amino acid composition. The method is anticipated to be an alternative tool for identifying membrane protein types. New metrics for evaluating performances of methods dealing with multi-label problems were also presented. The program of the method is available upon request.

Citation: Huang G, Zhang Y, Chen L, Zhang N, Huang T, Cai Y-D (2014) Prediction of Multi-Type Membrane Proteins in Human by an Integrated Approach. PLoS ONE 9(3): e93553. https://doi.org/10.1371/journal.pone.0093553

Editor: Yang Zhang, University of Michigan, United States of America

Received: October 23, 2013; Accepted: March 5, 2014; Published: March 27, 2014

Copyright: © 2014 Huang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was supported by grants from National Basic Research Program of China (2011CB510101, 2011CB510102), National Natural Science Foundation of China (31371335, 81171342, 81201148), Innovation Program of Shanghai Municipal Education Commission (12ZZ087), the grant of “The First-class Discipline of Universities in Shanghai”, Scientific Research Fund of Hunan Provincial Science and Technology Department (2011FJ3197), Hunan National Science Foundation (Grant: 13JJ3118), and Scientific Research Fund of Hunan Provincial Education Department (Grant: 11C1125). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Membrane proteins are one of the three main protein classes. It is approximately estimated that 20-30% of all genes in most genomes encode membrane proteins [1]. Gao et al. [2] estimated the number of membrane proteins was about 8000 in human. Membrane proteins also play wide varieties of important roles invloved cellular processes [3], in the immune response, serve as enzymes, etc. In addition, membrane proteins constitute 60% of drug targets [4], which were crucial to new drug discovery as well as to understand the mechanism of the cellular activities [4], [5], [6]. It is reported that functions of a membrane protein are closely associated with its type [7]. However, it is time-consuming and costly to determine types of uncharacterized membrane proteins by using traditional biophysical methods [8]. Thus, there is a growing need for effective computational methods to predict the membrane protein types.

In the past decades, it was considered the importance of the predictions. Many tools were developed based on machine learning techniques, in which both features extracted from samples and the learning algorithms were equally affecting. Features used for the predictions were mainly including: amino acid composition (AAC), sequence homology information such as position-specific scoring matrices (PSSMs), pseudo amino acid composition (PseAAC), physicochemical properties of amino acids and functional domains. AAC was the simplest but often the most efficient to represent protein sequence. Cai et al. [9] used only AAC feature by using support vector machine (SVM) to predict membrane protein types. Reflecting the order of amino acid in the sequence and complementing AAC, PseAAC of protein sequence is considered to be the extension of it. Those features were widely applied to recognize types of uncharacterized membrane proteins [7], [10], [11], [12], [13]. Wang et al. [14], however, pointed out that the feature space of PseAAC was redundant, and they utilized a Supervised Locally Linear Embedding (SLLE) algorithm [15] for nonlinear dimensionality reduction. Pu et al. [16] proposed IAMPC, in which they extracted features from both PSSMs and protein primary sequences by using a so-called SVM fusion to identify membrane proteins types. Hayat et al. [17] used both the split amino acid composition (SAAC) and seven different physicochemical properties of proteins to represent protein sequences. They also employed SVM as the classifier. The method has been implemented in a web servicer called Mem-PHybrid (http://111.68.99.218/Mem-PHybrid). The hybrid features of PSSM and the SAAC of protein sequences were also used by Hayat et al. [18] to construct a web-based predictor. Ding et al. [19] used tripeptide compositions to recognize mycobacterial membrane protein types. Jia et al. [8] constructed a classifier based on hybrid feature space of domain profiles and physiochemical properties of membrane proteins. Hayat et al. [20] used features of split amino acid as well as the ensemble classifier. Chen et al. [21] suggested novel multiple-source features such as transmembrane segments, lipid-binding domains, signal peptides, signal anchors, GPI-anchoring signals, surface amino acids, cationic patches, and so on. On the other hand, some machine learning algorithms have been applied for the prediction of membrane protein types. For example, Fourier spectrum [22], [23], wavelet analysis and cascaded neural network [24], wavelet transform (DWT) combined with SVM [25], neighborhood preserving embedding algorithm and K-nearest neighbor algorithm [26]. However, most of these methods did not consider the fact that one membrane protein possesses multiple types, which is common for membrane proteins.

Sequence homology reflects evolutionary relationships, which in turn helps infer structures and functions of proteins. Large bodies of protein-protein interactions available provide global perspective on relationship between different proteins at system-level. It is reported that proteins share identical properties and functions with interactive proteins more probably than with none-ones. Therefore, it is possible to identify membrane protein types by means of these. In this study, we proposed an integrated method by using both the homologies between protein sequences and the similar properties between interactive proteins to predict multi-types of membrane proteins in human. To widely examine the method, three datasets were constructed from UniProt database [27]. It was suggested from the performance of the method that it could be quite effective to identify membrane protein types.

Materials and Methods

Datasets

3,789 sequences of experimentally verified membrane proteins of human were downloaded from the UniProt database (release 2012_09 - Oct 3, 2012) [27]. According to their intramolecular arrangements and positions in a cell, membrane proteins are generally classified into the following six types: (1) GPI (Glycosylphosphatidylinisotol)-anchor; (2) lipid-anchor; (3) multi-pass; (4) peripheral; (5) single-pass type I; (6) single-pass type II membrane proteins [28]. Correct identification of the types of membrane proteins can help distinguish proteins between different functions.

To evaluate the perfomance of the prediction method, we employed the sequence clustering program CD-HIT (Cluster Database at High Identity with Tolerance) [29] to construct three datasets: S1, S2, and S3 from 3,789 proteins. S1 contained 2,883 protein sequences in which proteins had less than 70% sequence identity. S2 consisted of 2,081 protein sequences with sequence identity lower than 40%. S3 had 1,469 protein sequences with sequence identity less than 25%. As one membrane protein can have one or more types, Figure 1(A, B, C) shows the number of proteins having 1-6 types. It can be seen that no proteins have four or more types. The average numbers of types of proteins in datasets S1, S2 and S3 were 1.028, 1.035, 1.037, respectively. We also represented the distribution of membrane proteins to their types in the three datasets in Figure 1(D, E, F). Detail information containing protein IDs, sequences and types were available in Table S1.

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Figure 1. The distributions of membrane proteins in different types and in different multi-types.

A, B and C depict the distribution of the type number on three datasets, D, E and F show the distribution of types of membrane proteins on three datasets, respectively.

https://doi.org/10.1371/journal.pone.0093553.g001

Prediction method

In this section, the detailed procedure of the integrated prediction method was described. The integrated method included three methods: BLAST/PSI-BLAST method, network-based method and shortest-distance method.

Suppose there are n proteins, say , with known types in the training set S. For one protein in S, its types were encoded into a vector(1)where (2)

BLAST/PSI-BLAST method.

The basic local alignment search tool (BLAST), initiated by Altschul et al. [30], can be used to search for local similarity between two sequences. The idea underlying BLAST is to employ similarity measure optimized by background probabilities to directly approximate sequence alignments based on dynamic programming algorithm. This method can detect weak but more biologically significant sequence similarities. Gapped BLAST and PSI-BLAST (Positional Specific Iterated BLAST) [31] are the improved versions, improving on three major aspects: consuming-time of the extension, gapped alignment, and iterative search. Here, the BLAST/PSI-BLAST program (ftp://ftp.ncbi.nlm.nih.gov/blast/executables/release/2.2.26/) was adopted to calculate the alignment score, denoted by , of proteins p1 and p2. For an uncharacterized membrane protein , we searched a protein in S whose . Then the types of were assigned to as the predicted results. However, if the protein has no homologous proteins in S, this method cannot provide meaningful predicted results. In this case, the following two prediction methods make further predictions.

Network-based method.

This method utilized the information of protein interactions in human retrieved from STRING [32] (http://string.embl.de/), which was a well-known database covering 5,214,234 proteins with their interactions in 1,133 organisms. These interactions consist of known and predicted protein interactions including direct (physical) and indirect (functional) associations derived from the following four sources: (1) Genomic context; (2) High-throughput experiments; (3) (Conserved) coexpression; (4) Previous knowledge. There was a confidence score for every pairwise interaction quantifying the likelihood of the interaction occurrence. Let us denote the confidence score of a pairwise interaction between two proteins p1 and p2 as . Two proteins were regarded interactive in this study if the confidence score of the interaction between them was greater than zero. For an uncharacterized protein , the confidence scores of interactions between and proteins in S were represented by(3)where wjq denoted the confidence score of the interaction between and in S, i.e. . The probability of assigning the protein the type was calculated by (4)The greater the probability was, the more probably the query protein belonged to the type. To handling multiple types, we took the top t highest probability types as the predicted results, where t was regarded as the smallest integer larger than or equal to the average number of types in the dataset. That is, if, the protein would be predicted to have type. This method was very similar to that in our previous work [33], which was used to predict protein functions. However, this method was not always effective. If there were no interactive proteins with in S, the outputs of equation (4) were all zeros. In this case, the last method described below would make the final prediction.

Shortest-distance method.

As mentioned above, the higher the confidence score between two proteins, the stronger the interaction, and the more the two proteins shared identical or similar types. As shown in Figure 2(A), proteins a and b were assumed to have stronger interaction with each other than any other pairs, followed by proteins b and c. In this case, protein a would more likely have the same types as protein b, and protein b would more likely have the same types as protein c. Consequently, it could be inferred that proteins a and c would more probably share the same types, however they were not interacted directly with each other. In view of this, we constructed a weighted graph G in which nodes represent proteins and an edge between two nodes existed if and only if the confidence score of the interaction between them was greater than zero. In addition, the weight of the edge between nodes n1 and n2 was defined as (5)where was the confidence score of the interaction between proteins p1 and p2 which are corresponding proteins of nodes n1 and n2. As shown in Figure 2(B), proteins a and c were each other the shortest-distance neighbors except their direct neighbors. Therefore, the shortest-distances between proteins in G hold the clue to function relationship between them. This kind of weighted graph had been used in our previous work [34]. Therefore, even if two proteins had no direct interaction, it was possible to utilize the shortest distance in G to infer its types. By applying Dijkstra's algorithm which is one of the most well-known shortest-distance algorithms, the shortest distance between any two proteins p1 and p2 was calculated, denoted by and termed as shortest-distance score. For an uncharacterized membrane protein , its types were predicted as the same as those of protein in the training set S whose

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Figure 2. A sample of PPI interaction network and the weighted graph conversion.

A represents a sample of the interaction network with edges representing confidence scores and B is the weighted graph derived from A.

https://doi.org/10.1371/journal.pone.0093553.g002

Integrated method for predicting membrane protein multiple types.

The integrated method combined the BLAST/PSI-BLAST method, the network-based method and the shortest-distance method, as shown in Figure 3. For an uncharacterized protein , the prediction procedure by using the integrated method was as follows:

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Figure 3. The program flow diagram of the integrated method.

https://doi.org/10.1371/journal.pone.0093553.g003

  1. If there was a homologous protein within the training set S, i.e. for some k, only the BLAST/PSI-BLAST method was applied.
  2. If no homologous proteins but there was an interactive protein with in the training set S, i.e. for some k, only the network-based method was applied.
  3. If it was not the two cases above, the shortest-distance method was finally used.

Metrics

Among the three ways examining performances of classification algorithms: cross-validation test, leave-one-out test and independent test, the leave-one-out test is considered as the best [35], [36], because the independent test requires additional datasets and the cross-validation test generates unstable results, while the leave-one-out test always provides the unique results for a given a dataset. Here, the leave-one-out test was adopted.

The commonly used mesurements sensitivity, specificity, accuracy and matthew's correlation coefficient in binary classification problems were not applicable to this study problem as one protein may simultaneously have more than one types. Deng et al. [37] used both concepts of Precision and Recall to measure performances of methods for multi-label classification problems. Assume as a label set of actual types for the i-th protein in the dataset, and as its predicted label set of types, the Precision and Recall were defined as:(6)where |A| denoted the number of elements in set A, and n refered to the total number of predicted proteins. However, neither Precision nor Recall can be used alone for the evaluation. If predicted types contained all the elements in actual types , Recall would be equal to 1. This is of no mean, because any one can tell all possible types of a membrane protein. On the other hand, if the predicted types was a non-empty subset of the actual types , i.e. , Precision would be equal to 1. To overcome these weaknesses, we presented a new evaluation indicator called Accuracy (Acc), which was defined as:(7)The first part on the right of the equation was approximately equivalent to Recall, while was a penalty for due to incorrect predictions. When was identical to , the penalty was of no role. On the contrary, the more incorrect type the included, the lower the Acc was. Therefore, Acc reflected both Precision and Recall.

Results and Discussion

Choice of E-value in the BLAST/PSI-BLAST method

It was ploted in Figure 4 the performances of the BLAST/PSI-BLAST method with different E-values. It was obviously observed from Figure 4(A) that the lower the threshold of E-value chosed in the BLAST program, the higher the Acc was, however the more the number of annotated proteins remained from Figure 4(B). The commonly expected goal of prediction is of course to reach high Acc and simultaneously reduce the number of unannotated membrane proteins. However, this ideal objective is impossible to obtain because we are caught in the dilemma of choosing the cutoff of E-value. While lower E-values increased the statistical significance of alignment scores, it would be too strict and would accordingly lessen the number of proteins considered homologies. On the contrary, higher E-values may result in wrong homology, and thus reduce Acc. To balance Acc and the number of unannotated proteins, we set E-value 0.01 in this study.

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Figure 4. The performance of the BLAST/PSI-BLAST method under various E-values.

https://doi.org/10.1371/journal.pone.0093553.g004

Performances of the individual methods and the integrated method

Three methods and the integrated method were all conducted on the three datasets S1, S2 and S3, respectively, evaluated by the leave-one-out test. Results were shown in Tables 1-3. It can be observed from Table 1 that the BLAST/PSI-BLAST method achieved the best performance with the highest Acc 94.71%, 91.15% and 85.02% on datasets S1, S2 and S3, respectively. However, 481, 529 and 620 proteins cannot be annotated because there were no homologous proteins in the corresponding datasets. The network-based method achieved the second highest Acc, i.e. 66.68%, 62.46%, 58.75% on the three datasets, respectively. Since no interactive proteins can be found in the corresponding datasets, there were 86, 38, 41 proteins unannotated. Although Acc was lower, the number of unannotated proteins was much less than those in the BLAST/PSI-BLAST method. In the shortest-distance method, the lowest Acc achieved (54.97%, 48.75%, 44.99% on the three datasets, respectively). However, all proteins can be annotated. The shortest distance method was capable of annotating all proteins, although it was least effective.

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Table 1. Performances of the three single methods tested on the three datasets.

https://doi.org/10.1371/journal.pone.0093553.t001

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Table 2. Contributions of the three methods to the integrated method.

https://doi.org/10.1371/journal.pone.0093553.t002

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Table 3. Comparison of the integrated method with NNA based on PseACC and RWC on the three datasets.

https://doi.org/10.1371/journal.pone.0093553.t003

The integrated method combined the three methods above. The detail contributions of the three single methods were shown in Table 2. It was obvious that the BLAST/PSI-BLAST method contributed the most, annotating 2,402, 1,552 and 849 proteins and achieved Acc of 94.71%, 91.15% and 85.02% on datasets S1, S2 and S3, respectively. The network-based method annotated 467 of the remaining 481 unannotated proteins in S1, 513 of the remaining 529 in S2 and 596 of the remaining 620 in S3, respectively, and obtained Acc of 53.10%, 53.51%,52.60% on the three datasets. The rest proteins in the three datasets were predicted by the shortest-distance method with Acc of 28.57%, 28.13%, 18.75% on the three datasets. By combining the above predicted results for each dataset, the integrated method reached Acc of 87.65%, 81.39% and 70.79% on S1, S2, S3, respetively, which was listed in column 2 of Table 3. It was suggested that the integrated method could balance the prediction accuracy and the ratio of annotated proteins, benefiting from the high accuracy of BLAST/PSI-BLAST method and the wide range of the network-based method and shortest-distance method.

Membrane proteins may have multiple types. Statistical analysis were performed on the proteins having 1, 2, 3 types in each of the three datasets, respectively, with results shown in Figure 5. It can be seen that the prediction of 1 type proteins was the best, the 2 type ones was the second, and the 3 type ones was the least. For the 2 type proteins, nearly 50% could be correctly predicted or partly correctly predicted. It was indicated that the presented method could handle the multi-type membrane protein predictions.

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Figure 5. Distributions of correct and incorrect predictions for different multi-type proteins.

The subfigures A, B and C were the predicted results on the dataset S1, while D, E and F were for those on the dataset S2, and G, H and I for S3. c denotes the number of completely correct predictions of membrane proteins, e is the number of incorrect predicted membrane proteins, and p represents the number of partly correct type predictions.

https://doi.org/10.1371/journal.pone.0093553.g005

Comparison with other methods

We compared our method to two other methods: the nearest neighbor algorithm (NNA) based on pseudo amino acid composition (PseAAC) and the real weighted combination (RWC). The procedure of these two methods was described in the following two paragraphs and their performances on datasets S1, S2 and S3 were in the fourth paragraph of this section.

NNA based on PseAAC.

The concept of PseAAC was originally initiated by Chou to predict protein subcellular localization and membrane protein types [38]. The main idea of this method was to encode each protein sequence into a numeric vector containing information of sequence order effects. Here, the brief description of this method was as follows. Detail description about this method can be found in Chou's work [38]. Without loss of generality, let be a protein sequence with L amino acid residues. Accordingly, a set of discrete correlation factors can be computed by(8)where is defined by(9)F(Pi) is a feature value of the amino acid Pi. In fact, it is converted from the 20 original feature values of 20 amino acids by (10)where AAS is a set consisting of 20 amino acids. If the original feature values were known, can be computed by equations (8), (9) and (10). Table 4 listed the original feature values of 20 amino acids for some physicochemical and biochemical properties of amino acids. Then, the PseAAC of a protein sequence can be encoded into a numeric vector:(11)where can be computed by(12)where is the weight for the sequence order effects, () the occurrence frequency of the 20 amino acids in the protein sequence. In this study, we set , and considered the following five physicochemical and biochemical properties of amino acids: (1) Codon diversity; (2) Electrostatic charge; (3) Molecular volume; (4) Polarity; (5) Secondary structure. Their original feature values of each amino acid were listed in Table 4, which were retrieved from 39,40,41]. Each of the five properties contributed components to contain the sequence order effects. Therefore, there are totally 20+50×5 = 270 components which can comprise a 270-D vector to represent a protein sequence. The same PseAAC scheme to encode protein sequences including same parameters had been applied in Wang et al.'s study [42]. Given an uncharacterized protein and the training set , where is the size of the training set, the PseAAC of these proteins can be easily obtained. The NNA based on PseAAC computed the distance between and by the following equation:(13)where is the PseAAC of protein Pi. The query protein was then assigned the types of protein pk in the training set whose

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Table 4. Original feature values of physicochemical and biochemical properties of the 20 amino acids.

https://doi.org/10.1371/journal.pone.0093553.t004

RWC.

RWC utilized another integrated way combining the information of BLAST alignment score, protein interaction confidence score and shortest-distance score. For two proteins p1 and p2, the score measuring their relationship was calculated by (14)Given an uncharacterized membrane protein , its types were predicted to be the types of protein pk in the training set whose

Performance of NNA based on PseAAC and RWC.

The NNA based on PseAAC and RWC were also conducted on the three datasets S1, S2 and S3, respectively, evaluated by leave-one-out test, with results listed in Table 3. The Acc obtained by NNA based on PseAAC on S1, S2 and S3 was 70.41%, 61.70% and 56.33%, respectively, while it was 81.34%, 71.40% and 56.82% obtained by RWC. It can be seen that our method performed significantly better than the two methods. Acc obtained by our method was on average 17% higher than that by NNA based on PseAAC and 10% higher than that by RWC. These suggested the efficiency of the proposed integrated method in predicting membrane protein multi-types.

Discussions

Membrane proteins are embedded in or temporarily attached to the phospholipid bi-layer of the membrane. They interact with membranes via specific structures or comformations. Therefore, the protein structures were regarded as the determinate factors in the in vivo types. Based on the principle of Lock and Key Theory, the affinity and specificity of molecules is strictly determined by the local binding site shape and binding pocket size [43], [44]. To date, many efforts have been made to computationally predict the protein structures [45], [46], [47], [48], [49], [50] and to identify the membrane protein types based on structures, although some of them are devised for water-soluble proteins with large molecular volumes [51], [52], [53], [54].

In this paper, we employed the sequence alignment rather than protein structures for the general concept that similar amino acid sequences were prone to have similar structures. Indeed, homology modeling predictions derived from sequence alignment methods were regarded as precise and ubiquitous for analyzing structures and types [55]. Therefore, the BLAST/PS-BLAST was efficient to acquire the optimal prediction. Take the membrane protein ICAM5_HUMAN (uniprot: Q9UMF0) as an example. This protein belongs to single-pass type I. In the dataset S3, it was correctly predicted by the integrated method (via BLAST/PSI-BLAST method indeed) but wrongly by NNA based on PseAAC and RWC. The homologous proteins with Q9UMF0 in S3 and their types were shown in Figure 6(A). It was obvious that the protein shared the same or similar types with most of its homologous proteins (number 5 in the Figure 6(A)).

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Figure 6. The homologous proteins of the membrane protein Q9UMF0 (A) and the interactive proteins of the membrane protein O00623 (B) in the dataset S3.

The numbers represent the membrane protein types: 1 GPI-anchor, 2 lipid-anchor, 3 multi-pass, 4 peripheral, 5 single-pass type I and 6 single-pass type II.

https://doi.org/10.1371/journal.pone.0093553.g006

Interactive proteins could share the same or similar types since they have close distance when they perform biological functions in vivo. In Figure 6(B), shown were all the interactive proteins of another example membrane protein PEX12_HUMAN (uniprot: O00623) in the dataset S3, with their types. By our integrated method (via network-based method indeed), it was correctly predicted to be type of multi-pass, but wrongly to be type of peripheral by NNA based on PseAAC and to be type of single-pass single I by RWC. In the integrated method, the BLAST/PSI-BLAST can utilize the entire sequence order, and STRING can directly use the comprehensive functional-links. However, pseudo amino acid composition only can represent local information of sequence order. In RWC, the score calculated by equation (14) between membrane proteins was the weighted combination of alignment score, confidence score and shortest-distance score. The three types of scores were obtained in different ways; simple combination of them cannot characterize the complex in type relationships between membrane proteins. Experimental results also suggested that the combination of the three scores was less efficient than those by using separate ones.

However, the integrated method had some drawbacks because the BLAST/PSI-BLAST method was not primarily designed for specific proteins. Applying the “low-complexity filter” option in BLAST/ PSI-BLAST was not recommended for membrane proteins consisting of hydrophobic regions, because these regions could be the main factor determining the conformation and this would probably causes omission of the hydrophobic regions [56].

There was another problem that BLAST method would fail when membrane proteins have no homologies with known membrane proteins. However, it was still possible to discover the types according to the similarity of motifs or certain patterns with known proteins. Considering the variability of membrane proteins and the limited number of experimental high-resolution membrane protein structures, we did not further employ the similarity of motifs. This would probably reduce the prediction performances. Instead, the network-based method was implemented. Interaction of proteins might help and thus prediction would be more accurate. Although our prediction accuracy (Acc = 87.65%, 81.39%, 70.79%) prima facie suggested our integrated method performed better than individual predictions (except the BLAST method which was the most reliable one), this method come with a caveat. As the BLAST/PSI-BLAST method was suitable to annotate 2,402 of 2,883, 1,552 of 2,081, and 849 of 1,469 proteins and obtained Acc of 94.71%, 91.15% and 85.02%, respectively, it was quite possible that higher accuracy of the combined method was ascribed to the constitution of our dataset where most proteins have homologies and were annotated by the BLAST/PSI-BLAST method. However, in most cases, we were confronted with a dataset with few proteins having homologies with known proteins. Despite the most reliable predictions by BLAST, the network-based method was still required to annotate the remaining proteins. Here, we combined these different approaches to complement each other. Our integrated method benefited from the BLAST method and employed network-based method utilizing protein-protein interactions as complement, achieving nice performance.

Supporting Information

Table S1.

The protein IDs, sequences and their types in three datasets.

https://doi.org/10.1371/journal.pone.0093553.s001

(PDF)

Author Contributions

Conceived and designed the experiments: LC NZ TH YDC. Performed the experiments: GH YZ. Analyzed the data: YZ LC TH YDC. Contributed reagents/materials/analysis tools: GH NZ TH. Wrote the paper: GH YZ LC NZ.

References

  1. 1. Krogh A, Larsson B, von Heijne G, Sonnhammer EL (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305: 567–580.
  2. 2. Gao QB, Ye XF, Jin ZC, He J (2010) Improving discrimination of outer membrane proteins by fusing different forms of pseudo amino acid composition. Anal Biochem 398: 52–59.
  3. 3. Almen MS, Nordstrom KJ, Fredriksson R, Schioth HB (2009) Mapping the human membrane proteome: a majority of the human membrane proteins can be classified according to function and evolutionary origin. BMC Biol 7: 50.
  4. 4. Arinaminpathy Y, Khurana E, Engelman DM, Gerstein MB (2009) Computational analysis of membrane proteins: the largest class of drug targets. Drug Discov Today 14: 1130–1135.
  5. 5. Davey J (2004) G-protein-coupled receptors: new approaches to maximise the impact of GPCRS in drug discovery. Expert Opin Ther Targets 8: 165–170.
  6. 6. Terstappen GC, Reggiani A (2001) In silico research in drug discovery. Trends Pharmacol Sci 22: 23–26.
  7. 7. Wang J, Li Y, Wang Q, You X, Man J, et al. (2012) ProClusEnsem: predicting membrane protein types by fusing different modes of pseudo amino acid composition. Comput Biol Med 42: 564–574.
  8. 8. Jia P, Qian Z, Feng K, Lu W, Li Y, et al. (2008) Prediction of membrane protein types in a hybrid space. J Proteome Res 7: 1131–1137.
  9. 9. Cai YD, Ricardo PW, Jen CH, Chou KC (2004) Application of SVM to predict membrane protein types. J Theor Biol 226: 373–376.
  10. 10. Wang M, Yang J, Liu GP, Xu ZJ, Chou KC (2004) Weighted-support vector machines for predicting membrane protein types based on pseudo-amino acid composition. Protein Eng Des Sel 17: 509–516.
  11. 11. Wang SQ, Yang J, Chou KC (2006) Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition. J Theor Biol 242: 941–946.
  12. 12. Mahdavi A, Jahandideh S (2011) Application of density similarities to predict membrane protein types based on pseudo-amino acid composition. J Theor Biol 276: 132–137.
  13. 13. Hayat M, Khan A (2010) Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition. J Theor Biol 271: 10–17.
  14. 14. Wang M, Yang J, Xu ZJ, Chou KC (2005) SLLE for predicting membrane protein types. J Theor Biol 232: 7–15.
  15. 15. Ridder Dd, Kouropteva O, Okun O, Pietikäinen M, Duin RPW (2003) Supervised locally linear embedding. Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing. Istanbul, Turkey: Springer-Verlag. pp. 333–341.
    • 16. Pu X, Guo J, Leung H, Lin Y (2007) Prediction of membrane protein types from sequences and position-specific scoring matrices. J Theor Biol 247: 259–265.
    • 17. Hayat M, Khan A (2012) Mem-PHybrid: hybrid features-based prediction system for classifying membrane protein types. Anal Biochem 424: 35–44.
    • 18. Hayat M, Khan A (2012) MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM. J Theor Biol 292: 93–102.
    • 19. Ding C, Yuan LF, Guo SH, Lin H, Chen W (2012) Identification of mycobacterial membrane proteins and their types using over-represented tripeptide compositions. J Proteomics 77: 321–328.
    • 20. Hayat M, Khan A, Yeasin M (2012) Prediction of membrane proteins using split amino acid and ensemble classification. Amino Acids 42: 2447–2460.
    • 21. Chen YK, Li KB (2013) Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou's pseudo amino acid composition. J Theor Biol 318: 1–12.
    • 22. Liu H, Wang M, Chou KC (2005) Low-frequency Fourier spectrum for predicting membrane protein types. Biochem Biophys Res Commun 336: 737–739.
    • 23. Guo YZ, Li M, Lu M, Wen Z, Wang K, et al. (2006) Classifying G protein-coupled receptors and nuclear receptors on the basis of protein power spectrum from fast Fourier transform. Amino Acids 30: 397–402.
    • 24. Rezaei MA, Abdolmaleki P, Karami Z, Asadabadi EB, Sherafat MA, et al. (2008) Prediction of membrane protein types by means of wavelet analysis and cascaded neural networks. J Theor Biol 254: 817–820.
    • 25. Qiu JD, Sun XY, Huang JH, Liang RP (2010) Prediction of the types of membrane proteins based on discrete wavelet transform and support vector machines. Protein J 29: 114–119.
    • 26. Wang L, Yuan Z, Chen X, Zhou Z (2010) The prediction of membrane protein types with NPE. Ieice Electronic Express 7: 397–402.
    • 27. UniProt C (2010) The Universal Protein Resource (UniProt) in 2010. Nucleic Acids Res 38: D142–148.
    • 28. Chou KC, Cai YD (2005) Prediction of membrane protein types by incorporating amphipathic effects. J Chem Inf Model 45: 407–413.
    • 29. Li W, Godzik A (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22: 1658–1659.
    • 30. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215: 403–410.
    • 31. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, et al. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25: 3389–3402.
    • 32. Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, et al. (2011) The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 39: D561–568.
    • 33. Hu L, Huang T, Shi X, Lu WC, Cai YD, et al. (2011) Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties. PLoS One 6: e14556.
    • 34. Li BQ, Huang T, Liu L, Cai YD, Chou KC (2012) Identification of Colorectal Cancer Related Genes with mRMR and Shortest Path in Protein-Protein Interaction Network. PLoS One 7: e33393.
    • 35. Chen L, Zeng W-M, Cai Y-D, Feng K-Y, Chou K-C (2012) Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities. PLoS ONE 7: e35254.
    • 36. Chen L, Lu J, Zhang N, Huang T, Cai Y-D (2014) A hybrid method for prediction and repositioning of drug Anatomical Therapeutic Chemical classes. Molecular BioSystems 10: 868–877.
    • 37. Deng M, Zhang K, Mehta S, Chen T, Sun F (2003) Prediction of protein function using protein-protein interaction data. J Comput Biol 10: 947–960.
    • 38. Chou KC (2001) Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins 43: 246–255.
    • 39. Atchley WR, Zhao J, Fernandes AD, Drüke T (2005) Solving the protein sequence metric problem. Proceedings of the National Academy of Sciences of the United States of America 102: 6395–6400.
    • 40. Rubinstein ND, Mayrose I, Pupko T (2009) A machine-learning approach for predicting B-cell epitopes. Molecular immunology 46: 840–847.
    • 41. Huang T, Shi XH, Wang P, He Z, Feng KY, et al. (2010) Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks. PLoS ONE 5: e10972.
    • 42. Wang P, Hu L, Liu G, Jiang N, Chen X, et al. (2011) Prediction of antimicrobial peptides based on sequence alignment and feature selection methods. PLoS ONE 6: e18476.
    • 43. Keskin O, Gursoy A, Ma B, Nussinov R (2008) Principles of protein-protein interactions: what are the preferred ways for proteins to interact? Chemical reviews 108: 1225–1244.
    • 44. Nussinov R, Ma B (2012) Protein dynamics and conformational selection in bidirectional signal transduction. BMC Biol 10: 2.
    • 45. Levitt M (1992) Accurate modeling of protein conformation by automatic segment matching. J Mol Biol 226: 507–533.
    • 46. Sali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234: 779–815.
    • 47. Canutescu AA, Shelenkov AA, Dunbrack RL Jr (2003) A graph-theory algorithm for rapid protein side-chain prediction. Protein Sci 12: 2001–2014.
    • 48. Petrey D, Xiang Z, Tang CL, Xie L, Gimpelev M, et al. (2003) Using multiple structure alignments, fast model building, and energetic analysis in fold recognition and homology modeling. Proteins 53 Suppl 6430–435.
    • 49. Jacobson MP, Pincus DL, Rapp CS, Day TJ, Honig B, et al. (2004) A hierarchical approach to all-atom protein loop prediction. Proteins 55: 351–367.
    • 50. Yohannan S, Faham S, Yang D, Whitelegge JP, Bowie JU (2004) The evolution of transmembrane helix kinks and the structural diversity of G protein-coupled receptors. Proc Natl Acad Sci U S A 101: 959–963.
    • 51. Bourne PE (2003) CASP and CAFASP experiments and their findings. Methods Biochem Anal 44: 501–507.
    • 52. Koh IY, Eyrich VA, Marti-Renom MA, Przybylski D, Madhusudhan MS, et al. (2003) EVA: Evaluation of protein structure prediction servers. Nucleic Acids Res 31: 3311–3315.
    • 53. Moult J (2005) A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction. Curr Opin Struct Biol 15: 285–289.
    • 54. Rychlewski L, Fischer D (2005) LiveBench-8: the large-scale, continuous assessment of automated protein structure prediction. Protein Sci 14: 240–245.
    • 55. Petrey D, Honig B (2005) Protein structure prediction: inroads to biology. Mol Cell 20: 811–819.
    • 56. Hedman M, Deloof H, Von Heijne G, Elofsson A (2002) Improved detection of homologous membrane proteins by inclusion of information from topology predictions. Protein Sci 11: 652–658.