Motivation: A large number of proteins contain metal ions that are essential for their stability and biological activity. Identifying and characterizing metal-binding sites through computational methods is necessary when experimental clues are lacking. Almost all published computational methods are designed to distinguish metal-binding sites from non-metal-binding sites. However, discrimination between different types of metal-binding sites is also needed to make more accurate predictions.
Results: In the present work, we proposed a novel algorithm called mFASD, which could discriminate different types of metal-binding sites effectively based on 3D structure data and is useful for accurate metal-binding site prediction. mFASD captures the characteristics of a metal-binding site by investigating the local chemical environment of a set of functional atoms that are considered to be in contact with the bound metal. Then a distance measure defined on functional atom sets enables the comparison between different metal-binding sites. The algorithm could discriminate most types of metal-binding sites from each other with high sensitivity and accuracy. We showed that cascading our method with existing ones could achieve a substantial improvement of the accuracy for metal-binding site prediction.