Proteome-wide Prediction of Self-interacting Proteins Based on Multiple Properties

Properties of Self-interacting Proteins

To understand the role that self-interacting proteins play in cellular functions from an overall perspective, we systematically analyzed their properties from multiple aspects.

First, to obtain a list of known human self-interacting proteins as comprehensive as possible, we collected human self-interaction proteins supported by at least one piece of experimental evidence from multiple publicly available resources (see “Materials and Methods”). In total, 1537 nonredundant known human self-interacting proteins were obtained (supplemental Table S1).

Based on this integrated dataset, we studied the properties of self-interacting proteins from the following aspects. First, we compared the average domain number of self-interacting proteins with that of other proteins. Protein domains are the basic structural and functional units of a protein, and the domain number generally can reflect the complexity of a protein's structure and function (33). We find that, compared with other proteins, self-interacting proteins contain significantly more domains, indicating they tend to have a more complex structure and function (Table I). Then, by computing evolutionary rates and assessing original ages of proteins, we observe that self-interacting proteins tend to be evolutionarily conserved and ancient (Table I). Evolutionary conservation usually implies functional importance. The result on original age is also consistent with two previous studies based on the three-dimensional structural data of yeast proteins (34) and a small scale yeast homodimeric protein dataset (35), which both showed that ancient proteins are highly enriched with homodimeric proteins. Furthermore, self-interacting proteins are shown to be significantly enriched with enzyme genes and housekeeping genes, which are just consistent with our previous knowledge on the importance of homo-oligomerization in functional implementation of enzymes and more extensive cellular functions (Table II) (2). Surprisingly, about 40% of known human self-interacting proteins we collect are known drug targets, which is several times higher than that of other proteins (Table II). Finally, in the topological aspect of the network, we analyzed the degree and betweenness centrality of self-interacting proteins in PINs. These two topological indices with different emphases can both be used to quantify the topological importance of individual nodes in the network. Degree is the number of direct neighbors of a given node, capturing the information of a node's local importance. In contrast, betweenness centrality is related to the frequency with which a node appears on all the shortest paths between all pairs of nodes in the network (see “Materials and Methods”), thus not only considering local information but also reflecting a semi-global manner of how a node influences the “communication” between other nodes in the network (25, 36). Self-interacting proteins are found to have a significantly higher degree and betweenness centrality than other proteins in the PIN (Table I), suggesting the crucial role self-interacting proteins play in PINs. The result on a higher degree of self-interacting proteins is also consistent with a previous study (3). The higher degree of self-interacting proteins in PINs might be related to gene duplication. Different from a nonself-interacting protein, the duplication of a self-interacting protein will lead to an additive interaction partner (paralogous interactor) because of the conservation of self-interaction (8). Therefore, as proteins continuously duplicate, self-interacting proteins will accumulate more interaction partners than other proteins.

These results in structural, evolutionary, functional, and network topological aspects all indicate the importance of self-interacting proteins in cellular functions, and they also suggest the potential special role they play in drug research. In addition, we also obtain similar results in yeast, indicating these conclusions are universal across different organisms (supplemental Tables S2 and S3).

Features Used to Predict Self-interacting Proteins

To construct a self-interacting protein prediction system, features were first collected, which can efficiently discriminate between self-interacting proteins and other proteins.

From the analyses above, we found that on multiple properties the self-interacting proteins are significantly different from other proteins. Theoretically, all these features can be used to predict self-interacting proteins. We use likelihood ratio (LR) to measure the discrimination power or confidence level of feature f, which is defined as the ratio of the probability of feature f observed in GSP set to that in GSN set (31). In theory, LR(f) >1 means that feature f has the ability to distinguish self-interacting proteins from other proteins. As shown in Fig. 1, A–G, as we expected all these features have the ability to predict self-interacting proteins. Furthermore, different bins of the same feature have different prediction abilities, and the tendency is in agreement with the results in Tables I and II for all these features. For example, for feature “degree in the PIN,” proteins with different degrees have different confidence levels to be a self-interacting protein. The higher the degree of a protein is, the more possibly this protein can interact with itself. This tendency is consistent with the previous result that self-interacting proteins tend to have higher degree in the PIN (Table I). Besides the features above, we also consider another four features as shown below.

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Fig. 1.

LRs of various features used to predict self-interacting proteins. LR of feature f is calculated as the ratio between the fraction of proteins with feature f in the GSP set and that in the GSN set. A, domain number. B and C, evolutionary rate and age (see “Materials and Methods”). The evolutionary rate/age of proteins in the first bin cannot be estimated. D, housekeeping gene. Here, housekeeping genes are referred to as those expressed in at least 16 of 18 tissues in human (see “Materials and Methods”). Expression breadth is the number of tissues where a gene is expressed. The housekeeping genes are further grouped into three bins based on the expression breadth (H2 to H4). E, enzyme. F and G, degree and betweenness centrality in the PIN. The first bin of each of these two features is composed of those proteins that are not in the PIN and hence have no degree/betweenness centrality. H, model organism self-interacting protein. Not supported by model organism means that a protein's orthologs in any model organism (fly, worm, and yeast) are not known self-interacting proteins. I and J, GER and DER. If a protein contains multiple enriched GO terms/domains, the largest one of the corresponding GERs/DERs is used. K, paralogous interactor. Proteins having paralogous interaction partners are further binned based on their sequence similarity and BLAST E-value with their paralogous interactors (see “Materials and Methods”). When a protein has multiple paralogous interactors, the largest sequence similarity and the smallest E-value are used. GSN is repeatedly constructed 100 times (see “Materials and Methods”), and thus error bars in these figures represent SDs of LRs of 100 times.

Model Organism Self-interacting Protein

This feature was successfully used previously to predict protein interactions (not including protein self-interactions) (31, 37). Here, we introduce it to predict protein self-interactions. Based on interaction conservation across different organisms (38, 39), if a human protein's ortholog in another model organism can interact with itself, this human protein could be a self-interacting protein. To predict human self-interacting proteins, we collected self-interacting proteins of three model organisms, including fly, worm, and yeast. We see that the LRs of bins supported by model organism self-interacting proteins are larger than 1, and a protein supported by self-interacting proteins in more model organisms has a higher confidence level to self-interact, indicating the prediction efficacy of this feature (Fig. 1H).

DER and GER

It is thought that self-interacting proteins tend to contain special domains or have special biological functions, and hence novel self-interacting proteins could be predicted by identifying enriched domains or GO functional terms among known self-interacting proteins. To test this logic, the GSP set is randomly divided into three equal subsets, two are used to define enriched domains/GO terms and compute corresponding DERs/GERs (see “Materials and Methods”), and the remaining one is used to test the prediction ability of the enriched domains/GO terms. This process was repeated three times, and the LRs were averaged. We observed a clear correlation between LR and DER/GER, suggesting that these two features are suitable for self-interacting protein prediction (Fig. 1, I and J). A complete list of enriched domains/GO terms in the GSP set is available in supplemental Table S4. We speculate that many of these enriched domains can self-interact, and thus proteins that contain such domains can self-interact by means of domain-domain self-interactions. An example is the “S-100/ICaBP-type calcium-binding domain” (Pfam AC: PF01023, DER = 5.31). As shown by PDB structure 1BT6, the self-interaction of protein “S100-A10” (SwissProt AC: P60903) in the GSP set is mediated by this domain's self-interaction. As for the reason of GER's effectiveness for self-interacting protein prediction, we think that implementation of the many functions relies on the homo-oligomerizational ability of proteins, and therefore a protein with such function could be a self-interacting protein. For example, for the GO term “glutathione transferase activity” (GO: 0004364), there are 13 proteins with this molecular function in the GSP set, which is significantly larger than random expectation (GER = 7.46), suggesting the importance of homodimerization for glutathione transferase activity. Therefore, a protein with glutathione transferase activity has a high probability to be a homodimer.

Paralogous Interactor

When self-interacting proteins duplicate, inheritance of self-interactions will result in the interactions between paralogous proteins. It has been proposed that most current interactions between paralogs are derived from the duplication of ancestral self-interacting proteins (3). Therefore, we speculate that a protein having paralogous interactors (i.e. interaction partners) may still maintain its ancestor's self-interaction ability. The probability of this protein to be a self-interacting protein could be correlated to its sequence similarity with its paralogous interactors. The higher the protein's sequence similarity is with its paralogous protein, then the higher its sequence similarity is with their common ancestral protein, and therefore, the more the protein possibly maintains the ancestor's self-interaction ability. The LR result validates these speculations (Fig. 1K).

Ranking Features and Feature Selection

By computing LRs, we confirmed that all the 11 features can be used to predict self-interacting proteins. However, apparently their prediction abilities were different. Meanwhile, they are not independent with each other (supplemental Table S5). It has been recognized that the combination of all individually efficient features does not necessarily produce the best prediction performance (40). Often low relevant or high redundant features have no positive impact and even sometimes have negative impact on the classification performance, simultaneously leading to the decrease of generalization capability of prediction model and resource waste. Therefore, before integrating multiple features to construct the prediction model, feature selection is necessary.

First, based on MI, we compared the discriminative power of these features, i.e. their relevance to the classification variable. MI quantifies the mutual dependence between two random variables, i.e. it measures how much knowing one variable reduces uncertainty about the other (see “Materials and Methods”). Therefore, MI between feature f and protein classification (self-interacting protein class and nonself-interacting protein class) by and large could reflect the ability of feature f to predict self-interacting proteins. Because multiple features as well as protein classification are categorical variables, when computing MI we convert continuous variables into discrete variables by binning shown in Fig. 1. As a result, we find that the features of GER, degree, and betweenness centrality in the PIN have relatively high self-interacting protein prediction power, followed by paralogous_interactor and DER, although two evolutionary features and “model organism self-interacting protein” have low discrimination ability (Table III).

Besides the relevance of features to the target classification, their mutual redundancy is also an important factor that should be considered in feature selection. Here, we used mRMR feature selection method that ranks features based on both relevance and redundancy. Starting from the most relevant feature, mRMR sequentially selects a feature from the to-be-selected feature set, which maximizes its relevance to the classification variable and simultaneously minimizes its redundancy with features in the already-selected feature set (see “Materials and Methods”). We apply mRMR to these 11 features. We see that the features ranked at the bottom are either those with low relevance to the protein classification (such as evolutionary_rate and age) or those relatively high redundant with the features ranked in front (for example, as shown by supplemental Table S5, degree has high correlation with betweenness_centrality, and domain_number has high correlation with DER) (Table IV).

Self-interacting Protein Prediction Model Based on Logistic Regression

We use logistic regression model (LR model) to integrate multiple features to construct the self-interacting protein prediction model. LR model previously has been successfully used to predict protein (nonself) interactions (29, 41). It can integrate various heterogeneous data of discrete or continuous type and is robust against redundancy between features to some extent (42). Meanwhile, it provides a comparable, normalized probability_score for each interested protein, which measures its possibility to be a self-interacting protein (see “Materials and Methods”). The ROC curve is used to assess the performance of the prediction model. Each point on the ROC curve denotes the model's sensitivity and specificity against a particular probability_score_cutoff, and thus the ROC curve shows the tradeoff between sensitivity and specificity (see under “Materials and Methods”). A good prediction model has a ROC curve climbing rapidly toward the top left corner of the graph, which can be quantified by the area under the curve (AUC). AUC = 1.0 indicates a perfect classifier, whereas AUC = 0.5 means a noninformative prediction. The closer the AUC is to 1.0, the more efficient the classifier is (29).

Using the LR model, we first constructed a classifier for each feature. Assessment based on a 5-fold cross-validation shows that these single-feature prediction models have different prediction abilities (Fig. 2). The order of these features according to AUCs is by and large consistent with that according to their MI with protein classification shown in Table III.

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Fig. 2.

ROC curves of single-feature prediction models of self-interacting proteins based on the 5-fold cross-validation. Different models are distinguished by different colors. GSN is repeatedly constructed 100 times (see “Materials and Methods”), and thus the presented ROC AUC in the figure is mean ± S.D. of the results of 100 times. The features are ordered according to the decreasing AUC's average values of 100 times. The ROC curves in the figure are drawn based on GSP and a random GSN.

Furthermore, we constructed prediction models integrating multiple features. Starting from the most efficient single-feature model of GER, according to the order of features given by mRMR (Table IV), we added the features one by one into the prediction model. Ultimately 11 models were constructed, from the one only composed of feature GER (Model_1) to the one integrating all the 11 features (Model_11). As shown in Fig. 3, we first observe that the performance of all the prediction models integrating multiple features is better than that of any single-feature model. Furthermore, as the features are added into the model one by one, in the beginning stage the efficacy of the model gradually increases; however, after the number of features exceeds 6, the AUC does not rise any more but slightly declines. This indicates that the more features integrated into the classifier, the better the performance of the classifier is not necessary to be. We can obtain good classification efficacy only by integrating “representative” features with relatively high relevance and low mutual redundancy. The self-interacting protein prediction model integrating six features, including GER, paralogous interactor, betweenness centrality in the PIN, enzyme, DER, and model organism self-interacting protein (Model_6), has the highest AUC of 0.884, suggesting that it has strong ability to predict self-interacting proteins (Fig. 3).

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Fig. 3.

ROC curves of self-interacting protein prediction models integrating n features based on 5-fold cross-validation. Starting from the single-feature model of GER (Model_1), the features are integrated into the model one by one according to the order given by mRMR shown in Table IV. In total, 11 models are constructed, from Model_1 to Model_11 integrating all the 11 features. Their ROC curves are plotted by different colors. GSN is repeatedly constructed 100 times (see “Materials and Methods”), and thus the presented AUCs of the 11 models in the figure are the mean ± S.D. of the results of 100 times. The ROC curves in the figure are drawn based on GSP and a random GSN.

Besides 5-fold cross-validation, we also use an independent test set to assess the performance of Model_6. Previously, in total we collect 1537 known human self-interaction proteins, from which we choose 1198 high quality ones to construct the GSP set (see “Materials and Methods”). The remaining 339 proteins can be viewed as an independent test set. We find that the average probability_score of the proteins in this test set given by Model_6 is 0.60, which is significantly higher than that of other proteins (rank sum test, p < 10⁻⁴) (Fig. 4). This result further indicates the power of Model_6 to discriminate self-interacting proteins from others.

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Fig. 4.

Average probability_score of known human self-interacting proteins removing the GSP set (D1) and other proteins (that is the whole human proteome removing the GSP set and D1) (D2). The result is based on Model_6, which is constructed based on GSP and a random GSN from 100 repeatedly constructed ones (see “Materials and Methods”).

In addition, besides human, we also applied this prediction model integrating six features to yeast, and we obtained similarly good prediction performance (supplemental Figs. S1 and S2).

Web Service for the Self-interacting Protein Prediction

We developed the self-interacting protein prediction model integrating six features (Model_6) into a user-friendly web service SLIPPER, which can be assessed on line. Users may submit a group of proteins (Fig. 5A), and then SLIPPER will return the probability_scores measuring their possibility to be self-interacting proteins (Fig. 5B) and various related annotation information such as interaction partners in the PIN, domains, evolutionary rate, and so on, facilitating their further functional researches (Fig. 5C).

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Fig. 5.

Web service SLIPPER for the self-interacting protein prediction. A, homepage of SLIPPER. By this page, users may submit a group of proteins. Currently, SLIPPER supports self-interacting protein prediction for human and yeast. B, results page. On this page, for each submitted protein, SLIPPER will show its probability_score assigned by Model_6, whether or not it is a known self-interacting protein together with its self-interaction type, and likelihood ratios of its six features (DER, GER, betweenness_centrality, paralogous_interactor, model_organism, and enzyme). For each feature, LR >1 means that this feature can act as an evidence to support the self-interaction of this protein. Clicking on the “detail” button on the right side of the result table will lead to the annotation page of this protein. C, annotation page. This page presents a protein's various related annotation information.

By SLIPPER, we find that some proteins with high probability_scores that are not in the known human self-interacting protein dataset integrated by us have already been experimentally validated to be a self-interacting protein. For example, transcriptional factor HNF4A (SwissProt AC: P41235, probability_score = 0.8826) has been found early to bind DNA as a homodimer. Type 1 maturity-onset diabetes of the young (MODY1) is just caused by HNF4A homodimerization failure (43). Stoffel et al. (43) found that because of a nonsense mutation in exon 7, HNF4A fails to homodimerize and bind DNA, and it loses the transcriptional transactivation activity. This leads to abnormal expression of downstream genes involved in glucose transport and glycolysis, further causes the defect of glucose-stimulated insulin secretion, and ultimately results in diabetes. As another example, DCC (SwissProt AC: P43146, probability_score = 0.7936) homodimerization induced by netrin has been found to play a key role in the axon guidance (44). These examples further indicate that our prediction system works well and can provide clues for elucidating protein functions.