Literature DB >> 29680238

In Silico Tools and Databases for Designing Peptide-Based Vaccine and Drugs.

Salman Sadullah Usmani1, Rajesh Kumar1, Sherry Bhalla2, Vinod Kumar1, Gajendra P S Raghava3.   

Abstract

The prolonged conventional approaches of drug screening and vaccine designing prerequisite patience, vigorous effort, outrageous cost as well as additional manpower. Screening and experimentally validating thousands of molecules for a specific therapeutic property never proved to be an easy task. Similarly, traditional way of vaccination includes administration of either whole or attenuated pathogen, which raises toxicity and safety issues. Emergence of sequencing and recombinant DNA technology led to the epitope-based advanced vaccination concept, i.e., small peptides (epitope) can stimulate specific immune response. Advent of bioinformatics proved to be an adjunct in vaccine and drug designing. Genomic study of pathogens aid to identify and analyze the protective epitope. A number of in silico tools have been developed to design immunotherapy as well as peptide-based drugs in the last two decades. These tools proved to be a catalyst in drug and vaccine designing. This review solicits therapeutic peptide databases as well as in silico tools developed for designing peptide-based vaccine and drugs.
© 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Drug designing; Immunotherapeutic; Peptide databases; Peptide therapeutics; Vaccine

Mesh:

Substances:

Year:  2018        PMID: 29680238     DOI: 10.1016/bs.apcsb.2018.01.006

Source DB:  PubMed          Journal:  Adv Protein Chem Struct Biol        ISSN: 1876-1623            Impact factor:   3.507


  11 in total

1.  PRRDB 2.0: a comprehensive database of pattern-recognition receptors and their ligands.

Authors:  Dilraj Kaur; Sumeet Patiyal; Neelam Sharma; Salman Sadullah Usmani; Gajendra P S Raghava
Journal:  Database (Oxford)       Date:  2019-01-01       Impact factor: 3.451

2.  Prediction of Antitubercular Peptides From Sequence Information Using Ensemble Classifier and Hybrid Features.

Authors:  Salman Sadullah Usmani; Sherry Bhalla; Gajendra P S Raghava
Journal:  Front Pharmacol       Date:  2018-08-28       Impact factor: 5.810

3.  ImmunoSPdb: an archive of immunosuppressive peptides.

Authors:  Salman Sadullah Usmani; Piyush Agrawal; Manika Sehgal; Pradeep Kumar Patel; Gajendra P S Raghava
Journal:  Database (Oxford)       Date:  2019-01-01       Impact factor: 3.451

Review 4.  Potential Challenges for Coronavirus (SARS-CoV-2) Vaccines Under Trial.

Authors:  Salman Sadullah Usmani; Gajendra P S Raghava
Journal:  Front Immunol       Date:  2020-09-29       Impact factor: 7.561

5.  In-Silico Tool for Predicting, Scanning, and Designing Defensins.

Authors:  Dilraj Kaur; Sumeet Patiyal; Chakit Arora; Ritesh Singh; Gaurav Lodhi; Gajendra P S Raghava
Journal:  Front Immunol       Date:  2021-11-22       Impact factor: 7.561

6.  Prediction and Activity of a Cationic α-Helix Antimicrobial Peptide ZM-804 from Maize.

Authors:  Mohamed F Hassan; Abdelrahman M Qutb; Wubei Dong
Journal:  Int J Mol Sci       Date:  2021-03-05       Impact factor: 5.923

7.  A Web Resource for Designing Subunit Vaccine Against Major Pathogenic Species of Bacteria.

Authors:  Gandharva Nagpal; Salman Sadullah Usmani; Gajendra P S Raghava
Journal:  Front Immunol       Date:  2018-10-02       Impact factor: 7.561

8.  AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees.

Authors:  Balachandran Manavalan; Shaherin Basith; Tae Hwan Shin; Leyi Wei; Gwang Lee
Journal:  Comput Struct Biotechnol J       Date:  2019-07-03       Impact factor: 7.271

9.  Design of a Multiepitope-Based Peptide Vaccine against the E Protein of Human COVID-19: An Immunoinformatics Approach.

Authors:  Miyssa I Abdelmageed; Abdelrahman H Abdelmoneim; Mujahed I Mustafa; Nafisa M Elfadol; Naseem S Murshed; Shaza W Shantier; Abdelrafie M Makhawi
Journal:  Biomed Res Int       Date:  2020-05-11       Impact factor: 3.411

10.  Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach.

Authors:  Longendri Aguilera-Mendoza; Yovani Marrero-Ponce; César R García-Jacas; Edgar Chavez; Jesus A Beltran; Hugo A Guillen-Ramirez; Carlos A Brizuela
Journal:  Sci Rep       Date:  2020-10-22       Impact factor: 4.379

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