Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2

• Published in: Briefings in bioinformatics Vol. 23; no. 1
• Main Authors: Manavalan, Balachandran, Basith, Shaherin, Lee, Gwang
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 17.01.2022; Oxford Publishing Limited (England)
• Subjects: Algorithms; Antiviral Agents - chemistry; Antiviral Agents - therapeutic use; Comparative analysis; Computer applications; Coronaviruses; COVID-19; COVID-19 - drug therapy; COVID-19 - epidemiology; Epitopes; Humans; Interleukin 6; Learning algorithms; Machine Learning; Pandemics; Pandemics - prevention & control; Peptides; Peptides - chemistry; Peptides - therapeutic use; Performance assessment; Performance evaluation; Predictions; Public health; Review; SARS-CoV-2 - metabolism; Severe acute respiratory syndrome; Severe acute respiratory syndrome coronavirus 2; Software; State-of-the-art reviews; Viral diseases; IL-6 inducing peptides; SARS-CoV-2; antiviral peptides; bioinformatics; performance assessment; coronavirus; machine learning
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbab412

Abstract Coronavirus disease 2019 (COVID-19) has impacted public health as well as societal and economic well-being. In the last two decades, various prediction algorithms and tools have been developed for predicting antiviral peptides (AVPs). The current COVID-19 pandemic has underscored the need to develop more efficient and accurate machine learning (ML)-based prediction algorithms for the rapid identification of therapeutic peptides against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Several peptide-based ML approaches, including anti-coronavirus peptides (ACVPs), IL-6 inducing epitopes and other epitopes targeting SARS-CoV-2, have been implemented in COVID-19 therapeutics. Owing to the growing interest in the COVID-19 field, it is crucial to systematically compare the existing ML algorithms based on their performances. Accordingly, we comprehensively evaluated the state-of-the-art IL-6 and AVP predictors against coronaviruses in terms of core algorithms, feature encoding schemes, performance evaluation metrics and software usability. A comprehensive performance assessment was then conducted to evaluate the robustness and scalability of the existing predictors using well-constructed independent validation datasets. Additionally, we discussed the advantages and disadvantages of the existing methods, providing useful insights into the development of novel computational tools for characterizing and identifying epitopes or ACVPs. The insights gained from this review are anticipated to provide critical guidance to the scientific community in the rapid design and development of accurate and efficient next-generation in silico tools against SARS-CoV-2.