PhyCysID: Plant Cystatin Protein Prediction by an Artificial Intelligence Approach

Abstract

Phytocystatins are proteinaceous inhibitors found in plants that competitively target various classes of cysteine proteinases, including papain-like enzymes, cathepsins, and legumains. Based on structural characteristics and gene organization, phytocystatins can be classified into four subtypes: intronless (I1 and I2), intron-containing (IwI), and multidomain cystatins containing more than one inhibitory region (II). This work presents PhyCysID, a dedicated web server designed for the rapid classification of phytocystatin subtypes. PhyCysID uses a set of 21 features derived from amino acid composition, in combination with 15 distinct machine learning algorithms, to classify phytocystatin sequences into one of the four subtypes. Initially, the input sequence is analyzed to verify if it comprises a true phytocystatin sequence. If so, the input sequence is further analyzed using a specialized classification pipeline called PhyCysID 12M, which integrates 12 machine learning models to assign it to one of the four defined phytocystatin classes. As a case study, a curated dataset of phytocystatin sequences from the UniProt database was used to evaluate the algorithm’s performance. The PhyCysID web server enables rapid classification of both individual and batch-submitted sequences in less than 15 s, providing high-throughput analysis for an accurate identification of phytocystatin class and function. PhyCysID is freely available at https://www.ufrgs.br/labec/phycysid.