{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T18:43:56Z","timestamp":1775760236184,"version":"3.50.1"},"reference-count":60,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T00:00:00Z","timestamp":1636329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"National Agricultural Science Fund"},{"name":"National Supercomputing Mission"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Due to the rapid emergence of multi-drug resistant (MDR) bacteria, existing antibiotics are becoming ineffective. So, researchers are looking for alternatives in the form of antibacterial peptides (ABPs) based medicines. The discovery of novel ABPs using wet-lab experiments is time-consuming and expensive. Many machine learning models have been proposed to search for new ABPs, but there is still scope to develop a robust model that has high accuracy and precision. In this work, we present StaBle-ABPpred, a stacked ensemble technique-based deep learning classifier that uses bidirectional long-short term memory (biLSTM) and attention mechanism at base-level and an ensemble of random forest, gradient boosting and logistic regression at meta-level to classify peptides as antibacterial or otherwise. The performance of our model has been compared with several state-of-the-art classifiers, and results were subjected to analysis of variance (ANOVA) test and its post hoc analysis, which proves that our model performs better than existing classifiers. Furthermore, a web app has been developed and deployed at https:\/\/stable-abppred.anvil.app to identify novel ABPs in protein sequences. Using this app, we identified novel ABPs in all the proteins of the Streptococcus phage T12 genome. These ABPs have shown amino acid similarities with experimentally tested antimicrobial peptides (AMPs) of other organisms. Hence, they could be chemically synthesized and experimentally validated for their activity against different bacteria. The model and app developed in this work can be further utilized to explore the protein diversity for identifying novel ABPs with broad-spectrum activity, especially against MDR bacterial pathogens.<\/jats:p>","DOI":"10.1093\/bib\/bbab439","type":"journal-article","created":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T19:36:35Z","timestamp":1633030595000},"source":"Crossref","is-referenced-by-count":59,"title":["StaBle-ABPpred: a stacked ensemble predictor based on biLSTM and attention mechanism for accelerated discovery of antibacterial peptides"],"prefix":"10.1093","volume":"23","author":[{"given":"Vishakha","family":"Singh","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, Uttar Pradesh, India"}]},{"given":"Sameer","family":"Shrivastava","sequence":"additional","affiliation":[{"name":"Division of Veterinary Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar, 243122, Uttar Pradesh, India"}]},{"given":"Sanjay","family":"Kumar Singh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, Uttar Pradesh, India"}]},{"given":"Abhinav","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, Uttar Pradesh, India"}]},{"given":"Sonal","family":"Saxena","sequence":"additional","affiliation":[{"name":"Division of Veterinary Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar, 243122, Uttar Pradesh, India"}]}],"member":"286","published-online":{"date-parts":[[2021,11,8]]},"reference":[{"issue":"1","key":"2022011921341252900_ref1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s42003-021-02137-7","article-title":"The lexicon of antimicrobial peptides: a complete set of arginine and tryptophan sequences","volume":"4","author":"Clark","year":"2021","journal-title":"Communications biology"},{"issue":"1","key":"2022011921341252900_ref2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1038\/s41579-018-0125-x","article-title":"The cost of antimicrobial resistance","volume":"17","author":"Hofer","year":"2019","journal-title":"Nat Rev Microbiol"},{"issue":"1","key":"2022011921341252900_ref3","doi-asserted-by":"crossref","first-page":"24","DOI":"10.3390\/antibiotics9010024","article-title":"Development and challenges of antimicrobial peptides for therapeutic applications","volume":"9","author":"Chen","year":"2020","journal-title":"Antibiotics"},{"issue":"2","key":"2022011921341252900_ref4","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1093\/bib\/bbaa423","article-title":"Identifying anti-coronavirus peptides by incorporating different negative datasets and imbalanced learning strategies","volume":"22","author":"Pang","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022011921341252900_ref5","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.ins.2019.08.072","article-title":"Deep feature learning for histopathological image classification of canine mammary tumors and human breast cancer","volume":"508","author":"Kumar","year":"2020","journal-title":"Inform Sci"},{"issue":"1","key":"2022011921341252900_ref6","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1109\/TFUZZ.2020.2995968","article-title":"CoMHisP: A novel feature extractor for histopathological image classification based on fuzzy SVM with within-class relative density","volume":"29","author":"Kumar","year":"2021","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"2","key":"2022011921341252900_ref7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3424221","article-title":"A Novel Cloud-Assisted Secure Deep Feature Classification Framework for Cancer Histopathology Images","volume":"21","author":"Kumar","year":"2021","journal-title":"ACM Transactions on Internet Technology (TOIT)"},{"issue":"1","key":"2022011921341252900_ref8","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/TCBB.2020.2980831","article-title":"Imbalanced breast cancer classification using transfer learning","volume":"18","author":"Singh","year":"2020","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"2","key":"2022011921341252900_ref9","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1136\/bjophthalmol-2018-313173","article-title":"Artificial intelligence and deep learning in ophthalmology","volume":"103","author":"Ting","year":"2019","journal-title":"British Journal of Ophthalmology"},{"key":"2022011921341252900_ref10","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108111","article-title":"MetaMed: Few-shot medical image classification using gradient-based meta-learning","author":"Singh","year":"2021","journal-title":"Pattern Recognition"},{"key":"2022011921341252900_ref11","doi-asserted-by":"crossref","first-page":"104348","DOI":"10.1016\/j.compbiomed.2021.104348","article-title":"Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases","volume":"132","author":"Ibrahim","year":"2021","journal-title":"Comput Biol Med"},{"issue":"2","key":"2022011921341252900_ref12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3397679","article-title":"A novel multiobjective gdwcn-pso algorithm and its application to medical data security","volume":"21","author":"Bharti","year":"2021","journal-title":"ACM Transactions on Internet Technology (TOIT)"},{"issue":"1","key":"2022011921341252900_ref13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-8-263","article-title":"Analysis and prediction of antibacterial peptides","volume":"8","author":"Lata","year":"2007","journal-title":"BMC bioinformatics"},{"key":"2022011921341252900_ref14","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab065","article-title":"Deep-ABPpred: identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec","author":"Sharma","year":"2021","journal-title":"Brief Bioinform"},{"issue":"1","key":"2022011921341252900_ref15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/srep42362","article-title":"Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou\u2019s general PseAAC","volume":"7","author":"Meher","year":"2017","journal-title":"Sci Rep"},{"key":"2022011921341252900_ref16","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab242","article-title":"AniAMPpred: artificial intelligence guided discovery of novel antimicrobial peptides in animal kingdom","author":"Sharma","year":"2021","journal-title":"Brief Bioinform"},{"issue":"9","key":"2022011921341252900_ref17","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1093\/bioinformatics\/btm068","article-title":"AMPer: a database and an automated discovery tool for antimicrobial peptides","volume":"23","author":"Fjell","year":"2007","journal-title":"Bioinformatics"},{"issue":"D1","key":"2022011921341252900_ref18","doi-asserted-by":"crossref","first-page":"D1154","DOI":"10.1093\/nar\/gkt1157","article-title":"CAMP: Collection of sequences and structures of antimicrobial peptides","volume":"42","author":"Waghu","year":"2014","journal-title":"Nucleic Acids Res"},{"issue":"W1","key":"2022011921341252900_ref19","doi-asserted-by":"crossref","first-page":"W199","DOI":"10.1093\/nar\/gks450","article-title":"AVPpred: collection and prediction of highly effective antiviral peptides","volume":"40","author":"Thakur","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2022011921341252900_ref20","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab263","article-title":"AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches","author":"Pang","year":"2021","journal-title":"Brief Bioinform"},{"issue":"1","key":"2022011921341252900_ref21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-19752-w","article-title":"AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest","volume":"8","author":"Bhadra","year":"2018","journal-title":"Sci Rep"},{"issue":"5","key":"2022011921341252900_ref22","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1109\/TCBB.2012.89","article-title":"ClassAMP: a prediction tool for classification of antimicrobial peptides","volume":"9","author":"Joseph","year":"2012","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"13","key":"2022011921341252900_ref23","doi-asserted-by":"crossref","first-page":"7290","DOI":"10.1021\/acsomega.9b04119","article-title":"BIPEP: Sequence-based prediction of biofilm inhibitory peptides using a combination of nmr and physicochemical descriptors","volume":"5","author":"Fallah Atanaki","year":"2020","journal-title":"ACS omega"},{"issue":"10","key":"2022011921341252900_ref24","doi-asserted-by":"crossref","first-page":"4691","DOI":"10.1021\/acs.jcim.0c00841","article-title":"IAMPE: NMR-assisted computational prediction of antimicrobial peptides","volume":"60","author":"Kavousi","year":"2020","journal-title":"J Chem Inf Model"},{"key":"2022011921341252900_ref25","article-title":"A large-scale structural classification of antimicrobial peptides","volume":"2015","author":"Lee","year":"2015","journal-title":"Biomed Res Int"},{"issue":"8","key":"2022011921341252900_ref26","doi-asserted-by":"crossref","first-page":"14531","DOI":"10.3390\/ijms150814531","article-title":"New milk protein-derived peptides with potential antimicrobial activity: An approach based on bioinformatic studies","volume":"15","author":"Dziuba","year":"2014","journal-title":"Int J Mol Sci"},{"issue":"2","key":"2022011921341252900_ref27","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1093\/bib\/bbaa259","article-title":"Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19","volume":"22","author":"Dhall","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022011921341252900_ref28","doi-asserted-by":"crossref","DOI":"10.21236\/ADA164453","volume-title":"Learning internal representations by error propagation. Technical report, California Univ San Diego La Jolla Inst for","author":"Rumelhart","year":"1985"},{"issue":"4","key":"2022011921341252900_ref29","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1109\/TCBB.2019.2903800","article-title":"Classification of antibacterial peptides using long short-term memory recurrent neural networks","volume":"17","author":"Youmans","year":"2019","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2022011921341252900_ref30","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab083","article-title":"Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides","author":"Xu","year":"2021","journal-title":"Brief Bioinform"},{"issue":"1","key":"2022011921341252900_ref31","doi-asserted-by":"crossref","DOI":"10.1186\/1471-2105-8-263","article-title":"AntiBP2: improved version of antibacterial peptide prediction","volume":"11","author":"Lata","year":"2010","journal-title":"BMC bioinformatics"},{"issue":"2","key":"2022011921341252900_ref32","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.ab.2013.01.019","article-title":"iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types","volume":"436","author":"Xiao","year":"2013","journal-title":"Anal Biochem"},{"key":"2022011921341252900_ref33","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab209","article-title":"iAMP-CA2L: a new CNN-BiLSTM-SVM classifier based on cellular automata image for identifying antimicrobial peptides and their functional types","author":"Xiao","year":"2021","journal-title":"Brief Bioinform"},{"issue":"16","key":"2022011921341252900_ref34","doi-asserted-by":"crossref","first-page":"2740","DOI":"10.1093\/bioinformatics\/bty179","article-title":"Deep learning improves antimicrobial peptide recognition","volume":"34","author":"Veltri","year":"2018","journal-title":"Bioinformatics"},{"key":"2022011921341252900_ref35","first-page":"9(8)","article-title":"Long short-term memory","author":"Hochreiter","year":"1997","journal-title":"Neural Comput"},{"issue":"1","key":"2022011921341252900_ref36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-019-3006-z","article-title":"PTPD: predicting therapeutic peptides by deep learning and word2vec","volume":"20","author":"Wu","year":"2019","journal-title":"BMC bioinformatics"},{"key":"2022011921341252900_ref37","first-page":"5998","article-title":"Attention is all you need","volume-title":"Advances in neural information processing systems","author":"Vaswani"},{"key":"2022011921341252900_ref38","first-page":"294","article-title":"Recent trends in nature inspired computation with applications to deep learning","volume-title":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","author":"Bharti"},{"issue":"2","key":"2022011921341252900_ref39","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1128\/jb.166.2.623-627.1986","article-title":"Bacteriophage involvement in group A streptococcal pyrogenic exotoxin A production","volume":"166","author":"Johnson","year":"1986","journal-title":"J Bacteriol"},{"key":"2022011921341252900_ref40","volume-title":"Regression and ANOVA: an integrated approach using SAS software","author":"Muller","year":"2003"},{"issue":"D1","key":"2022011921341252900_ref41","doi-asserted-by":"crossref","first-page":"D1087","DOI":"10.1093\/nar\/gkv1278","article-title":"APD3: the antimicrobial peptide database as a tool for research and education","volume":"44","author":"Wang","year":"2016","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"2022011921341252900_ref42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-019-0154-y","article-title":"DRAMP 2.0, an updated data repository of antimicrobial peptides","volume":"6","author":"Kang","year":"2019","journal-title":"Scientific data"},{"issue":"2","key":"2022011921341252900_ref43","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s13594-013-0153-2","article-title":"MilkAMP: a comprehensive database of antimicrobial peptides of dairy origin","volume":"94","author":"Th\u00e9olier","year":"2014","journal-title":"Dairy Sci Technol"},{"issue":"15","key":"2022011921341252900_ref44","doi-asserted-by":"crossref","first-page":"2553","DOI":"10.1093\/bioinformatics\/btv180","article-title":"Overlap and diversity in antimicrobial peptide databases: compiling a non-redundant set of sequences","volume":"31","author":"Aguilera-Mendoza","year":"2015","journal-title":"Bioinformatics"},{"issue":"22","key":"2022011921341252900_ref45","doi-asserted-by":"crossref","first-page":"4739","DOI":"10.1093\/bioinformatics\/btz260","article-title":"Graph-based data integration from bioactive peptide databases of pharmaceutical interest: toward an organized collection enabling visual network analysis","volume":"35","author":"Aguilera-Mendoza","year":"2019","journal-title":"Bioinformatics"},{"issue":"1","key":"2022011921341252900_ref46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-75029-1","article-title":"Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach","volume":"10","author":"Aguilera-Mendoza","year":"2020","journal-title":"Sci Rep"},{"issue":"D1","key":"2022011921341252900_ref47","doi-asserted-by":"crossref","first-page":"D506","DOI":"10.1093\/nar\/gky1049","article-title":"UniProt: a worldwide hub of protein knowledge","volume":"47","author":"Consortium","year":"2019","journal-title":"Nucleic Acids Res"},{"issue":"11","key":"2022011921341252900_ref48","doi-asserted-by":"crossref","first-page":"1422","DOI":"10.1093\/bioinformatics\/btp163","article-title":"Biopython: freely available Python tools for computational molecular biology and bioinformatics","volume":"25","author":"Cock","year":"2009","journal-title":"Bioinformatics"},{"issue":"7","key":"2022011921341252900_ref49","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1093\/bioinformatics\/btt072","article-title":"propy: a tool to generate various modes of Chou\u2019s PseAAC","volume":"29","author":"Cao","year":"2013","journal-title":"Bioinformatics"},{"issue":"13","key":"2022011921341252900_ref50","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1093\/bioinformatics\/btl158","article-title":"Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences","volume":"22","author":"Li","year":"2006","journal-title":"Bioinformatics"},{"key":"2022011921341252900_ref51","article-title":"Efficient estimation of word representations in vector space","author":"Mikolov","year":"2013"},{"issue":"1","key":"2022011921341252900_ref52","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1515\/jisys-2020-0021","article-title":"Deep Bidirectional LSTM Network Learning-Based Sentiment Analysis for Arabic Text","volume":"30","author":"Elfaik","year":"2021","journal-title":"Journal of Intelligent Systems"},{"key":"2022011921341252900_ref53","article-title":"Neural machine translation by jointly learning to align and translate","author":"Bahdanau","year":"2014"},{"issue":"2","key":"2022011921341252900_ref54","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1109\/TCBB.2015.2462364","article-title":"Improving recognition of antimicrobial peptides and target selectivity through machine learning and genetic programming","volume":"14","author":"Veltri","year":"2015","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2022011921341252900_ref55","article-title":"Tensorflow: Large-scale machine learning on heterogeneous distributed systems","author":"Abadi","year":"2016"},{"key":"2022011921341252900_ref56","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.future.2017.09.054","article-title":"A novel cost-efficient approach for deadline-constrained workflow scheduling by dynamic provisioning of resources","volume":"79","author":"Singh","year":"2018","journal-title":"Future Generation Computer Systems"},{"issue":"3","key":"2022011921341252900_ref57","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s10723-019-09490-2","article-title":"An energy efficient algorithm for workflow scheduling in IAAS cloud","volume":"18","author":"Singh","year":"2020","journal-title":"Journal of Grid Computing"},{"issue":"3","key":"2022011921341252900_ref58","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s10822-016-9902-7","article-title":"Pep-Calc. com: a set of web utilities for the calculation of peptide and peptoid properties and automatic mass spectral peak assignment","volume":"30","author":"Lear","year":"2016","journal-title":"J Comput Aided Mol Des"},{"issue":"18","key":"2022011921341252900_ref59","doi-asserted-by":"crossref","first-page":"2101","DOI":"10.1093\/bioinformatics\/btn392","article-title":"HELIQUEST: a web server to screen sequences with specific $\\alpha $-helical properties","volume":"24","author":"Gautier","year":"2008","journal-title":"Bioinformatics"},{"issue":"6","key":"2022011921341252900_ref60","doi-asserted-by":"crossref","first-page":"20160153","DOI":"10.1098\/rsfs.2016.0153","article-title":"What can machine learning do for antimicrobial peptides, and what can antimicrobial peptides do for machine learning?","volume":"7","author":"Lee","year":"2017","journal-title":"Interface Focus"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/1\/bbab439\/42230094\/bbab439.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/1\/bbab439\/42230094\/bbab439.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T03:08:41Z","timestamp":1725851321000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbab439\/6423526"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,8]]},"references-count":60,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1,17]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbab439","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,1]]},"published":{"date-parts":[[2021,11,8]]},"article-number":"bbab439"}}