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Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach
BMC Genomics volume 19, Article number: 672 (2018)
Abstract
Background
Antimicrobial peptides are a promising alternative for combating pathogens resistant to conventional antibiotics. Computerassisted peptide discovery strategies are necessary to automatically assess a significant amount of data by generating models that efficiently classify what an antimicrobial peptide is, before its evaluation in the wet lab. Model’s performance depends on the selection of molecular descriptors for which an efficient and effective approach has recently been proposed. Unfortunately, how to adapt this method to the selection of molecular descriptors for the classification of antimicrobial peptides and the performance it can achieve, have only preliminary been explored.
Results
We propose an adaptation of this successful feature selection approach for the weighting of molecular descriptors and assess its performance. The evaluation is conducted on six highquality benchmark datasets that have previously been used for the empirical evaluation of stateofart antimicrobial prediction tools in an unbiased manner. The results indicate that our approach substantially reduces the number of required molecular descriptors, improving, at the same time, the performance of classification with respect to using all molecular descriptors. Our models also outperform stateofart prediction tools for the classification of antimicrobial and antibacterial peptides.
Conclusions
The proposed methodology is an efficient approach for the development of models to classify antimicrobial peptides. Particularly in the generation of models for discrimination against a specific antimicrobial activity, such as antibacterial. One of our future directions is aimed at using the obtained classifier to search for antimicrobial peptides in various transcriptomes.
Background
Antimicrobial peptides (AMPs) are components of the host defense mechanism against bacteria and fungi, including multidrug resistant pathogens such as Methicillinresistant Staphylococcus aureus and vancomycinresistant Enterococci [1]. AMPs also exhibit other biological properties like antitumor, antiviral, and antiparasitic activities. With the rapid increase in number of antibioticresistant bacteria, AMPs have received much attention as a template for the development of new drugs for the treatment of infectious diseases.
From the computational point of view, Virtual Screening (VS) [2–4] is usually applied at early stages of the drug discovery process. It contributes to the identification of putative AMPs from large peptide libraries [3, 5]. In this context, Quantitative StructureActivity Relationship (QSAR) is of great importance for models’ generation to classify active (AMPs) and inactive (nonAMPs) peptides [6]. QSAR modeling defines mathematical relationship between the peptides’ physicochemical properties (molecular descriptors) to their biological activity [6] to classify the activity of new peptides. Machine learning approaches are tools for the generation of models that describe this relationship from a set of peptides with known activities. Admittedly, the model’s performance depends on the selection of molecular descriptors since they define the chemical space in which each peptide is projected. The selection of appropriate molecular descriptors to discriminate between AMPs and nonAMPs is a hard goal to achieve due to the large number of molecular descriptors that can be calculated in peptides and to their complex interrelationships. Furthermore, new features can be added to this large set of molecular descriptors through feature construction methods [7]. Recently, an evolutionary approach [8] was proposed for AMP recognition which combines sequencebase features such as motif and positional sequence into more complex features leading to promising results. These results motivate the inclusion of these new features to the existing set to participate in the feature selection process afterwards.
In earlier studies, the selection of molecular descriptors has often been made based on chemical intuition or observed properties that give rise to the antimicrobial activity [3, 9]. In contrast, recent works employ handpicked features (molecular descriptors) procedures or filtering methods that independently evaluate the features according to a given criterion to select the top k of them [8–11]. However, these approaches present some disadvantages considering that the biological activity of peptides depends on complex interrelationships of many molecular descriptors. Therefore, we need a more rigorous feature selection procedure to improve the performance of AMPs classification [12].
Feature selection methods can be categorized into three major classes based on the features’ assessment: filter, wrapper, and hybrid. First, in the filter methods, the quality of features is evaluated from the data, ignoring the effect of the selected features on the classifier algorithm performance [13]. Examples of evaluation functions used on filter methods are distance, information, and dependence measure [14]. Second, the wrapper methods incorporate the classifier’s performance (e.g., error rate, accuracy) to evaluate the quality of the selected features [13]. Finally, hybrid methods combine both, the filter and wrapper methods [15]. Wrappers usually outperform filter methods, mainly because the selection of optimal features is biased towards the effect of these features on the classifier’s performance. Additionally, wrapper methods have a high computational cost because they require to induce and test a classifier for each evaluated features’ subset. In contrast, since filter methods are independent of classification algorithms, they may be computed efficiently [13]. Furthermore, filter methods can improve their performances by using evaluation measures for a specific classification algorithm [13]. For example, the intraclass distance could be appropriate for the instancebased learning algorithms, whereas the information gain for the decision trees classifiers.
An efficient and effective filter approach for the selection of features, based on their weighting has been recently proposed [16]. In this approach, the weights are assigned in such a manner that objects in different classes tend to be far away from each other, whereas objects within the same class tend to be close together. Unfortunately, there is a tradeoff between these distances and, that is why the feature weighting challenge is modeled as a multiobjective optimization problem. In a recent work [17] we applied this formulation to the antimicrobial peptides classification problem and improved it by taking into account that molecules with similar structure tend to possess similar biological activity [18]. The central idea is that it makes no sense to minimize the distance among nonAMPs as it should be done [16], since they may have different biological activities. The proofofconcept of our formulation in [17] showed a good performance capability for the binary classification of AMPs. The present work builds upon our improved formulation [17] and extends its results. Besides dealing with a significantly larger dataset, the statistical significance of the observed difference is assessed. We now also show the ability of our proposal to classify a subset of AMPs that explicitly targeted bacteria.
Problem statement
The general problem to solve is referred to as feature weighting problem [19], and it is known to be NPHard [20]. For our purposes, we model this problem as a multiobjective optimization problem (MOP) to find a set of weight vectors that simultaneously minimize the distance between AMPs and maximize the distances between AMPs and nonAMPs. To define the MOP, we follow a similar approach to the one presented in [16], where the main differences are as follows: first, the general problem of weighting feature in [16] simultaneously minimizes the intraclass distance for all classes. Instead, our approach [17] minimizes only the intraclass distance of AMPs, since the nonAMPs set might contain peptides with different biological activities, thus trying to reduce the intraclass distance for nonAMP would be contradictory with the similarity property principle [18]. Furthermore, in our approach, the number of nonzero weights are used as a tiebreaker criterion for the weight vectors with the same intra or interclass distances.
Notation and definitions
Before presenting the formal definition of the problem, some notation, and definitions are introduced.

\(\mathcal {X}\) is a feature set {X_{1},…,X_{m}}. In this paper, we use, without distinction, the term molecular descriptor and feature.

\(\mathcal {Y}\) is the set of class labels {C_{1},…,C_{c}}, with c the number of classes. For instance, \(\mathcal {Y}=\{C_{1}, C_{2}\}\), with C_{1}= “AMP” and C_{2}= “NonAMP”.

\(\mathcal {D}\) is the training dataset composed of n peptides with a known biological activity {(x_{1},y_{1}),…,(x_{n},y_{n})}, where x_{i} is an mdimensional vector [x_{i1},…,x_{im}]^{T} that captures the physicochemical properties into real values, each component x_{ij} encodes the value for the jth molecular descriptor (i.e., feature) of the ith peptide sequence. \(y_{i} \in \mathcal {Y}\) denotes whether x_{i} has the antimicrobial activity or not. \(\mathcal {D}\) can be expressed as a matrix with n x (m+1) elements whose rows are given by \(\mathbf {x}_{i}^{T}\) and y_{i}.
$$ \mathcal{D} = \left[\begin{array}{ccccc} x_{11} & x_{12} & \cdots & x_{1m} & y_{1}\\ x_{21} & x_{22} & \cdots & x_{2m} &y_{2}\\ \vdots & & \ddots & \vdots & \vdots\\ x_{n1} & x_{n2} & \cdots & x_{nm} & y_{n}\\ \end{array}\right] $$(1)This data matrix \(\mathcal {D}\) is also known as a descriptor matrix [21].

w=[w_{1},…,w_{m}]^{T} is a weight vector that specifies the rescaling value of each feature, the corresponding weight for the ith feature is given by [16]:
$$ w_{i} =\left\{ \begin{array}{ll} [\!1,\mathcal{A} ] & \text{if the feature \(X_{i}\) is selected};\\ 0 & \text{if the feature \(X_{i}\) is rejected}. \end{array} \right. $$(2)where \(\mathcal {A}\) is the maximum weight for w_{i} and it takes any positive real number. As in [16], \(\mathcal {A}=10\) in this work.

The weighted distance (also known as weighted Manhattan distance) between two data points x_{p} and x_{q} is defined as:
$$ d\left(\mathbf{w}, \mathbf{x}_{p}, \mathbf{x}_{q} \right)=\sum\limits_{i=1}^{m}w_{i}x_{pi}x_{qi} $$(3)where . represents the L_{1} norm. Let y=AMP the class label of interest, then the intraclass distance for the class of interest is defined as follows:
$$ D_{intra} \left(\mathbf{w},\mathcal{D} \right) = \sum\limits_{p=1}^{n1} \sum\limits_{q=p+1 \atop y_{p},y_{q}=AMP}^{n} d(\mathbf{w}, \mathbf{x}_{p}, \mathbf{x}_{q}) $$(4)Additionally, the interclass distance is defined as:
$$ D_{inter}(\mathbf{w},\mathcal{D}) = \sum\limits_{p=1}^{n1} \sum\limits_{q=p+1\atop y_{p} \neq y_{q}}^{n} d(\mathbf{w}, \mathbf{x}_{p}, \mathbf{x}_{q}) $$(5)
A multiobjective approach to the feature weighting problem
Let \(\mathcal {D}\) be a training dataset with n instances and m candidate input features, we assume that for each instance \(\mathbf {x}_{i}^{T} \in \mathcal {D}\), the value x_{ij} is in the interval \([\!1, \mathcal {A}]\), where x_{ij} is the jth component of the vector \(\mathbf {x}_{i}^{T} \). Then, the multiobjective feature weighting problem can be stated as:
where,
here, the term [ min{1,w}]^{T}1 is the number of weights that are different from zero (i.e., w_{i}>0 for i=1,…,m.). This term promotes a weight vector with a smaller number of features than any other weight vector with the same intraclass or interclass distances.
Results
To evaluate the effectiveness of our approach, called MultiObjective Approach for Feature Weighting (MOEAFW), we conducted experiments on six highquality benchmark datasets that have recently been used for empirical evaluation of stateofart antimicrobial prediction tools in an unbiased manner [12]. These datasets were selected because they are composed of manually curated and experimentally validated AMPs; in these datasets, the nonAMPs have the same peptide distribution as that observed in AMPs (see “Methods” section). This experimental study was divided into four parts. In the first part, we aimed at selecting the appropriate molecular descriptors for each dataset through their scaling. Whereas, in the second part, different classification models are induced by four machine learning algorithms (MLAs) with the transformed datasets. In the third part, the best classification models generated were used to predict the antimicrobial activity for new peptides sequences, i.e., peptide sequences that have not been used either for obtaining the weight vectors or for the crossvalidation test to choose the best classifiers. Finally, we compared our result with those presented in a recent work [12] that evaluates different AMP predictors.
Performance measure
To compare the best compromise solutions found by our MOEAFW algorithm, for each dataset, a performance estimation method was employed to evaluate the efficiency of the model to classify antimicrobial peptides. The performance estimation method employed 10fold CrossValidation (10fold CV) as a resampling method and a diverse set of evaluation metrics. In the 10fold CV, the dataset is partitioned into 10 nonempty disjoint subsets (i.e., fold); each subset has roughly equal size. Nine folds are employed for the machine learning algorithm to induce a classifier, and the classifier is tested on the remaining subset, this procedure is repeated ten times. Additionally, the performance of the classifier was estimated by using the average values from the tests. To test the classification performance, the following metrics were used: accuracy, Matthews correlation coefficient, precision, specificity, sensitivity, balance accuracy, and the area under a ROC Curve (AUC). We consider the instances of the class AMP as positive and the instances of the class nonAMP as negative; then the metrics can be formally defined as follows:

Accuracy (Acc) [22]:
$$ Acc = \frac{TP + TN }{TP + TN + FP + FN } $$(7) 
Matthews correlation coefficient (MCC) [22]:
$$ {}MCC = \frac{TP \times TN  FN \times FP}{\sqrt[]{(TP+FN)(TN+FP)(TP+FP)(TN+FN)}} $$(8) 
Precision (Prec):
$$ Prec = \frac{TP}{TP+FP} $$(9) 
Sensitivity (Sens):
$$ Sens = \frac{TP}{TP+FN} $$(10) 
Balance Accuracy (Bal Acc) [12]:
$$ Bal Acc = \frac{1}{2} \left(\frac{TP}{TP+FN} \right) + \frac{1}{2} \left(\frac{TN}{TN+FP} \right) $$(11)
where TP, TN, FP, and FN are the number of true positive, true negative, false positive, and false negative, respectively. Given that the considered datasets are imbalanced classes (i.e., the AMPs and nonAMPs are not represented equally into the datasets), we used the balance accuracy and the AUC to obtain a better measure of the inducedmodels’ performance.
Weighting of molecular descriptors
Figure 1 displays the consolidated nondominated front obtained by our approach (MOEAFW) for each dataset. The consolidated nondominated front is generated after 30 independent runs of MOEAFW. The diamond and square marker (i.e., λ_{1}=0.55 and λ_{1}=0.6) represent the values for the best compromise solutions that encourage the objective f_{1} (i.e., minimize the distance between peptides with antimicrobial activity). Alternatively, λ_{1}=0.45 and λ_{1}=0.4 represent the values for the best compromise solutions that encourage the objective f_{2} (i.e., maximize the distance between AMPs and nonAMPs). Furthermore, λ_{1}=0.5 represents the value for the best compromise solution where both objectives are equally important.
The percentage of number of molecular descriptors reduction shown in Fig. 2 indicates a similar behavior on the six datasets for each best compromise solution. In particular, the best compromise solution λ_{1}=0.5 has, on average, a reduction in the number of molecular descriptors of 52.7%, i.e., on average, the bestweighted solution has 128 features out of 272. Nevertheless, the DAMP_BACTERIOCIN dataset shows an increment of this measure for solution λ_{1}=0.45. These findings indicate that solutions supporting the objective f_{1} (i.e., interclass distance) have, on average, fewer molecular descriptors than those that support the objective f_{2} (i.e., intraclass distance).
Model selection
Next, for each best compromise solution earlier obtained (i.e., a weight vector w), the original datasets were transformed (i.e., weighted; see “Methods” section). Then, for each transformed data, four classification models were constructed by the following machine learning algorithms: random forest (RF), knearest neighbor (KNN), multilayer perceptron (MLP), and a linear support vector machine (SVML).
As mentioned earlier, the balance accuracy (BalAcc) was considered as a measure to determine the best model on the six datasets weighted by the best compromise solutions. We applied the nonparametric Friedman’s test [23] and Nemenyi post hoc test [24] to verify whether there are significant differences among the classifiers’ performance. The Friedman [23] and Nemenyi tests have been widely used in the literature for statistical comparison of classifiers on multiple datasets (the interested reader is referred to [24] for more information about how to perform both tests).
Our results indicated that the best compromise solution, with λ_{1}=0.5, allows to induce on average, better classification models regardless the machine learning algorithm, the BalAcc was 87.52% (see Additional file 1).
The statistical analysis of the MLAs’ performance identified (by the Friedman test) a significant difference in the BalAcc (\(\chi ^{2}_{f} (3) = 55.2\), pvalue = 6.224e12) of the four MLAs on multiple datasets. Our results show that, on average, SVML ranked first (with rank 1.23), KNN second (with rank 2.43), RF third (2.63), and MLP fourth (3.7) (see Additional file 1: Table S1). Furthermore, we found that SVML performed significantly better than MLP (Nemenyi: z=7.4, pvalue = 8.40e13), RF (Nemenyi: z=4.2, pvalue = 0.00016), and KNN (Nemenyi: z= 3.6, pvalue = 0.00181). Similarly, KNN performed significantly better than MLP (z = 3.8, pvalue = 0.00083). Although the KNN performs a little better than RF, there was no statistical significant difference (pvalue = 0.932) between them.
In particular, considering only the best compromise solution with λ_{1}=0.5, the average of BalAcc for SVML was 92.65% and for KNN 90.13% (Summary statistics on BalAcc(%) for all best compromise solutions can be found in the Additional file 1). Hence, our findings indicate that for the six datasets, the best compromise solution with λ_{1}=0.5 using SVML and KNN induced better classification models.
Table 1 summarizes the result obtained by SVML and KNN with the best compromise solution at λ_{1}=0.5 (detailed results are presented in Additional files 2, 3, 4, 5, 6 and 7). The metric’s values represent the average for the 10fold crossvalidation. In this table, a Wilcoxon test is also performed on the observed differences between KNN and SVML for Sens(%), Spec(%), Prec(%), BalAcc, Acc(%), MCC, and AUC values; if the difference is statistically significant, at a confidence level of 95%, then an asterisk is added to the winner value (in bold). In most cases, the classification models generated by the KNN showed better specificity and precision than the ones generated by the SVML, i.e., models correctly predict 96% of nonAMPs, and correctly classify 80% of predicted AMPs. In comparison, the classification model obtained by SVML showed good sensitivity, namely, the model correctly classifies 88.33% of AMPs.
To determine the effect of MOEAFW on the efficiency of the model to classify AMPs for each dataset, we compared the performance of two classifiers generated by the same machine learning algorithm, one applying the MOEAFW and the other one, by using all candidate input features (i.e., baseline). We selected the best machine learning algorithm per database, this is according to the balanced accuracy column in Table 1. We run the Wilcoxon’s test on the BalAcc resulting from the 10fold crossvalidation of our proposed method and the baseline for each dataset (Additional files 2, 3, 4, 5, 6 and 7). The models generated by MOEAFW shows a significant improvement over the baseline models on the BalAcc. For each dataset, the significant difference in BalAcc between MOEAFW and baseline were as follows: DAMPD_AMP (pvalue = 0.00976), APD3_AMP (pvalue = 0.00195), DAMPD_ANTIBACTERIAL (pvalue = 0.00195), APD3_ANTIBACTERIAL (pvalue = 0.00976), DAMPD_BACTERIOCIN (pvalue = 0.051), and APD3_ BACTERIOCIN (pvalue = 0.08398). Similar results were observed for the other metrics, they are summarized in Fig. 3. In this figure, an asterisk indicates that the observed different is statistically significant.
On the other hand, if we take into consideration other metrics (i.e., Sens, Spec, AUC, MCC, Pres, Acc) to compare both models, the results show that the models generated by using MOEAFW achieve a comparable or superior performance than those obtained by using all candidate input features. In particular, for the datasets DAMPD_AMP, APD3_AMP, and APD3_ANTIBACTERIAL, the MOEAFW shows an improvement over the baseline (see Fig. 3). In contrast, datasets DAMPD_BACTERIOCIN and APD3_BACTERIOCIN showed a decrease in the precision measure with respect to the baseline. This result suggests that our proposal cannot find a suitable chemical space for BACTERIOCIN datasets, whereby an efficient model could be induced to discriminate what a bacteriocin is. Conjectures of why this is happening are given in the “Discussion” section.
Model assessment
After selecting the best models obtained with the best compromise solution given λ_{1}=0.5, and using KNN and SVML, we measured their prediction capacity over new peptide sequences, this is, peptide sequences that have not been used either for obtaining the weight vectors or for crossvalidation tests to choose the best classifiers (see “Methods” section). We observed that all classifiers induced by SVML have an AUC value >0.83, this means that the models generated by SVML have an excellent capacity to learn what an antimicrobial peptide is. Whereas, the model generated by KNN maintain an excellent specificity (as the results presented in Table 1 indicate).
On the other hand, comparing the results for DAMP_BACTIBASE set, espacially for bacteriocin, in Tables 1 and 2, the considerable difference in sensitivity (Sens(%)) may be because of the small number of bacteriocins in the test set.
Comparison with existing AMP classifiers
The best model generated by our approach MOEAFW was compared with others AMP predictors that used the same datasets. It is important to note that the number of instances between our test and the test showed in [12] are different, because in [12] the evaluation of AMP’s predictors was performed by using the full examples of the six datasets, whereas in our method, we used only 20% of them (i.e., the other 80% of the dataset was used in the optimization process, see “Methods” section). However, this comparison is intended to observe the predictive capacity of the classification models generated with our approach and those presented by the stateoftheart methods.
The classifier performances presented in this work and those reported by stateofart methods for the AMP prediction are summarized in Tables 3 and 4. Our results reflect that the models produced by our approach have a better performance than the stateoftheart methods for the classification of antimicrobial and antibacterial peptides. It is worth noting that models derived from our approach to classify antibacterial peptides outperformed AntiBP [25] and AntiBP2 [26] (see Tables 3 and 4). However, our method is improved by BAGEL3 [27] for the BACTERIOCIN datasets.
Discussion
Our approach aims to identify a weight for each molecular descriptor, in such manner that, peptides with antimicrobial activity tend to be close together, whereas peptides with different biological activities tend to be far away from each other. Our results indicate that the best compromise solution with λ_{1}=0.5 allows, on average, the best balance accuracy for all six databases. Furthermore, this solution allows a reduction of at least 52% in the number of molecular descriptors. It is important to note that in our previous work [17], the best solution, for a smaller database, was found with λ_{1}=0.55, and it reduced the number of descriptors by 67.90%. The difference may be a consequence of having unbalanced datasets in this case. With the best compromise solution (λ_{1}=0.5), we transform (weight the features) the datasets and build models for the binary classification of AMPs and nonAMPs. Our results indicate that both KNN and SVML allow to achieve reliable models for classification of antimicrobial and antibacterial peptides. These results support the idea that our MOEAFW approach allows generating better models for a specific antimicrobial activity, in this particular case, antibacterial activity. In this direction, we expect to use this approach in the future, to classify other specific antimicrobial activities, such as antiviral, antifungal, and antiparasitic, accordingly to determine whether this classification performance is also observed in those particular antimicrobial activities.
As mentioned earlier, the models generated by KNN achieve high specificity and precision, while models induced by SVML produce high sensitivity (see Table 1). These results suggest that, combining the models generated by KNN and SVML, we could exploit their properties to generate even more efficient models.
On the other hand, the lowest performance model generated by MOEAFW was for the classification of peptides which source and target are bacteria (i.e., bacteriocins). In this case, our approach was not able to produce a chemical space where both, the peptide activity and their source could be discriminated. It is important to note, that BAGEL3 [27] and BACTIBASE [28] use properties related to sequence similarity to classify bacteriocins.
Conclusions
This work deals with the problem of molecular descriptors weighting by modeling it as a multiobjective optimization problem, such that peptides with different biological activities tend to be far away from each other, whereas, AMPs tend to be close together. To solve this problem, a variant of a general methodology [16] based on a multiobjective evolutionary algorithm (MOEA/DDE) [29, 30] was employed. Also, we introduce a multicriteria decisionmaking approach to select the weight vectors with different degrees of satisfaction between the intraclass and interclass distances for the target class. Then, with these weight vectors, we scaled the datasets where the peptides are represented by molecular descriptors, and generated different models for the binary classification of AMPs. The analysis of experimental results, on six unbalanced datasets, indicates that the proposed methodology is effective on the development of models to predict antimicrobial peptides. Particularly, in the generation of models for discrimination against a specific antimicrobial activity, such as antibacterial. Given this last result, future research direction aims at constructing classifiers that specialize in specific antimicrobial activities such as antiviral, antifungal, antitumor, among others.
Methods
The scheme of the methodology adopted in this study is shown in Fig. 4. Each process is described in detail in this section, including selection and splitting of a dataset, computing and preprocessing of molecular descriptors, molecular descriptor weighting, and classification of antimicrobial peptides.
Data collection
For this study, we used six sets of peptide sequences, for which AMPs are experimentally validated whereas nonAMPs were randomly selected from a supersequence generated from the concatenation of proteins retrieved from UniProt. None of the retrieved proteins have been annotated as antimicrobial, and some of them are intracellular. From the supersequence, six nonAMPs are randomly extracted for each AMP in the dataset [12]. The datasets were obtained from the publicly available supplementary data of a recent work [12]. Then, we removed the peptide sequence that contains nonstandard residues (e.g., peptide sequences with undetermined amino acids such as ’X’, ’B’, ’J’ or ’Z’). We named these datasets according to i) the database from which the AMPs were recovered and, ii) their annotated activity. Regarding their database, we named the datasets DAMPD and APD3, because they come from the Dragon Antimicrobial Peptide Database (DAMPD) [31] and the Antimicrobial Peptide Database (APD3) [32], respectively. Regarding their annotated activity, we named the datasets as AMP, ANTIBACTERIAL and BACTERIOCIN. AMP are peptides that have antimicrobial activity. ANTIBACTERIAL is a proper subset of AMP since they are antimicrobial peptides that explicitly targeted bacteria. Additionally, BACTERIOCIN is a proper subset of ANTIBACTERIAL, the source organisms of such peptides are also bacteria (these peptides are referred to as bacteriocins, the interested reader is referred to [33] for more information on peptide naming and classification).
Each dataset was split into two random parts, training and test sets. The training set contains 80% of randomly selected sequences from the original dataset, while the test set contains the remaining sequences (see Table 5). The training set is used in the next steps of this section, while the test set is only used to test the effectiveness of the models generated by our approach.
Computation of molecular descriptors
Molecular descriptors are derived from a logical and mathematical procedure which transform physical and chemical information encoded in a molecule representation into useful numbers [34]. Nowadays, there are many proposed descriptors, that can be grouped according to their dimensionality from 0D to 3D. The 0D descriptors are very simple molecular properties (e.g., molecular mass and atom count), that depend only on the molecular composition of the peptide. The 1D descriptors encode information about molecular structural fragments (e.g., distance between two cysteine residues, hydrophobic moment). The 2D descriptors are also known as topological descriptors, and they give us information contained in a molecular graph (e.g., Weiner index). Furthermore, 3D descriptors capture the molecular geometry, stereochemical, and surface properties [6].
Two free software packages were used to extract molecular descriptors: Tango [35–37] and the inhouse Java Peptide Descriptor from Sequences (JPEDES) tool [17]. The first one was used to compute the following physicochemical properties: αhelix propensity, βsheet propensity, turn structure propensity, and in vitro aggregation. Whereas, JPEDES [17] was used to codify OD and 1D descriptors. Unfortunately, the 3D descriptors were not computed due to unavailability 3Dstructures for most known AMPs. Altogether four molecular descriptors were computed using Tango [35–37] and other 268 with JPEDES tool [17]. Those descriptors were extracted for each peptide sequence in the training and test datasets.
Preprocessing
We conducted a twolevel preprocessing for the descriptor matrix previously generated. First, we applied a preprocessing at the instance level that consisted of removing outliers; these are vectors labeled with the same class that are very different from the rest, and that might affect the performance of chemical space characterization. Second, we applied a preprocessing at the descriptor level that renders all molecular descriptor values to the same range. This is because the employed molecular descriptors have different range values, e.g., the isoelectric point takes values in the order of 10^{0} to 10^{1} pH units, whereas the molecular weight in the order of 10^{2} to 10^{3} Daltons.
To remove isolated vectors concerning their neighborhood, the Local Outlier Factor (LOF) [38] method was used. It should be noted that the LOF was applied to each class (e.g., AMP and nonAMP) from each dataset. Regarding preprocessing at the descriptor level, we applied the MinMax scaling method, which maps the measures for each descriptor into a range of 1 to 10 [17]. As a result, we obtained a normalized descriptor matrix \(\mathcal {D}\).
Multiobjective evolutionary approach for feature weighting (MOEAFW)
The multiobjective evolutionary algorithm based on decomposition (MOEA/DDE) [29, 30] was employed to solve the multiobjective feature weighting problem earlier formulated (see Problem statement). The work in [30] shows that MOEA/DDE performs better than the well known NSGAII [39] for continuous optimization problems, like the one described in this study (see Eq. 6).
In short, MOEA/DDE decomposes the multiobjective optimization problem into N singleobjective optimization problems by adopting the Tchebycheff approach. Then, these N problems are solved simultaneously (for a detailed description of this method we refer to the interested reader to [29, 30]).
In general, this algorithm receives as input the descriptor matrix \(\mathcal {D}\) and gives as output a set of approximated N optimal solutions to (6), this is called approximate Pareto set: \(\mathcal {P^{*}}=\left \{\mathbf {w^{1}}, \ldots, \mathbf {w^{N}}\right \}\). It should be noted that each solution is a weight vector \(\mathbf {w^{k}} =\left [w_{1}^{k}, \ldots, w_{m}^{k}\right ]^{T}\), where the ith component is the scale factor for the ith molecular descriptor. For each solution w^{k} in \(\mathcal {P^{*}}\), an objective vector F(w^{k})=[f_{1}(w^{k}),f_{2}(w^{k})]^{T} is assigned. Then the set of all these objective vectors is called the approximate Pareto front [40]: PF={F(w^{1}),…,F(w^{N})}.
It is important to note that, solutions in \(\mathcal {P^{*}}\) cannot be considered better among themselves in both objectives since they are in a tradeoff relation. This means that, some solutions in \(\mathcal {P^{*}}\) are better in objective f_{1} than in f_{2} and viceversa (see Fig. 5). To draw a few solutions from \(\mathcal {P^{*}}\), taking into account different satisfiability levels of the objectives, we employed a wellestablished process in multicriteria decision making [40].
Multicriteria decision making approach to select weight vectors
For the problem of choosing a few weight vectors from the approximate Pareto set \(\mathcal {P^{*}}\), we followed a process that receive as input \(\mathcal {P^{*}}\). The main steps can be described as follows:

Step 1: for each solution, \(\mathbf {w} \in \mathcal {P^{*}}\), scale the values for objective functions f_{1}(w) and f_{2}(w) to a range between 0 and 1, where 1 means full satisfaction for a particular objective, and 0 indicates dissatisfaction. Perform the scaling of a solution w^{k} for the objective f_{i} as follows [16]:
$$ \mu_{i}^{k} =\left\{ \begin{array}{ll} 1 & \text{if } f_{i}\left(\mathbf{w^{k}}\right) = f_{i}^{\min},\\ \frac{f_{i}^{\max}  f_{i}\left(\mathbf{w^{k}}\right)}{f_{i}^{\max} f_{i}^{\min}} & \text{if } f_{i}^{\min} < f_{i}^{k} < f_{i}^{\max},\\ 0 & \text{if } f_{i}\left(\mathbf{w^{k}}\right) = f_{i}^{\max}, \end{array} \right. $$(12)where,
$$\begin{array}{*{20}l} f_{i}^{\min} &= \underset{ 1 \leq j \leq N }{\min} \left\{f_{i}\left(\mathbf{w^{j}}\right) \right\}, \end{array} $$(13)$$\begin{array}{*{20}l} f_{i}^{\max} &= \underset{ 1 \leq j \leq N }{\max} \left\{f_{i}\left(\mathbf{w^{j}}\right) \right\}. \end{array} $$(14)Here \(\mathbf {\mu ^{k}} = \left [\mu _{1}^{k},\mu _{2}^{k}\right ]^{T}\) is the objective vector constrained to the [0,1] range for the solution w^{k} in the approximate Pareto set \(\mathcal {P^{*}}\).

Step 2: perform a weighted sum approach given a weight vector λ= [ λ_{1},λ_{2}]^{T}. Here λ_{1} and λ_{2} are used to set the preference over objectives f_{1} and f_{2}, respectively. For instance, if we want a solution that satisfies f_{1} more than f_{2}, then a greater value should be assigned to λ_{1} than to λ_{2} (see Fig. 5). Given λ, the weighted sum for each objective vector μ^{k} is calculated as follows:
$$ g^{bcs}(\mathbf{\mu} \lambda_{1}) = \lambda_{1} \mu_{1} + (1\lambda_{1}) \mu_{2} $$(15) 
Step 3: Find the best compromise solution given λ, namely, the weight vector w^{k∗} with the maximum value of g^{bcs} (formally described in Eq. 1).
$$ k^{*}=\underset{k \in [1,N]}{\arg \max} \;\;\; g^{bcs}\left(\mathbf{\mu^{k}} \lambda_{1}\right) $$(16)
In this work, for each dataset, we selected five of the best compromise solutions by using λ_{1} equals to 0.4, 0.45, 0.5, 0.55, and 0.60.
Classification algorithms
This section describes an assessment method to validate the performance of the MOEAFW method. In this method, we evaluated the classification task before (i.e., baseline), and after applying our MOEAFW algorithm. For each classification task, we built four models: random forest (RF), knearest neighbor (KNN), a linear support vector machine (SVML), and a multilayer perceptron (MLP). A training dataset without weight factors was used before applying our MOEAFW algorithm, and the weighted molecular descriptors are used after that. Later, we compared the classification performance between the models and measured indirectly the quality of our proposal. To accomplish this, each best compromise solution (i.e., the weight vector w^{k∗}) was applied to dataset \(\mathcal {D}\) resulting in a new dataset \(\mathcal {\hat {D}}_{\mathbf {W}}\), where:
In this way, after applying our proposal, the rejected molecular descriptors correspond to columns whose values are zero and those columns were deleted.
Implementation details
All experiments were performed under the following condition; OS: ubuntu 16.04 LTS; CPU: Intel i7 at 2.40GHz; and RAM memory: 12 GB.
The MOEA/DDE algorithm was implemented in Java using the framework of Metaheuristics for solving multiobjective optimization problems MOEA Framework 2.1 (available from http://www.moeaframework.org). The main parameters for MOEA/DDE were set according to the values recommended in [30] for 2objective problems, the specific parameter settings are summarized in [17].
The classification algorithms were implemented in Python 3.6 using the Scikitlearn [41]. Scikitlearn is an efficient set of tools for the implementation of machine learning algorithms for data mining tasks. The machine learning algorithms’ hyperparameters are summarized as a following: KNN (p=1,weight=distance) and k=19,22,3 for the antimicrobial, antibacterial, and bacteriocin datasets, respectively; SVML (class_weight= balanced) and the penalty parameter C = 0.001, 0.1, and 0.001 for the antimicrobial, antibacterial, and bacteriocin datasets, respectively; RF (criterion =gini, max_features =sqrt); finally for MLP we used the default hyperparameters.
Abbreviations
 10fold CV:

10fold CrossValidation
 Acc:

Accuracy
 AMPs:

Antimicrobial peptides
 APD3:

Antimicrobial Peptide Database
 AUC:

Area under a ROC curve
 Bal Acc:

Balance accuracy
 DAMPD:

Dragon Antimicrobial Peptide Database
 JPEDES:

Java Peptide Descriptor from Sequences
 KNN:

knearest neighbor
 LOF:

Local Outlier Factor
 MCC:

Matthews correlation coefficient
 MLAs:

machine learning algorithms
 MLP:

Multilayer perceptron
 MOEAFW:

MultiObjective Approach for Feature Weighting
 MOEA/DDE:

Multiobjective evolutionary algorithm based on decomposition
 MOP:

Multiobjective optimization problem
 Prec:

Precision
 QSAR:

Quantitative StructureActivity Relationship
 RF:

Random forest
 Sens:

Sensitivity
 SVML:

Linear support vector machine
 VS:

Virtual Screening
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Acknowledgements
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of this paper.
Funding
Publications costs were funded by Center for Scientific Research and Higher Education of Ensenada (CICESE).
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This article has been published as part of BMC Genomics Volume 19 Supplement 7, 2018: Selected articles from the IEEE BIBM International Conference on Bioinformatics & Biomedicine (BIBM) 2017: genomics. The full contents of the supplement are available online at https://bmcgenomics.biomedcentral.com/articles/supplements/volume19supplement7.
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JAB and CAB defined the problem and designed the methodology; JAB performed the experiments and analyzed the data; JAB, LAM and CAB. wrote the paper. JAB and CAB edited the final version. All authors read and approved the final manuscript.
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Additional files
Additional file 1
Performance comparison of the best compromise solutions given λ_{1} for four different machine learning algorithms. The values are related to the average of the balance accuracy. (PDF 341 kb)
Additional file 2
Predictions of antimicrobial activity for DAMPD_AMP. Evaluation of different models with DAMP_AMP after applying the best compromise solutions and four machine learning algorithms. This file shows the results obtained by each of the best compromise solutions given λ_{1} in each fold of the 10fold crossvalidation test. (CSV 33 kb)
Additional file 3
Predictions of antimicrobial activity for APD3_AMP. Evaluation of different models with APD3_AMP after applying the best compromise solutions and four machine learning algorithms. This file shows the results obtained by each of the best compromise solutions given λ_{1} in each fold of the 10fold crossvalidation test. (CSV 34 kb)
Additional file 4
Predictions of antibacterial activity for DAMPD_ANTIBACTERIAL. Evaluation of different models with DAMPD_ANTIBACTERIAL after applying the best compromise solutions and four machine learning algorithms. This file shows the results obtained by each of the best compromise solutions given λ_{1} in each fold of the 10fold crossvalidation test. (CSV 32 kb)
Additional file 5
Predictions of antibacterial activity for APD3_ANTIBACTERIAL. Evaluation of different models with APD3_ANTIBACTERIAL after applying the best compromise solutions and four machine learning algorithms. This file shows the results obtained by each of the best compromise solutions given λ_{1} in each fold of the 10fold crossvalidation test. (CSV 34 kb)
Additional file 6
Predictions of bacterocins for DAMPD_BACTERIOCIN. Evaluation of different models with DAMPD_BACTERIOCIN after applying the best compromise solutions and four machine learning algorithms. This file shows the results obtained by each of the best compromise solutions given λ_{1} in each fold of the 10fold crossvalidation test. (CSV 22 kb)
Additional file 7
Predictions of bacterocins for APD3_BACTERIOCIN. Evaluation of different models with APD3_BACTERIOCIN after applying the best compromise solutions and four machine learning algorithms. This file shows the results obtained by each of the best compromise solutions given λ_{1} in each fold of the 10fold crossvalidation test. (CSV 31 kb)
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Beltran, J.A., AguileraMendoza, L. & Brizuela, C.A. Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach. BMC Genomics 19 (Suppl 7), 672 (2018). https://doi.org/10.1186/s1286401850301
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DOI: https://doi.org/10.1186/s1286401850301