Applications
Since PseAA composition was introduced, it has been widely used to predict various attributes of proteins, such as structural classes of proteins, enzyme family classes and subfamily classes, GABA(A) receptor proteins, protein folding rates, cyclin proteins, supersecondary structure, subcellular location of proteins, subnuclear location of proteins, apoptosis protein subcellular localization, submitochondria localization, protein quaternary structure, bacterial secreted proteins, conotoxin superfamily and family classification, protease types, GPCR types, human papillomaviruses, outer membrane proteins, transmembrane regions in protein, protein secondary structural contents, subcellular localization of mycobacterial proteins, lipase types, DNA-binding proteins, cell wall lytic enzymes, cofactors of oxidoreductases, among many other protein attributes and protein-related features (see, e.g., the review paper by Gonzalez-Diaz et al. as well as the relevant references cited therein).
Ever since the concept of PseAA composition was introduced, it has been widely utilized to predict various protein attributes. It has also been used to incorporate the protein domain or FunD (functional domain) information and GO (gene ontology) information for improving the prediction quality for the subcellular localization of proteins. as well as their other attributes.
Meanwhile, the concept of PseAA composition has also stimulated the generation of pseudo-folding topological indices and pseudo-folding lattice network.
Read more about this topic: Pseudo Amino Acid Composition