PredIL13: Stacking a variety of machine and deep learning methods with ESM-2 language model for identifying IL13-inducing peptides

Hiroyuki Kurata; Md. Harun-Or-Roshid; Sho Tsukiyama; Kazuhiro Maeda

doi:10.1371/journal.pone.0309078

Peer Review History

Original SubmissionMay 23, 2024
2 Jul 2024 Decision Letter - Shahid Akbar, Editor PONE-D-24-20864PredIL13: stacking a variety of machine and deep learning methods by weight coefficient-based single-feature model selection for identifying IL13-inducing peptidesPLOS ONE Dear Dr. Kurata, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Major Revision Please submit your revised manuscript by Aug 16 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: In the abstract, the authors should mention the feature encoding methods , more specifically the protein language model. 2. In The introduction, the literature section is very poor, the authors should add some computational methods based papers related to PredIL13. 3. The contribution should be clearly mentioned in points at the end of introduction section. 4. The authors should mention the provided the hyper-parameters used for training the used machine learning and deep learning in the form of a table. 5. The code of the interpretation methods used in paper should be provided for the reimplementation purposes. 6. For the reader concerns the authors are advised to add the recent predictors such as AIPs-SnTCN, DeepAVP-TPPred, Deepstacked-AVPs,iAFPs-Mv-BiTCN and pAVP_PSSMDWT-EnC models 7.The feature vector size of the encoding methods should be provided in the paper. 8. What are limitations of the proposed model. Reviewer #2: 1. How the authors handle the imbalance data while training the model. Did they used any oversampling or under sampling approach, authors should clearly mention. 2. What is justification of using the machine learning models in the proposed model? As there are several other training models available in the literature. 3. As the authors used several feature extraction methods to formulate peptide sequence. However, It should be convincing if the authors highlight the high contributory features using SHAP analysis. 4. What should be the future direction of the proposed model. ****** 6. 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Revision 1
15 Jul 2024 Author Response Reviewer #1: Q1.In the abstract, the authors should mention the feature encoding methods, more specifically the protein language model. A1. Thank you for your useful comment. We mentioned the ESM-2 protein language model in Abstract. In addition, we revised the title to attract many readers as follows: PredIL13: stacking a variety of machine and deep learning methods with ESM-2 language model for identifying IL13-inducing peptides. Q2. In The introduction, the literature section is very poor, the authors should add some computational methods based papers related to PredIL13. A2. A thorough search of the literature reveals that IL-13Pred (2022) is the first to predict the IL-13 inducing and non-inducing peptides from its amino acid sequence. To data only a few predictors have been presented. We mentioned that the development of IL-13 predictors has just begun in Introduction. Q3. The contribution should be clearly mentioned in points at the end of introduction section. A3. According to the suggestions, we mentioned the following points at the end of Introduction: (1) PredIL13 is a novel meta-classifier that effectively stacks a variety of single-feature models by using logistic regression to predict IL13-inducing peptides; (2) The SDIWC method is proposed to effectively select the 16 optimal single-feature models out of the 168 single-feature models according to their importance; (3) Language models including ESM-2 are effective in increasing the prediction performance and greatly outperforms state-of-the-art methods in terms of prediction performances. Q4. The authors should mention the provided the hyper-parameters used for training the used machine learning and deep learning in the form of a table. A4. We provided the hyper-parameters used for training the used machine learning and deep learning in Table S1 at the end of the ML and DL classifiers section. Q5. The code of the interpretation methods used in paper should be provided for the reimplementation purposes. A5. We mentioned the availability of the data and program codes in Data availability statement. The source codes are freely accessible at https://github.com/kuratahiroyuki/PredIL13. The web application is freely available at http://kurata35.bio.kyutech.ac.jp/PredIL13. Q6. For the reader concerns the authors are advised to add the recent predictors such as AIPs-SnTCN, DeepAVP-TPPred, Deepstacked-AVPs,iAFPs-Mv-BiTCN and pAVP_PSSMDWT-EnC models A6. These predictors are not directly related to prediction of interleukin 13-inducing peptides. Instead of them we added a general review on the computational approach of prediction of anti‑inflammatory in Introduction. Q7.The feature vector size of the encoding methods should be provided in the paper. A7. We described the vector size or the number of descriptors of all the features in the feature encoding method section. Q8. What are limitations of the proposed model. A8. We added the Limitation section to Results and discussion. One limitation of this study is that the number of experimentally validated IL-13-inducing peptides is small. Thus, we need to enlarge the dataset to increase the prediction performance. Furthermore, using the large dataset we construct a generative AI to design de novo peptide sequences and to ensure the generated sequences are biologically functional and potentially beneficial for medication. Reviewer #2: We revised the title to attract many readers from “PredIL13: stacking a variety of machine and deep learning methods by weight coefficient-based single-feature model selection for identifying IL13-inducing peptides” to “PredIL13: stacking a variety of machine and deep learning methods with ESM-2 language model for identifying IL13-inducing peptides” Q1. How the authors handle the imbalance data while training the model. Did they use any oversampling or under sampling approach, authors should clearly mention. A1. We neither used over-sampling nor under-sampling methods. We added this statement in the section of Dataset preparation. Q2. What is justification of using the machine learning models in the proposed model? As there are several other training models available in the literature. A2. We focused on widely-used, typical machine and deep learning methods. They would be sufficient to construct the best model or PredIL13. Q3. As the authors used several feature extraction methods to formulate peptide sequence. However, It should be convincing if the authors highlight the high contributory features using SHAP analysis. A3. At the section of Importance of each single-feature model, we discussed SHapley Additive exPlanations (SHAP) analysis, while making Figure S2. Feature selection methods other than AWCLR, which represents the contribution of each single-feature model (feature) to the log-odds of binary classification, are known. For example, SHAP analysis provides a more nuanced view by showing how each feature contributes to individual predictions (Figure S2). In this study we used the AWCLR as it has a theoretical intelligible basis to overcome the-state-of-the-art methods. SHAP analysis will be considered in a next step. Q4. What should be the future direction of the proposed model. A4. We added the Limitation section to Results and discussion to mention the future direction. One limitation of this study is that the number of experimentally validated IL-13-inducing peptides is small. Thus, we need to enlarge the dataset to increase the prediction performance. Furthermore, using the large dataset we construct a generative AI to design de novo peptide sequences and to ensure the generated sequences are biologically functional and potentially beneficial for medication. https://doi.org/10.1371/journal.pone.0309078.r002
6 Aug 2024 Decision Letter - Shahid Akbar, Editor PredIL13: stacking a variety of machine and deep learning methods with ESM-2 language model for identifying IL13-inducing peptides PONE-D-24-20864R1 Dear Dr. Kurata, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. 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If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The required comments are successfully incorporated and I think the paper is significantly improved. The paper is acceptable from my side Reviewer #2: the authors successfully addressed my previous concerns and now the paper is significantly improved. i think the paper can be accepted . no further comments from my side ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No ******** https://doi.org/10.1371/journal.pone.0309078.r003
Formally Accepted
13 Aug 2024 Acceptance Letter - Shahid Akbar, Editor PONE-D-24-20864R1 PLOS ONE Dear Dr. Kurata, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Shahid Akbar Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0309078.r004

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