Chandra et al. BMC Molecular and Cell Biology 2019, 20(Suppl 2):57
https://doi.org/10.1186/s12860-019-0240-1

BMC Molecular andCell Biology

RESEARCH

Open Access

Bigram-PGK: phosphoglycerylation
prediction using the technique of bigram
probabilities of position specific scoring
matrixAbel Chandra1* , Alok Sharma1,2,3,4,5*, Abdollah Dehzangi6, Daichi Shigemizu3,4,5,7 and Tatsuhiko Tsunoda3,4,5,8
From International Conference on Bioinformatics (InCoB 2019)
Jakarta, Indonesia. 10-12 Septemebr 2019

Abstract
Background: The biological process known as post-translational modification (PTM) is a condition whereby proteomes
are modified that affects normal cell biology, and hence the pathogenesis. A number of PTMs have been discovered in
the recent years and lysine phosphoglycerylation is one of the fairly recent developments. Even with a large number ofproteins being sequenced in the post-genomic era, the identification of phosphoglycerylation remains a big challenge
due to factors such as cost, time consumption and inefficiency involved in the experimental efforts. To overcome thisissue, computational techniques have emerged to accurately identify phosphoglycerylated lysine residues. However,the computational techniques proposed so far hold limitations to correctly predict this covalent modification.
Results: We propose a new predictor in this paper called Bigram-PGK which uses evolutionary information of aminoacids to try and predict phosphoglycerylated sites. The benchmark dataset which contains experimentally labelled sites
is employed for this purpose and profile bigram occurrences is calculated from position specific scoring matrices ofamino acids in the protein sequences. The statistical measures of this work, such as sensitivity, specificity, precision,accuracy, Mathews correlation coefficient and area under ROC curve have been reported to be 0.9642, 0.8973, 0.8253,
0.9193, 0.8330, 0.9306, respectively.
Conclusions: The proposed predictor, based on the feature of evolutionary information and support vector machineclassifier, has shown great potential to effectively predict phosphoglycerylated and non-phosphoglycerylated lysine
residues when compared against the existing predictors. The data and software of this work can be acquired fromhttps://github.com/abelavit/Bigram-PGK.
Keywords: Post-translational modification, Phosphoglycerylation, Lysine residue, Computational technique,
Evolutionary information* Correspondence: abelavit@gmail.com; alok.sharma@griffith.edu.au
1
School of Engineering and Physics, Faculty of Science Technology andEnvironment, University of the South Pacific, Suva, Fiji
Full list of author information is available at the end of the article© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Chandra et al. BMC Molecular and Cell Biology 2019, 20(Suppl 2):57

BackgroundThe biological process of enzymatic change in proteinsbrought about after the translation in the ribosome is
known as post-translational modification (PTM). The highinterest in PTM for various organisms have emerged as aresult of efforts in high-throughput proteomics for the
study of site-specific PTM as well as enzymes which cause
these modifications [1]. The genetic code comprises 20
amino acids and out of which, lysine is the most commonly modified [2, 3]. From the literature [4], some of the
major covalent modifications of lysine residues are acetyl[5], glycosyl [6], methyl [7], succinyl [8], pupyl [9], crotonyl [10], and propionyl [11]. These various amino acidmodifications, as well as their regulatory enzymes, areassociated with several human diseases including heartdisease, rheumatoid arthritis, multiple sclerosis, neurodegenerative disorders, and celiac disease [12–15].
Phosphoglycerylation, which is a non-enzymatic lysine
modification, is a type of PTM that has been recently
discovered in human cells and mouse liver [16, 17]. Cardiovascular disease, such as heart failure, is a highly
probable condition caused by phosphoglycerylation since
this chemical modification is associated with glycolytic
pathways and glucose metabolism [18, 19]. Phosphoglycerylation is dynamic and reversible and occurs as a result of reaction between primary glycolytic intermediate(1,3-BPG) and lysine residue, which result in the formation of 3-phosphoglyceryl-lysine (pgK) [17]. Glycolytic
enzymes are affected by pgk. It also builds up on cells
having high exposure to glucose. As a result, potential
feedback mechanism which leads to build up and redirection of glycolytic intermediates to different biosynthetic pathways is established. As this PTM is relatively
new to the field, it is important to identify and analyze
its functional aspects to be able to understand the selectivity mechanism and its regulatory roles for better diagnosis and treatment of affected persons.
The method of computational techniques to identify
phosphoglycerylation sites is getting popular nowadays[20–34] as the pure experimental methods, such as mass
spectrometry, is quite inefficient, time-consuming and at
the same time expensive [35–37]. The promising resultsof computational techniques has resulted in great
confidence to identify phosphoglycerylated and nonphosphoglycerylated sites over the traditional method.
In the recent years, there have been a number of studies involved to identify phosphoglycerylation using computational technique. Phogly-PseAAC was the earliest
work to be carried out where it utilized a KNN-based
predictor to predict phosphoglycerylation using a feature
set of pseudo amino acid composition [38]. The second
work called CKSAAP_PhoglySite uses the compositionof k-spaced amino acid pairs (CKSAAP) as features andemploys a fuzzy support vector machine to predict [16].

Page 2 of 9

Finally, the recent work named iPGK-PseAAC was proposed and it uses a four tier amino acid pairwise coupling technique alongside a SVM operation engine forprediction [39].
The proposed predictors of phosphoglycerylation in
the literature are still limited in terms of their performance. In this regard, we are introducing a novel predictor
called Bigram-PGK which employs evolutionary
information to predict phosphoglycerylated and nonphosphoglycerylated lysine residues. A total of 91 protein sequences were used in this work which contained
experimentally confirmed phosphoglycerylated sites andtheir profile bigram was obtained from the positionspecific scoring matrix (PSSM). With the evolutionary
information of the sequences, different segment sizes foreach lysine residue was analyzed in terms of theperformance. The residue window of ±32 performed thebest on Mathews correlation coefficient (MCC) metricwhen the size of ±1 to ±32 was considered (seeAdditional file 1). Residue window sizes further than ±32 could not be taken into account due to constrain of
the protein sequence length. Hence a lysine residue,
whether phosphoglycerylated or non-phosphoglycerylated,
was considered by encompassing a stretch of 32 upstream
and 32 downstream amino acids to the lysine with thelysine residue at the center. The number of phosphoglycerylated residues were small compared to the nonphosphoglycerylated, therefore a k-nearest neighbors
cleaning treatment was implemented to deal with the classimbalance [35, 40, 41]. The balanced dataset was thenused to construct the Bigram-PGK predictor using
LibSVM package which showed a superior performanceover the existing methods on the 10-fold cross-validation
procedure. The performance of Bigram-PGK on the metrics sensitivity, specificity, precision, accuracy, MCC andarea under the ROC curve (AUC) was 0.9642, 0.8973,
0.8253, 0.9193, 0.8330, 0.9306, respectively.

Results and discussion
Dataset balancing

The phosphoglycerylation dataset obtained from PLMD
was found to be imbalanced, whereby the phosphoglycerylated sites were much less compared to that of nonphosphoglycerylated. The 111 phosphoglycerylated sites
compared to 3249 non-phosphoglycerylated sites resulted in an imbalance ratio of 1:29. Having imbalance
ratio of this magnitude will easily bias the classification
process. Resolving the class imbalance is critical in orderto build a reliable predictor. To deal with the imbalanceissue, we employed the commonly used k-nearest neighbor cleaning treatment which removed instances fromthe majority class (non-phosphoglycerylated in this case)when they are one of the k neighbors of a positive instance (phosphoglycerylated site) [35, 37, 40, 42, 43].

Chandra et al. BMC Molecular and Cell Biology 2019, 20(Suppl 2):57

The cleaning treatment was initiated with a k value
equal to the imbalance ratio i.e. 29. The intention was toremove those negative instances which were among the
29 neighbors of every positive instance. With a k valueof 29, the imbalance ratio remained undesirable hence
the threshold was further increased until the final data
set attained an imbalance ratio of 1:2. As a result, thenumber of non-phosphoglycerylated sites was reduced
to 224 instances after applying a k value of 111. The
final dataset of 111 positive instances and 224 negative
instances, obtained using a k value of 111, was used tovalidate the performance of the predictor.
Statistical measures

In proposing any new predictor, it is crucial to assess its
performance. In this work, we have employed five statistical measures including sensitivity, specificity, precision,accuracy, and Mathews correlation coefficient [16, 35,
38, 41, 42, 44–47]. Furthermore, we have calculated thearea under the ROC curve of the predictor and it isdepicted in the later section.
The first metric, sensitivity, determines the ability of
the classifier to correctly predict phosphoglycerylated lysine sites. The measure ranges from 0 to 1 where ahigher value indicates the better the predictor is in classifying the phosphoglycerylated sites. Specificity is thesecond metric and it measures the ability of the classifier
to correctly predict non-phosphoglycerylated lysine sites.
This metric also takes on the 0 to 1 range of values
where a high value indicates that the predictor is effective at predicting non-phosphoglycerylated sites. The
third and fourth metrics are precision and accuracy, respectively, and they take on the same range of values assensitivity and specificity. Precision metric assesses capability of the predictor to correctly classify phosphoglycerylated sites from all the phosphoglycerylated sites
predicted. The accuracy metric evaluates how correctly
the predictor distinguishes between phosphoglycerylatedsites and non-phosphoglycerylated sites. Mathews correlation metric, which is the fifth measure, assesses thequality of the predictor. The range of values of MCC
metric is − 1 to + 1 where − 1 signifies a completely
negative correlation, while + 1 indicates a highly positive
correlation. These five statistical measures can be written as equations as shown below:
TP
Sensitivity ¼
TP þ FN

ð1Þ

Accuracy ¼

TN þ TPFN þ FP þ TN þ TP

ð4ÞðTN  TP Þ−ðFN  FP Þ
MCC ¼ pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ðTP þ FP ÞðTP þ FN ÞðTN þ FP ÞðTN þ FN Þ

ð5ÞIn the equations above, FN, FP, TN, and TP represents
false negatives, false positives, true negatives and true
positives, respectively. False negatives represents instances which are phosphoglycerylated sites but predicted as non-phosphoglycerylated. False positives arethose that are non-phosphoglycerylated sites yet predicted as phosphoglycerylated. The true negatives are instances correctly predicted as non-phosphoglycerylatedsites and finally, true positives are instances correctlypredicted as phosphoglycerylated sites. It is desirable forthe best predictor to have high scores in all of the statistical measures. Nevertheless, the proposed predictor
should at least have higher sensitivity measure comparedto the existing predictors.
Validation scheme

The statistical measures outlined in the previous section
to assess the predictor’s performance was carried out
using the 10-fold cross-validation scheme. In the literature, there are three common ways of determining theeffectiveness of a predictor and these are n-fold crossvalidation test, independent dataset test, and the jackknife test [48, 49]. Though the jackknife test is regarded
to be the least arbitrary of the three and outputs distinctive result on dataset [50], we employed the n-foldcross-validation scheme to avoid high computationaltime, with n equal to 10. The below steps highlight the
10-fold cross-validation procedure:
Step 1: Divide the dataset into 10 equal parts
Step 2: Train predictor by combining the 9 parts andtest it using the part left out
Step 3: Adjust the classifier parameters with training
set
Step 4: Obtain the statistical measures with the test set
Step 5: Repeat steps 2 to 4 until all the folds have beenused as test sets and average the statistical measures
The result of 10-fold cross-validation scheme on
Bigram-PGK is presented in the following section.
Bigram-PGK comparison with available predictors

TNSpecificity ¼
TN þ FP

ð2Þ

TPTP þ FP

ð3Þ

Precision ¼

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There are three predictors in the literature which carryout the classification of phosphoglycerylated and nonphosphoglycerylated sites, and these are Phogly_PseAAC[38], CKSAAP_PhoglySite [16], and iPGK_PseAAC [39].
Firstly, we obtained the predictions of these methods on

Chandra et al. BMC Molecular and Cell Biology 2019, 20(Suppl 2):57

all the lysine residues in our benchmark dataset. Thiswas carried out by preparing the dataset in FASTA format and uploading it to the webservers of PhoglyPseAAC and iPGK-PseAAC predictors, and for the
CKSAAP_PhoglySite predictor by inputting the file to
the Matlab software package. It is intuitive to point out
that these predictors may have been trained using samples which are being used to carry out the performance
evaluation and therefore the results can be biased intheir favor. The performance comparison of our predictor against the existing methods was carried out on
the validation set, the sets put aside as test sets during
the 10-fold cross-validation scheme. Likewise, the same
validation set was used to obtain the performance of theother methods by investigating their predictions on
those samples when the benchmark dataset was
uploaded to the respective webserver/software packages.
The comparison result of Phogly_PseAAC [38],CKSAAP_PhoglySite [16], iPGK_PseAAC [39], and our
predictor Bigram-PGK is shown in Table 1. In Table 1,
we have also added the AUC measure for all the predictors for a more robust comparison since the predictor
with the highest AUC measure is always favorable. It
can be seen from the results that Bigram-PGK gives thehighest performance on the metrics sensitivity, accuracy,
MCC and AUC. The sensitivity measure increased by
16.4%, accuracy by 1.7%, MCC by 6.5%, and AUC by
5.1%. These improved performances goes on to say that
Bigram-PGK is quite an effective predictor for the phosphoglycerylation problem. From Table 1, it can also be
realized that iPGK_PseAAC predictor [39] obtained thehighest specificity measure (0.9864) but its sensitivity
measure is very low (0.4555), which shows that almost
55% of the phosphoglycerylated sites were undetected bythis method.
The promising result in Table 1 clearly illustrate theability of Bigram-PGK to correctly predict phosphoglycerylated and non-phosphoglycerylated lysine residues.
This can be credited to the use of underlying important
evolutionary information in protein sequences. The information is captured in PSSM of amino acids surrounding the lysines and when this information is transformed
into the matrix of bigram occurrences, it produces thenecessary characteristics for identifying the modified lysines. Furthermore, the improved performance can also

Page 4 of 9

be attributed to the SVM algorithm for its effective data
handling.
Insights into phosphoglycerylation prediction

In the Additional file 2, we present the analysis of phosphoglycerylation sites predicted by iPGK_PseAAC [39],CKSAAP_PhoglySite [16], Phogly_PseAAC [38], andBigram-PGK on the 10-fold cross-validation procedure.
It has been observed that for the proteins having multiple phosphoglycerylation sites, not all the predictors
were able to detect them entirely. In fact, only the
Bigram-PGK predictor managed to detect almost all ofthese proteins. The only protein which went undetected
was Beta-globin (UniProt Accession A8DUK4) which isa subunit of a larger protein named hemoglobin [51],and this protein was successfully identified by the
Phogly_PseAAC [38] predictor alone. Moreover,
Bigram-PGK was the only one that effectively detected
all phosphoglycerylation sites of the protein Carbamoylphosphate synthase (UniProt Accession Q8C196) whichplays a vital role in the removal of surplus ammonia
from the cell of ureotelic animals [52]. Moving on to theproteins with single phosphoglycerylation site, there
were a number of proteins which only the Bigram-PGK
predictor was able to detect. These proteins include ArfGAP with SH3 domain (UniProt Accession E9QMI7)
which regulates the formation of post-Golgi vesicles andcontrols constitutive secretion [53], 14–3-3 protein beta/alpha (UniProt Accession A2A5N1) which regulates
both general and specialized signaling pathways [54],60S ribosomal protein L31 (UniProt Accession P62900)
which is heavily involved in RNA binding and structural
integrity of the ribosome [55], and Zinc finger protein
GLI1 (UniProt Accession P47806) which acts as a transcriptional activator [56]. There were also proteins thatthe Bigram-PGK could not detect but were detected by
the rest of the predictors. These proteins include Glutamate receptor ionotropic (UniProt Accession B1AS29)
which acts as an excitatory neurotransmitter at many
synapses in the central nervous system [57], and EH
domain-containing protein 4 (UniProt Accession
Q9EQP2) that binds ATP and membrane and it could
likely control membrane reorganization upon ATP hydrolysis [58]. Furthermore, none of the predictors wereable to detect phosphoglycerylation site of a couple ofTable 1 Comparison of the three existing prediction methods with the Bigram-PGK predictor using 10-fold cross-validation scheme
Predictor

Sensitivity

Specificity

Precision

Accuracy

MCC

AUC

iPGK_PseAAC [39]0.45550.9864

0.95480.81190.56920.7230CKSAAP_PhoglySite [16]0.82850.94200.87650.90430.78180.8854

Phogly_PseAAC [38]0.69270.71930.55180.71020.39510.7062

Bigram-PGK

0.96420.89730.82530.91930.83300.9306Highest values of the metrics are highlighted in bold

Chandra et al. BMC Molecular and Cell Biology 2019, 20(Suppl 2):57

the proteins. One such protein is Proline synthase cotranscribed bacterial homolog protein (UniProt Accession Q80ZV3) which selectively and non-covalently
interacts with biologically active form of vitamin B6 andother vitamin B elements [59]. Nevertheless, all the predictors successfully predicted the phosphoglycerylationsite of many of the proteins such as ATP-dependent 6phosphofructokinase (UniProt Accession P47857) whichacts as a catalyst in phosphorylation of D-fructose 6phophate to fructose 1,6-bisphosphate by ATP [60],Farnesyl pyrophosphate synthase (UniProt Accession
Q920E5) which plays a key role in isoprenoid biosynthesis [61], Calcium-binding mitochondrial carrier
protein Aralar2 (UniProt Accession Q9QXX4) whichacts as a catalyst in calcium-dependent exchange ofcytoplasmic glutamate with mitochondrial aspartate [62],Triosephosphate isomerase (UniProt Accession P60174)
that catalyzes interconversion between dihydroxyacetone
phosphate and D-glyceraldehyde-3-phosphate in glycolysis [63], Kinectin (UniProt Accession Q86UP2) which is
involved in kinesin-driven vesicle motility [64], Fructosebisphosphate aldolase (UniProt Accession A6ZI44)
which plays an important role in glycolysis and gluconeogenesis [65], and Eukaryotic translation initiation
factor 4E-binding protein 1 (UniProt Accession Q13541)
which is a repressor of translational initiation that controls EIF4E activity [66].

Conclusion
This paper presents a novel predictor Bigram-PGK,
which utilizes the feature PSSM + bigram to predict
phosphoglycerylation. The underlying evolutionary information in PSSM of protein sequences and its transformation to bigram occurrences appears to be a crucial
property in detecting the lysine modification. The use ofstudied feature in this work and the SVM classifier withpolynomial kernel to obtain a decent hyperplane separation was effective to distinguish between the modified
and unmodified lysine sites.

Page 5 of 9

and in each sequence, there were more than one lysine
residue. From these sequences, 3360 lysine residues
were found. Three thousand two hundred forty-ninelysines were non-phosphoglycerylated and 111 were
phosphoglycerylated.
Position specific scoring matrix

Evolutionary feature captures how proteins have evolved
in relative to its structural, functional and sequential
similarities with other protein sequences [70]. PSSM calculates the substitution probability of amino acids in thesequence to all the amino acids of the genetic code.
PSSM profiles is a highly revered feature in the area ofproteomics [71–73]. The profiles are obtained using PSIBLAST toolbox [74] which aligns protein sequences tosimilar sequences stored in protein data bank [75]. The
outputs of PSI-BLAST are two matrices with a dimension of L × 20; L being the length of the queried protein
sequence and 20 being the 20 amino acids of the genetic
code. Of the two matrices, one being log odds and theother the amino acid linear probabilities, the latter wasused in this work. The PSSM for the purpose of thiswork was produced on non-redundant proteins using athreshold value of 0.001 of the PSI-BLAST toolbox withthree iterations.
Feature extraction

This section deals with the segment sizing for each lysine residue and its corresponding feature extraction. To
represent each sample, we have used the evolutionary information of 32 upstream and 32 downstream aminoacids to the lysine K portrayed in Fig. 1a. In the cases
where lysine residue did not have enough amino acids,
either upstream or downstream, the mirror technique[35] was used to create the missing amino acids asshown in Fig. 1b. The segment consisting of 32 upstream and 32 downstream neighboring amino acids oflysine K can be denoted by P as:
P ¼ fA−32 ; …; A−2 ; A−1 ; K ; A1 ; A2 ; …; A32 g

ð6Þ

Materials and methods
Protein dataset

The benchmark dataset used in this work was obtained
from the Compendium of Protein Lysine Modifications(CPLM) repository, accessed 1 March 2017 (available at
http://cplm.biocuckoo.org) which has now been
upgraded to Protein Lysine Modification Database(PLMD). PLMD contains a number of different protein
lysine modifications that have been experimentally identified. Phosphoglycerylation dataset obtained was initiallyprepared by removing sequences which had 40% or
higher sequential similarities, which is a widely usedlevel in the literature [40, 67, 68], using the Cd-hit tool[69]. As a result, a total of 91 sequences were attained

From eq. (6), the downstream amino acids are represented by An where 1 ≤ n ≤ 32 while the upstream by An where 1 ≤ n ≤ 32. Moreover, it can be seen that asegment consists of a total of 65 amino acids, including
the lysine K at the center. The segment P is attachedwith an experimentally confirmed label of either 1 or a 0
indicating a phosphoglycerylated site or a nonphosphoglycerylated site, respectively. The acquired submatrix by segment P describing each lysine wasconverted to a frequency vector of bigrams (PSSM +bigram) resulting in the matrix of size 20 × 20. Each lysine was then represented by transforming this matrix toChandra et al. BMC Molecular and Cell Biology 2019, 20(Suppl 2):57

Page 6 of 9

Fig. 1 Depiction of lysine residue with its upstream and downstream amino acids. a Lysine residue with sufficient neighbors. b Scenario of lysine
residue with insufficient neighbors on either the upstream or downstream portion

a 400 dimensional row vector capturing evolutionary information of the segment P.
Profile bigrams

The profile bigrams technique has displayed promising
results in dealing with discriminatory information[76–79]. For the purpose of explanation, let’s assume
that the PSSM of a protein sequence is denoted by amatrix M. Every element in matrix M, indicated by mij,
can be said to be the transitional probability of j-th aminoacid at i-th location within the given protein sequence.
The segment P, consisting of 65 amino acids (a fraction of
the protein sequence), is hence represented by a 65 × 20
feature matrix in which 20 denotes the amino acids of thegenetic code. Therefore the PSSM was calculated basedon the substitution probabilities of each amino acid in thesegment to the 20 amino acids. For the matrix M, its profile bigram is calculated by.
X64
Bp;q ¼m m
where 1≤ p ≤ 20 and 1≤ q ≤20
k¼1 k;p kþ1;qð7Þ
From the above equation, the resulting dimension of
matrix B representing the PSSM + bigram is 20 × 20. Finally, matrix B is converted to a 400 dimensional row

vector indicated by Eq. (8) which represents the 400transitional probabilities pertaining to evolutionary information of each lysine residue.

F ¼ B1;1 ; B1;2 ; …; B1;20 ; B2;1 ; B2;2 ; …; B2;20 ; B20;1 ; B20;2 ; …; B20;20

ð8Þ

Support vector machine

Support vector machine is one of the supervised learningmodel listed under the topic of machine learning. The
algorithm is commonly used in classification and regression applications. It is a discriminative classifier thatworks by defining a separating hyperplane. Usage of SVM
is not only popular in protein problems [20, 80–83], butalso in other areas of biology, such as genomes [84, 85].
With a given set of training data, the algorithm produces
an optimal hyperplane separating the two classes and forevery new data points presented, it is able to categorize
based on this defined hyperplane. The data points represent a point in n-dimensional space where n corresponds
to the number of features it possesses. These data pointsof two class problem are not always linearly separable,
hence non-linear kernels are used to carry out classification. The non-linear kernels project the nonlinear input

Chandra et al. BMC Molecular and Cell Biology 2019, 20(Suppl 2):57

space to a higher dimensional space where the classes
are linearly separable. For the purpose of this work,
LibSVM package on Matlab software was used tocarry out the identification of phosphoglycerylated
and non-phosphoglycerylated sites using C-SVC type
SVM and a polynomial kernel.

Supplementary information
Supplementary information accompanies this paper at https://doi.org/10.
1186/s12860-019-0240-1.
Additional file 1. MCC values for different segment sizes.
Additional file 2. Number of Phosphoglycerylation sites detected byeach predictor.
Abbreviations
AUC: Area under the ROC curve; MCC: Mathews correlation coefficient;
PSSM: Position-specific scoring matrix; PTM: Post-translational modification;
SVM: Support vector machineAcknowledgements
Not applicable.
About this supplement
This article has been published as part of BMC Molecular and Cell Biology,
Volume 20 Supplement 2, 2019: 18th International Conference on
Bioinformatics. The full contents of the supplement are available at https://
bmcmolcellbiol.biomedcentral.com/articles/supplements/volume-20supplement-2.
Authors’ contributions
AC and AS conceived and wrote the first manuscript. AC and AD performed
analysis and experiments. DS and TT contributed in manuscript write-up. All
authors read and approved the final manuscript.
Funding
Publication of this supplement was funded by JST CREST Grant Number
JPMJCR1412, Japan, and JSPS KAKENHI Grant Numbers 17H06307 and
17H06299, Japan, and Nanken-Kyoten, TMDU, Japan.
Availability of data and materials
The datasets used and analyzed during the current study are publicallyavailable online at https://github.com/abelavit/Bigram-PGK or www.alok-ailab.com.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
School of Engineering and Physics, Faculty of Science Technology andEnvironment, University of the South Pacific, Suva, Fiji. 2Institute forIntegrated and Intelligent Systems, Griffith University, Brisbane, QLD 4111,
Australia. 3Department of Medical Science Mathematics, Medical ResearchInstitute, Tokyo Medical and Dental University (TMDU), Tokyo 113-8510,
Japan. 4Laboratory for Medical Science Mathematics, RIKEN Center for
Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan. 5CREST,
JST, Tokyo 102-8666, Japan. 6Department of Computer Science, Morgan State
University, Baltimore, MD, USA. 7Medical Genome Center, National Center for
Geriatrics and Gerontology, Obu, Aichi 474-8511, Japan. 8Laboratory for
Medical Science Mathematics, Department of Biological Sciences, Graduate
School of Science, The University of Tokyo, Tokyo 108-8639, Japan.

Page 7 of 9

Received: 3 November 2019 Accepted: 20 November 2019
Published: 20 December 2019
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