Dataset

The P53 Mutant dataset available at the University of California, Irvine (UCI) Machine Learning (ML) Repository that can be accessed at http://archive.ics.uci.edu/ml/p53Mutants [29]–[32] was utilized as the benchmark dataset to train and test the proposed predictor system. Biophysical models of mutant p53 proteins yielded the features to predict the transcriptional activity. All class labels were determined via in vivo assays [31]. There were a total of 5409 attributes per instance. The attribute description is provided as Table S1. Attributes named V1–V4826 represented 2D electrostatic and surface based features. Attributes V4827–V5408 represented 3D distance based features. The target attribute was denoted by V5409 that carried two possible values to represent p53 transcriptional activity (Active/Inactive). The dataset initially comprised of 16772 p53 mutant records. This was primarily analyzed to filter the records that could not be encoded (records held missing values specific to 2D/3D structural properties). This resulted in the removal of 180 instances thus reducing the total size of the data to 16592 records. The data was further partitioned to identify the structural features pertaining to specific secondary-site mutations resulting in 5 subsets as depicted in Table 1.

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Table 1. Site-Specific P53 Mutant Records.

https://doi.org/10.1371/journal.pone.0055401.t001

Once the data was pre-processed to suit the software specifications, the computational techniques were explored to generate the prediction of the p53 transcriptional activity. The preliminary requirement as mentioned by Huang et al. [20] was said to be the formulation of peptide samples with a potential mathematical relation to design an effective predictor system. The expression needed to significantly portray the intrinsic correlation of the predictor with the target to be predicted. The set of predictors was given by the following relation(1)where the subscript reflected the dimension of the vector and its value, while the components were defined by a series of features as elaborated below.

2D Structure Features

The 2D features were also known as the Surface Property Maps. The structure features for each mutant were obtained using the homology models described in [31]–[32]. The structures of mutant proteins were simulated centred on the configuration of wild type p53 by substitution with mutant amino acids following which the structure features were extracted from the energy minimized mutant model [30]. The 2D surface property maps were annotated with surface properties, such as electrostatics or h-bond donor/acceptor status provided by the electrostatic add-ons to AMBER 6 by Luo et al. [33].The molecular surface was mapped to a sphere, following which steric and depth information was recorded, and the sphere was mapped to a plane. The resulting surface map was subtracted from the wild-type map to obtain the resulting 2D features. The attributes 1–4826 of structure features (V1–V4826) were calculated based on the 2D surface map of the mutant protein [30]–[32].

3D Structure Features

3D features were also termed the Structure Distance Maps. Attributes 4827–5408 (V4827–V5408) of structure features were calculated based on the 3D distance difference map between mutant and wild-type p53 [30]–[32]. Mutation of amino acid in p53 could be responsible for alteration in the protein 3D structure. The 3D distance map was an N×N matrix giving the Cartesian distance between N residue alpha carbons. It reflected structural shifts induced by the mutation [32]. The wild-type distance map was subtracted, leaving a difference map. The p53 core domain had 197 residues, hence resulted in a 197×197 matrix that was collapsed to a distance vector that gave the magnitudes of the distance changes [32]. This resulted in a 197 length vector map portraying three features for each residue, the directional i, j, and k vectors. This summed up to 591 features per mutant of which 582 features alone were retained as significant attributes [31]–[32]. The 3D distance difference map features symbolized the magnitudes of the distance changes in the 3D structure [30]–[32].

Both the 2D structure features and 3D structure features were downloaded from the UCI Machine Learning Repository [29] and their annotations are supplied as Table S1.Thus a total of 5408 features constituted the attribute (feature) vector for each record in the p53 mutant dataset while attribute 5409 indicated the target category.

Record Space of Site-specific Mutants

Table 1 depicts the breakdown of the number of records reflecting the total number of active and inactive records in each site-specific subset. There were a total of 5 subsets, partitioned based on the primary and co-occurring mutations sites.

General Computational Approach

The proposed approach for p53 mutant transcriptional activity prediction through computational approaches is portrayed in Figure 1. The approach comprised of the Training phase followed by the Prediction Phase. The former involved preparing the mutation data for process on software tools, data partitioning, and relevance detection of each attribute in the partitioned subset, construction of the prediction models and generation of prediction rules pertaining to each site. The SPSS software [34] was used to visualize and pre-process the mutant structural details according to the mutation sites. Data are available at www.shomonagjacob-research.com containing structural properties of site-specific mutations. The computational analysis of the data was done with data recorded on Microsoft Excel’s Comma Separated Version files. The Machine Learning Tool WEKA [35] was employed to perform attribute subset evaluation of the attributes using the CFS subset attribute evaluator algorithm [35]. The classification techniques utilized to build the predictor models for the mutation sites with the generated feature sets were also implemented in WEKA. The Prediction phase involved validation of the prediction accuracy and performance evaluation of the classifier. Jack-knife cross-validation [36] was employed wherein each of the statistical samples was taken to be the test case with the remaining samples considered the training set. The average MCC, accuracy, sensitivity and specificity was recorded to rate the performance in prediction of p53 mutant transcriptional activity and identify the classification algorithm that generated the highest MCC. The description of the attribute evaluator (feature ranking) and classification techniques in the proposed predictor methods are detailed below.

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Figure 1. Novel Computational Approach to Predict Site-Specific P53 Mutant Transcriptional Activity.

https://doi.org/10.1371/journal.pone.0055401.g001

Correlation Feature Selection (CFS) Subset Attribute Evaluator Method

Feature Selection [21] [23]–[24] played a crucial role in classifier design as several reports [20]–[26] have previously affirmed with acceptable justification. The most important phase in construction of classifiers was to identify the most representative set of predictor attributes [37]–[38]. The CFS hypothesis [35] [39] suggested that the most predictive features needed to be highly correlated to the target class and least relevant to other predictor attributes.

The following equation dictated the merit of a feature subset S that consisted of ‘k’ features [40]–[41]:(2)where, was the average value of all feature-classification correlations, and was the average value of all feature-feature correlations. The CFS criterion [39]–[41] was defined as follows:(3)where and variables are referred to as correlations. The attributes that portrayed a high correlation to the target class and least relevance to each other were chosen as the best subset of attributes [42].

The attributes filtered by the CFS Subset Evaluator method were added in an incremental manner to identify the optimal set of features that contributed to prediction of p53 activity. This methodology is reported below.

Incremental Feature Selection (IFS) Method

Utilizing the predictor attributes reported by the CFS Subset Attribute Evaluator method, Incremental Feature Selection (IFS) [43]–[51] was applied to determine the minimal and optimal set of features. The predictors generated by the CFS Subset evaluator were the feature set under consideration for Incremental Feature Selection. On adding each feature, a new feature set was obtained and the n^th feature set could be stated as(4)Where M denoted the total number of predictor subsets. On constructing each feature set, the predictor model was constructed and tested through Jack-knife cross-validation method. The MCC/Accuracy of cross-validation was measured, leading to the formation of the IFS table with the number of features and their performance. FS_mo was the minimal and optimal feature set that achieved the highest MCC for each site-specific mutation.

The novel predictor methods proposed for the site-wise mutation activity prediction are detailed below.

Independent Predictor Method

The independent predictor method applied to detecting the transcriptional activity of one-site mutations based on their 2D and 3D structural features. The method involved attribute evaluation by CFS Subset evaluator method followed by Incremental Feature Selection to determine the predictive accuracy of the classifier. The Adaboost algorithm using the Decision Stump (ABDS) prediction technique was utilized to categorize the functional activity of p53 one-site mutations. The algorithm execution used 100 iterations to obtain the most reliable results. The algorithms are briefed about in the following sections.

Decision Stump Algorithm

The Decision Stump algorithm was introduced by Wayne Iba and Pat Langley in 1992 [52]. It was a machine learning model that generated a single level decision tree that comprised of a single node connected to the leaf nodes [52]–[53]. The decision stump made a prediction based on the value of just a single input feature. In the case of data that contained continuous values, a threshold feature value was selected, and the stump contained two leaves, one for values below and the other for values above the threshold. Multiple thresholds when chosen lead to generation of more leaf nodes. Decision stumps have been widely used as components (weak learners/base learners) in machine learning ensemble techniques like boosting [54]–[55].

Adaboost Ensemble Learning with Decision Stump Method (ABDS)

Adaptive Boosting (AdaBoost), a machine learning algorithm, was formulated by Yoav Freund and Robert Schapire [56]–[57]. AdaBoost, a meta-algorithm, was used in conjunction with many other learning algorithms to improve their performance. AdaBoost was adaptive such that subsequent classifiers built were modified in favour of those instances misclassified by previous classifiers. Adaboost, an ensemble method of prediction used a combination of models [58]. Each combined a series of ‘k’ learned models with the aim of creating a composite model. Initially, Adaboost assigned each training instance an equal weight that equalled 1/number of training instances [56]. Later ‘k’ classifiers were generated that required ‘k’ rounds. In each round, instances from the dataset were sampled by weight to form the training set [55]–[56]. A classifier model was derived and its error rate was computed with the training set that later served as the test set. The instance weights were adjusted according to the error-rate. Records correctly classified were weighed less while misclassified records were made to weigh more [58]. Those weights were considered to generate the training tuples for the subsequent round. The error-rate of a generated model M_k, was computed as follows [56]–[58]:(5)Where Err(X_j) was the misclassification rate of instance X_j. If it was a misclassified instance the value was 1 else it was 0. If the performance of the classifier M_k was very poor (>0.5), it was abandoned and a new training set was generated to construct a new classifier model. If an instance was correctly classified in round ‘k’, its weight was updated by

(6)The weights of all the correctly classified instances were updated likewise while the weights of the unclassified tuples were normalized to restore their sums to the initial value. Normalization was done by multiplying it by the sum of the old weights divided by the sum of the new weights.

After generation of the classifier ensemble, boosting assigned a weight to each classifier’s vote based on its performance. The weight of a classifier’s (M_k) vote [56] [58]was given by(7)

For each class, the sum of the weights of each classifier that assigned class c to an instance ‘X’ was determined. The class with the highest sum was considered as the category of the instance X.

The predictor method for imbalanced set of mutant data is discussed in the ensuing section.

Imbalanced Predictor Method

The imbalanced predictor method was applied to mutation data that contained either too few or very large number of instances. This prediction technique comprised of attribute evaluation via CFS Subset evaluator followed by Incremental Feature Selection with the Random Committee Ensemble classifier with Random Tree (RCRT) algorithm. The algorithms are discussed below.

Random Tree Classifier

Random trees were first introduced by Leo Breiman and Adele Cutler [59]. Random trees referred to a collection (ensemble) of tree predictors [60].The input feature vector was given to the classifier that classified it with every tree in the forest, and output the class label that received the majority of votes (weights) [61]. All the trees were trained with the same parameters, but on different training sets, that were generated from the original training set using the bootstrap procedure, i.e., for each training set vectors were selected randomly that equalled the number in the original set [62]. The vectors were chosen with replacement, i.e., some vectors occurred more than once and some did not occur at all. At each node of each tree trained, only a random subset of the nodes was used to identify the best split [63]–[64]. With each node a new subset was generated, whose size was fixed for all the nodes and all the trees. This referred to the training parameter denoted by √number of variables. In random trees the error was estimated internally during the training phase [63].

Random Committee with Random Tree Classifier (RCRT)

The Random Committee generated an ensemble of classifiers for any base classifier that executed the Randomnizable Interface [35] [37]. We utilized the RCRT approach that constructed an ensemble of classifiers with Random Tree as the base classifier [64]. The random committee algorithm raised a diverse ensemble of random tree classifiers [65]. The random committee algorithm generated predictions by averaging probability estimates over the generated classification trees.The final prediction was a straight average of the predictions generated by the individual base classifiers [64]–[66].The algorithm was implemented in WEKA [35] with default parameters.

The prediction techniques that generated higher MCC for prediction of balanced and acceptable number of mutant records are given below.

Balanced Predictor Method

Our investigations revealed that the number of mutation records and the class balance did play a pivotal role in deciding classifier results. Hence we attempted to compare three benchmark classification techniques to identify the algorithms that generated higher MCC and accuracy in prediction with the CFS Subset Evaluator attributes on data that contained balanced records. Our comparisons revealed that the Bayesian Network algorithm generated a higher MCC and accuracy than previously reported results on classification of site-3 and site-4 mutation data with 112 and 31 records respectively, much higher that the site-5 data subset and much smaller than the site-2 data subset. This predictor method employed the features returned by the CFS Subset evaluator method with the Bayesian Network Learning Algorithm.

Bayesian Belief Network Learning Algorithm

A Bayesian network was a probabilistic graphical model/statistical model that represented a set of random variables and their conditional dependencies via a directed acyclic graph (DAG) whose nodes represented random variables [67]–[68]. The edges represented conditional dependencies while unconnected nodes represented variables that were conditionally independent of each other. Each node was associated with a probability function that took in as input a particular set of values for the node's parent variables and gave the probability of the variable represented by the node [69]–[70]. In this research, we utilized the Bayesian network to model the relationship between structural properties of mutants and their functional activity. Given the structural details, the network was used to compute the probabilities of the possible functional activity (active/inactive).

The learning task consisted of finding an appropriate Bayesian network given a data set D over U where U = {u₁, u_n}, n ≥1 was the set of input variables [67] [69]. The classification task consisted of classifying a variable y = x₀ called the class variable (active/inactive) given a set of variables U = u₁... u_n. A classifier C: u → y was a function that mapped an instance of u to a value of y. The classifier was learned from a dataset D that consisted of samples over (u, y) [68]. A Bayesian network over a set of variables U was a network structure B_s, a directed acyclic graph (DAG) over the set of variables U and a set of probability tables given by(8)

Where pa(u) was the set of parents of u in B_S and the network represented a probability distribution given by(9)

The inference made from the Bayesian Network was to allocate the category with the maximum probability [70]–[71]. The Simple Estimator with the K2 local search method using Bayes Score were utilized (default parameters) for the execution of the algorithm in WEKA [35].

The performance evaluation methods and parameters are briefed about in the subsequent section.

Jack-knife Cross-Validation Method

Statistical prediction methods generally involved verification of the predictor performance to estimate their effectiveness in practical applications [72]–[73]. Cross-validation (rotation estimation), was a technique that assessed how the results of a statistical analysis could generalize to an independent data set. It was a way to predict the fit of a model to a hypothetical validation set when an explicit validation set was not available [72]–[73]. In k-fold cross-validation, the original sample was randomly partitioned into k equal size subsamples. Of the k subsamples, a single subsample was retained as the validation data for testing the model, and the remaining (k –1) subsamples were used as training data [73]. The cross-validation process was then repeated k times with each of the k subsamples used exactly once as the validation data. The k results from the folds were later averaged to produce a single estimation. In this study, the jack-knife cross validation method was used for validation since previous reports have stated it to be least arbitrary in nature and widely recognized by researchers to assess the performance of predictors [20] [72]–[73]. In jack-knife cross-validation, each one of the statistical samples in the training dataset was in turn singled out as a tested sample and the predictor was trained by the remaining samples. During the jack-knifing process, both the training dataset and testing dataset were actually open, and a statistical sample moved from one set to the other [20]. However since the second site mutations held voluminous records, in order to reduce the memory effects and computational complexity we used the three-fold cross-validation technique to rate and compare the performance of the prediction techniques. Moreover the analysis of the second site p53 mutation dataset exposed heavy imbalance of the active and inactive records. In view of this, the following indexes were adopted to test our proposed predictors.(10)(11)(12)(13)where reflected the Mathews Correlation Coefficient; reflected the accuracy, i.e., the rate of correctly predicted mutation activity; reflected the sensitivity, i.e., the rate of inactive records correctly predicted; reflected the specificity, i.e., the rate of active records that were correctly predicted.

TP, TN, FP and FN denoted the number of true positives, true negatives, false positives and false negatives, respectively [20] [22]. However the MCC parameter was believed to estimate more precisely the performance of a predictor model on heavily imbalanced data and hence was given precedence.

CFS Subset Attribute Evaluation Results

The feature set size filtered by the attribute evaluator techniques are tabulated in Table 2. The CFS Subset Evaluator, Information Gain, Gain Ratio and Symmetric Uncertainty Attribute Evaluators were compared in this work. It is evident from the results that the minimal feature set was generated by the CFS Subset Evaluator. Hence focus was placed on exploiting this technique to build predictor models with the minimal set of predictive features. Moreover the rank and score of the predictors generated by the other predictor models were more often negligible and hence their contribution to the prediction was questionable. Combining the CFS Subset Evaluator with the feature ranking methods was found to be very time-consuming and computationally expensive since the data spanned large number of attributes. On smaller datasets, the results showed only marginal variation. In the case of the site –two mutation data, the CFS subset Evaluator was applied on subsets of the mutation records with the 2D and 3D features being considered separately for analysis in order to speed up the execution process. Since the evaluator method filtered attributes with respect to its contribution to the target class and relevance to the other attributes, the cumulative results of the subset data were taken as the minimal feature set for the site-two predictor model. The CFS Subset Attribute Evaluator results for site-specific mutant data are provided as Table S2.

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Table 2. Performance of Attribute Evaluator Algorithms on Site-Wise P53 Mutants Transcriptional Activity.

https://doi.org/10.1371/journal.pone.0055401.t002

Incremental Feature Selection Results

The predictor attributes were used to build individual predictors by inserting features in an incremental manner beginning at the first filtered attribute and proceeding till the attribute that generated the highest MCC was obtained. We tested each of the individual predictors and obtained the IFS results for all the filtered predictors. The Incremental Feature Selection for the site-specific mutation data was given as Table S3. The IFS Curves for the site-specific mutation data are portrayed in Figure 2A, 2B, 2C, and 2D respectively. The MCC of the site-two mutation data was compared using 3-fold cross –validation method. The optimal performance of the proposed predictor models is tabulated in Table 3.

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Figure 2. The IFS Curves for one-site, two-site, three-site, and four-site p53 mutants.

In the IFS curve, the x-axis is the number of features used for classification, and the y-axis is the Mathew’s correlation coefficients (MCC). (A) The IFS curve for one-site p53 mutants. The peak of MCC is 0.775 with 7 features. The top 7 features derived by the CFS Subset Evaluator approach form the optimal feature set for one-site p53 mutants. (B) The IFS curve for two-site p53 mutants. The peak of MCC is 0.341 with 52 features. The top 52 features derived by the CFS Subset Evaluator approach form the optimal feature set for two-site p53 mutants. (C) The IFS curve for three-site p53 mutants. The peak of MCC is 0.784 with 30 features. The top 30 features derived from the CFS Subset Evaluator approach form the optimal feature set for three-site p53 mutants. (D) The IFS curve for four-site p53 mutants. The peak of MCC is 0.916 with 15 features. The top 15 features derived from the CFS Subset Evaluator approach form the optimal feature set for four-site p53 mutants.

https://doi.org/10.1371/journal.pone.0055401.g002

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Table 3. Optimal Performance of Novel Predictor Methods on Site-Wise P53 Mutants Transcriptional Activity.

https://doi.org/10.1371/journal.pone.0055401.t003

Performance Comparison of Proposed Predictors with Other Methods

We investigated the performance of Bayesian and Ensemble learning methods and found that a single technique did not generate optimal results on all site-specific mutation data with the CFS Subset attribute evaluator methods. So we attempted to identify the specific combination of attribute evaluator and prediction algorithm that generated optimal results with minimal features. The improved performance of our work was validated by the results of the previous work on predicting site-specific p53 mutant activity by Huang et al. [20]. The comparative performance of the classification algorithms on site-specific p53 mutation data is given as Table 4, Table 5, Table 6, Table 7 and Table 8 for one-, two-, three-, and four- and five-site mutation data. The Information Gain, Gain Ratio and Symmetric Uncertainty Attribute Evaluators used the Ranking method to generate the attribute evaluation results. With respect to the Independent and Balanced Predictor models, we considered all the ranked values for comparison. However for the two-site mutation data, since the features spanned large dimensions we set the information gain score to 0.02, gain ratio and symmetric uncertainty score to 0.05 to select the ranked attributes and the comparison among the methods was recorded accordingly. All the performance parameters were obtained by Jack-knife cross-validation approach for one-site, three-site, four-site and five-site mutants. However in order to reduce the memory effects and computational complexity, we used the three-fold cross-validation approach to compare the performance of two-site predictor models.

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Table 4. Performance Comparison of Site-1 P53 Mutants Transcriptional Activity.

https://doi.org/10.1371/journal.pone.0055401.t004

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Table 5. Performance Comparison of Site-2 P53 Mutants Transcriptional Activity.

https://doi.org/10.1371/journal.pone.0055401.t005

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Table 6. Performance Comparison of Site-3 P53 Mutants Transcriptional Activity.

https://doi.org/10.1371/journal.pone.0055401.t006

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Table 7. Performance Comparison of Site-4 P53 Mutants Transcriptional Activity.

https://doi.org/10.1371/journal.pone.0055401.t007

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Table 8. Performance Comparison of Site-5 P53 Mutants Transcriptional Activity.

https://doi.org/10.1371/journal.pone.0055401.t008

The Independent Predictor (IP) model utilized the CFS Subset Evaluator followed by the ABDS algorithm to obtain the optimal MCC. The algorithm was executed with default parameters with the number of iterations set to 100 to avoid over fitting of the data and obtain reliable results. Though the other attribute evaluator methods also showed promising results, the size of the feature set was taken into consideration to choose the most optimal approach. However the proposed approach equalled or bettered the other compared methods as depicted in Table S3 using Incremental Feature Selection. The Imbalanced Mutation Predictor (IMP) model utilized the CFS Subset Evaluator with RCRT algorithm to obtain the optimal MCC. The algorithm was run with default parameters and evaluated by 3-fold cross validation for two-site mutation data on account of large number of instances and increased computational complexity. The execution time of Jack-knife cross validation on site-two mutation data with default parameters was 24 hours and 17 minutes to validate the RCRT approach and reported an MCC of 0.293 with 52 features. The same predictor model was applied to five-site mutation data and generated an MCC of 0.655 with the smallest feature set comprising of 1 feature. However attributes generated by the Information Gain, Gain Ratio and Symmetric Uncertainty Attribute Evaluators generated a high MCC of 1 using ABDS algorithm with default parameters on the five-site mutation data. Since the results appeared to over fit the data on account of very small number of instances, the imbalanced predictor model was believed to be a more reliable technique. The Balanced Predictor (BP) model utilized the CFS Subset Evaluator with Bayesian Network Learning Algorithm and obtained an optimal MCC of 0.784 with 30 features on the three-site mutation data and an optimal MCC of 0.916 with 15 features on the site-four mutation data. The results were validated by Jack-knife cross-validation method. The feature set analysis of site-specific mutants is discussed below.

Site –Wise Feature Set Analysis

On analysis of the feature sets that generated optimal results, it was concluded that the 2D features played a dominating role when compared to the 3D features and hence an in-depth analysis of 2D structural properties could provide novel insights into p53 functional mechanism. Site-1, Site-3 and Site-5 mutation data attained the optimal MCC with the inclusion of 2D features alone. However site-2 and site-4 mutation data attained the highest MCC only on inclusion of the 3D predictor features. We also attempted to explore the 2D and 3D features that were found relevant for the different site-wise subsets representing p53 transcriptional activity using structural properties and identify if there existed any common relevant features that deserved further attention. Hence we made use of visualization tool NodeXL available at [http://nodexl.codeplex.com/releases/view/96383] that generated the site-wise feature-activity relevance graph depicted in Figure 3 to represent the relevant features reported for each site. The visualization of the p53 site-wise feature relevance graph is shown in Figure 3. We used the top 10 features for the site-5 mutation data. The graph clearly depicts that not a single feature was commonly relevant to any of the sites. The features were mutually exclusive and hence we believed it was acceptable that any further investigations of the p53 functional activity would certainly warrant a site-wise analysis of structure and function.

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Figure 3. Site-wise Feature Relevance Graph.

The sites are represented in purple as solid diamonds. The optimal features for each site are represented by directed edges from the site to the feature. Site-specific features are displayed in different colours.

https://doi.org/10.1371/journal.pone.0055401.g003

Comparison to Previous Work

The most recent and previously reported results of predicting p53 mutants transcriptional activity was stated by Huang et al., in 2011. The comparative performance to the previous work is depicted in Table 9. One-site mutation was optimally predicted at 0.678 MCC with 8 features whereas the proposed predictor model predicts at 0.775 MCC with 7 features while two-site mutation data was predicted at an optimal 0.314 MCC with 50 features while our approach attained an optimal prediction of 0.341 with 52 features. However they have excluded the memory effects of running Jack-knife cross validation on the 16376 records. Our results were drawn with 3-fold cross-validation that is reported to be a benchmark validation technique for large datasets [50]–[51],[73]. For the three-site mutation data, the proposed approach generated an optimal MCC of 0.784 with 30 features while the previous optimal MCC of 0.705 included 282 features. The four-site mutation data was predicted at 0.916 by our proposed approach with 15 features while the previous approach reported an optimal MCC of 0.907 with 25 features. Our findings agree with the previous results stating 2D features to be the major contributory factors to p53 mutant transcriptional activity prediction. The MCC and accuracy parameters of the predictor methods were found to be highly irrelevant in estimation of predictor performance of unbalanced datasets. Since this research was oriented towards both balanced and unbalanced datasets, MCC was utilized as the primary criterion for ranking the predictor models.

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Table 9. Comparison to Previous Work on P53 Mutants Transcriptional Activity Prediction.

https://doi.org/10.1371/journal.pone.0055401.t009

CFS Subset Vs mRMR Method

Previous work on prediction of p53 transcriptional activity made use of the Maximum Relevance and Minimum Redundancy (mRMR) approach in order to select the features most relevant to the target class and least redundant to one another [20]. The mRMR method ranked features based on the Mutual Information criterion [77]–[79]. In this study however we chose to investigate other possible feature selection algorithms for three main reasons: (i) Performance of the mRMR method has already been discussed in p53 transcriptional activity prediction [20]whereas this is the first study on utilization of CFS Subset evaluator and the other ranking methods (Information Gain, Gain Ratio and Symmetric Uncertainty) in p53 activity prediction (ii) Human intervention is required in deciding the feature subset size for the mRMR method [21]whereas in the CFS Subset method, the default parameters of Best First Search with a search termination threshold of five, generated the appropriate and relevant feature subset [35] (iii) It is evident from the work on p53 transcriptional activity prediction by Huang et al. [20] where roughly 100 to 1000 ranked features from the mRMR method were included for the Incremental Feature Selection process to obtain optimal results whereas in this investigation the feature subset size returned by the CFS Subset Evaluator on the same datasets was of considerably smaller dimension thus entailing less human effort and time while generating improved results. Moreover we believed the CFS Subset Evaluator would certainly prove to be an effective algorithm in other biological data prediction also and hence propose a reasonably acceptable alternative to the mRMR method. Further extensions to this work would involve investigating the use of this novel methodology in DNA and protein sequence analysis.

Influence of Structure on P53 Function

This research has clearly revealed the contribution of the structural features in predicting p53 transcriptional activity. Previous authors [20] [25]–[26] [30],[44] have stated that structural features played a dominant role in p53 status prediction. However this study has clearly portrayed through the use of computational techniques that 2D properties played the most contributing role in P3 transcriptional activity prediction. A characteristic feature of the p53 mutational map is the frequency of missense point mutations [74]–[75]. Structural studies have revealed a higher concentration of amino acid residues pertaining to the mutation hot spots of p53 within the central region (residues 102–292), encoding the central DNA binding domain of the protein, and a trivial number of p53 mutations in the regulatory domains (N terminus, residues 1–99; C terminus, residues 301–393) [74]–[76]. This drives research focus towards concluding that intense analysis of p53 structure could reveal yet unknown facts on p53 activity thus leading to novel therapeutic solutions.

Rewards of Computational Strategies

Previous work on p53 Mutants and related studies have brought to light the hurdles encountered in in-vitro experimentation with mutation data in view of the resources, labour and time involved, but with irresolute rewards [2] [6] [13] [16] [20] [30]–[32]. On the contrary, computational strategies and algorithms expend comparatively less time, resources and labour with a clear idea of expected end results [19]–[21] [25]–[28]. The broad goal of this work was to provide an influential assessment of the functional activity of p53 cancer mutants and their secondary-site suppressor mutations through the use of computational techniques. A functional census of suppressor mutations for p53 cancer mutants was believed to appreciably further existing knowledge of p53 rescue mechanisms [32] [74],[76]. Knowledge of possible regions of the p53 core domain that generated stability when altered provided insights in detecting probable alteration sites for small molecules. The methodology could be generalized to other mutational systems where mutants needed to be classified as functional/non-functional. Moreover computational classifiers that predicted mutant function would allow experimentalists to map structure/function relationships for proteins in other mutation-related diseases.

With the advances in technology and their applications in the field of biology and medicine influencing the focus of research in remarkable ways, we believed research and analysis of the effects of computational methods on biological data analysis was certainly an essential breakthrough. However a limiting factor in computational analysis was the measure of time spent on preparing biological data for process on software tools. Efficient data pre-processing techniques specific to biological data could spur great opportunities for further investigation in the field of Bioinformatics and Computer Science.

Conclusion

Intense research on p53, its structure, function and therapeutic strengths has drawn the attention of researchers from varied domains that include medical science, technology and informatics. This research was focused on revealing the significance of computational techniques in predicting the most optimal set of structural features that contributed predominantly to designating the nature of p53 transcriptional activity. We compared the performance of four feature evaluator and three classification techniques to determine the optimal set of features that predicted p53 activity with higher MCC. Our findings revealed the optimal MCC in prediction of p53 transcriptional activity with the most predictive feature set for each site-specific mutation subsets. Moreover visualization of the site-specific relevant features indicated that the contributing features were mutually exclusive for each site and appeared only on a section of the mutation sites. This could be attributed to the fact that unselected mutations contributed nothing to the p53 activity while selected features played the crucial role in regulation of p53 activity. We also warrant the fact that the 2D structural properties deserved more attention and further analysis of their influence on p53 mutations could reveal latent facts on the underlying mechanism of p53 and provide novel and informative insights into p53 transcriptional activity and their restoration.