The benchmark dataset used in this study is exactly the same as that in pLoc_bal-mVirus [4]; i.e.,

S = S 1 ∪ S 2 ∪ S 3 ∪ S 4 ∪ S 5 ∪ S 6 (1)

where S 1 only contains the virus protein samples from the “Viral capsid” location (cf. Table 1), S 2 only contains those from the “Host cell membrane” location, and so forth; ∪ denotes the symbol for “union” in the set theory. For readers’ convenience, the detailed sequences of these protein samples and their accession numbers (or ID codes) are given in Supporting Information S1 that are also available at http://www.jci-bioinfo.cn/pLoc_bal-mVirus/Supp1.pdf, in which none of proteins included has ≥25% sequence identity to any other in the same subset (subcellular location). But such a cutoff treatment was not imposed for the protein sequences in the “viral capsid” subset; otherwise it would contain too few protein samples to be of statistical significance as explained in the original paper [18].

^aSee Equation (4) for the definition of the metrics. ^bSee [4], where the reported metrics rates were obtained by the jackknife test on the benchmark dataset of Supporting Information S1 that contains experiment- confirmed proteins only. ^cThe proposed predictor; to assure that the test was performed on exactly the same experimental data as reported in [4] for pLoc_bal-mVirus.

Now let us consider the 2^nd step of the 5-step rule [17]; i.e., how to formulate the biological sequence samples with an effective mathematical expression that can truly reflect their essential correlation with the target concerned. Given a protein sequence P, its most straightforward expression is

P = R 1 R 2 R 3 R 4 R 5 R 6 R 7 ⋯ R L (2)

where L denotes the protein’s length or the number of its constituent amino acid residues, R 1 is the 1^st residue, R 2 the 2^nd residue, R 3 the 3^rd residue, and so forth. Since all the existing machine-learning algorithms} can only handle vectors as elaborated in [3], one has to convert a protein sample from its sequential expression Equation (2) to a vector. But a vector defined in a discrete model might completely miss all the sequence-order or pattern information. To deal with this problem, the Pseudo Amino Acid Composition [19] or PseAAC [20]. Ever since then, the concept of “Pseudo Amino Acid Composition” has been widely used in nearly all the areas of computational proteomics with the aim to grasp various different sequence patterns that are essential to the targets investigated (see, e.g., [21-31] as well as a long list of references cited in [32]). Because it has been widely and increasingly used, recently three powerful open access soft-wares, called “PseAAC-Builder” [33], “propy” [34], and “PseAAC-General” [35], were established: the former two are for generating various modes of special PseAAC [36]; while the 3rd one for those of general PseAAC [17], including not only all the special modes of feature vectors for proteins but also the higher level feature vectors such as “Functional Domain” mode, “Gene Ontology” mode, and “Sequential Evolution” or “PSSM” mode. Encouraged by the successes of using PseAAC to deal with protein/peptide sequences, its idea and approach were extended to PseKNC (Pseudo K-tuple Nucleotide Composition) to generate various feature vectors for DNA/RNA sequences [37] that have proved very successful as well (see, e.g., [38,39].

According to the concept of general PseAAC [17], any protein sequence can be formulated as a PseAAC vector given by

P = [ Ψ 1   Ψ 2   ⋯   Ψ u   ⋯   Ψ Ω ] T (3)

where T is a transpose operator, while the integer Ω is a parameter and its value as well as the components Ψ u ( u = 1 , 2 , ⋯ , Ω ) will depend on how to extract the desired information from the amino acid sequence of P, as elaborated in [4]. Thus, by following exactly the same procedures as described in the Section 2.2 of [4], each of the protein samples in the benchmark dataset can be uniquely defined as a 6-D numerical vector as given in Supporting Information S2, which can also be directly downloaded at http://www.jci-bioinfo.cn/pLoc_bal-mVirus/Supp2.pdf.

In this study, we use the CNN (Convolutional Neural Network) model to predict the subcellular localization of virus proteins, as illustrated in Figure 1.

The CNN model consists of input layer, convolutional layer, average-pooling layer and fully connected layer. The input layer represents each virus protein with 6 features. The second layer is convolutional layer which extract dependency relationship between features subsequence of virus proteins. The filter stride is set to one. The activation function is set as “relu”. The average-pooling layer down-samples the features and compute the average values of the features. The fully connected layer consists of 2 hidden layers. Finally, the output of connected layer was concatenated into output layer with sigmoid activation function. The label of virus protein was decided by the threshold θ. If the output is greater than 0.5, the outcome was true; otherwise, false.

The other parameters of CNN model are as follows. 1) The algorithm of Adam was used to train the model and the loss function is set to binary cross-entropy. 2) The activation function of full connected layer and convolutional layer is ReLU [40], and the activation function of output layer is sigmoid. 3) Convolutional Layer used the filter size 2 * 1 to extract features of virus proteins. 4) The batch size is 26. 5) The model is trained for 120 epochs. 6) The metrics is set as “accuracy”.

The new predictor developed via the above procedures is called “pLoc_Deep-mVirus”, where “pLoc_Deep” stands for “predict subcellular localization by deep learning”, and “mVirus” for “multi-label virus proteins”.

Different from the metrics used to measure the prediction quality of single-label systems, the metrics for the multi-label systems are much more complicated [41]. To make them more intuitive and easier to understand for most experimental scientists, here we use the following intuitive Chou’s five metrics [42] or the “global metrics” that have recently been widely used for studying various multi-label systems (see, e.g., [43,44]). For the current study, the set of global metrics can be formulated as:

{ Aiming ↑   = 1 N q ∑ k = 1 N q ( ‖ L k ∩ L k * ‖ L k * )   ,   [ 0 , 1 ] Coverage ↑   = 1 N q ∑ k = 1 N q ( ‖ L k ∩ L k * ‖ L k )   ,   [ 0 , 1 ] Accuracy ↑   = 1 N q ∑ k = 1 N q ( ‖ L k ∩ L k * ‖ ‖ L k ∪ L k * ‖ )   ,   [ 0 , 1 ] Absolutetrue ↑   = 1 N q ∑ k = 1 N q Δ ( L k , L k * )   ,   [ 0 , 1 ] Absolutefalse ↓   = 1 N q ∑ k = 1 N q ( ‖ L k ∪ L k * ‖ − ‖ L k ∩ L k * ‖ M )   ,   [ 1 , 0 ] (4)

where N q is the total number of query proteins or tested proteins, M is the total number of different labels for the investigated system (for the current study it is L cell = 6 ), ‖ ‖ means the operator acting on the set therein to count the number of its elements, ∪ means the symbol for the “union” in the set theory, ∩ denotes the symbol for the “intersection”, L k denotes the subset that contains all the labels observed by experiments for the k-th tested sample, L k * represents the subset that contains all the labels predicted for the k-th sample, and

Δ ( L k , L k * ) = { 1 ,   if   all   the   labels   in   L k *   are   identical   to   those   in   L k 0 ,   otherwise (5)

In Equation (4), the first four metrics with an upper arrow ↑ are called positive metrics, meaning that the larger the rate is the better the prediction quality will be; the 5^th metrics with a down arrow ↓ is called negative metrics, implying just the opposite meaning.

From Equation (4) we can see the following: 1) the “Aiming” defined by the 1^st sub-equation is for checking the rate or percentage of the correctly predicted labels over the practically predicted labels; 2) the “Coverage” defined in the 2^nd sub-equation is for checking the rate of the correctly predicted labels over the actual labels in the system concerned; 3) the “Accuracy” in the 3^rd sub-equation is for checking the average ratio of correctly predicted labels over the total labels including correctly and incorrectly predicted labels as well as those real labels but are missed in the prediction; 4) the “Absolute true” in the 4^th sub-equation is for checking the ratio of the perfectly or completely correct prediction events over the total prediction events; 5) the “Absolute false” in the 5^th sub-equation is for checking the ratio of the completely wrong prediction over the total prediction events.

Listed in Table 1 are the rates achieved by the current pLoc_Deep-mVirus predictor via the cross validations on the same experiment-confirmed dataset as used in [4]. For facilitating comparison, listed there are also the corresponding results obtained by the pLoc_bal-mVirus [4], the existing most powerful predictor for identifying the subcellular localization of virus proteins with both single and multiple location sites. As shown in Table 1, the newly proposed predictor pLoc_Deep-mVirus is remarkably superior to the existing state-of-the-art predictor pLoc_bal-mVirus in all the five metrics. Particularly, it can be seen from the table that the absolute true rate achieved by the new predictor is over 97%, which is far beyond the reach of any other existing methods [45-50]. This is because it is extremely difficult to enhance the absolute true rate of a prediction method for a multi-label system as clearly elucidated in [4]. Actually, to avoid embarrassment, many investigators even chose not to mention the metrics of absolute true rate in dealing with multi-label systems (see, e.g., [51-57]).

Moreover, to in-depth examine the prediction quality of the new predictor for the proteins in each of the subcellular locations concerned (cf. Table 2), we used the “local metrics” [41] or a set of four intuitive metrics that were derived in [58] based on the Chou’s symbols introduced for studying protein signal peptides [59] and that have ever since been widely concurred or justified (see, e.g., [60-63]). For the current study, the set of local metrics can be formulated as:

^aSee Table 1 and the relevant context for further explanation. ^bSee Equation (6) for the metrics definition.

{ Sn ( i ) = 1 − N − + ( i ) N + ( i )                 0 ≤ Sn ≤ 1 Sp ( i ) = 1 − N + − ( i ) N − ( i )                 0 ≤ Sp ≤ 1 Acc ( i ) = 1 − N − + ( i ) + N + − ( i ) N + ( i ) + N − ( i )                 0 ≤ Acc ≤ 1 MCC ( i ) = 1 − ( N − + ( i ) N + ( i ) + N + − ( i ) N − ( i ) ) ( 1 + N + − ( i ) − N − + ( i ) N + ( i ) ) ( 1 + N − + ( i ) − N + − ( i ) N − ( i ) )       − 1 ≤ MCC ≤ 1 ( i = 1 , 2 , ⋯ , 6 ) (6)

where Sn, Sp, Acc, and MCC represent the sensitivity, specificity, accuracy, and Mathew’s correlation coefficient, respectively, and i denotes the i-th subcellular location (or subset) in the benchmark dataset. N + ( i ) is the total number of the samples investigated in the i-th subset, whereas N − + ( i ) is the number of the samples in N + ( i ) that are incorrectly predicted to be of other locations; N − ( i ) is the total number of samples in any locations but not the i-th location, whereas N + − ( i ) is the number of the samples in N − ( i ) that are incorrectly predicted to be of the i-th location.

Listed in Table 2 are the results achieved by pLoc_Deep-mVirus for the virus proteins in each of 12 subcellular locations. As we can see from the table, nearly all the success rates achieved by the new predictor for the virus proteins in each of the 12 subcellular locations are within the range of 90-100%, which is once again far beyond the reach of any of its counterparts

As pointed out in [64], user-friendly and publicly accessible web-servers represent the future direction for developing practically more useful predictors. Actually, user-friendly web-servers will significantly enhance the impacts of theoretical work because they can attract the broad experimental scientists [32]. In view of this, the web-server of the current pLoc_Deep-mVirus pedictor has also been established. Moreover, to maximize users’ convenience, a step-by-step guide is given below.

Step 1. Click the link at http://www.jci-bioinfo.cn/pLoc_Deep-mVirus/, the top page of the pLoc_Deep- mVirus web-server will appear on your computer screen, as shown in Figure 2. Click on the Read Me button to see a brief introduction about the predictor.

Step 2. Either type or copy/paste the sequences of query virus proteins into the input box at the center of Figure 2. The input sequence should be in the FASTA format. For the examples of sequences in FASTA format, click the Example button right above the input box.

Step 3. Click on the Submit button to see the predicted result. For instance, if you use the four protein sequences in the Example window as the input, after 10 seconds or so, you will see a new screen (Figure 3) occurring. On its upper part are listed the names of the subcellular locations numbered from (1) to (6) covered by the current predictor. On its lower part are the predicted results: the query protein “P01115” of example-1 corresponds to “2,” meaning it belongings to “Viral capsid” only; the query protein “P03495” of example-2 corresponds to “4, 5” meaning it belonging to “Host cytoplasm” and “Cytoplasm”; the query protein “P89873” of example-3 corresponds to “4, 5, 6”, meaning it belonging to “Host cytoplasm”, “Host nucleus”, and “Secreted”. All these results are perfectly consistent with experimental observations.

Step 4. As shown on the lower panel of Figure 2, you may also choose the batch prediction by entering your e-mail address and your desired batch input file (in FASTA format of course) via the Browse

button. To see the sample of batch input file, click on the button Batch-example. After clicking the button Batch-submit, you will see “Your batch job is under computation; once the results are available, you will be notified by e-mail.”

Step 5. Click on the Citation button to find the papers that have played the key role in developing the current predictor of pLoc_Deep-mVirus.

Step 6. Click the Supporting Information button to download the Supporting Informations mentioned in this paper.