Predicting mucin-type O-Glycosylation using enhancement value\nproducts from derived protein features
Mucin-type O-glycosylation is one of the most common post-translational modifications of proteins. This glycosylation is initiated in the Golgi by the addition of the sugar N-acetylgalactosamine (GalNAc) onto protein Ser and Thr residues by a family of polypeptide GalNAc transferases. In humans there are 20 isoforms that are differentially expressed across tissues that serve multiple important biological roles. Using random peptide substrates, isoform specific amino acid preferences have been obtained in the form of enhancement values (EV). These EVs alone have previously been used to predict O-glycosylation sites via the web based ISOGlyP (Isoform Specific O-Glycosylation Prediction) tool. Here we explore additional protein features to determine whether these can complement the random peptide derived enhancement values and increase the predictive power of ISOGlyP. The inclusion of additional protein substrate features (such as secondary structure and surface accessibility) was found to increase sensitivity with minimal loss of specificity, when tested with three different published in vivo O-glycoproteomics data sets, thus increasing the overall accuracy of the ISOGlyP predictions.
Introduction<!>Data Acquisition<!>Feature Generation<!>Feature Selection<!>Generation of Enhancement Values<!>Performance of O-Glycosylation Prediction<!>Accuracy<!>Sensitivity<!>Specificity<!>ROC curves and AUC<!>Data<!>Feature Generation<!>Feature Selection<!>Generation of Enhancement Values<!>Performance of O-Glycosylation Prediction<!>Conclusions
<p>Post-translational modifications (PTMs) are a diverse set of protein
modifications that impact protein function and structure. Mucin type O-glycosylation
(henceforth O-glycosylation) is one of the most common types of PTMs of secreted
proteins and is defined by the presence of the sugar N-acetylgalactosamine (GalNAc)
attached to Ser and Thr amino acid residues of proteins. These peptide linked GalNAc
residues can be further modified by the addition of other sugars, forming an array
of heterogeneous branched glycan structures.1,2 The addition of
GalNAc onto Ser and Thr residues is performed by a family of polypeptide GalNAc
transferases (GalNAc-Ts) that utilize UDP-GalNAc as the GalNAc donor. There are 20
GalNAc-Ts in human (T1 to T20) and similar numbers in most mammals. Fewer GalNAc-T
orthologues have been identified in the lower animals and even in single cell
parasites, such as Toxoplasma gondii.3,4</p><p>One of the major classes of O-glycosylated proteins are the mucin
glycoproteins (hence the name of this modification) found lining the respiratory,
digestive and urogenital tracts that serve to protect these surfaces. Mucins are
very large glycoproteins which are typically composed of over 50% O-linked
carbohydrates and commonly contain densely O-glycosylated tandem repeated domains.
These domains possess extended and rigid conformations and are highly resistant to
proteases, thus helping to protect cell surfaces from insult. In addition to mucins,
most secreted proteins are found to contain single to multiple O-glycosylation sites
that serve multiple functions. One critical function is the regulation of furin
cleavage sites of peptide hormones and enzymes where single sites of O-glycosylation
serve to modulate furin's ability to cleave these sites.5 In this manner O-glycosylation can modulate
important biological functions. O-glycosylation further modulates receptor
binding6–8 and cell-cell interactions critical to human
and fly development 9–11 and is aberrant in most
cancers.12 Even the
O-glycosylation of viral coat proteins, such as the herpes simplex virus type 1,
relies on host cellular glycosylation machinery.13 Understanding which GalNAc-T isoforms are responsible for
initiating O-glycosylation at these many diverse glycosites is critical to
understanding the roles of O-glycosylation in development as well as in health and
disease.</p><p>The cellular expression of the different GalNAc-Ts varies from tissue to
tissue. For example GalNAc-T20 is expressed only in the brain and the testis while
T1, T2, and T11 expressed across most tissues but at varying levels.1,14 The other GalNAc-Ts have more variable expression levels that
depend on the tissue where typically no more than 6 isoforms are expressed in a
given tissue.1 Mucin secreting
tissues show a broad range of expressed GalNAc-Ts, while tissues such as the heart
and liver, express fewer GalNAc-T isoforms.15</p><p>GalNAc-T knock out models and GWAS (Genome Wide Association Studies) clearly
show that multiple GalNAc-Ts are required for proper development and are linked to
many disease states. For example GalNAc-T9 and T1 are important for normal mouse
brain and heart valve development10 and several individual fly GalNAc-T homologues are absolutely
required for fly development.11
Abnormal GalNAc-Ts expression and mutations in several GalNAc-Ts are linked to many
types of human cancers.16–25 The modulation of furin cleavage
sites by O-glycosylation now appears to be largely GalNAc-T isoform
specific,5 where the first
GalNAc-T human disease was identified by GalNAc-T3's sole ability to
glycosylate the phosphate regulating peptide hormone FGF23.26 In most cases, however, the specific
glycosites where the disease associated GalNAc-Ts glycosylate are unknown. Thus
there is a need for effective tools capable of predicting and also analyzing
GalNAc-T isoform specific glycosylation sites.</p><p>Towards the goal of understanding what sites are O-glycosylated in health and
disease Steentoft and coworkers have developed approaches for the analysis of the
O-glycan proteome of cells in culture.27,28 These researchers
utilized the so called SimpleCell approach where they deleted the core-1 elongating
transferase in multiple cell lines such that all of the O-linked glycan structures
remained truncated at the peptide GalNAc residue.27 This loss of glycan heterogeneity
facilitated the isolation and analysis of individual glycosites in cell using mass
spectral analysis. Having such a large dataset of O-glycosites these workers used
their glycomics data to improve on the NetOGlyc 3.1 O-glycan predicting
program29 producing the
NetOGlyc 4.028 version for
predicting sites of O-glycosylation. NetOGlyc 4.0 uses a support vector machine that
was built using the SimpleCell and other O-glycomics data in addition to features
related to protein surface accessibility, protein disorder and located within a
transmembrane spanning sequence, as generated by the programs NetSurfP30, DISEMBL31, and TMHMM32 respectively. Importantly both versions of
NetOGlyc were trained on data that did not contain transferase isoform specific
information and hence these programs are unable to modify their results to take into
account the GalNAc-Ts that are or are not expressed in the SimpleCell lines
used.</p><p>Unlike O-glycosylation prediction programs such as NetOGlyc 4.028 and CKSAAP OGlySite33 that utilize prior determined
glycosites as learning data sets, the Isoform Specific O-Glycosylation Prediction
(ISOGlyP) tool utilizes transferase specific random peptide derived data for
predicting glycosites unique to a given GalNAc-T isoform. The current version of
ISOGlyP is implemented as a web service at ISOGlyP.utep.edu and a command line version is available for
download on GitHub (github.com/jonmohl/). The program uses experimentally determined
transferase specific enhancement values (EVs) for each GalNAc-T isoform to determine
the likelihood of glycosylation of a Ser or Thr residue in a given protein or
peptide sequence. Currently EVs, obtained from random peptide derived amino acid
preferences, for 11 of the 20 GalNAc-T isoforms are available.34–39 We take the EVs to represent the propensity for a given amino
acid residue flanking a given glycosite, to be found after glycosylation by a given
GalNAc-T isoform. EVs greater than one suggest a preference for that residue while
values less than one suggest glycosylation is inhibited by that amino acid residue.
By multiplying together the EVs associated with a given amino acid sequence flanking
the acceptor Thr or Ser (up to +/− 5 residues of the glycosite) an
enhancement value product (EVP) is obtained which represents the collective
propensity for the sequence to be glycosylated. EVP values greater than one suggest
that glycosylation at that position is likely while values less than one suggest
glycosylation would be unlikely. An advantage of ISOGlyP is its ability to indicate
which GalNAc-T isoform(s) is likely responsible for the glycosylation.</p><p>There are too few isoform specific O-glycomics data to date for rigorously
testing ISOGlyP, however, ISOGlyP is reasonably predictive for short peptide
substrates glycosylated in vitro by several GalNAc-T isoforms (i.e.
GalNAc-T1, T2, T3, T5, T11, T12, T14, & T16).34,37,40,41 We have tested ISOGlyP and NetOGlyc 4.0 against the
SimpleCell O-glycome which contains a mix of transferases and found that ISOGlyP has
a higher specificity compared to NetOGlyc 4.0 whereas NetOGlyc 4.0 gave a higher
sensitivity. This was again seen in a similar analysis of platelet glycomic
data42 in which the
overall accuracy of predictions for ISOGlyP was 70% and NetOGlyc was 75%. When
looking at only the sites not previously identified by Steentoft and used in the
creation of NetOGlyc, ISOGlyP's prediction accuracy dropped to 69% while
NetOGlyc's was 68%. In both platelet scenarios, the sensitivity for NetOGlyc
4.0 was again higher while the specificity of ISOGlyP was better.</p><p>In an attempt to improve the sensitivity of the ISOGlyP predictions we
examined whether including additional peptide sequence information and structural
features as used in the NetOGlyc 4.0 predictor would increase ISOGlyP's
overall prediction accuracy. We scanned a series of protein features for inclusion
in the EVP calculation (see below) for their effect on predicting mucin-type
O-glycosylation. We found that secondary structure and surface accessibility
increased ISOGlyP sensitivity with minimal loss of specificity thus increasing the
overall accuracy of the ISOGlyP predictions. This work demonstrates that inclusion
of protein substrate structural feature values calculated in a similar way as the
ISOGlyP EVPs increases the in vivo prediction accuracy of ISOGlyP.</p><!><p>The SimpleCell O-glycome data for this work was from a supplementary
Excel file containing the glycosite data for each of the cell lines with the
identified protein and positional information of the sites.28 The data used in this work were
generated from the HaCat, HeLa, and HEK cell lines expressing transferases
available within the ISOGlyP program providing 279, 466, and 658 confirmed
glycosites respectively, When combined, this provided 940 different positive
sites with 71,145 non-glycosylated (negative) sites across the three cell lines.
An Excel file containing the glycosite data for each of the cell lines with the
identified protein and positional information of the sites were obtained from
the supplementary data.
Note that only unambiguously labeled glycosites and the EVs for the GalNAc-Ts
that were reported to be expressed in each of the cell lines were included in
the ISOGlyP calculations.</p><!><p>In the analysis of the peptide sequence, examined features were single
and di-peptide amino acid compositions while protein structural features
examined included properties such as protein secondary structure, disorder and
surface accessibility. As most of these features are not experimentally
available for all proteins we utilized multiple available prediction tools for
their generation as described below.</p><p>The primary initial feature utilized in this work were the EVP based on
the EV data from random peptide studies of Gerken and coworkers.34–39 EV tables were compiled for 10 GalNAc-Ts
(T1–3, T5, T10–14, and T16). Each table contains 10 positional EVs
for each amino acid type (except Cys, Trp and Thr, whose EVs are all taken as
1.0). The EVs are derived from the change in composition of a particular amino
acid residue in a GalNAc-T random glycopeptide product compared to the
residue's composition in the starting non-glycosylated random
peptide.36,34 Using ISOGlyP the EVP values were
calculated for each of the GalNAc-Ts by multiplying together the EVs between
−3 to +3 residues of the potential Thr and Ser glycosite. In addition,
the maximum EVP value among the transferases reported by ISOGlyP was included
giving rise to a total of 11 numeric EVP based features. These were labeled
IsoGlyP-T1,...,IsoGlyP-T16 and IsoGlyP-Max.</p><p>For the next feature set, the program PseAAC-General was used to
calculate the percentage of each amino acid(s) within a window size of 10 for
both single and di-peptide compositions.43 This process retrieved 420 numeric features that were
added to the list of features (designated as pseb_s_1,...,pseb_s_20 for the
single amino acid compositions and pseb_di_1,...,pseb_di_400 for the dipeptide
compositions).</p><p>The TMHMM32 program,
which employs hidden Markov models, was used to identify the possible location
of the potential glycosite, i.e. whether within a transmembrane region (T), an
extracellular loop (E) or an intracellular loop (I) for inclusion as another
feature. NetSurfP30 examines
surface accessibility of proteins and returns two continuous measurements of
accessibility plus a simple buried or exposed categorical variable. For both
programs, the results file were parsed for one transmembrane feature (tmhmm),
and three surface accessibility features (netsurf_rsa, netsurf_asa, and
netsurf_cat).</p><p>The generation of features related to protein disorder utilized the
ESpritz program suite.44,45 Two types of prediction
methods within ESpritz were used, X-ray and Disprot, with parameter values
"X" and "D" respectively. The final results are
determined by using either a weighted score measure or 5% false positive rate
(FPR) cutoff set via "0" and "1" respectively. The
weighted score rewards the prediction with disordered regions while the 5% FPR
cutoff is considered more conservative. Each of the possible combination of
parameters returned a fasta sequence with either "D" or
"O", for disordered or ordered respectively for each amino acid
position. Values for each potential glycosite were extracted thus increasing the
feature set by four non-numerical values (espritz_X_0, espritz_X_1, espritz_D_0,
and espritz_D_1).</p><p>The Scratch program suite was used to generate relative solvent
accessibility values via ACCpro and secondary structure determinations via
SSpro.46,47 There are two versions of SSpro, one
that utilizes three secondary structure classes and the other, SSpro8, that has
eight possible secondary structure classes. Similarly, ACCpro comes in two
flavors, the first reports either the findings as "e" or
"-" for exposed or buried respectively. The second version,
ACCpro20, returns a value between 0 and 95% in increments of 5% with the higher
values indicating higher exposure. The features derived by the Scratch suite
expands the number of features by four, one numerical (scratch_acc20) and three
categorical values (scratch_ss, scratch_ss8, and scratch_acc).</p><p>Each of the above programs were downloaded and installed on a local
CentOS 7 machine. A python script ran each of the programs and compiled the
results into a single CSV file. For each potential glycosite within protein
sequence the total number of features generated was 443 of which 9 were
categorical.</p><!><p>To reduce the number of redundant or uninformative features and to
improve the prediction accuracy and efficiency multiple feature selection
techniques provided by R packages were used. In addition, the feature selection
process was conducted twice, with and without the ISOGlyP EVP values to verify
the relevance of ISOGlyP EVP values in predicting the O-glycosylation sites. The
combined data from the three cell lines was used for feature selection. If any
of the cell lines were identified as positive that site was considered positive
overall. 600 random sites were used as the training data and another 300 were
used for testing purposes with equal number of negatives included.</p><p>The R package FSelector48 was used for screening most of the features. The first
feature selection methods employed were the "Forward",
"Best First", and "Backward" searches.
"Forward" selection begins with the most informative feature and
then continues to add one feature at a time until no more features further
contribute to the value. "Best First" is similar to
"Forward" selection, except it can step back to the prior feature
set to replace a feature before moving forward again. Finally,
"Backward" selection begins with all the features included and
removes one feature at a time until only relevant features remain. In this study
a decision tree-based evaluator method within the R package rpart49 was used to determine which of
the features were to be selected.</p><p>Additionally, we used the correlation-based feature selection (cfs) and
the general information.gain functions in FSelector48 which employs the "Best
First" methodology to select relevant features. Finally, the
randomForest50 R
package was used to identify the set of features that were important for the
training of a random forest model. Random Forest Variable Importance (RFVI) uses
the mean decrease in Gini across multiple decision trees to determine a ranking
of feature importance. The decrease in Gini value is the difference of the Gini
index before a feature is considered and then again once the feature is used to
split the samples into two groups. The larger the decrease in Gini would
indicate the greater ability of the feature has in being able to separate the
samples into groups that are more homogeneous in relation to their class. The
randomForest package further generates various graphs showing the ranking of
each feature using the derived value.</p><!><p>To create the EV tables, the combined data from the three cell lines was
used. Again, if any of the cell lines were identified as positive that site was
considered positive overall. 600 random positive sites were selected with 46,500
negative sites. The process was repeated ten times with the final EVs considered
the average of the ten rounds.</p><p>EVs were obtained from the fraction of sites containing a particular
value found at known glycosites divided by the fraction of that the value found
at all Thr and Ser residues. Using espritz_D_1 as an example, the possible
values of the feature reported are an ordered "O" or disordered
"D" state. To calculate the EV of the ordered "O"
state, the number of times an "O" at a known glycosylation site
was tallied and then divided by the total number of positive glycosites giving
the fraction of sites that are "O". Next, the "O"
was counted for all possible glycosites and then divided by the total number of
Thr and Ser residues. The positive fraction was then divided by the all possible
fraction to give the final EV. This process is then repeated for the disordered
values, "D", to complete the EVs for the feature espritz_D_1.
Eq.(1) depicts this in an
equation form, where EVf,i is the
i-th enhancement value within feature f.
The value cf,i,+ is the number of
times a value is found within the positive subset of sites and
cf,+ is the count of positives
checked. Similarly, cf,i and
cf are the counts within all
possible sites for the i-th value and all possible values
respectively. Numerical features were converted into a categorical format by
dividing the values into nine bins and an EV calculated for each as described
above.</p><!><p>The effectiveness of adding features into the ISOGlyP O-glycosylation
predictions was tested against the site specific glycomics data from the HEK293,
HeLa and HaCaT SimpleCell lines of Steentoft and coworkers.28 Using the EV tables generated by the
Gerken lab and the feature derived EVs from the above section the extended
Feature Enhancement Value Product (FEVP) score was calculated by multiplying
together the individual EVf,i, for
all selected features f, see Eq.
(2). The max ISOGlyP EVP value among the present transferases was
used as the ISOGlyP feature in creation of FEVP. For features that are not
utilized, for example if the -4 and -5 positions are not selected, the value
will be set to one thus not changing the overall FEVP score. As with the
standard EVPs, FEVPs above 1.0 will be considered positive predictions of
O-glycosylation for general testing.</p><p>In order to determine the several accuracy metrics described below,
two-thirds of the experimentally confirmed glycosites along with an equal number
of negative sites were randomly selected from a cell line. This process was
repeated ten times and the results shown are an average with the standard
deviation for each cell line. As an external reference, predictions using
NetOGlyc 4.0 were also obtained for comparison for the same set of sites.
Finally, this process was repeated for all three cell lines described above. For
the various accuracy metrics, True Positives (TP) and True Negatives (TN) are
the number of correctly identified positives and negatives respectively. False
Positives (FP) are considered negative sites predicted to be positive while
False Negatives (FN) were positive sites not identified by the prediction
programs as being glycosylated.</p><!><p>The accuracy of a prediction is the total number of correctly
predicted positive (glycosylated) and correctly predicted negative
(non-glycosylated) sites divided by the total number of Thr and Ser sites
analyzed.</p><!><p>Sensitivity measures true positive glycosites as a ratio of
predicted true glycosites to the total actual glycosites. It measures how
well the prediction method identifies positive glycosylated instances.</p><!><p>Specificity of a prediction is a ratio of the predicted true
negatives (non-glycosylated sites) to the total actual negatives. It
measures how well the prediction method will identify the negative
sites.</p><!><p>To determine the optimal EVP and FEVP cutoffs, each cutoff was
incremented from 0.1 to 2.0 by steps of 0.1. Using the mean of the
sensitivity and specificity from the 10 replicates from above, the receiver
operating characteristic (ROC) curves were plotted in Excel. The area under
the curve (AUC) of the ROC curve, which represents the overall performance
of the prediction method across different cutoff values, was obtained using
R. To determine the optimal EVP cutoff value, the Youden index
(Sensitivity + Specificity − 1)
was calculated.</p><!><p>A list of proteins and sites that were glycosylated for the HaCat, HeLa,
and HEK SimpleCell cell lines were obtained in an Excel format. The protein
accession numbers and cell line specific unambiguous glycosylation data were
extracted from the file. The protein accession numbers were then used to
download the 660 protein sequences from UniProt. There were a total 940
confirmed glycosites of the 72,085 possible Thr and Ser sites within the
SimpleCell dataset. A list of the sites along with the amino acids surrounding
the site of interest are listed within the supplemental material.</p><!><p>Results of the various feature generating methods for each of the
possible glycosites were compiled into a single CSV by parsing the data with a
Python script. As the features were being generated, the average run times for
the processing of proteins using the programs TMHMM, Netsurf, Scratch and
ESpritz were recorded. As seen in Table 1
the run times varied greatly between the programs with the Scratch program
taking the longest with a mean of 696 seconds per protein. While the run times
of the other programs were relatively low, this processing time per sequence
increases dramatically over the original ISOGlyP predictions making this type of
glycomic analysis computationally expensive. Also, unlike the ESpritz program in
which one can choose a set of parameters desired to obtain the specific output
needed, the Scratch program produces all four features for every call by running
all of SSpro, SSpro8, ACCpro, and ACCpro20. Therefore it is not possible to
reduce the run time by reducing the features needed without modifying the
original program. As newer and more efficient software, such as the recent
NetSurfP 2.051 upgrade that
captures the same information, are created their use may help reduce the run
time of the predictions.</p><!><p>In the feature selection step, we made the assumption that all of the
transferase isoforms prefer similar traits around sites of interest. This was
necessary because there is insufficient isoform specific data to determine
whether the isoforms recognize these features differently. Additionally, all
tested feature selection methods returned similar results with only minor
differences that were related in nature, for example, espritz_D_1 with
espritz_X_1 where both features are related to protein disorder and order.
Therefore the focus here will be on the random forest based RFVI results. The
RFVI plot showed that except for the first twenty features, most of the
decreases in mean Gini values were low, see Figure
2 panel A. Based on cross validation testing, of all of the features
within the compiled features, only a little over twenty of the top ranked
features are needed to minimize the error, as shown in the scree plot in Figure 2 panel B.</p><p>Upon closer examination of the top 20 features displayed in Figure 2 panel A, it is clear that both
structural and sequence derived features are important. When including the
ISOGlyP EVP values, only a few amino acid composition features remained in the
top 20 features. This suggests that when using ISOGlyP's EVP values, the
additional amino acid composition values may be removed from the final
predictive model. (See Figure 2 panels
B)</p><p>The two relative solvent accessibility features generated by NetSurfP,
netsurf_asa and netsurf_rsa, are the most informative features. From the Scratch
program, features included both those including secondary structure information,
scratch_ss and scratch_ss8, and the relative solvent accessibility containing
twenty levels, scratch_acc20. ESpritz's more conservative 5% FPR
parameter was most beneficial when used with both the X-ray type, espritz_X_1,
and Disprot, espritz_D_1, and the features were among the top ranked structural
features identified. Amongst all of the tested features, netsurf_asa,
netsurf_rsa, scratch_ss, scratch_ss8, scratch_acc20, espritz_X_1 and espritz_D_1
were the chosen features for further incorporation into the extended ISOGlyP
predictions.</p><!><p>For the features that contained categorical values, espritz_D_1,
espritz_X_1, scratch_ss and scratch_ss8, the categories were used for the
generation of the EV, see Table 2 for
select listing. For the continuous features, scratch_acc20, netsurf_rsa, and
netsurf_asa, the results were split into nine bins ranging from two to nine
based on the numeric value of the feature at the glycosite. As an example,
netsurf_rsa calculated values for the glycoproteomics data ranged between 0.014
and 0.94, so each value was then assigned a bin as follows: Anything below 0.2
was assigned to bin 2, anything at 0.2 or higher but below 0.3 was assigned to
bin 3, and so on to the last bin 10 which would contain anything greater than or
equal to 0.9.</p><p>There are a total of 41 different EVs derived from the seven most
relevant features. A partial list of EVs are given in Table 2. From the displayed EVs, certain
characteristics of the glycosites can be seen with EVs above 1.0, such as the
preference of protein disorder, an EV of 1.8568 espritz_D_1's D, at the
site of interest and a preference for coiled regions, an EV of 1.4621 for
scratch_ss8's C, at sites of glycosylation. The results also suggest an
aversion of glycosylation by EVs below 1.0 at beta sheet structures, an EV of
0.2344 for scratch_ss8's B, or sites having low solvent/surface
accessibility, an EV of 0.2732 and 0.3503 for netsurf_asa's 2 and 3
respectively.</p><!><p>Using an EVP cutoff value of one, the accuracy of the ISOGlyP program
without the inclusion of the structural features ranged from 0.62 to 0.66
depending on the cell line, see Tables 3
& 4. Accuracy of NetOGlyc 4.0 ranged
between 0.75 to 0.76, but it should be noted that the data used to train
NetOGlyc the same data used within this comparison and thus there is a bias in
favor of the accuracy of NetOGlyc.</p><p>For the ISOGlyP predictions with the inclusion of other features while
maintaining a FEVP of greater than one to be considered positive, the overall
accuracy increased to 0.70. The increase in accuracy resulted from identifying
more positive glycosites thus increasing the sensitivity from 0.49 to 0.63 while
maintaining similar specificities of 0.74 and 0.76. This trend was also seen in
the HeLa and HEK293 data sets as well, see Table
4. This highlights how the incorporation of structural features
increases the number of positive glycosites identified while maintaining the
ability to correctly identify the negative glycosites. The values reported here
are only a subset of combinations of the features used with ISOGlyP, and they
are selected to show how accuracy increases with the inclusion of structural
features.</p><p>The ROC curves, displayed in Figure
3, were used to determine the optimal EVP and FEVP cutoff values. The
gray line represents a baseline curve with an AUC of 0.5 as a reference. The red
plotted curve, with an AUC of 0.67, represents ISOGlyP's EVP value while
incrementing the cutoff value by 0.1. The blue line, with an AUC of 0.77,
represents the curves using FEVP values from ISOGlyP and the netsurf_asa,
scratch_ss8, and espritz_D_1 features. The increase of the AUC and the shift up
of the ROC curve show the improvement obtained by including the three features.
For ISOGlyP without the structural features, the highest value was found at an
EVP cutoff of 0.6 where the Youden index was 0.27. For ISOGlyP with the
structural features, the optimal FEVP cutoff dropped to 0.5 but the Youden index
increased to 0.44.</p><p>The number of false positives in this study shifts the optimal cutoff
value lower. While optimal values are below the suggested EVP and FEVP cutoff of
one, there is an underlying expectation that not all the actual glycosylated
sites are reported in the glycoproteomics data. This is because of the inability
of experimentally identifying all of the glycosites from such glycoproteomics
studies, as current mass spectrometric approaches are not sufficiently
quantitative. Thus it is possible that the predicted false positive sites with
low EVP and FEVP values, could just be missed experimentally in the present
glycoproteomics data sets.</p><!><p>We have shown that the use of structural features increases the accuracy of
ISOGlyP predictions across three different glycoproteomic data sets. This was
accomplished by increasing the sensitivity of the program while making only minimal
change in specificity by including three additional features into the ISOGlyP
predictions. Incorporation of these structural features into ISOGlyP's EVP
helps in increasing the overall O-glycosylation prediction accuracy, but it comes at
a cost in processing time with the use of programs such as Scratch to generate the
needed scratch_ss8 feature. While this work focuses on the inclusion of structural
features, other features could also be screened for use. As more detail information
is revealed about different PTMs within the proteome, a search as to whether they
may be correlated with O-glycosylation could also be useful in further refining our
predictions. These could include factors such as proteolytic cleavage and
phosphorylation sites as in the case of FGF23R where O-glycosylation regulates its
cleavage by furin.5,52</p><p>In this study we have identified additional features outside of the primary
amino acid sequence that appear to affect a transferase's ability to
glycosylate a given site. As a result of the nature of these different features it
may be possible to perform additional studies to help explain their preferences and
how these transferases interact with the protein substrates.</p>