Built, designed and developed a multi-label and multi-class classification model for Protein Sub-Chloroplast Localization (PSCL)
Protein sub-chloroplast localization prediction is a crucial task in genomics and bioinformatics. Chloroplasts are organelles found in plant cells and are responsible for various essential biological processes, including photosynthesis and synthesis of amino acids and fatty acids. Proteins targeted to the chloroplast must be correctly localized within its subcompartments to carry out specific functions effectively. Sub-chloroplast localization prediction aims to determine the exact subcompartments within the chloroplast where a protein is likely to be located.
This project focuses on developing and deploying machine learning models for predicting the sub-chloroplast localization of proteins. By analyzing protein sequences and their associated features, such as amino acid composition, physicochemical properties, and sequence motifs, the models can classify proteins into different subcompartments within the chloroplast, such as the stroma, thylakoid membrane, and inner membrane.
The accurate prediction of sub-chloroplast localization aids in understanding the functions and interactions of proteins within the chloroplast, which has implications for agricultural research, biofuel production, and understanding plant physiology.