Literature DB >> 31902976

BioDADPep: A Bioinformatics database for anti diabetic peptides.

Susanta Roy1, Robindra Teron1.   

Abstract

The increasing number of cases for diabetes worldwide is a concern. Therefore, it is of interest to design therapeutic peptides to overcome side effects caused by the available drugs. It should be noted that data on several known anti-diabetic peptides is available in the literature in an organized manner. Hence, it is of interest to collect, glean and store such data in form of a searchable database supported by RDBMS. Data on anti-diabetic peptides and their related data are collected from the literature using manual search. Data on related peptides from other databases (THPdb, ADP3, LAMP, AHTPDB, AVPdb, BioPepDB, CancerPPD, CPPsite, DRAMP, SATPdb, CAMPR3 and MBPDB) are also included after adequate curation. Thus, we describe the development and utility of BioDADPep, a Bioinformatics database for anti-diabetic peptides. The database has cross-reference for antidiabetic peptides. The database is enabled with a web-based GUI using a simple Google-like search function. Data presented in BioDADPep finds application in the design of an effective anti-diabetic peptide.
© 2019 Biomedical Informatics.

Entities:  

Keywords:  Diabetes mellitus (DM); anti-diabetic peptides; cross reactivity; peptide database; text mining; therapeutic protein targets

Year:  2019        PMID: 31902976      PMCID: PMC6936660          DOI: 10.6026/97320630015780

Source DB:  PubMed          Journal:  Bioinformation        ISSN: 0973-2063


Background

Diabetes mellitus is a pandemic non-communicable chronic autoimmune disease affecting the quality of life (diet and lifestyle).It is estimated that diabetes mellitus will rise to 592 million by the year 2035 [1].Approximately 50% of patients with diabetes mellitus are believed to be unaware of their condition [2]. Diabetes Mellitus may go up from ninth to seventh most important reason of death worldwide by 2030 [3]. Based upon the etiology, diabetes mellitus can be divided into two main types, Type 1, "Juvenile Diabetes Mellitus" (Insulin Dependent Diabetes Mellitus) and Type 2, "adult type" (Non-Insulin Dependent Diabetes Mellitus) [4].Type 1 diabetes (Insulin Dependent Diabetes Mellitus, IDDM or juvenile onset diabetes), occurs when the pancreas do not produce enough insulin due to destruction of pancreatic β-cells mediated by auto reactive T-cells resulting in chronic insulitis [5]. In Type 2, "adult type" (Non-Insulin Dependent Diabetes Mellitus), primary insulin resistance, rather than defective insulin production due to β-cells destruction, seems to be the triggering alteration [6]. BioDADPep is an online database of published data about Type 1 and Type 2 diabetes mellitus peptides, their targets and other related data. A comprehensive visualization of all known antidiabetic peptides is important for peptide classification, modification and design. It is of interest to design different formulations of peptides for improved half-life, immunogenicity, chemical activity, solubility, side effects, toxicity and others [7].It is promising to modify the structure to enhance stability, increase the half-life and improve membrane permeability of the peptide after calculating the binding and selectivity using structural features [8].Several methods like truncations, PEGylation and cyclizations available to improve the properties and bioavailability using peptide mimetic design [8].Bioactive peptides that helps assess symptoms of diabetes are also included in the database [9].

Methodology

Database architecture and web interface

Collection and organization of data

Primary data:

Peptides and related data were collected using literature search. We used PubMed to search for such peptides in research and review articles. Peptides were collected based on Type 1 and Type 2 diabetes annotations in literature. References are collected and stored in BioDADPep(Figure 1). This data forms primary data for the database. The source (synthetic or natural) of the peptides is also included in the database. Similar or linked or related peptides from other databases also included in the database.
Figure 1

BioDADPep database Schema

Derived data:

Hydrophobicity, hydropathicity, hydrophilicity, charge, molecular weight and toxicity are derived from TOXINPRED [10]. Peptides in BioDADPep are compared with peptides in IEDB (Immune Epitope Database) [11] to extract antidiabetic peptides with cross reactivity features with anticancer, anti-inflammatory, anti-microbial, antioxidant, autoimmune and allergic. Such peptides in databases like THPdb [12], ADP3 [13]; LAMP [14]; AHTPDB [15]; AVPdb [16];BioPepDB [17]; CancerPPD [18]; CPPsite [19]; DRAMP 2.0 [20]; SATPdb [21]; HIPdb [22]; CAMPR3 [23] and MBPDB [24] are also used for enrichment of the database.

User interface

All primary and derived data are included in BioDADPep in respective columns. The data related to a peptide can be browsed using the following parameters (i) accession number, (ii) peptide sequence, (iii) protein name, (iv) peptide length, (v) peptide source (start position-end position), (vi) protein function, (vii) *ptm, (viii) organism, (ix) mhc allele name, (x) mhc class, (xi) host mhc types present, (xii) ic50(µm), (xiii) assay_method/preclinical/clinical studies, (xiv) hydrophobicity, (xv) hydropathicity, (xvi) hydrophilicity, (xvii) charge, (xviii) molecular weight, (xix) toxin/non-toxin, (xx) peptides cross reactivity, (xxi) type 1 diabetes/type 2 diabetes (xxii) natural peptide/synthetic peptide and (xxii) references.

Browsing:

BioDADPep interface has the following features (1) Home Page (2) BioDADPep Search (3) Data Statistics (4) Acknowledgment (5) Help (6) Contact

Home page:

Homepage give introduction on BioDADPep(Figure 2)
Figure 2

BioDADPep peptide search results

BioDADPep Search:

The BioDADPep database has search utility for keywords.

Data statistics:

Data statistics with graphs, figures and facts is made available.

Help:

The HELP page assists to navigate BioDADPep in a step-by-step manner.

Availability:

http://omicsbase.com/BioDADPep/
  24 in total

1.  BioPepDB: an integrated data platform for food-derived bioactive peptides.

Authors:  Qilin Li; Chao Zhang; Hongjun Chen; Jitong Xue; Xiaolei Guo; Ming Liang; Ming Chen
Journal:  Int J Food Sci Nutr       Date:  2018-03-12       Impact factor: 3.833

2.  Global estimates of the prevalence of diabetes for 2010 and 2030.

Authors:  J E Shaw; R A Sicree; P Z Zimmet
Journal:  Diabetes Res Clin Pract       Date:  2009-11-06       Impact factor: 5.602

Review 3.  Diabetes mellitus: an overview on its pharmacological aspects and reported medicinal plants having antidiabetic activity.

Authors:  D K Patel; R Kumar; D Laloo; S Hemalatha
Journal:  Asian Pac J Trop Biomed       Date:  2012-05

4.  CPPsite: a curated database of cell penetrating peptides.

Authors:  Ankur Gautam; Harinder Singh; Atul Tyagi; Kumardeep Chaudhary; Rahul Kumar; Pallavi Kapoor; G P S Raghava
Journal:  Database (Oxford)       Date:  2012-03-07       Impact factor: 3.451

5.  AHTPDB: a comprehensive platform for analysis and presentation of antihypertensive peptides.

Authors:  Ravi Kumar; Kumardeep Chaudhary; Minakshi Sharma; Gandharva Nagpal; Jagat Singh Chauhan; Sandeep Singh; Ankur Gautam; Gajendra P S Raghava
Journal:  Nucleic Acids Res       Date:  2014-11-11       Impact factor: 16.971

6.  THPdb: Database of FDA-approved peptide and protein therapeutics.

Authors:  Salman Sadullah Usmani; Gursimran Bedi; Jesse S Samuel; Sandeep Singh; Sourav Kalra; Pawan Kumar; Anjuman Arora Ahuja; Meenu Sharma; Ankur Gautam; Gajendra P S Raghava
Journal:  PLoS One       Date:  2017-07-31       Impact factor: 3.240

7.  Adaptive immunity, inflammation, and cardiovascular complications in type 1 and type 2 diabetes mellitus.

Authors:  Daniela Pedicino; Giovanna Liuzzo; Francesco Trotta; Ada Francesca Giglio; Simona Giubilato; Francesca Martini; Francesco Zaccardi; Giuseppe Scavone; Marco Previtero; Gianluca Massaro; Pio Cialdella; Maria Teresa Cardillo; Dario Pitocco; Giovanni Ghirlanda; Filippo Crea
Journal:  J Diabetes Res       Date:  2013-05-23       Impact factor: 4.011

8.  HIPdb: a database of experimentally validated HIV inhibiting peptides.

Authors:  Abid Qureshi; Nishant Thakur; Manoj Kumar
Journal:  PLoS One       Date:  2013-01-24       Impact factor: 3.240

9.  AVPdb: a database of experimentally validated antiviral peptides targeting medically important viruses.

Authors:  Abid Qureshi; Nishant Thakur; Himani Tandon; Manoj Kumar
Journal:  Nucleic Acids Res       Date:  2013-11-26       Impact factor: 16.971

10.  SATPdb: a database of structurally annotated therapeutic peptides.

Authors:  Sandeep Singh; Kumardeep Chaudhary; Sandeep Kumar Dhanda; Sherry Bhalla; Salman Sadullah Usmani; Ankur Gautam; Abhishek Tuknait; Piyush Agrawal; Deepika Mathur; Gajendra P S Raghava
Journal:  Nucleic Acids Res       Date:  2015-11-02       Impact factor: 16.971

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Authors:  You Li; Xueyong Li; Yuewu Liu; Yuhua Yao; Guohua Huang
Journal:  Pharmaceuticals (Basel)       Date:  2022-06-03

2.  AntiDMPpred: a web service for identifying anti-diabetic peptides.

Authors:  Xue Chen; Jian Huang; Bifang He
Journal:  PeerJ       Date:  2022-06-14       Impact factor: 3.061

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

Authors:  Alexander G B Grønning; Tim Kacprowski; Camilla Schéele
Journal:  Biol Methods Protoc       Date:  2021-11-23

4.  PrMFTP: Multi-functional therapeutic peptides prediction based on multi-head self-attention mechanism and class weight optimization.

Authors:  Wenhui Yan; Wending Tang; Lihua Wang; Yannan Bin; Junfeng Xia
Journal:  PLoS Comput Biol       Date:  2022-09-12       Impact factor: 4.779

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