Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance.

ABSTRACT: The emergence of viral epidemics throughout the world is of concern due to the scarcity of available effective antiviral therapeutics. The discovery of new antiviral therapies is imperative to address this challenge, and antiviral peptides (AVPs) represent a valuable resource for the development of novel therapies to combat viral infection. We present a new machine learning model to distinguish AVPs from non-AVPs using the most informative features derived from the physicochemical and structural properties of their amino acid sequences. To focus on those features that are most likely to contribute to antiviral performance, we filter potential features based on their importance for classification. These feature selection analyses suggest that secondary structure is the most important peptid

SUBMITTER: Chowdhury AS 

PROVIDER: S-EPMC7648056 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature