Gordon Life Science Institute, Boston, USA
Received Date: 28 May, 2020 ; Accepted Date: 29 May, 2020 ; Published Date: 06 June, 2020
In 2020, a very powerful web-server predictor has been established for identifying the subcellular localization of Gram-negative bacterial proteins based on the sequence information alone , in which a same protein may occur or move between two or more location sites and hence needs to be marked with the multi-label approach . The web-server predictor is called “pLoc_Deep-mGneg”, where “Deep” means the web-server has been further improved by the “Deep Learning” technique [3-6], and “m” means the capacity able to deal with the multi-label systems.
Step 1: Click the link at http://www.jci-bioinfo.cn/pLoc_Deep-mGneg/, the top page of the pLoc_Deep-mGneg web-server will appear on your computer screen, as shown in (Figure 1). 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 human proteins into the input box at the center of (Figure 1). 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 occurring (Figure 2). On its upper part are listed the names of the subcellular locations numbered from (1) to (8) covered by the current predictor. On its lower part are the predicted results: the query protein P22340 of example-1 corresponds to “2,” meaning it belongs to “Cell outer membrane” only; the query protein P04032 of example-2 corresponds to “8” meaning it belongs to “Periplasm”; the query protein P04825 of example-3 corresponds to “1, 3”, meaning it belongs to “Cell inner membrane” and “Cytoplasm”; the query protein P22251 of example 4 corresponds to “4, 6”, meaning it belongs to “Extracellular” and “Flagellum”. 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-mGneg.
Step 6: Click the Supporting Information button to download the Supporting Informations mentioned in this paper.
Citation: Chou KC (2020) Showcase to illustrate how the web-server pLoc_Deep-mGneg is working. Clin Med Case Rep J 1: 002. DOI: 10.29011/CMCRJ-001.000002