MPMABP: A CNN and Bi-LSTM-Based Method for Predicting Multi-Activities of Bioactive Peptides

Overview

Journal Pharmaceuticals (Basel)

Publisher MDPI

Specialty Chemistry

Date 2022 Jun 24

PMID 35745625

Authors

You Li

Xueyong Li

Yuewu Liu

Yuhua Yao

Guohua Huang

Affiliations

Soon will be listed here.

Abstract

Bioactive peptides are typically small functional peptides with 2-20 amino acid residues and play versatile roles in metabolic and biological processes. Bioactive peptides are multi-functional, so it is vastly challenging to accurately detect all their functions simultaneously. We proposed a convolution neural network (CNN) and bi-directional long short-term memory (Bi-LSTM)-based deep learning method (called MPMABP) for recognizing multi-activities of bioactive peptides. The MPMABP stacked five CNNs at different scales, and used the residual network to preserve the information from loss. The empirical results showed that the MPMABP is superior to the state-of-the-art methods. Analysis on the distribution of amino acids indicated that the lysine preferred to appear in the anti-cancer peptide, the leucine in the anti-diabetic peptide, and the proline in the anti-hypertensive peptide. The method and analysis are beneficial to recognize multi-activities of bioactive peptides.

Citing Articles

MFP-MFL: Leveraging Graph Attention and Multi-Feature Integration for Superior Multifunctional Bioactive Peptide Prediction.

Ge F, Zhou J, Zhang M, Yu D Int J Mol Sci. 2025; 26(3).

PMID: 39941085 PMC: 11818429. DOI: 10.3390/ijms26031317.

Advances of deep Neural Networks (DNNs) in the development of peptide drugs.

Niu Y, Qin P, Lin P Future Med Chem. 2025; 17(4):485-499.

PMID: 39935356 PMC: 11834456. DOI: 10.1080/17568919.2025.2463319.

Research Progress of Food-Derived Antihypertensive Peptides in Regulating the Key Factors of the Renin-Angiotensin System.

Yao X, Cao X, Chen L, Liao W Nutrients. 2025; 17(1.

PMID: 39796531 PMC: 11722916. DOI: 10.3390/nu17010097.

Machine learning tools for peptide bioactivity evaluation - Implications for cell culture media optimization and the broader cultivated meat industry.

Isaac K, Combe M, Potter G, Sokolenko S Curr Res Food Sci. 2024; 9:100842.

PMID: 39435450 PMC: 11491887. DOI: 10.1016/j.crfs.2024.100842.

CELA-MFP: a contrast-enhanced and label-adaptive framework for multi-functional therapeutic peptides prediction.

Fang Y, Luo M, Ren Z, Wei L, Wei D Brief Bioinform. 2024; 25(4).

PMID: 39038935 PMC: 11262836. DOI: 10.1093/bib/bbae348.

References

Dong G, Zheng L, Huang S, Gao J, Zuo Y . Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional Activities. Front Genet. 2021; 12:669328. PMC: 8093877. DOI: 10.3389/fgene.2021.669328. View

Tyagi A, Tuknait A, Anand P, Gupta S, Sharma M, Mathur D . CancerPPD: a database of anticancer peptides and proteins. Nucleic Acids Res. 2014; 43(Database issue):D837-43. PMC: 4384006. DOI: 10.1093/nar/gku892. View

Lawrence T, Carper D, Spangler M, Carrell A, Rush T, Minter S . amPEPpy 1.0: a portable and accurate antimicrobial peptide prediction tool. Bioinformatics. 2020; 37(14):2058-2060. DOI: 10.1093/bioinformatics/btaa917. View

Schaduangrat N, Nantasenamat C, Prachayasittikul V, Shoombuatong W . ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides. Molecules. 2019; 24(10). PMC: 6571645. DOI: 10.3390/molecules24101973. View

Hussain W, Rasool N, Khan Y . A Sequence-Based Predictor of Zika Virus Proteins Developed by Integration of PseAAC and Statistical Moments. Comb Chem High Throughput Screen. 2020; 23(8):797-804. DOI: 10.2174/1386207323666200428115449. View

Fu L, Niu B, Zhu Z, Wu S, Li W . CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012; 28(23):3150-2. PMC: 3516142. DOI: 10.1093/bioinformatics/bts565. View

Zahiri J, Khorsand B, Yousefi A, Kargar M, Shirali Hossein Zade R, Mahdevar G . AntAngioCOOL: computational detection of anti-angiogenic peptides. J Transl Med. 2019; 17(1):71. PMC: 6399940. DOI: 10.1186/s12967-019-1813-7. View

Manavalan B, Shin T, Kim M, Lee G . PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions. Front Immunol. 2018; 9:1783. PMC: 6079197. DOI: 10.3389/fimmu.2018.01783. View

Dai Q, Bao C, Hai Y, Ma S, Zhou T, Wang C . MTGIpick allows robust identification of genomic islands from a single genome. Brief Bioinform. 2016; 19(3):361-373. PMC: 6454522. DOI: 10.1093/bib/bbw118. View

10.

Manikkam V, Vasiljevic T, Donkor O, Mathai M . A Review of Potential Marine-derived Hypotensive and Anti-obesity Peptides. Crit Rev Food Sci Nutr. 2015; 56(1):92-112. DOI: 10.1080/10408398.2012.753866. View

11.

Manavalan B, Basith S, Shin T, Wei L, Lee G . AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees. Comput Struct Biotechnol J. 2019; 17:972-981. PMC: 6658830. DOI: 10.1016/j.csbj.2019.06.024. View

12.

Singh S, Chaudhary K, Dhanda S, Bhalla S, Usmani S, Gautam A . SATPdb: a database of structurally annotated therapeutic peptides. Nucleic Acids Res. 2015; 44(D1):D1119-26. PMC: 4702810. DOI: 10.1093/nar/gkv1114. View

13.

Ramaprasad A, Singh S, Gajendra P S R, Venkatesan S . AntiAngioPred: A Server for Prediction of Anti-Angiogenic Peptides. PLoS One. 2015; 10(9):e0136990. PMC: 4559406. DOI: 10.1371/journal.pone.0136990. View

14.

Zhao J, Bai L, Ren X, Guo J, Xia S, Zhang W . Co-immobilization of ACH antithrombotic peptide and CAG cell-adhesive peptide onto vascular grafts for improved hemocompatibility and endothelialization. Acta Biomater. 2019; 97:344-359. DOI: 10.1016/j.actbio.2019.07.057. View

15.

Tang X, Zheng P, Li X, Wu H, Wei D, Liu Y . Deep6mAPred: A CNN and Bi-LSTM-based deep learning method for predicting DNA N6-methyladenosine sites across plant species. Methods. 2022; 204:142-150. DOI: 10.1016/j.ymeth.2022.04.011. View

16.

Shi H, Zhang S . Accurate Prediction of Anti-hypertensive Peptides Based on Convolutional Neural Network and Gated Recurrent unit. Interdiscip Sci. 2022; 14(4):879-894. DOI: 10.1007/s12539-022-00521-3. View

17.

Li Y, Lyu J, Wu Y, Liu Y, Huang G . PRIP: A Protein-RNA Interface Predictor Based on Semantics of Sequences. Life (Basel). 2022; 12(2). PMC: 8879494. DOI: 10.3390/life12020307. View

18.

Blanco J, Porto-Pazos A, Pazos A, Fernandez-Lozano C . Prediction of high anti-angiogenic activity peptides in silico using a generalized linear model and feature selection. Sci Rep. 2018; 8(1):15688. PMC: 6200741. DOI: 10.1038/s41598-018-33911-z. View

19.

Charoenkwan P, Chiangjong W, Lee V, Nantasenamat C, Hasan M, Shoombuatong W . Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method. Sci Rep. 2021; 11(1):3017. PMC: 7862624. DOI: 10.1038/s41598-021-82513-9. View

20.

Laengsri V, Nantasenamat C, Schaduangrat N, Nuchnoi P, Prachayasittikul V, Shoombuatong W . TargetAntiAngio: A Sequence-Based Tool for the Prediction and Analysis of Anti-Angiogenic Peptides. Int J Mol Sci. 2019; 20(12). PMC: 6628072. DOI: 10.3390/ijms20122950. View