Current Development of Data Resources and Bioinformatics Tools for Anticoronavirus Peptide

Area of Science:

• Bioinformatics
• Computational Biology
• Drug Discovery

Background:

• Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 pandemic, necessitating effective antiviral strategies.
• Identifying anticoronavirus peptides (ACVPs) is vital for developing treatments and vaccines.
• Traditional peptide discovery methods are slow; bioinformatics and machine learning offer faster alternatives.

Purpose of the Study:

• To review existing data resources and predictors for ACVPs.
• To analyze factors influencing the performance of computational models for ACVP identification.
• To provide insights into the efficacy of bioinformatics tools for ACVP discovery.

Main Methods:

• Comprehensive literature review of ACVP databases and predictors over the past two decades.
• Analysis of factors affecting computational model performance.
• Assessment of 15 distinct peptide predictors for antiviral activity.

Main Results:

• Nine ACVP databases integrating effective coronavirus-fighting peptide information were reviewed.
• Fifteen peptide predictors for antiviral or specific anticoronavirus activity were assessed.
• Computational models offer rapid screening of potential antiviral peptide candidates.

Conclusions:

• The study offers insights into the performance of various computational methods for ACVP identification.
• It highlights the future direction of bioinformatics tools in the field of ACVPs.
• This work emphasizes the critical role of ACVPs in the ongoing fight against COVID-19.