You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


All relevant data are within the paper and its Supporting Information files.

Introduction {#sec001}
============

In 1992, the Nobel Prize in Physiology or Medicine was award to Edmond H. Fischer and Edwin G. Krebs for their discovery that reversible protein phosphorylation is a critical biological regulatory mechanism in biology \[[@pone.0203840.ref001]\]. Many studies in recent decades have been carried out to characterize the molecular mechanisms and functions of phosphorylation, and most were carried out in eukaryotes \[[@pone.0203840.ref002]--[@pone.0203840.ref004]\]. A number of recent studies identified that phosphorylation is also critical for signaling transduction in bacteria \[[@pone.0203840.ref005]--[@pone.0203840.ref009]\], while the regulation of phosphorylation in bacteria is complicated. For example, the phosphorylation of histidine and aspartate was found to play critical roles in two-components systems for signal transduction \[[@pone.0203840.ref008],[@pone.0203840.ref009]\]. Recently, a number of studies discovered that serine/threonine phosphorylation played important roles in cellular signaling and might be critical for the bacterial pathogenicity \[[@pone.0203840.ref005],[@pone.0203840.ref007]\]; however, the regulators of serine/threonine phosphorylation, serine/threonine kinases, could play critical roles in bacteria. As an important kinase in bacteria, prkC was first characterized as membrane-linked serine/threonine protein kinase, which is important for sporulation and biofilm formation in *Bacillus subtilis* \[[@pone.0203840.ref010]\]. Serine/threonine protein in bacteria show homology in their catalytic domains \[[@pone.0203840.ref011]\], and it has been implicated that prkC is homologous in *S*. *pyogenes* adherence, invasion and in *E*. *faecalis* persistence \[[@pone.0203840.ref012]\]. Further studies showed that prkC was implicated in various biological processes such as antimicrobial resistance and intestinal persistence \[[@pone.0203840.ref012]\], bacterial resuscitation \[[@pone.0203840.ref013]\] and gliding motility \[[@pone.0203840.ref014]\]. However, the detailed substrates of prkC needed further dissection.

To understand the detailed biological functions and molecular mechanisms of prkC, identification of its substrates and sites is fundamental. Although the development of state-of-art proteomics technologies such as high-throughput mass spectrometry enabled leading scientists to carry out large-scale profiling of serine/threonine phosphorylation events in bacteria \[[@pone.0203840.ref015],[@pone.0203840.ref016]\], the kinase-substrate regulatory relationships are still unknown. Experimental studies with conventional methods to identify substrates and sites for prkC are complicated. Recently, a number of state-of-art computational methods such as Scansite, PPSP, PKIS and GPS were developed to predict kinase-specific phosphorylation in eukaryotes \[[@pone.0203840.ref017]--[@pone.0203840.ref020]\], while NetPhosBac and cPhosBac were constructed for serine/threonine phosphorylation in bacteria \[[@pone.0203840.ref021],[@pone.0203840.ref022]\]. However, the predictor for kinase-specific phosphorylation in bacteria is still absent. Since there are limited experimental studies for kinase-specific phosphorylation in prokaryotes, more efforts should be made in this area to provide helpful information for further studies.

In this study, we developed a novel predictor for prkC-specific phosphorylation. According to the 5-step rule defined by Chou *et al*. \[[@pone.0203840.ref023],[@pone.0203840.ref024]\], we carried out the study and organized the manuscript with the following 5 steps: (1) benchmark dataset construction, (2) protein sample formulation, (3) algorithm classification, (4) cross validations and (5) web-server implementation. The experimentally identified substrates and prkC-specific phosphorylation sites were manually collected from the literature. A dataset of 36 phosphorylation sites in 14 substrates were constructed. The sequence preferences of these sites were analyzed, while the result showed that prkC has complicated specificity of the sequence. The amino acid location feature extraction method was used to predict the sequence encoding, and the support vector machine (SVM) was employed to distinguish potential prkC-specific phosphorylation sites from the background. 4-, 6-, 8- and 10-fold cross validations were employed to evaluate the performance and the results shows the prediction power is promising. Based on the predictor, hundreds of potential prkC-specific phosphorylation sites were annotated based on the known phosphorylation sites in bacteria. Taken together, it was anticipated that the computational prediction of prkC-specific phosphorylation might generate helpful information for further studies of phosphorylation regulation in bacteria.

Materials and methods {#sec002}
=====================

Data preparation and analysis {#sec003}
-----------------------------

Since no prkC-specific phosphorylation sites are currently available in public databases, we manually curated the experimentally identified prkC-specific phosphorylation sites from the literature in PubMed. We used 'prkC' and 'phosphorylation' as the key words to search the PubMed database and manually read the retrieved articles to curate the experimentally identified phosphorylated by prkC in *Bacillus subtilis*. Only the identified prkC-specific phosphorylation sites clearly described in the full text were reserved. In total, 36 phosphorylation sites in 14 substrates were obtained ([Table 1](#pone.0203840.t001){ref-type="table"}). In this study, the 512 non-phosphorylated serine/threonine residues were regarded as negative. To analyze the sequence preferences of prkC-specific phosphorylation, WebLogo 3 software \[[@pone.0203840.ref025]\] was used to present the amino acid preference of the phosphorylation sites, and Two Sample Logo software \[[@pone.0203840.ref026]\] was employed to compare the adjacent around the phosphorylation sites and non-phosphorylated serine/threonine residues.

10.1371/journal.pone.0203840.t001

###### Experimentally identified prkC-specific phosphorylation sites.

![](pone.0203840.t001){#pone.0203840.t001g}

  Acc      Position   Gene   Organism              PMID(s)
  -------- ---------- ------ --------------------- -------------------
  P16263   182        odhB   *Bacillus subtilis*   24390483
  P38494   365        ypfD   *Bacillus subtilis*   24390483
  P37561   88         yabS   *Bacillus subtilis*   24390483
  P37561   90         yabS   *Bacillus subtilis*   24390483
  P45740   565        thiC   *Bacillus subtilis*   24390483
  P42974   49         ahpF   *Bacillus subtilis*   24390483
  O34948   281        ykwC   *Bacillus subtilis*   24390483
  O34507   162        prkC   *Bacillus subtilis*   12842463
  O34507   163        prkC   *Bacillus subtilis*   12842463
  O34507   165        prkC   *Bacillus subtilis*   12842463
  O34507   167        prkC   *Bacillus subtilis*   12842463
  O34507   214        prkC   *Bacillus subtilis*   20389117;12842463
  O34507   290        prkC   *Bacillus subtilis*   20389117;12842463
  O34507   313        prkC   *Bacillus subtilis*   20389117;12842463
  O34507   320        prkC   *Bacillus subtilis*   20389117;12842463
  O34507   417        prkC   *Bacillus subtilis*   20389117
  O34507   498        prkC   *Bacillus subtilis*   20389117
  P19669   26         tal    *Bacillus subtilis*   20389117
  P19669   54         tal    *Bacillus subtilis*   20389117
  P19669   82         tal    *Bacillus subtilis*   20389117
  P19669   125        tal    *Bacillus subtilis*   20389117
  P19669   159        tal    *Bacillus subtilis*   20389117
  P19669   184        tal    *Bacillus subtilis*   20389117
  P12425   26         glnA   *Bacillus subtilis*   20389117
  P12425   147        glnA   *Bacillus subtilis*   20389117
  P12425   207        glnA   *Bacillus subtilis*   20389117
  P12425   286        glnA   *Bacillus subtilis*   20389117
  P39126   138        icd    *Bacillus subtilis*   20389117
  P39126   147        icd    *Bacillus subtilis*   20389117
  P39126   396        icd    *Bacillus subtilis*   20389117
  Q04777   88         alsD   *Bacillus subtilis*   20389117
  P08877   12         ptsH   *Bacillus subtilis*   20389117
  O34530   166        rsgA   *Bacillus subtilis*   22544754
  O34530   192        rsgA   *Bacillus subtilis*   19246764
  O34530   226        rsgA   *Bacillus subtilis*   19246764
  P33166   385        tuf    *Bacillus subtilis*   19246764

The amino acid location feature extraction method {#sec004}
-------------------------------------------------

To perform the prediction, the amino acid location feature extraction method, which was developed previously and widely used to predict various protein post-translational modifications \[[@pone.0203840.ref027]\], was employed to encode the sequence.

According to peptide fragment encoding equation, $$P = R_{‑15}R_{‑14}\cdots R_{‑1}\ S/T\ R_{1}R_{2}{\cdots R}_{15}$$

[Eq 1](#pone.0203840.e001){ref-type="disp-formula"} and the concept of the amino acid location feature extraction, the peptide sequences in the training dataset can be formulated as [Eq 2](#pone.0203840.e002){ref-type="disp-formula"} $$\mathbf{P}_{\xi = 31}\left( S/T \right) = \left\lbrack {\Psi_{1}\ \Psi_{2}\ \cdots\ \Psi_{u}\ \cdots\ \Psi_{\Omega}} \right\rbrack^{\mathbf{T}}$$ where the components Ψ~u~ (u = 1,2,⋯,Ω) are defined to extract useful features from the relevant training sequences. Since the length of peptides in the benchmark dataset is 31, [Eq 1](#pone.0203840.e001){ref-type="disp-formula"} can be simplified as $$\mathbf{P} = R_{1}R_{2}\mspace{2mu}\cdots\mspace{2mu} R_{15}R_{16}R_{17}\mspace{2mu}\cdots\mspace{2mu} R_{30}R_{31}$$ where R~16~ = S/T, and R~i~(i = 1,2,⋯,31,i ≠ 16) can be any of the twenty native amino acids. Thus, the 31 components in its amino acid location feature vector are defined as follows.

For each position of the fragment, we have $$\left\{ \begin{array}{l}
{\ {\psi_{1} = p\left( R_{1} \right)}} \\
{\ {\psi_{2} = p\left( R_{2} \right)}} \\
{\quad{\vdots \qquad \vdots}} \\
{\psi_{31} = p\left( R_{31} \right)} \\
{\ {\psi_{32} = n\left( R_{1} \right)}} \\
{\ {\psi_{33} = n\left( R_{2} \right)}} \\
{\quad{\vdots \qquad \vdots}} \\
{\psi_{62} = n\left( R_{31} \right)} \\
\end{array} \right.$$

In [Eq 4](#pone.0203840.e004){ref-type="disp-formula"}, p(R~1~) is the occurrence frequency of R~1~ at position 1 for the positive peptide sequence of [Eq 2](#pone.0203840.e002){ref-type="disp-formula"} in the training dataset, and p(R~2~) is the occurrence frequency of R~2~ at position 2. n(R~1~) is the occurrence frequency of R~1~ at position 1 for the negative peptide sequence of [Eq 2](#pone.0203840.e002){ref-type="disp-formula"} in the training dataset, n(R~2~) is the occurrence frequency of R~2~ at position 2, and so forth.

After deriving these amino acid location feature values from the training data, we use the SVM classifier LibSVM \[[@pone.0203840.ref028]\] to build the classifier for prediction. The extracted features were the input and the best parameters were adjusted to perform better prediction. The most important parameters are the gamma (g) and cost (C), where the g parameter is used to configure the kernel function, and the C parameter is the penalty factor of the support vectors when the prediction is wrong. The steps of this process are as follows: (a) feature extraction, (b) data standardization, (c) cross validation and (d) best parameters combination selection. Finally, we constructed the prkC-PSP with the parameters of g = 0.5 and C = 32.0.

Performance evaluation {#sec005}
----------------------

As previously described, four measurements of sensitivity (*Sn*), specificity (*Sp*), accuracy (*Ac*), and Mathew's Correlation Coefficient (*MCC*) were employed to evaluate the prediction performance. The four measurements were defined as follows: $$Sn = \frac{TP}{TP + FN},\ Sp = \frac{TN}{TN + FP},\ Ac = \frac{TP + TN}{TP + FP + TN + FN},\ {and}$$ $$MCC = \frac{\left( {TP*TN} \right)*\left( FN*FP \right)}{\sqrt{\left( {TP + FN} \right)*\left( {TN + FP} \right)*\left( {TP + FP} \right)*\left( TN + FN \right)}}.$$

To evaluate the prediction performance and robustness of prkC-PSP, the training data set was used to perform the *n*-fold cross-validations. That is, the data set is split into n parts randomly and evenly. A candidate model will be built based on n-1 parts of the data set, and prediction accuracy of this model will be evaluated on the validation data set, the holdout part of the data set. In this study, the 4-, 6-, 8- and 10-fold cross-validations were performed; the receiver operating characteristic (ROC) curves and AROCs (area under ROCs) were analyzed.

Implementation of the online service {#sec006}
------------------------------------

The online service of the prkC-specific phosphorylation sites prediction (prkC-PSP) software was implemented in Python and is freely available at <http://free.cancerbio.info/prkc/>.

Results {#sec007}
=======

Sequence preferences of prkC-specific phosphorylation sites {#sec008}
-----------------------------------------------------------

Although a number of studies were carried out for prkC and its substrates, the sequence features and motifs for prkC substrate recognition are still to be dissected. With the collected prkC-specific phosphorylation sites ([Table 1](#pone.0203840.t001){ref-type="table"}), the sequence features were analyzed with WebLogo 3 \[[@pone.0203840.ref025]\] and two sample logo software packages \[[@pone.0203840.ref026]\]. The amino acid preferences are shown in [Fig 1A](#pone.0203840.g001){ref-type="fig"}, while the enriched and depleted amino acid types around the prkC-specific phosphorylation sites are presented in [Fig 1B](#pone.0203840.g001){ref-type="fig"}. It was observed that, in the current stage, most prkC-specific phosphorylation sites were threonine residues. Among the residues around the prkC-specific phosphorylation sites, lysine was enriched at the -10, -4, +7 and +11 positions ([Fig 1](#pone.0203840.g001){ref-type="fig"}). Another positive charge residue arginine was enriched at the -9 and -1 positions, while histidine was also enriched in the -13 and +12 positions ([Fig 1](#pone.0203840.g001){ref-type="fig"}). Interestingly, none of the negative residues such as aspartic acid and glutamic acid were enriched around the prkC-specific phosphorylation sites. Taken together, it was indicated that the positive charge residues around the recognition site were preferred by prkC. Furthermore, small residues including alanine and glycine were enriched in the upstream positions including -10, -9, -5, -3 and -2 ([Fig 1](#pone.0203840.g001){ref-type="fig"}). Aromatic residue phenylalanine was enriched near the recognition site including -4 and +3 positions ([Fig 1](#pone.0203840.g001){ref-type="fig"}).

![Preferences (A) and comparisons (B) of amino acids around the prkC-specific phosphorylation sites.](pone.0203840.g001){#pone.0203840.g001}

Since the sequence preferences seemed to be evident, we tried to identify the potential motif for prkC-specific phosphorylation. The motif analysis for the dataset was carried out with the Motif-All software \[[@pone.0203840.ref029]\]. However, no significant motif was observed with a p-value lower than 0.0001. This observation indicated that the prediction of the prkC-specific phosphorylation might be difficult.

Performance evaluation {#sec009}
----------------------

To develop an accurate predictor for prkC-specific phosphorylation, several widely used computational models, including amino acid location feature extraction (location) \[[@pone.0203840.ref027]\], PseAAC \[[@pone.0203840.ref030]\] and CKSAAP \[[@pone.0203840.ref022]\], were tested. These models were combined with LibSVM to perform the prediction, while the 10-fold cross validation was employed for accuracy evaluation. The ROC curves were shown in [S1 Fig](#pone.0203840.s001){ref-type="supplementary-material"}, which indicated that the location-based model was much better than the others. Since the PseAAC and CKSAAP models achieved great success in prediction of other PTMs with a relatively huge dataset \[[@pone.0203840.ref022],[@pone.0203840.ref030]\], we anticipated that the location-based model might be more suitable for small datasets such as prkC-specific phosphorylation. Thus, we employed the location-based model in this study.

To evaluate the performance of our prediction, the 4-, 6-, 8-, 10-fold cross validations were carried out. The ROC curves for these validations are presented in [Fig 2](#pone.0203840.g002){ref-type="fig"}, while [Table 2](#pone.0203840.t002){ref-type="table"} presents the detailed values of the performance. Since the 4-, 6-, 8-, and 10-fold cross validations performances were consistent, it was indicated that the prediction was robust. Since the *n*-fold cross validations could represent the prediction of new or unknown sites, the results show that our prediction achieved promising performance. For the 4-fold cross validation, the prediction achieved an accuracy of 94.89%, sensitivity of 91.67%, specificity of 95.12%, MCC of 0.6989 and AROC (area under ROC) of 0.9798. For the 6-fold cross validation, the performance was an accuracy of 95.07%, sensitivity of 94.44%, specificity of 95.12%, MCC of 0.7159 and AROC of 0.9778. For the 8-fold cross validation, the prediction achieved an accuracy of 94.71%, sensitivity of 88.89%, specificity of 95.12%, MCC of 0.6817 and AROC of 0.9823. For the 10-fold cross validation, the performance was an accuracy of 94.89%, sensitivity of 91.67%, specificity of 95.12%, MCC of 0.6989 and AROC of 0.9770. From the results, it was observed that the performance was promising.

![Cross-validation performance of prkC-PSP.\
The ROC curves of the 4-, 6-, 8-, and 10-fold cross validations. The AROC values were calculated and shown.](pone.0203840.g002){#pone.0203840.g002}

10.1371/journal.pone.0203840.t002

###### Cross-validation (CV) performances of prkC-PSP.

![](pone.0203840.t002){#pone.0203840.t002g}

  *n*-fold CV   Threshold   *Sn* (%)   *Sp* (%)   *Ac* (%)   *MCC*    *AROC*
  ------------- ----------- ---------- ---------- ---------- -------- --------
  4-fold                    91.67      95.12      94.89      0.6989   0.9798
  6-fold                    94.44      95.12      95.07      0.7159   0.9778
  8-fold                    88.89      95.12      94.71      0.6817   0.9823
  10-fold       High        91.67      95.12      94.89      0.6989   0.9770
                Medium      94.44      90.04      90.33      0.5782   0.9770
                Low         94.44      85.16      85.77      0.4923   0.9770

Development of the prkC-PSP online prediction service {#sec010}
-----------------------------------------------------

With performance taken into consideration, we developed a novel predictor of prkC-PSP (prkC-specific Phosphorylation Sites Prediction) software for online prediction service. The prkC-PSP was implemented in PHP and Python, and the prediction page was as shown in [Fig 3](#pone.0203840.g003){ref-type="fig"}. The example button presented the format for the input, which should be entered in the text box. Three specificity levels in the 10-fold cross validation were provided to set the cut-off values for prediction. The high threshold indicated a sensitivity of 91.67%, specificity of 95.12% and cut-off value of -0.6442. The medium threshold indicated a sensitivity of 94.44%, specificity of 90.04% and cut-off value of -0.9774. The low threshold indicated a sensitivity of 94.44%, specificity of 85.16% and cut-off value of -0.6442. Thus, the low, medium and high thresholds indicated that the false discovery rates of the prediction were approximately 15%, 10% and 5%. Users could choose the cut-off by themselves to perform prediction of the prkC-specific phosphorylation sites.

![Prediction page of prkC-PSP predictor.](pone.0203840.g003){#pone.0203840.g003}

Here, we presented the *Bacillus subtilis* odhB, ypfD and tal proteins (UniProt accessions: P16263, P38494 and P12425) as examples to demonstrate the simplicity and precision of the prkC-PSP. These sequences were pasted into the text box, and the high threshold was chosen; then, we clicked the 'Submit' button, and the results were shown on the result page ([Fig 4](#pone.0203840.g004){ref-type="fig"}). There were 8 predicted hits (S182 in odhB, S365 in ypfD, T26, S54, T82, T125, T159 and T184 in tal), which meant these sites would be phosphorylated by prkC specifically.

![Prediction results from prkC-PSP predictor for the example sequences with high threshold.\
There are 8 predicted hits (S182 in odhB, S365 in ypfD, T26, S54, T82, T125, T159 and T184 in tal).](pone.0203840.g004){#pone.0203840.g004}

Large-scale prediction of prkC-specific phosphorylation sites in bacteria {#sec011}
-------------------------------------------------------------------------

Although the development of state-of-art proteomics technologies, such as high-throughput mass spectrometry, enabled leading scientists to carry out large-scale identification of serine/threonine phosphorylation in bacteria, the kinase-substrate regulatory relationships were still unknown. Experimental studies with conventional methods to identify substrates and sites for prkC were complicated. The homology of the bacterial serine/threonine protein is hypothesized to have similar substrates as with prkC. Here, we applied the prkC-PSP predictor to identify potential prkC-specific phosphorylation sites. To perform the large-scale prediction, we downloaded the dbPSP dataset \[[@pone.0203840.ref031]\], which curated massive phosphorylation data in 96 prokaryotes. However, only 38 bacteria species had the prkC kinase, and only 1,513 phosphorylated sites in these organisms were reserved. With the prkC-PSP predictor, we found that approximately 8.5% of the sites could be phosphorylated by prkC with the high threshold, while the medium threshold is of 13.2% and the low threshold is of 19.4% ([Fig 5](#pone.0203840.g005){ref-type="fig"}, [S1 Table](#pone.0203840.s002){ref-type="supplementary-material"}). The prediction results should be useful for further experimental investigations. Several proteins were picked as examples, and their prediction results were visualized in [Fig 6](#pone.0203840.g006){ref-type="fig"} with IBS software \[[@pone.0203840.ref022]\].

![Counts and coverage ratios of phosphorylation sites predicted by prkC-PSP at three different thresholds.\
The ratio ranges from high to low threshold is 8.5% to 19.4%.](pone.0203840.g005){#pone.0203840.g005}

![Examples of large-scale prediction by prkC-PSP.\
Here, we predicted the potential prkC-specific phosphorylation sites among the experimentally identified protein phosphorylation sites with a high threshold. (A) *M*. *tuberculosis* pknL (P9WI62); (B) *E*. *coli* mdh (P61889); (C) *B*. *subtilis* pyk (P80885); (D) *E*. *coli* tufA (P0CE47).](pone.0203840.g006){#pone.0203840.g006}

The serine/threonine-protein kinase pknL (P9WI62) could phosphorylate the DNA-binding protein MT2231 in *Mycobacterium tuberculosis* and was predicted to be involved in transcriptional regulation and cell division \[[@pone.0203840.ref027]\]. We predicted that prkC might phosphorylate pknL at T32, T173 and T175 ([Fig 6A](#pone.0203840.g006){ref-type="fig"}). Site T173 was required for autophosphorylation and transphosphorylation activities, and T175 was critical for full kinase activity. These results indicated that prkC might be the upstream kinase of pknL. As an important role in tricarboxylic acid cycle (TAC), malate dehydrogenase mdh (P61889) catalyzed the reversible oxidation of malate to oxaloacetate \[[@pone.0203840.ref031]\]. Here, we predicted that prkC phosphorylated malate dehydrogenase at T211 and S193 in the lactate dehydrogenase/glycoside hydrolase domain, which meant the T211 and S193 phosphorylation by prkC might regulate the malate dehydrogenase activity in TAC ([Fig 6B](#pone.0203840.g006){ref-type="fig"}). Deletion of pyruvate kinase pyk (P80885) activity was a possible route for elimination of acid formation in *Bacillus subtilis* grown on glucose minimal media, while metabolic analysis indicated a dramatic increase in intracellular pools of phosphoenolpyruvate (PEP) and glucose-6-P in the pyk mutant \[[@pone.0203840.ref032]\]. Previous studies showed that pyk could be phosphorylated at S36, S538 and S546 \[[@pone.0203840.ref033]\]. We predicted that prkC phosphorylated pyk at S538 and S546 ([Fig 6C](#pone.0203840.g006){ref-type="fig"}). Since the two sites located in the PEP-utilizing enzyme domain, pyk phosphorylation by prkC might be relevant to PEP accumulation. Elongation factor Tu 1 tufA (P0CE47) in *Escherichia coli* played a stimulatory role in trans-translation through binding to tmRNA \[[@pone.0203840.ref034]\] and could be phosphorylated at T383 *in vitro* by several kinases such as HipA and doc \[[@pone.0203840.ref035],[@pone.0203840.ref036]\]. Here, we predicted that T383 could be phosphorylated by prkC as well ([Fig 6D](#pone.0203840.g006){ref-type="fig"}).

Discussion {#sec012}
==========

As a dynamic regulatory mechanism, protein phosphorylation played important roles in regulation of various cellular processes in prokaryotes \[[@pone.0203840.ref005]--[@pone.0203840.ref009]\]. Identifying the phosphorylation events and their upstream kinases was critical for dissecting the molecular details of phosphorylation signaling \[[@pone.0203840.ref005]--[@pone.0203840.ref009]\]. Since experimental methods to detect kinase-specific phosphorylation were time-consuming and labor-intensive, convenient computational prediction could provide great help to narrow down the candidate sites for experiments. Since all the computational prediction methods were based on known datasets, the accumulation of known kinase-specific phosphorylation sites should be enough for the construction of prediction models. However, the currently known substrates and sites for most kinases among prokaryotes were limited. As one of the most important kinase in bacteria, prkC could phosphorylate serine and threonine and regulate various biological functions \[[@pone.0203840.ref013],[@pone.0203840.ref026],[@pone.0203840.ref037]\]. Through careful curation, we found that prkC had many known substrates and sites. Thus, we predicted prkC-specific phosphorylation as the initial step for further kinome-wide prediction of kinase-specific phosphorylation in bacteria.

In this study, we carried out computational prediction for prkC-specific phosphorylation sites. The experimentally identified prkC-specific phosphorylation sites were manually collected, and the sequence preferences were analyzed. There are many feature extraction methods and algorithms developed for predicting biological features. For example, *Butt et al* used statistical moments to extract features and Multilayer Neural Network (MNN) to predict membrane proteins \[[@pone.0203840.ref038]\], *Akmal et al* extracted the protein feature with multiple methods and combined with MNN to identify glycosylation sites \[[@pone.0203840.ref039]\], and *Ehsan et al* used neuro network for classification of signal peptides \[[@pone.0203840.ref040]\]. With our dataset, the amino acid location feature extraction method and the SVM algorithm were employed to perform prediction. These studies could serve as a promising start while a number of improvements could be implemented in the future. For example, a complex feature selection method could be developed to provide better prediction, while introducing other features such as secondary structure and solvent-accessible surface areas might provide better prediction. Furthermore, since there are a large number of known kinase-specific phosphorylation sites in eukaryotes, the kinase-substrate recognition patterns might be used to perform predictions in prokaryotes.

Taken together, this study provides a start for kinase-specific prediction of phosphorylation sites in prokaryotes. Computational prediction will help advancing studies of serine/threonine phosphorylation in bacteria.

Supporting information {#sec013}
======================

###### Comparison of different feature extraction methods.

The ROC curves of the 10-fold cross validation for different algorithms including the location model used in this study and other models such as PseAAC and CKSAAP. The AROC values were calculated and are shown.

(TIF)

###### 

Click here for additional data file.

###### The annotation of potential prkC-specific phosphorylation sites from dbPSP database.

\(a\) The phosphorylation sites in bacteria species that have prkC kinase. (b) The sites that were potentially phosphorylated by prkC kinase were annotated by the predictor prkC-PSP.

(XLSX)

###### 

Click here for additional data file.

The authors thank Dr. Yan Xu for her helpful discussions.

SVM

:   Support Vector Machine

Sn

:   sensitivity

Sp

:   specificity

Ac

:   accuracy

MCC

:   Mathew's Correlation Coefficient

ROC

:   Receiver Operating Characteristic

AROC

:   Area under ROCs

prkC-PSP

:   prkC-specific Phosphorylation Sites Prediction

[^1]: **Competing Interests:**The authors have declared that no competing interests exist.