BioDADPep: A Bioinformatics database for anti-diabetic peptides

Roy, Susanta; Teron, Robindra

doi:10.6026/97320630015780

Cited by 20 publications

(6 citation statements)

References 24 publications

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“…A total of 1,482,409 peptide sequences with therapeutic functions were finally obtained, with average 12,251 in each species ( Figure 1 ). The therapeutic function category includes 21 unique functional characteristics, such as anti-angiogenic peptide (AAP) ( Zhang and Zou, 2020 ), anti-bacterial peptide (ABP) ( Xiao et al., 2021 ), anti-cancer peptide (ACP) ( Agrawal et al., 2021 ), anti-coronavirus peptide (ACVP) ( Pang et al., 2021 ), and anti-diabetic peptide (ADP) ( Roy and Teron, 2019 ). These peptides were classified into 41 therapeutic peptides categories with different functional characteristics ( Table 1 ).…”

Section: Resultsmentioning

confidence: 99%

MFPPDB: a comprehensive multi-functional plant peptide database

Yang,

Wu,

Gao

et al. 2023

Front. Plant Sci.

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Plants produce a wide range of bioactive peptides as part of their innate defense mechanisms. With the explosive growth of plant-derived peptides, verifying the therapeutic function using traditional experimental methods are resources and time consuming. Therefore, it is necessary to predict the therapeutic function of plant-derived peptides more effectively and accurately with reduced waste of resources and thus expedite the development of plant peptides. We herein developed a repository of plant peptides predicted to have multiple therapeutic functions, named as MFPPDB (multi-functional plant peptide database). MFPPDB including 1,482,409 single or multiple functional plant origin therapeutic peptides derived from 121 fundamental plant species. The functional categories of these therapeutic peptides include 41 different features such as anti-bacterial, anti-fungal, anti-HIV, anti-viral, and anti-cancer. The detailed physicochemical information of these peptides was presented in functional search and physicochemical property search module, which can help users easily access the peptide information by the plant peptide species, ID, and functions, or by their peptide ID, isoelectric point, peptide sequence, and molecular weight through web-friendly interface. We further matched the predicted peptides to nine state-of-the-art curated functional peptide databases and found that at least 293,408 of the peptides possess functional potentials. Overall, MFPPDB integrated a massive number of plant peptides have single or multiple therapeutic functions, which will facilitate the comprehensive research in plant peptidomics. MFPPDB can be freely accessed through http://124.223.195.214:9188/mfppdb/index.

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Section: Resultsmentioning

confidence: 99%

MFPPDB: a comprehensive multi-functional plant peptide database

Yang,

Wu,

Gao

et al. 2023

Front. Plant Sci.

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“…The data used for training, validation, and testing of our models were downloaded from APD3 [15], BioDADPep [ 16 ], BIOPEP-UWM [ 18 ], CancerPPD [ 19 ], CAMPR3 [20], DBAASP [2 1 ], LAMP2 [22], NeuroPedia [ 23 ], NeuroPep [ 24 ], PeptideDB [ 25 ], and SATPdb [ 17 ].…”

Section: Methodsmentioning

confidence: 99%

MultiPep: a hierarchical deep learning approach for multi-label classification of peptide bioactivities

2021

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Peptide-based therapeutics are here to stay and will prosper in the future. A key step in identifying novel peptide-drugs is the determination of their bioactivities. Recent advances in peptidomics screening approaches hold promise as a strategy for identifying novel drug targets. However, these screenings typically generate an immense number of peptides and tools for ranking these peptides prior to planning functional studies are warranted. Whereas a couple of tools in the literature predict multiple classes, these are constructed using multiple binary classifiers. We here aimed to use an innovative deep learning approach to generate an improved peptide bioactivity classifier with capacity of distinguishing between multiple classes. We present MultiPep: a deep learning multi-label classifier that assigns peptides to zero or more of 20 bioactivity classes. We train and test MultiPep on data from several publically available databases. The same data are used for a hierarchical clustering, whose dendrogram shapes the architecture of MultiPep. We test a new loss function that combines a customized version of Matthews correlation coefficient with binary cross entropy (BCE), and show that this is better than using class-weighted BCE as loss function. Further, we show that MultiPep surpasses state-of-the-art peptide bioactivity classifiers and that it predicts known and novel bioactivities of FDA-approved therapeutic peptides. In conclusion, we present innovative machine learning techniques used to produce a peptide prediction tool to aid peptide-based therapy development and hypothesis generation.

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“…Peptides cross reactivity and toxicity check studies were performed as per published protocol in BioDADpep Publication [ 28 ].…”

Section: Peptides Cross Reactivity and Toxicity Checkmentioning

confidence: 99%

“…These peptides can be classified according to their antidiabetic actions such as α‐amylase and α-glucosidase inhibitors, GLP-1 receptor agonists and DPP-IV inhibitors [ 27 ]. A comprehensive list of Antidiabetic Plant Derived Bioactive Peptides from varied sources can be searched in our work BioDADPep database [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

PhytoSelectDBT: A database for the molecular models of anti-diabetic targets docked with bioactive peptides from selected ethno-medicinal plants

Roy

2023

Bioinformation

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It is of interest to assess the effectiveness of bioactive peptides derived from 41 ethno-medicinal plants, classify them according to their anti-diabetic protein targets (DPP-IV, alpha-amylase, alpha-glucosidase, GRK2, GSK3B, GLP-1R, and AdipoR1), and create a web tool named PhytoSelectDBT by using the top seven peptides per target. If one of the target-based medicinal plant suggestions made by PhytoSelectDBT is unsuccessful, alternative target-based possibilities are presented by PhytoSelectDBT for treating the condition and any other related complications. The results provide a useful resource for the management of type 2 diabetes and emphasize the significance of utilising ethnomedical knowledge for the identification of potent anti-diabetic plants and their peptides. We used molecular docking to investigate interactions between anti-diabetic targets (DPP-IV, alpha-amylase, alpha-glucosidase, GRK2, GSK3B, GLP-1R, and AdipoR1) and projected bioactive peptides from 41 ethnomedicinal plants. All bioactive peptides were cross-checked against several databases to determine their allergenicity, toxicity, and cross-reactivity. The presence of B and T cell epitopes was also examined in all simulated digested bioactive peptides for reference. This data is archived at the PhytoselectDBT database.

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BioDADPep: A Bioinformatics database for anti-diabetic peptides

Cited by 20 publications

References 24 publications

MFPPDB: a comprehensive multi-functional plant peptide database

MFPPDB: a comprehensive multi-functional plant peptide database

MultiPep: a hierarchical deep learning approach for multi-label classification of peptide bioactivities

PhytoSelectDBT: A database for the molecular models of anti-diabetic targets docked with bioactive peptides from selected ethno-medicinal plants

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