Literature DB >> 29112936

Designing anti-Zika virus peptides derived from predicted human-Zika virus protein-protein interactions.

Tom Kazmirchuk1, Kevin Dick2, Daniel J Burnside1, Brad Barnes2, Houman Moteshareie1, Maryam Hajikarimlou1, Katayoun Omidi1, Duale Ahmed3, Andrew Low1, Clara Lettl1, Mohsen Hooshyar1, Andrew Schoenrock4, Sylvain Pitre4, Mohan Babu5, Edana Cassol6, Bahram Samanfar7, Alex Wong1, Frank Dehne6, James R Green2, Ashkan Golshani8.   

Abstract

The production of anti-Zika virus (ZIKV) therapeutics has become increasingly important as the propagation of the devastating virus continues largely unchecked. Notably, a causal relationship between ZIKV infection and neurodevelopmental abnormalities has been widely reported, yet a specific mechanism underlying impaired neurological development has not been identified. Here, we report on the design of several synthetic competitive inhibitory peptides against key pathogenic ZIKV proteins through the prediction of protein-protein interactions (PPIs). Often, PPIs between host and viral proteins are crucial for infection and pathogenesis, making them attractive targets for therapeutics. Using two complementary sequence-based PPI prediction tools, we first produced a comprehensive map of predicted human-ZIKV PPIs (involving 209 human protein candidates). We then designed several peptides intended to disrupt the corresponding host-pathogen interactions thereby acting as anti-ZIKV therapeutics. The data generated in this study constitute a foundational resource to aid in the multi-disciplinary effort to combat ZIKV infection, including the design of additional synthetic proteins.
Copyright © 2017. Published by Elsevier Ltd.

Entities:  

Keywords:  Anti-Zika virus peptides; Host-virus interactions; In silico drug design; Protein-protein interaction prediction; Synthetic peptide design

Mesh:

Substances:

Year:  2017        PMID: 29112936     DOI: 10.1016/j.compbiolchem.2017.10.011

Source DB:  PubMed          Journal:  Comput Biol Chem        ISSN: 1476-9271            Impact factor:   2.877


  4 in total

1.  In silico identification of drug target pathways in breast cancer subtypes using pathway cross-talk inhibition.

Authors:  Claudia Cava; Gloria Bertoli; Isabella Castiglioni
Journal:  J Transl Med       Date:  2018-06-05       Impact factor: 5.531

2.  Multi-schema computational prediction of the comprehensive SARS-CoV-2 vs. human interactome.

Authors:  Kevin Dick; Anand Chopra; Kyle K Biggar; James R Green
Journal:  PeerJ       Date:  2021-04-05       Impact factor: 2.984

3.  Reciprocal Perspective for Improved Protein-Protein Interaction Prediction.

Authors:  Kevin Dick; James R Green
Journal:  Sci Rep       Date:  2018-08-03       Impact factor: 4.379

Review 4.  Computer-Assisted and Data Driven Approaches for Surveillance, Drug Discovery, and Vaccine Design for the Zika Virus.

Authors:  Subhash C Basak; Subhabrata Majumdar; Ashesh Nandy; Proyasha Roy; Tathagata Dutta; Marjan Vracko; Apurba K Bhattacharjee
Journal:  Pharmaceuticals (Basel)       Date:  2019-10-16
  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.