Journal of Bioinformatics and Neuroscience (JBINS), 3 (3): 91-98, December 29, 2017
e-ISSN: 2432-5422, p-ISSN: 2188-8116
Applied Science and Computer Science Publications

Making protein-protein interaction prediction more reliablewith a large-scale dataset at the proteome level
Yoichi Murakami and Kenji Mizuguchi

Abstract: Reliable information about protein-protein interactions (PPIs) enables us to better
understand biological processes, pathways and functions. However, there are many experimental
problems in identifying complete PPI-networks in a cell or organism. To supplement the limitations ofcurrent experimental techniques, we have previously proposed PSOPIA, a computational method topredict whether two proteins interact or not (http://mizuguchilab.org/PSOPIA/) [1]. In the PPI
prediction, the selection of datasets is a big issue for accurately evaluating the performance of different
algorithms [2, 3]. It is generally believed that increasing the size and diversity of examples makes thedataset more representative and reduces the noise effects; however, for many algorithms, it isimpractical to use a large-scale dataset because of the memory and CPU time requirements. In thisstudy, PSOPIA was retrained on a highly imbalanced large-scale dataset having a diverse set ofexamples at the proteome level. The dataset consisted of 43,060 high-confidence direct physical PPIs
obtained from TargetMine [4] (as PPIs being only 0.13% of the total) and 33,098,951 non-PPIs. As aresult, the new prediction model achieved a higher AUC of 0.89 (pAUCFPR≤0.5% = 0.24) than theprevious model of PSOPIA. Furthermore, it was applied to the problem of filtering out protein pairs
incorrectly labeled as interacting from a low-confidence human PPI dataset. Here, we suggest that adiverse set of large-scale examples is key to more reliable PPI prediction, demonstrating theperformance of PSOPIA at the proteome level.
Keywords: Prediction of protein-protein interactions; Homology; Machine Learning; Averaged OneDependence EstimatorsI.

physiological conditions. In addition, proteins that are
expressed and transported to different subcellularlocations may never interact in vivo, although inprinciple it may be possible to get them to interact invitro.
To supplement such limitations of experimental
techniques, many computational methods for predicting
PPIs have been developed based on features obtained
from known PPIs [14-23], such as the number of aminoacid triplets in each sequence [14, 19, 22], a product ofsignatures defined as a set of subsequences [16], autocorrelation values of seven different physicochemical
scales [20, 24] and normalized counts of single or pairsof consecutive amino acid residues [21]. Furthermore,methods have been developed based on interologs,
which are defined as interaction-homologs; “if two
proteins are known to interact in species A and areconserved in species B, these proteins have potential to

INTRODUCTIONThe identification of PPIs is essential for a better
understanding of biological functions of many
proteins in living cells and also for elucidating
biochemical pathways. PPIs have become attractive
targets for rational drug discovery, because their highspecificity potentially enables researchers to target
specific disease-related pathways [5-7]. A variety ofhigh-throughput experimental techniques, such as yeast
two-hybrid assays and methods based on mass
spectrometry, have been used to discover PPIs inseveral organisms. However, it is still a difficult task toidentify experimentally the whole set of PPIs (aninteractome) in a cell or organism, because of various
physicochemical factors such as transient dynamics,
post-translational modifications (PTMs) [8, 9], proteins
with intrinsically disordered regions [10-13] andCorresponding Authors:

Yoichi Murakami. Department of Informatics, Faculty of Informatics, Tokyo University ofInformation Sciences, 4-1 Onaridai Wakaba-ku Chiba-shi Chiba, 265-8501, Japan, Tel: +81-43235-7342, E-mail: ym206508@rsch.tuis.ac.jp
Kenji Mizuguchi. National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 SaitoAsagi Ibaraki-shi Osaka, 567-0085, Japan, Tel: +81-72-641-9890, E-mail: kenji@nibiohn.go.jp;

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Journal of Bioinformatics and Neuroscience (JBINS), 3 (3):91-98, 2017
interact in species B” [25, 26]. Yu et al. found that PPIs
can be transferred when two protein pairs have thegeometric mean of the sequence identities >80% or the
BLAST e-values <10-70 [26]. For example, Wiles et al.
predicted new PPIs from known PPIs in five species
and developed InterologFinder, a web server to search
for information about predicted as well as
experimentally determined PPIs for given proteins ofinterest [27]. Chen et al. developed PPISearch, a web
server to search for homologous PPIs given a single
protein pair of interest against an integrated database of
PPIs in 576 species [28]. Garcia et al. developed BIPS,
a web server to predict PPIs based on information about
known PPIs in multiple species and additional
information about domain interactions and GO
annotations [29]. In these methods, collecting as many
PPIs as possible in multiple species is important forincreasing the reliability of the predicted PPIs, i.e., they
are largely dependent on the existence of orthologous
PPIs, so it would be difficult to detect a novel PPI withno interologs.
To increase the performance of the PPI prediction,
we have previously adopted a machine learning (ML)approach, the Averaged One-Dependence Estimators(AODE; [30]), and have trained and tested models
using three features: (I) sequence similarities to known
interacting proteins (FSeq) (Fig.1a), (II) an average valueof statistical propensities of domain pairs observed in
known interacting proteins (FDom) (Fig.1b) and (III) a
sum of edge weights along the shortest path between
homologous proteins in a PPI network (FNet) (Fig.1c)[1]. The idea of the feature (III) is based on the
hypothesis that two proteins would have higher
potential to interact if their homologs exist in proximityof each other in a known PPI network. Such a proximal
pair, even if not known to interact directly, may form acomplex with other proximal proteins or reside incommon subcellular locations, thereby increasing thechances of their homologues interacting directly. The
best AODE model is named PSOPIA and available at
http://mizuguchilab.org/ PSOPIA.
The selection of datasets used for training and testingis a big issue for the PPI prediction. Park and Marcotte
have found that the reported performances ofpreviously developed methods for the PPI prediction
are biased, because methods requiring two proteins as
an input show better performance for test pairs when
proteins in the pairs are seen in the training set,
compared to when no proteins are shared between the
training and test sets [2]. They recently introduced threeclasses for a test pair (proteins A and B): (i) class 1(C1), if both A and B are shared with the training set:(ii) class 2 (C2), if either A or B are shared with thetraining set, (iii) class 3 (C3), if neither A nor B are
shared with the training set. They demonstrated that theprediction for C3, i.e., the prediction without any priorknowledge of both proteins, is more difficult than thatfor C1, and also suggested that the methods should betested for each distinct class [2].
Furthermore, the selection of datasets, especiallynon-interacting protein pairs (non-PPI), is also a big
issue for the PPI prediction. The performance of ML
methods depends on the size and diversity of thedatasets used for training. Although, in reality, thenumber of non-PPIs is much larger than that of PPIs,
for many PPI prediction algorithms, dealing with alarge number of non-PPIs at the proteome level isimpractical because of the memory and CPU timerequirements. Thus, most of the methods introduced
above typically use a manageable number of non-PPIs
generated by randomly sampling protein pairs notknown to interact. Most up-to-date ML algorithms draw
benefit from large datasets, often called ‘big data’. For
example, DeepMind's AlphaGo uses deep neuralnetworks that were trained on big data to infer a million
or billion parameters [31]. It is believed that increasing
the size and diversity of examples makes the dataset
more representative and reduces the noise effects.
In this study, we evaluate PSOPIA using three
distinct datasets (C1-3) proposed by Park and Marcotte[29], and also compare the performances with those ofpreviously reported methods benchmarked by theseauthors. Furthermore, to make PSOPIA more reliable,
the AODE models were retrained on the highly
imbalanced human PPI dataset with a huge number of
non-PPIs at the proteome level. In addition, the new
PSOPIA was applied to the problem of filtering out
false positives from the low-confidence human PPI
dataset.
II.

METHODOLOGYThe AODE [30] is an ML algorithm, a variant of theNaïve Bayes classifier (NBC) and it weakens NBC’s
independence assumption by allowing a onedependence. So far, AODE has been used to combine
the outputs of several prediction methods; it has beenshown to be useful for extracting distinctive
information from large imbalanced datasets without
increasing the computational cost significantly and it
can also be retrained easily and efficiently [30, 32, 33].
In addition, AODE requires no model selection orparameter optimization. These strengths, therefore,
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Journal of Bioinformatics and Neuroscience (JBINS), 3 (3):91-98, 2017
allowed us to train AODE on large-scale PPI data
without incurring a large computational cost.
In PSOPIA, the three features for two given proteins
are used for the development of the AODE model andthe prediction. A feature vector for two proteins isconstructed in order to avoid bias from reciprocalinteractions, i.e., there are two combinations A-B, B-A
for two proteins (A, B). For FDom and FNet, one feature
value is computed, but for FSeq, there are at least two
combinations of feature values, FSeq = {e-valueA,
mincovA, e-valueB, mincovB} and FSeq’ = {e-valueB,
mincovB, e-valueA, mincovA}, where e-value andmincov are a BLAST e-value and a minimum coverageagainst a longer sequence between a protein of a test
pair and one of training proteins. We used a set of either
FSeq or FSeq’ which lie in a half-space of the symmetricalhigh-dimensional feature space to make them
independent of the protein order. See [1] for moredetails.

A. The dataset for cross-validation
The Park and Marcotte datasets were used [29]. The
number of PPIs used in their dataset is 24,718
constructed from 20,117 proteins. They divided the
PPIs into forty splits, each consisting of three testingdatasets (C1-3) for one training dataset. In thesedatasets, each test pair was assigned to one of the threeclasses C1-3, as described in the Introduction. non-PPI
examples were generated by randomly sampling protein
pairs not known to interact. The ratio between PPIs andnon-PPIs is balanced (1:1) in their dataset. See [29] formore details.
B. The large-scale dataset at the proteome level
In reality, the number of non-PPIs is much larger
than that of PPIs. In the BioGrid database, aninteraction repository with data complied throughcomprehensive curation efforts [34], 310,453 nonredundant physical interactions constructed from21,432 unique genes have been registered for Homosapiens (as of August 2017). Thus, it is roughly
estimated that there are 229,654,596 possible protein
pairs (=21,432 × (21,432 – 1) /2) and 229,344,143
protein pairs (=229,654,596 – 310,453) are assumed as
non-PP. The ratio between PPIs and non-PPIs is about
1:734.
In order to obtain a highly imbalanced non-redundantdataset that reflects the nature of the proteome seen in
BioGrid, the following steps were carried out; (i): A
dataset of high-confidence direct physical interactions(HDCP) were obtained from TaregtMine [4]. Thisdataset consists of 152,562 interactions including
reciprocal interactions (17,652 unique proteins, 145
discrete networks), each of which was cited in morethan two publications or confirmed in more than two
experimental methods. (ii): 17,652 protein sequenceswere clustered using CD-HIT [35] with sequencesimilarity threshold of ≥0.4 and alignment coverage forthe longer sequence of ≥0. (iii): All possible cluster
pairs were examined; when a cluster pair or a single
cluster contains one interaction, its interaction isretained in the new dataset (Fig 2a). When it contains
more than two interactions, a resultant vector (RV) of
two sequence similarities against representative
sequences of the clusters is calculated for each of theinteractions. Then, one interaction having the largest
RV is selected and retained in the new dataset (Fig 2b).
When it does not contain any interactions,representative proteins are paired and regarded as a

Fig. 1 Features used in PSOPIA

Fig. 2 Selection of interactions and generation of noninteractions from all possible sequence cluster pairs

93Journal of Bioinformatics and Neuroscience (JBINS), 3 (3):91-98, 2017
non-PPI in the new dataset (Fig 2c). This procedure
retained 43,060 PPIs and generated 33,098,951 nonPPIs. The ratio between the interactions and the noninteractions is 1:769, similar to that seen in BioGrid.

based on the co-occurrences of sub-sequences between
known positives, and does not require non-PPIs fortraining and prediction [18].
Table 1 shows the prediction performances of
PSOPIA, PSOPIA’, M2 and M6. The AUC (upper row)and the pAUCFPR≤0.5% (lower row) values calculatedwith 40-fold CV on Park and Marcotte’s datasets areshown. The values given are the mean ± standard
deviation. The AUC and pAUCFPR≤0.5% of PSOPIA’ are+0.01~0.02 and +0.01 higher than M2 in C2 and C3,
respectively. In C1, the AUC of PSOPIA’ is +0.03
higher than M2 and pAUCFPR≤0.5% of PSOPIA’ is +0.05
higher than M6. As Park and Marcotte demonstrated,
the performance for C3 is poorer than that for C1 andC2 in all methods. In this evaluation on Park andMarcotte’s datasets, the inclusion of FDom into the
PSOPIA algorithm caused the degradation in overall
performance; the AUC of original PSOPIA is 0.12, 0.01
and 0.02 lower than PSOPIA’ in C1, C2 and C3,
respectively. However, PSOPIA’ performed the best in
all classes C1-3.

C. Evaluation measures and validation
To evaluate the performance of the methods, n-foldcross validation (CV) is generally used. In the n-fold
CV, a dataset is divided into n subsets, and each subset
is used as a testing set and the remaining n-1 subsets areused as a training set. This process is repeated n times,
and then the performances are averaged over all the test
results. In this study, 40-fold CV was used on the Parkand Marcotte datasets, and 3-fold CV was used on thelarge scale dataset at the proteome level. The
performance of AODEs is estimated by the Area Under
the Curve (AUC), which gives an AUC = 1.0 for a
perfect model and gives an AUC = 0.5 for a random
model. The AUC is known to be insensitive toimbalanced data [36] and it would be a reliable measurefor the prediction performance. In addition,
performances were also estimated by a normalized
partial AUC up to the FPR ≤ x% (pAUC x%), following[14] and [23]. We set x to be 0.5. A prediction modelwith a high pAUC can predict more true positives withfew false positives, so such a model is known to be
most useful for users to identify PPIs from the topranked predictions [14].

Table 1 Performance comparison on Park and Marcotte’s
datasets
Park and Marcotte’s datasets

Methods

III. RESULTS
A. Performance of PSOPIA in class C1-3
Following the procedure introduced by Park andMarcotte [29], we evaluated PSOPIA in 40-fold CV
using their datasets. In addition, PSOPIA trained using
only two features, FSeq and FNet (denoted PSOPIA’
hereafter), was also evaluated because FDom, thestatistical propensities to discriminate between domain
pairs on PPIs and those on non-PPIs, could not be
accurately computed using their balanced datasets, inwhich the numbers of PPIs and non-PPIs are small,about 13,000 PPIs on average. We compared theperformance of PSOPIA and PSOPIA’ to that of two
sequence-based methods, M2 and M6, abbreviated inPark and Marcotte, which performed well comparedwith other methods in their assessment [29]. M2 is a
support vector machine (SVM), in which feature
vectors for protein pairs are formed by applying themetric learning pairwise kernel [37]. M6 is a method

C1

C2

C3

PSOPIA

0.76 ± 0.02
0.17 ± 0.04

0.65 ± 0.01
0.06 ± 0.02

0.58 ± 0.02
0.03 ± 0.01

PSOPIA’

0.88 ± 0.01
0.19 ± 0.04

0.66 ± 0.01
0.06 ± 0.02

0.60 ± 0.02
0.03 ± 0.01

M2 (SVM)

0.85 ± 0.01
0.07 ± 0.02

0.60 ± 0.01
0.01 ± 0.00

0.58 ± 0.02
0.00 ± 0.00

M6 (PIPE2)

0.77 ± 0.01
0.14 ± 0.02

0.64 ± 0.01
0.06 ± 0.02

0.59 ± 0.02
0.02 ± 0.01

* Upper and lower rows show AUC and pAUCFPR≤ 0.5%, respectively.

B.

Cross validation on the large-scale dataset atthe proteome level

The AODE models of PSOPIA were retrained on thelarge scale human PPI dataset at the proteome level,
which is a highly imbalanced dataset containing only
0.13% PPIs (1:734), and also evaluated in 3-fold CV.
As a result, the model trained with all the three features,
FNet, FDom and FSeq, achieved the highest AUC of 0.89
(pAUCFPR≤0.5% = 0.24) (Table 2). Even though adding
FDom to the model did not contribute to the prediction
on the balanced dataset with small PPI examples, i.e.,
Park and Marcotte’s datasets, it contributed to the94Journal of Bioinformatics and Neuroscience (JBINS), 3 (3):91-98, 2017prediction on the large-scale human PPI dataset,enhancing the performance in terms of AUC and pAUC
FPR≤0.5%. In addition, adding FNet to the models
significantly contributed to the prediction, suggestingthat FNet is an important feature to enhance thepredictability of PPIs in PSOPIA. In addition, AUC andpAUC FPR≤0.5% of PSOPIA were improved by 0.11 and
0.08, respectively, compared to the previous version of
PSOPIA trained on the imbalanced dataset (1:400) withsmall PPI examples [1].

D.

Experimental methods for identifying PPIs have
limitations, as described in the Introduction. Thus, there
are strong needs to accurately filter out false positives
from experimentally determined low-confidence PPIs.
We applied the new PSOPIA to the removal of false
positives in the low-confidence human PPIs obtained
from TaregtMine [4], 726,606 PPIs (as of July, 2016).
In this dataset, there are 674,740 (86.6%), 49,487(6.81%) and 2,379 (0.33%) PPIs in C1, C2 and C3,
respectively. We distinguished between these classes of
low-confidence interacting pairs based on sequencesimilarity (BLAST e-value = 10-3) to the proteins of the
training dataset used in the new PSOPIA, not based onwhether A and/or B are shared with the training dataset.
Table 4 shows the number of predicted pairs of the lowconfidence protein pairs at different thresholds. Thesepairs are considered as more reliable positive pairs in
the dataset; for example, 57,882 pairs (8.0%) caninteract with high accuracy (with a precision of 30%
and a sensitivity of 16.1%). We added information
about the PSOPIA score of each of low-confidence
protein pairs to TargetMine [4].

Table 2 Performance comparison of AODE models on thehuman dataset at the proteome level
Different AODE
modelsHuman PPI dataset at proteome level
AUC

pAUC FPR≤ 0.5%

FNet

0.85 ± 001

0.04 ± 0.00

FDom0.72 ± 0.00

0.11 ± 0.00

FSeq

0.77 ± 0.00

0.20 ± 0.00

FNet + FDom

0.88 ± 0.00

0.17 ± 0.00

FNet + FSeq

0.88 ± 0.00

0.23 ± 0.00

FDom + FSeq

0.79 ± 0.00

0.21 ± 0.01

FNet + FDom + FSeq

0.89 ± 0.00

0.24 ± 0.00

Table 3 Strict thresholds of PSOPIA (FNet + FDom + FSeq)

Table 4 Predicted pairs of the low-confidence human PPI
datasetPrecision (%)

ThresholdSensitivity (%)

≥ 10%

0.975

29.6 ± 0.5

Threshold

≥ 20%

0.992

20.6 ± 0.8

0.975

90,706( 12.5% )

≥ 30%

0.996

16.0 ± 0.8

0.992

68,500( 9.5% )11.1 ± 0.8

0.996

57,882( 8.0% )

0.997

53,957( 7.4% )

≥ 50%

C.

Applying PSOPIA to the low-confident human
PPI dataset

0.997

Determination of the thresholds at highprecisions

Predicted pairs

IV. DISCUSSION

To predict whether a protein pair given by a user isinteracting or not, and also discriminate true positives
from the entire population of PPIs reliably, the best
thresholds were determined based on the results of the
3-fold CV (Table 3). In reality, the predictions at highspecificity are important and required by experimental
biologists. We therefore determined the thresholds at
different high precision values, ≥10%, 20%, 30% and50%. These are strict and rigid threshold values thatguarantee successful predictions; for example, at athreshold of 0.996, true positives are theoretically
predicted with a precision of 30% and a sensitivity of
16.1%.

The computational approaches to the PPI prediction
are necessary to supplement the limitations of thecurrent experimental approaches and characterize a vast
number of PPIs, in order to generate PPI-networks forunderstanding biological processes, pathways andprotein functions, and also add to the robustness of
biological insights obtained from the PPI-networkbased analysis. Recent advances in protein-protein
docking (PPD) have made it possible to build reliable
protein complex models. However, docking proteins
having large conformational changes is difficult [38,39] and PPD does not work for proteins of unknown
structure. Thus, computational methods based on only
sequence information are necessary to predict PPIs.
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Journal of Bioinformatics and Neuroscience (JBINS), 3 (3):91-98, 2017
The performance of ML methods depends on thediversity of the datasets used for training and testing.
However, the limited availability of high-confidence
non-PPIs impedes performance improvement of the PPI
prediction. For example, only 6,542 non-PPIsconstructed with 3,475 proteins are available in the‘Combined’ dataset (‘Manual’ and ‘PDB’ datasets) of
the Negatome database 2.0 [40]. In reality, the numberof non-PPIs is much larger than that of known PPIs. In
our statistics based on the non-redundant physicalhuman interactions registered in BioGrid (as of August2017), the ratio between PPIs and non-PPIs is estimatedto be approximately 1:734 (310,453 PPIs and remaining
229,654,596 protein pairs regarded as non-PPIs). For
many computational methods, the dataset containing
these non-PPIs is too large to be used because of thememory and CPU time requirements. Therefore, mostof the methods typically use a manageable size of nonPPIs generated by randomly sampling protein pairs notknown to interact. Yet, the AODE is able to deal withlarge and imbalanced PPI dataset effectively without
incurring a larger computational cost. Thus, it enables
PSOPIA to train on the highly imbalanced human PPI
dataset containing 43,060 PPIs and an exhaustive
number of non-PPIs, 33,098,951 non-PPIs (1:769), andthen achieved an AUC of 0.89 (pAUCFPR≤0.5% = 0.24)
better than the previous PSOPIA trained on theimbalanced human PPI dataset containing 4,430 PPIs
and non-PPIs 400 times larger in number than the PPIs
(1:400), indicating that a diverse set of PPIs and nonPPIs leads to the improvement of the prediction
performance.
There are a few interolog-based servers, which
estimate the possibility of each protein registered in a
database to interact with a single protein [27, 29, 41].
These servers are not based on ML and are largely
dependent on the existence of orthologous or
homologous PPIs, so it is difficult to detect novel PPIs
without information about interologs. PSOPIA is also
dependent on interologs, and the FSeq feature does notwork when PPIs homologous to the query protein pairare unknown. In such cases, however, other features,
FDom and FNet, would compensate for theunpredictability of FSeq in PSOPIA. As our future work,
we will apply the new PSOPIA, based on ML, to theprediction of interacting proteins with a given protein.
This system will assess the potential interactions
between a single protein and each of all human proteinsregistered in a database, and then it will prioritize thepredicted interactions according to their prediction

scores.
V.

CONCLUSIONSThe PSOPIA method for predicting PPIs wasevaluated on three distinct datasets (C1-C3) introduced
by Park and Marcotte [2], and then it was shown thatPSOPIA has higher performance than the previously
proposed computational algorithms, M2 and M6, which
performed well compared with other methods in theirassessment. Furthermore, PSOPIA has been improved
using the highly imbalanced large-scale dataset (1:734)
having a diverse set of examples at the proteome level.
It achieved higher performance than the previous
version of PSOPIA trained on an imbalanced dataset(1:400) having smaller PPI examples. This result
indicates that increasing the size and diversity of
training examples indeed makes the dataset more
representative, leading to the improvement of theprediction performance. The new PSOPIA can provide
successful PPI predictions for not only finding novel
PPIs but also filtering out false positives from lowconfident interacting protein pairs experimentally
determined. It will hopefully contribute to theestimation of complete PPI networks, and reveal
disease-causing PPI networks and pathways.
ACKNOWLEDGEMENTS
We thank Dr. Chen Yi-An (Laboratory of
Bioinformatics, National Institutes of Biomedical
Innovation, Health and Nutrition, Osaka, Japan) for
kindly providing the high/low confidence human PPI
datasets from TargetMine. This study was in part
supported by JSPS KAKENHI Grant Numbers 26870045(to YM) and 17K07268 (to KM).
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