Volume 11 Supplement 1
Predicting the protein-protein interactions using primary structures with predicted protein surface
© Chang et al; licensee BioMed Central Ltd. 2010
Published: 18 January 2010
Many biological functions involve various protein-protein interactions (PPIs). Elucidating such interactions is crucial for understanding general principles of cellular systems. Previous studies have shown the potential of predicting PPIs based on only sequence information. Compared to approaches that require other auxiliary information, these sequence-based approaches can be applied to a broader range of applications.
This study presents a novel sequence-based method based on the assumption that protein-protein interactions are more related to amino acids at the surface than those at the core. The present method considers surface information and maintains the advantage of relying on only sequence data by including an accessible surface area (ASA) predictor recently proposed by the authors. This study also reports the experiments conducted to evaluate a) the performance of PPI prediction achieved by including the predicted surface and b) the quality of the predicted surface in comparison with the surface obtained from structures. The experimental results show that surface information helps to predict interacting protein pairs. Furthermore, the prediction performance achieved by using the surface estimated with the ASA predictor is close to that using the surface obtained from protein structures.
This work presents a sequence-based method that takes into account surface information for predicting PPIs. The proposed procedure of surface identification improves the prediction performance with an F-measure of 5.1%. The extracted surfaces are also valuable in other biomedical applications that require similar information.
The different types of interactions among proteins are essential to various biological functions in a living cell. Information about these interactions provides a basis to construct protein interaction networks and improves our understanding of the general principles of the functioning of biological systems . Recent years have seen the development of various experimental techniques for systematic protein-protein interaction (PPI) analysis [2–5]. At present, however, experimentally detected interactions represent only a small fraction of the real interaction network [6, 7]. Therefore, a number of computational approaches have been proposed to expedite the PPI detection process based on only experimental techniques .
Computational methods that depend on not only sequence information but also some prior knowledge of, for example, localization data , structural data [10, 11], expression data [12, 13] or information on the interactions of orthologs [14, 15] cannot be applied on some essential proteins that are observed in most organisms . To solve this problem, several sequence-based algorithms have been developed to detect potentially interacting protein pairs when no auxiliary information is available [17–23].
This work presents a novel sequence-based method which involves a mechanism for identifying the protein surface to help PPI prediction. This method employs the conjoint triad feature  for describing protein sequences and the relaxed variable kernel density estimator (RVKDE)  for classification. Conjoint triads, which treat three continuous amino acids as a single unit, have been shown to be a useful set of features in predicting protein-protein interactions . This work improves this feature set by focusing on conjoint triads at the protein surface. This improvement is based on the assumption that protein-protein interactions are more related to amino acids at the surface than those at the core. To maintain the advantage of depending on only sequence information, this method employs an accurate accessible surface area (ASA) predictor, recently proposed by the authors , to determine the protein surface.
In this study, a collection of 691 PPIs is used to evaluate the prediction performance with and without the proposed mechanism for identifying the protein surface. The experimental results show that the surface information promotes PPI prediction based on feature encoding with conjoint triads. Furthermore, the quality of the predicted surface is analyzed using a number of protein structures collected from the Protein Data Bank (PDB) . The experimental results demonstrate that the performance of PPI prediction achieved using the predicted surface is close to that achieved using the surface obtained from protein structures.
Results and discussion
This section first describes the workflow of the proposed method. Next, the measurements and datasets for performance evaluation are presented. The proposed method is evaluated and compared with another sequence-based PPI predictor. At the end of the section, the predicted surface is compared to those obtained from protein structures.
Proposed PPI prediction scheme
A challenge in preparing protein-protein interaction datasets is the presence of some interactions that are observed in the laboratory experimentation but do not occur physiologically . To ensure the quality of PPI data, an interaction should be consistent with other types of information , such as metabolomic  and gene-gene relationship data . Though these types of data are often incomplete in most organisms at present, the interaction network of transcription factors (TF) of Saccharomyces cerevisiae is an extensively studied system in which all of such information are currently available . Therefore, this study collects 691 interactions of 211 yeast TFs from several studies and databases [32–36] to generate a PPI dataset, SC691. In this dataset, the 691 interactions are used as positive instances, while other protein pairs created by coupling the 211 TFs are used as negative instances.
Evaluation of PPI prediction
In the experiment, the SC691 dataset is randomly split into three subsets of 341, 175 and 175 interacting pairs. These subsets also contain 341, 175 and 175 non-interacting pairs obtained by arbitrarily sampling of the negative instances in the SC691 dataset. Care is taken to ensure that different subsets will not share identical instances. In this experiment, the first subset is used as the training set to predict the other two subsets. The predicted results of the second subset are used for parameter selection, while the predicted results of the third subset indicate the prediction performance of a PPI predictor. Therefore, an evaluation process is performed by first using the first subset to predict the second subset. Then the parameters that maximize the F-measure are used to predict the third subset. Since the procedure for generating these subsets involves randomness, the evaluation process is performed ten times to eliminate the evaluation bias in a single evaluation process.
Performance achieved by considering and by neglecting surface information.
Without surface information
Shen et al.'s work
68.2 ± 4.3
70.4 ± 3.2
66.4 ± 5.1
75.4 ± 5.4
61.0 ± 10.2
Surface identified using different o
72.3 ± 1.4
73.7 ± 1.6
70.3 ± 2.3
77.8 ± 4.4
66.9 ± 5.1
72.1 ± 3.2
74.0 ± 2.2
69.7 ± 4.2
79.3 ± 3.7
64.9 ± 8.3
74.1 ± 2.0
75.5 ± 2.0
71.8 ± 2.4
79.7 ± 3.5
68.6 ± 4.0
71.7 ± 3.8
73.4 ± 2.3
69.8 ± 4.9
77.9 ± 5.9
65.4 ± 11.5
As a result, the average Acc., Fm., Prec., Sens. and Spec. of the developed method are 74.1%, 75.5%, 71.8%, 79.7% and 68.6%, respectively. All five measurements are superior to those delivered by the predictor without surface information. These results show that the proposed mechanism for identifying the protein surface helps to predict protein-protein interactions based on feature encoding with conjoint triads.
Evaluation of predicted surface
Proteins in the SC691 dataset that have structures in PDB
PDB ID: chain
Transcription initiation protein
Galactose/lactose metabolism regulatory protein
RNA polymerase II mediator complex subunit 18
RNA polymerase II mediator complex subunit 20
RNA polymerase II holoenzyme component SRB7
Mitochondrial replication protein
Nonhistone protein 6A
Cyclin, negatively regulates phosphate metabolism
Transcription initiation factor IIA large chain
Transcription initiation factor IIA small chain
Overlap between predicted and structural surface.
85.8 ± 8.4
88.3 ± 7.0
85.7 ± 9.7
91.9 ± 9.0
77.7 ± 15.2
Performance achieved using predicted and structural surface.
96.1 ± 0.6
39.8 ± 1.7
58.5 ± 11.0
31.1 ± 3.5
98.9 ± 0.7
96.2 ± 0.2
40.7 ± 1.3
58.1 ± 6.0
31.5 ± 1.3
99.0 ± 0.3
For Med18, the present method successfully excludes 80 (accounting for ~26.1%) from total 307 residues while preserving 48 (accounting for ~92.3% of the 52) interface residues. As shown in Figure 2(a), most interface residues, specified in yellow, are included. However, for Med20, the proposed method misses 24 (accounting for ~54.5% of the 44) interface residues in the predicted surface in Figure 2(b). Figure 2(b) reveals that the predicted surface misses the segment (residues 86-107) of Med20 that acts like an arm stretching to Med18. A comparison with the interface shown in Figure 2(a) suggests that the present method may perform better at handling flatter interfaces. Since protein subunits may interact and form relatively flat or twisted surfaces , the good performance of the present method probably results from the fact that most of the collected S. cerevisiae TFs have relatively flat surfaces.
These results also reveal that the proposed mechanism for identifying the surfaces of proteins with relatively twisted surfaces must be improved.
An enormous gap exists between the number of protein structures and the huge number of protein sequences. Hence, predicting protein functions directly from amino acid sequences remains one of the most important problems in life science. This work presents a computational approach for PPI prediction based on only sequence information. Notably, a mechanism of extracting surface information is proposed to refine the feature vector for representing a protein sequence. This method is analyzed in terms of a) the performance in predicting PPIs and b) the quality of the predicted surface. The experimental results show that the present method improves on the prediction performance of PPI with an F-measure of 5.1%. Furthermore, the predicted surface of yeast TFs is consistent with that obtained from structures, which encourages applying the present steps of surface identification in other biomedical problems that require similar information.
The second stage encodes a protein sequence based on neighboring solvent accessibility [26, 45]. The i-th residue in a protein sequence is represented as a 2w2+1 dimensional vector v = (ai-h, ti-h, ai-h+1, ti-h+1, ..., a i , t i , ..., ai+h, ti+h, l), where a i is the predicted RSA value of the i-th residue in the first regression, t i is the terminal flag as either 1 (a null/terminal residue) or 0 (otherwise), l is the sequence length and w2 = 2h+1 is window size (w2 = 5 in our implementation).
where K() is the kernel function, and b and w i are numerical parameters determined by minimizing the prediction error on training samples. The problem is to find the support vectors and determine parameters b and w i , which can be solved by constrained quadratic optimization . The LIBSVM package (version 2.86)  is used for SVR implementation in this study.
The employed ASA predictor makes predictions at the residue level. The predicted RSA value of each residue enables surface residues to be defined as those whose RSA values are equal to or larger than a threshold t. These identified surface residues are frequently scattered throughout the protein sequences. This work develops a process for generating a set of surface segments each of which is a consecutive sub-sequence of minimum length. Because a conjoint triad represents three continuous amino acids, these consecutive segments are more suitable than scattered surface residues for being encoded with conjoint triads.
Amino acid groups used herein.
Ala, Gly, Val
Ile, Leu, Phe, Pro
Tyr, Met, Thr, Ser
His, Asn, Gln, Tpr
Relaxed variable kernel density estimator
R(s i ) is the maximum distance between si and its ks nearest training instances;
Γ(·) is the Gamma function ;
β and ks are parameters to be set either through cross-validation or by the user.
where |S j | is the number of class-j training instances, and (v) is the kernel density estimator corresponding to class-j training instances. In this study, j is either 'interacting' or 'non-interacting'. Current RVKDE implementation includes only a limited number, denoted by kt, of the nearest class-j training instances of v while computing (v) in order to improve the efficiency of the predictor. The kt is also a parameter to be set either through cross-validation or by the user.
The authors would like to thank the National Science Council of the Republic of China, Taiwan, for financially supporting this research under Contract Nos. NSC 97-2627-P-001-002, NSC 96-2320-B-006-027-MY2 and NSC 96-2221-E-006-232-MY2. Ted Knoy is appreciated for his editorial assistance.
This article has been published as part of BMC Bioinformatics Volume 11 Supplement 1, 2010: Selected articles from the Eighth Asia-Pacific Bioinformatics Conference (APBC 2010). The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/11?issue=S1.
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