Genomics Inform.  2017 Dec;15(4):162-169. 10.5808/GI.2017.15.4.162.

Prediction of Metal Ion Binding Sites in Proteins from Amino Acid Sequences by Using Simplified Amino Acid Alphabets and Random Forest Model

• Kumar S

• KMID: 2404054
• DOI: http://doi.org/10.5808/GI.2017.15.4.162

Abstract

Metal binding proteins or metallo-proteins are important for the stability of the protein and also serve as co-factors in various functions like controlling metabolism, regulating signal transport, and metal homeostasis. In structural genomics, prediction of metal binding proteins help in the selection of suitable growth medium for overexpression's studies and also help in obtaining the functional protein. Computational prediction using machine learning approach has been widely used in various fields of bioinformatics based on the fact all the information contains in amino acid sequence. In this study, random forest machine learning prediction systems were deployed with simplified amino acid for prediction of individual major metal ion binding sites like copper, calcium, cobalt, iron, magnesium, manganese, nickel, and zinc.

Keyword

amino acid sequence; binding sites; machine learning; proteins

MeSH Terms

Amino Acid Sequence*
Binding Sites*
Calcium
Carrier Proteins
Cobalt
Computational Biology
Copper
Forests*
Genomics
Homeostasis
Iron
Machine Learning
Magnesium
Manganese
Metabolism
Nickel
Zinc
Calcium
Carrier Proteins
Cobalt
Copper
Iron
Magnesium
Manganese
Nickel
Zinc