LARGE-SCALE COMPARATIVE REVIEW AND ASSESSMENT OF COMPUTATIONAL METHODS FOR PHAGE VIRION PROTEINS IDENTIFICATION
Issued Date
2022-01-03
Resource Type
eISSN
16112156
Scopus ID
2-s2.0-85126018757
Journal Title
EXCLI Journal
Volume
21
Start Page
11
End Page
29
Rights Holder(s)
SCOPUS
Bibliographic Citation
EXCLI Journal Vol.21 (2022) , 11-29
Suggested Citation
Kabir M., Nantasenamat C., Kanthawong S., Charoenkwan P., Shoombuatong W. LARGE-SCALE COMPARATIVE REVIEW AND ASSESSMENT OF COMPUTATIONAL METHODS FOR PHAGE VIRION PROTEINS IDENTIFICATION. EXCLI Journal Vol.21 (2022) , 11-29. 29. doi:10.17179/excli2021-4411 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/86885
Title
LARGE-SCALE COMPARATIVE REVIEW AND ASSESSMENT OF COMPUTATIONAL METHODS FOR PHAGE VIRION PROTEINS IDENTIFICATION
Other Contributor(s)
Abstract
Phage virion proteins (PVPs) are effective at recognizing and binding to host cell receptors while having no deleterious effects on human or animal cells. Understanding their functional mechanisms is regarded as a critical goal that will aid in rational antibacterial drug discovery and development. Although high-throughput experimental methods for identifying PVPs are considered the gold standard for exploring crucial PVP features, these procedures are frequently time-consuming and labor-intensive. Thusfar, more than ten sequence-based predictors have been established for the in silico identification of PVPs in conjunction with traditional experimental approaches. As a result, a revised and more thorough assessment is extremely desirable. With this purpose in mind, we first conduct a thorough survey and evaluation of a vast array of 13 state-of-the-art PVP predictors. Among these PVP predictors, they can be classified into three groups according to the types of machine learning (ML) algorithms employed (i.e. traditional ML-based methods, ensemble-based methods and deep learning-based methods). Subsequently, we explored which factors are important for building more accurate and stable predictors and this included training/independent datasets, feature encoding algorithms, feature selection methods, core algorithms, performance evaluation metrics/strategies and web servers. Finally, we provide insights and future perspectives for the design and development of new and more effective computational approaches for the detection and characterization of PVPs.