mFASD: a structure-based algorithm for discriminating different types of metal-binding sites.
Bioinformatics
; 31(12): 1938-44, 2015 Jun 15.
Article
in En
| MEDLINE
| ID: mdl-25649619
ABSTRACT
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 this 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. AVAILABILITY AND IMPLEMENTATION Source code and data used are freely available from http//staff.ustc.edu.cn/â¼liangzhi/mfasd/
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Proteins
/
Discriminant Analysis
/
Metals
Type of study:
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Bioinformatics
Journal subject:
INFORMATICA MEDICA
Year:
2015
Type:
Article
Affiliation country:
China