*Result*: AIP-TranLAC: A Transformer-Based Method Integrating LSTM and Attention Mechanism for Predicting Anti-inflammatory Peptides.
Title:
AIP-TranLAC: A Transformer-Based Method Integrating LSTM and Attention Mechanism for Predicting Anti-inflammatory Peptides.
Authors:
Source:
Interdisciplinary sciences, computational life sciences [Interdiscip Sci] 2026 Mar; Vol. 18 (1), pp. 253-266. Date of Electronic Publication: 2025 Aug 19.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 101515919 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1867-1462 (Electronic) Linking ISSN: 18671462 NLM ISO Abbreviation: Interdiscip Sci Subsets: MEDLINE
Imprint Name(s):
Original Publication: [Heidelberg] : Springer-Verlag
MeSH Terms:
References:
Dadar M, Shahali Y, Chakraborty S et al (2019) Antiinflammatory peptides: current knowledge and promising prospects. Inflamm Re 68:125–145. https://doi.org/10.1007/s00011-018-1208-x. (PMID: 10.1007/s00011-018-1208-x)
Biji CA, Balde A, Nazeer RA (2024) Anti-inflammatory peptide therapeutics and the role of sulphur containing amino acids (cysteine and methionine) in inflammation suppression: a review. Inflamm Re 73:1203–1221. https://doi.org/10.1007/s00011-024-01893-6. (PMID: 10.1007/s00011-024-01893-6)
Wang SC, Fan LM, Pan HY et al (2023) Antimicrobial peptides from marine animals: sources, structures, mechanisms and the potential for drug development. Front Mar Sci 9:1112595. https://doi.org/10.3389/fmars.2022.1112595. (PMID: 10.3389/fmars.2022.1112595)
Zouki C, Ouellet S, Filep JG (2000) The anti-inflammatory peptides, antiflammins, regulate the expression of adhesion molecules on human leukocytes and prevent neutrophil adhesion to endothelial cells. FASEB J 14:572–580. https://doi.org/10.1096/fasebj.14.3.572. (PMID: 10.1096/fasebj.14.3.57210698973)
Bhol NK, Bhanjadeo MM, Singh AK et al (2024) The interplay between cytokines, inflammation, and antioxidants: mechanistic insights and therapeutic potentials of various antioxidants and anti-cytokine compounds. Biomed Pharmacother 178:117177. https://doi.org/10.1016/j.biopha.2024.117177. (PMID: 10.1016/j.biopha.2024.11717739053423)
Megha KB, Joseph X, Akhil V et al (2021) Cascade of immune mechanism and consequences of inflammatory disorders. Phytomedicine 91:153712. https://doi.org/10.1016/j.phymed.2021.153712. (PMID: 10.1016/j.phymed.2021.153712345112648373857)
Li LH, Yu R, Cai T et al (2020) Effects of immune cells and cytokines on inflammation and immunosuppression in the tumor microenvironment. Int Immunopharmacol 88:106939. https://doi.org/10.1016/j.intimp.2020.106939. (PMID: 10.1016/j.intimp.2020.10693933182039)
Navegantes KC, de Souza Gomes R, Tártari Pereira PA et al (2017) Immune modulation of some autoimmune diseases: the critical role of macrophages and neutrophils in the innate and adaptive immunity. J Transl Med 15:1–21. https://doi.org/10.1186/s12967-017-1141-8. (PMID: 10.1186/s12967-017-1141-8)
Lawrence T, Willoughby DA, Gilroy DW (2002) Anti-inflammatory lipid mediators and insights into the resolution of inflammation. Nat Rev Immunol 2:787–795. https://doi.org/10.1038/nri915. (PMID: 10.1038/nri91512360216)
Liu YC, Zou XB, Chai YF et al (2014) Macrophage polarization in inflammatory diseases. Int J Biol Sci 10:520–529. https://doi.org/10.7150/ijbs.8879. (PMID: 10.7150/ijbs.8879249105314046879)
Guo Q, Jin YZ, Chen XY et al (2024) NF-κB in biology and targeted therapy: new insights and translational implications. Signal Transduct Target Ther 9:53. https://doi.org/10.1038/s41392-024-01757-9. (PMID: 10.1038/s41392-024-01757-93843328010910037)
Cuevas BD, Abell AN, Johnson GL (2007) Role of mitogen-activated protein kinase kinase kinases in signal integration. Oncogene 26:3159–3171. https://doi.org/10.1038/sj.onc.1210409. (PMID: 10.1038/sj.onc.121040917496913)
Chen Y, Fang ZM, Yi X et al (2023) The interaction between ferroptosis and inflammatory signaling pathways. Cell Death Dis 14:205. https://doi.org/10.1038/s41419-023-05716-0. (PMID: 10.1038/s41419-023-05716-03694460910030804)
Deng Z, Liu S (2021) Inflammation-responsive delivery systems for the treatment of chronic inflammatory diseases. Drug Deliv Transl Res 11:1475–1497. https://doi.org/10.1007/s13346-021-00977-8. (PMID: 10.1007/s13346-021-00977-8338604478048351)
Eming SA, Wynn TA, Martin P (2017) Inflammation and metabolism in tissue repair and regeneration. Science 356:1026–1030. https://doi.org/10.1126/science.aam7928. (PMID: 10.1126/science.aam792828596335)
Li M, Lu W, Sun Y et al (2023) Antimicrobial peptides: sources, expression systems, and applications. Curr Protein Pept Sci 24:640–654. https://doi.org/10.2174/1389203724666230727101636. (PMID: 10.2174/138920372466623072710163637497701)
Grieco P, Rossi C, Gatti S et al (2005) Design and synthesis of melanocortin peptides with candidacidal and anti-TNF-alpha properties. J Med Chem 48:1384–1388. https://doi.org/10.1021/jm040890j. (PMID: 10.1021/jm040890j15743181)
Geetha C, Venkatesh SG, Bingle L et al (2005) Design and validation of anti-inflammatory peptides from human parotid secretory protein. J Dent Res 84:149–153. https://doi.org/10.1177/154405910508400208. (PMID: 10.1177/15440591050840020815668332)
Park KH, Nan YH, Park YK et al (2009) Cell specificity, anti-inflammatory activity, and plausible bactericidal mechanism of designed Trp-rich model antimicrobial peptides. Biochim Biophys Acta Biomembr 1788:1193–1203. https://doi.org/10.1016/j.bbamem.2009.02.020. (PMID: 10.1016/j.bbamem.2009.02.020)
Zhang Y (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 9:1. https://doi.org/10.1186/1471-2105-9-40. (PMID: 10.1186/1471-2105-9-40)
Thomas S, Karnik SD, Barai RS et al (2010) CAMP: a useful resource for research on antimicrobial peptides. Nucleic Acids Res 38:D774–D780. https://doi.org/10.1093/nar/gkp1021. (PMID: 10.1093/nar/gkp102119923233)
Khan S, Noor S, Javed T et al (2025) XGBoost-enhanced ensemble model using discriminative hybrid features for the prediction of sumoylation sites. BioData Min 18:12. https://doi.org/10.1186/s13040-024-00415-8. (PMID: 10.1186/s13040-024-00415-83990127911792219)
Noor S, Naseem A, Awan HH et al (2024) Deep-m5U: a deep learning-based approach for RNA 5-methyluridine modification prediction using optimized feature integration. BMC Bioinformatics 25:360. https://doi.org/10.1186/s12859-024-05978-1. (PMID: 10.1186/s12859-024-05978-13956323911577875)
Khan S, Khan MA, Khan M et al (2023) Optimized feature learning for Anti-Inflammatory peptide prediction using parallel distributed computing. Appl Sci 13:7059. https://doi.org/10.3390/app13127059. (PMID: 10.3390/app13127059)
Gupta S, Sharma AK, Shastri V et al (2017) Prediction of anti-inflammatory proteins/peptides: an insilico approach. J Transl Med 15:1–11. https://doi.org/10.1186/s12967-016-1103-6. (PMID: 10.1186/s12967-016-1103-6)
Manavalan B, Shin TH, Kim MO et al (2018) AIPpred: sequence-based prediction of anti-inflammatory peptides using random forest. Front Pharmacol 9:276. https://doi.org/10.3389/fphar.2018.00276.
Khatun MS, Hasan MM, Kurata H (2019) PreAIP: computational prediction of anti-inflammatory peptides by integrating multiple complementary features. Front Genet 10:129. https://doi.org/10.3389/fgene.2019.00129. (PMID: 10.3389/fgene.2019.00129308910596411759)
Zhang JH, Zhang ZH, Pu LR et al (2021) AIEpred: an ensemble predictive model of classifier chain to identify anti-inflammatory peptides. IEEE/ACM Trans Comput Biol Bioinform 18:1831–1840. https://doi.org/10.1109/TCBB.2020.2968419. (PMID: 10.1109/TCBB.2020.296841931985437)
Zhao DX, Teng ZX, Li YJ et al (2021) iAIPs: identifying anti-inflammatory peptides using random forest. Front Genet 12:773202. https://doi.org/10.3389/fgene.2021.773202. (PMID: 10.3389/fgene.2021.773202349171308669811)
Gaffar S, Hassan MT, Tayara H et al (2024) IF-AIP: a machine learning method for the identification of anti-inflammatory peptides using multi-feature fusion strategy. Comput Biol Med 168:107724. https://doi.org/10.1016/j.compbiomed.2023.107724. (PMID: 10.1016/j.compbiomed.2023.10772437989075)
Xu T, Wang Q, Yang ZG et al (2024) A BERT-based approach for identifying anti-inflammatory peptides using sequence information. Heliyon 10:e32951. https://doi.org/10.1016/j.heliyon.2024.e32951. (PMID: 10.1016/j.heliyon.2024.e329513898853711234020)
Khan S, AlQahtani SA, Noor S et al (2024) PSSM-Sumo: deep learning based intelligent model for prediction of sumoylation sites using discriminative features. BMC Bioinformatics 25:284. https://doi.org/10.1186/s12859-024-05917-0. (PMID: 10.1186/s12859-024-05917-03921523111363370)
Fu LM, Niu BF, Zhu ZW et al (2012) CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28:3150–3152. https://doi.org/10.1093/bioinformatics/bts565. (PMID: 10.1093/bioinformatics/bts565230606103516142)
Li H, Gong XJ, Yu H et al (2018) Deep neural network based predictions of protein interactions using primary sequences. Molecules 23:1923. https://doi.org/10.3390/molecules23081923.
Chen SZ, Li Q, Zhao JP et al (2022) NeuroPred-CLQ: incorporating deep Temporal convolutional networks and multi-head attention mechanism to predict neuropeptides. Brief Bioinform 23:bbac319. https://doi.org/10.1093/bib/bbac319. (PMID: 10.1093/bib/bbac31935988921)
Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. arXiv. https://doi.org/10.48550/arXiv.1706.03762.
Li ZS, Jin JR, Wang Y et al (2023) ExamPle: explainable deep learning framework for the prediction of plant small secreted peptides. Bioinformatics 39:btad108. https://doi.org/10.1093/bioinformatics/btad108. (PMID: 10.1093/bioinformatics/btad1083689703010027287)
Jumper J, Evans R, Pritzel A et al (2021) Highly accurate protein structure prediction with alphafold. Nature 596:583–589. https://doi.org/10.1038/s41586-021-03819-2. (PMID: 10.1038/s41586-021-03819-2342658448371605)
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735. (PMID: 10.1162/neco.1997.9.8.17359377276)
Davi GS, Anderson AMM (2023) Comparing long short-term memory (LSTM) and bidirectional LSTM deep neural networks for power consumption prediction. Energy Rep 10:3315–3334. https://doi.org/10.1016/j.egyr.2023.09.175. (PMID: 10.1016/j.egyr.2023.09.175)
Van Houdt G, Mosquera C, Nápoles G (2020) A review on the long short-term memory model. Artif Intell Rev 53:5929–5955. https://doi.org/10.1007/s10462-020-09838-1. (PMID: 10.1007/s10462-020-09838-1)
Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv. https://doi.org/10.48550/arXiv.1409.0473.
Singh V, Shrivastava S, Kumar Singh S et al (2022) StaBle-ABPpred: a stacked ensemble predictor based on biLSTM and attention mechanism for accelerated discovery of antibacterial peptides. Brief Bioinform 2022, 23:1–17. https://doi.org/10.1093/bib/bbab439.
Jones DT, Kandathil SM (2018) High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features. Bioinformatics 34:3308–3315. https://doi.org/10.1093/bioinformatics/bty341. (PMID: 10.1093/bioinformatics/bty341297181126157083)
Diederik P, Kingma JB (2017) Adam: a method for stochastic optimization. arXiv. https://doi.org/10.48550/arXiv.1412.6980.
Akiba T, Sano S, Yanase T et al (2019) Optuna: a next-generation hyperparameter optimization framework. arXiv. https://doi.org/10.48550/arXiv.1907.10902.
Sharma R, Shrivastava S, Kumar Singh S et al (2021) Deep-ABPpred: identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec. Brief Bioinform 22:1–19. https://doi.org/10.1093/bib/bbab065. (PMID: 10.1093/bib/bbab065)
Wei ZT, Gao YL, Meng FLZ et al (2021) iDMer: an integrative and mechanism-driven response system for identifying compound interventions for sudden virus outbreak. Brief Bioinform 22:976–987. https://doi.org/10.1093/bib/bbaa341. (PMID: 10.1093/bib/bbaa341333022927799233)
Du ZJ, Ding XJ, Xu YX et al (2023) UniDL4BioPep: a universal deep learning architecture for binary classification in peptide bioactivity. Brief Bioinform 24:bbad135. https://doi.org/10.1093/bib/bbad135. (PMID: 10.1093/bib/bbad13537020337)
Charoenkwan P, Kanthawong S, Nantasenamat C et al (2020) iDPPIV-SCM: a sequence-based predictor for identifying and analyzing dipeptidyl peptidase IV (DPP-IV) inhibitory peptides using a scoring card method. J Proteome Res 19:4125–4136. https://doi.org/10.1021/acs.jproteome.0c00590. (PMID: 10.1021/acs.jproteome.0c0059032897718)
Agrawal P, Bhagat D, Mahalwal M et al (2021) AntiCP 2.0: an updated model for predicting anticancer peptides. Brief Bioinform 22:bbaa153. https://doi.org/10.1093/bib/bbaa153. (PMID: 10.1093/bib/bbaa15332770192)
Wei Z, Shen Y, Tang X et al (2025) AVPpred-BWR: antiviral peptides prediction via biological words representation. Bioinformatics 41:btaf126. https://doi.org/10.1093/bioinformatics/btaf126. (PMID: 10.1093/bioinformatics/btaf1264015225011968319)
Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579–2605. https://jmlr.org/papers/v9/vandermaaten08a.html.
Noor S, Awan HH, Hashmi AS et al (2025) Optimizing performance of parallel computing platforms for large-scale genome data analysis. Computing 107:86. https://doi.org/10.1007/s00607-025-01441-y. (PMID: 10.1007/s00607-025-01441-y)
Biji CA, Balde A, Nazeer RA (2024) Anti-inflammatory peptide therapeutics and the role of sulphur containing amino acids (cysteine and methionine) in inflammation suppression: a review. Inflamm Re 73:1203–1221. https://doi.org/10.1007/s00011-024-01893-6. (PMID: 10.1007/s00011-024-01893-6)
Wang SC, Fan LM, Pan HY et al (2023) Antimicrobial peptides from marine animals: sources, structures, mechanisms and the potential for drug development. Front Mar Sci 9:1112595. https://doi.org/10.3389/fmars.2022.1112595. (PMID: 10.3389/fmars.2022.1112595)
Zouki C, Ouellet S, Filep JG (2000) The anti-inflammatory peptides, antiflammins, regulate the expression of adhesion molecules on human leukocytes and prevent neutrophil adhesion to endothelial cells. FASEB J 14:572–580. https://doi.org/10.1096/fasebj.14.3.572. (PMID: 10.1096/fasebj.14.3.57210698973)
Bhol NK, Bhanjadeo MM, Singh AK et al (2024) The interplay between cytokines, inflammation, and antioxidants: mechanistic insights and therapeutic potentials of various antioxidants and anti-cytokine compounds. Biomed Pharmacother 178:117177. https://doi.org/10.1016/j.biopha.2024.117177. (PMID: 10.1016/j.biopha.2024.11717739053423)
Megha KB, Joseph X, Akhil V et al (2021) Cascade of immune mechanism and consequences of inflammatory disorders. Phytomedicine 91:153712. https://doi.org/10.1016/j.phymed.2021.153712. (PMID: 10.1016/j.phymed.2021.153712345112648373857)
Li LH, Yu R, Cai T et al (2020) Effects of immune cells and cytokines on inflammation and immunosuppression in the tumor microenvironment. Int Immunopharmacol 88:106939. https://doi.org/10.1016/j.intimp.2020.106939. (PMID: 10.1016/j.intimp.2020.10693933182039)
Navegantes KC, de Souza Gomes R, Tártari Pereira PA et al (2017) Immune modulation of some autoimmune diseases: the critical role of macrophages and neutrophils in the innate and adaptive immunity. J Transl Med 15:1–21. https://doi.org/10.1186/s12967-017-1141-8. (PMID: 10.1186/s12967-017-1141-8)
Lawrence T, Willoughby DA, Gilroy DW (2002) Anti-inflammatory lipid mediators and insights into the resolution of inflammation. Nat Rev Immunol 2:787–795. https://doi.org/10.1038/nri915. (PMID: 10.1038/nri91512360216)
Liu YC, Zou XB, Chai YF et al (2014) Macrophage polarization in inflammatory diseases. Int J Biol Sci 10:520–529. https://doi.org/10.7150/ijbs.8879. (PMID: 10.7150/ijbs.8879249105314046879)
Guo Q, Jin YZ, Chen XY et al (2024) NF-κB in biology and targeted therapy: new insights and translational implications. Signal Transduct Target Ther 9:53. https://doi.org/10.1038/s41392-024-01757-9. (PMID: 10.1038/s41392-024-01757-93843328010910037)
Cuevas BD, Abell AN, Johnson GL (2007) Role of mitogen-activated protein kinase kinase kinases in signal integration. Oncogene 26:3159–3171. https://doi.org/10.1038/sj.onc.1210409. (PMID: 10.1038/sj.onc.121040917496913)
Chen Y, Fang ZM, Yi X et al (2023) The interaction between ferroptosis and inflammatory signaling pathways. Cell Death Dis 14:205. https://doi.org/10.1038/s41419-023-05716-0. (PMID: 10.1038/s41419-023-05716-03694460910030804)
Deng Z, Liu S (2021) Inflammation-responsive delivery systems for the treatment of chronic inflammatory diseases. Drug Deliv Transl Res 11:1475–1497. https://doi.org/10.1007/s13346-021-00977-8. (PMID: 10.1007/s13346-021-00977-8338604478048351)
Eming SA, Wynn TA, Martin P (2017) Inflammation and metabolism in tissue repair and regeneration. Science 356:1026–1030. https://doi.org/10.1126/science.aam7928. (PMID: 10.1126/science.aam792828596335)
Li M, Lu W, Sun Y et al (2023) Antimicrobial peptides: sources, expression systems, and applications. Curr Protein Pept Sci 24:640–654. https://doi.org/10.2174/1389203724666230727101636. (PMID: 10.2174/138920372466623072710163637497701)
Grieco P, Rossi C, Gatti S et al (2005) Design and synthesis of melanocortin peptides with candidacidal and anti-TNF-alpha properties. J Med Chem 48:1384–1388. https://doi.org/10.1021/jm040890j. (PMID: 10.1021/jm040890j15743181)
Geetha C, Venkatesh SG, Bingle L et al (2005) Design and validation of anti-inflammatory peptides from human parotid secretory protein. J Dent Res 84:149–153. https://doi.org/10.1177/154405910508400208. (PMID: 10.1177/15440591050840020815668332)
Park KH, Nan YH, Park YK et al (2009) Cell specificity, anti-inflammatory activity, and plausible bactericidal mechanism of designed Trp-rich model antimicrobial peptides. Biochim Biophys Acta Biomembr 1788:1193–1203. https://doi.org/10.1016/j.bbamem.2009.02.020. (PMID: 10.1016/j.bbamem.2009.02.020)
Zhang Y (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 9:1. https://doi.org/10.1186/1471-2105-9-40. (PMID: 10.1186/1471-2105-9-40)
Thomas S, Karnik SD, Barai RS et al (2010) CAMP: a useful resource for research on antimicrobial peptides. Nucleic Acids Res 38:D774–D780. https://doi.org/10.1093/nar/gkp1021. (PMID: 10.1093/nar/gkp102119923233)
Khan S, Noor S, Javed T et al (2025) XGBoost-enhanced ensemble model using discriminative hybrid features for the prediction of sumoylation sites. BioData Min 18:12. https://doi.org/10.1186/s13040-024-00415-8. (PMID: 10.1186/s13040-024-00415-83990127911792219)
Noor S, Naseem A, Awan HH et al (2024) Deep-m5U: a deep learning-based approach for RNA 5-methyluridine modification prediction using optimized feature integration. BMC Bioinformatics 25:360. https://doi.org/10.1186/s12859-024-05978-1. (PMID: 10.1186/s12859-024-05978-13956323911577875)
Khan S, Khan MA, Khan M et al (2023) Optimized feature learning for Anti-Inflammatory peptide prediction using parallel distributed computing. Appl Sci 13:7059. https://doi.org/10.3390/app13127059. (PMID: 10.3390/app13127059)
Gupta S, Sharma AK, Shastri V et al (2017) Prediction of anti-inflammatory proteins/peptides: an insilico approach. J Transl Med 15:1–11. https://doi.org/10.1186/s12967-016-1103-6. (PMID: 10.1186/s12967-016-1103-6)
Manavalan B, Shin TH, Kim MO et al (2018) AIPpred: sequence-based prediction of anti-inflammatory peptides using random forest. Front Pharmacol 9:276. https://doi.org/10.3389/fphar.2018.00276.
Khatun MS, Hasan MM, Kurata H (2019) PreAIP: computational prediction of anti-inflammatory peptides by integrating multiple complementary features. Front Genet 10:129. https://doi.org/10.3389/fgene.2019.00129. (PMID: 10.3389/fgene.2019.00129308910596411759)
Zhang JH, Zhang ZH, Pu LR et al (2021) AIEpred: an ensemble predictive model of classifier chain to identify anti-inflammatory peptides. IEEE/ACM Trans Comput Biol Bioinform 18:1831–1840. https://doi.org/10.1109/TCBB.2020.2968419. (PMID: 10.1109/TCBB.2020.296841931985437)
Zhao DX, Teng ZX, Li YJ et al (2021) iAIPs: identifying anti-inflammatory peptides using random forest. Front Genet 12:773202. https://doi.org/10.3389/fgene.2021.773202. (PMID: 10.3389/fgene.2021.773202349171308669811)
Gaffar S, Hassan MT, Tayara H et al (2024) IF-AIP: a machine learning method for the identification of anti-inflammatory peptides using multi-feature fusion strategy. Comput Biol Med 168:107724. https://doi.org/10.1016/j.compbiomed.2023.107724. (PMID: 10.1016/j.compbiomed.2023.10772437989075)
Xu T, Wang Q, Yang ZG et al (2024) A BERT-based approach for identifying anti-inflammatory peptides using sequence information. Heliyon 10:e32951. https://doi.org/10.1016/j.heliyon.2024.e32951. (PMID: 10.1016/j.heliyon.2024.e329513898853711234020)
Khan S, AlQahtani SA, Noor S et al (2024) PSSM-Sumo: deep learning based intelligent model for prediction of sumoylation sites using discriminative features. BMC Bioinformatics 25:284. https://doi.org/10.1186/s12859-024-05917-0. (PMID: 10.1186/s12859-024-05917-03921523111363370)
Fu LM, Niu BF, Zhu ZW et al (2012) CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28:3150–3152. https://doi.org/10.1093/bioinformatics/bts565. (PMID: 10.1093/bioinformatics/bts565230606103516142)
Li H, Gong XJ, Yu H et al (2018) Deep neural network based predictions of protein interactions using primary sequences. Molecules 23:1923. https://doi.org/10.3390/molecules23081923.
Chen SZ, Li Q, Zhao JP et al (2022) NeuroPred-CLQ: incorporating deep Temporal convolutional networks and multi-head attention mechanism to predict neuropeptides. Brief Bioinform 23:bbac319. https://doi.org/10.1093/bib/bbac319. (PMID: 10.1093/bib/bbac31935988921)
Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. arXiv. https://doi.org/10.48550/arXiv.1706.03762.
Li ZS, Jin JR, Wang Y et al (2023) ExamPle: explainable deep learning framework for the prediction of plant small secreted peptides. Bioinformatics 39:btad108. https://doi.org/10.1093/bioinformatics/btad108. (PMID: 10.1093/bioinformatics/btad1083689703010027287)
Jumper J, Evans R, Pritzel A et al (2021) Highly accurate protein structure prediction with alphafold. Nature 596:583–589. https://doi.org/10.1038/s41586-021-03819-2. (PMID: 10.1038/s41586-021-03819-2342658448371605)
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735. (PMID: 10.1162/neco.1997.9.8.17359377276)
Davi GS, Anderson AMM (2023) Comparing long short-term memory (LSTM) and bidirectional LSTM deep neural networks for power consumption prediction. Energy Rep 10:3315–3334. https://doi.org/10.1016/j.egyr.2023.09.175. (PMID: 10.1016/j.egyr.2023.09.175)
Van Houdt G, Mosquera C, Nápoles G (2020) A review on the long short-term memory model. Artif Intell Rev 53:5929–5955. https://doi.org/10.1007/s10462-020-09838-1. (PMID: 10.1007/s10462-020-09838-1)
Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv. https://doi.org/10.48550/arXiv.1409.0473.
Singh V, Shrivastava S, Kumar Singh S et al (2022) StaBle-ABPpred: a stacked ensemble predictor based on biLSTM and attention mechanism for accelerated discovery of antibacterial peptides. Brief Bioinform 2022, 23:1–17. https://doi.org/10.1093/bib/bbab439.
Jones DT, Kandathil SM (2018) High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features. Bioinformatics 34:3308–3315. https://doi.org/10.1093/bioinformatics/bty341. (PMID: 10.1093/bioinformatics/bty341297181126157083)
Diederik P, Kingma JB (2017) Adam: a method for stochastic optimization. arXiv. https://doi.org/10.48550/arXiv.1412.6980.
Akiba T, Sano S, Yanase T et al (2019) Optuna: a next-generation hyperparameter optimization framework. arXiv. https://doi.org/10.48550/arXiv.1907.10902.
Sharma R, Shrivastava S, Kumar Singh S et al (2021) Deep-ABPpred: identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec. Brief Bioinform 22:1–19. https://doi.org/10.1093/bib/bbab065. (PMID: 10.1093/bib/bbab065)
Wei ZT, Gao YL, Meng FLZ et al (2021) iDMer: an integrative and mechanism-driven response system for identifying compound interventions for sudden virus outbreak. Brief Bioinform 22:976–987. https://doi.org/10.1093/bib/bbaa341. (PMID: 10.1093/bib/bbaa341333022927799233)
Du ZJ, Ding XJ, Xu YX et al (2023) UniDL4BioPep: a universal deep learning architecture for binary classification in peptide bioactivity. Brief Bioinform 24:bbad135. https://doi.org/10.1093/bib/bbad135. (PMID: 10.1093/bib/bbad13537020337)
Charoenkwan P, Kanthawong S, Nantasenamat C et al (2020) iDPPIV-SCM: a sequence-based predictor for identifying and analyzing dipeptidyl peptidase IV (DPP-IV) inhibitory peptides using a scoring card method. J Proteome Res 19:4125–4136. https://doi.org/10.1021/acs.jproteome.0c00590. (PMID: 10.1021/acs.jproteome.0c0059032897718)
Agrawal P, Bhagat D, Mahalwal M et al (2021) AntiCP 2.0: an updated model for predicting anticancer peptides. Brief Bioinform 22:bbaa153. https://doi.org/10.1093/bib/bbaa153. (PMID: 10.1093/bib/bbaa15332770192)
Wei Z, Shen Y, Tang X et al (2025) AVPpred-BWR: antiviral peptides prediction via biological words representation. Bioinformatics 41:btaf126. https://doi.org/10.1093/bioinformatics/btaf126. (PMID: 10.1093/bioinformatics/btaf1264015225011968319)
Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579–2605. https://jmlr.org/papers/v9/vandermaaten08a.html.
Noor S, Awan HH, Hashmi AS et al (2025) Optimizing performance of parallel computing platforms for large-scale genome data analysis. Computing 107:86. https://doi.org/10.1007/s00607-025-01441-y. (PMID: 10.1007/s00607-025-01441-y)
Grant Information:
2024JC-YBMS-004 Natural Science Basic Research Program of Shaanxi; TZJH2024028 Xidian University Specially Funded Project for Interdisciplinary Exploration
Contributed Indexing:
Keywords: Anti-inflammatory peptide; Bi-LSTM; Multi-head attention; Transformer
Substance Nomenclature:
0 (Anti-Inflammatory Agents)
0 (Peptides)
0 (Peptides)
Entry Date(s):
Date Created: 20250819 Date Completed: 20260213 Latest Revision: 20260213
Update Code:
20260213
DOI:
10.1007/s12539-025-00761-z
PMID:
40830309
Database:
MEDLINE
*Further Information*
*Anti-inflammatory peptides (AIPs) have emerged as potential therapeutic candidates for managing various inflammatory disorders, but their computational identification remains challenging. We propose AIP-TranLAC, a novel deep learning framework that integrates Transformer-based embedding, bidirectional long short-term memory (Bi-LSTM), multi-head attention, and convolutional neural network (CNN) to classify AIPs accurately. Our model achieves superior performance on benchmark and independent test datasets, demonstrating significant improvements over existing methods. The hybrid architecture effectively captures local and global sequence patterns, while interpretability analyses reveal critical amino acid residues. With robust performance on imbalanced data and open-source availability, AIP-TranLAC provides a powerful tool for accelerating therapeutic peptide discovery and inflammation research. For reproducibility purposes, we have released the codebase, trained models, and all supporting data on GitHub ( https://github.com/Renjingyi123/AIP-TranLAC ).
(© 2025. International Association of Scientists in the Interdisciplinary Areas.)*
*Declarations. Conflict of Interest: The authors declare that they have no conflict of interest.*