Abstract
The rapid growth of the Protein Data Bank (PDB) highlights a great challenge for researchers to predict the binding sites of protein for specific metal ion(s). Experimental determination of functional features of a protein is expensive, time consuming and difficult to automate. Therefore, there is a great demand of computational methods for predicting functional features of protein. This review sheds light on currently available in-silico methods including different tools and databases which are based on various information of metal ion and their binding sites (protein residue length, amino acid composition, geometrical and molecular information etc.) and determines the efficiency, speed and accuracy by using diverse algorithms which make the tools beneficial.
Keywords: Computational method, Metal binding site, Motif, PDB, Metalloprotein, Stand alone tools, Metal binding Site Predictor, GRID, CHED, MSDsite
Current Bioinformatics
Title: Tools for Predicting Metal Binding Sites in Protein: A Review
Volume: 6 Issue: 4
Author(s): Medhavi Mallick, Ambarish Sharan Vidyarthi and Shankaracharya
Affiliation:
Keywords: Computational method, Metal binding site, Motif, PDB, Metalloprotein, Stand alone tools, Metal binding Site Predictor, GRID, CHED, MSDsite
Abstract: The rapid growth of the Protein Data Bank (PDB) highlights a great challenge for researchers to predict the binding sites of protein for specific metal ion(s). Experimental determination of functional features of a protein is expensive, time consuming and difficult to automate. Therefore, there is a great demand of computational methods for predicting functional features of protein. This review sheds light on currently available in-silico methods including different tools and databases which are based on various information of metal ion and their binding sites (protein residue length, amino acid composition, geometrical and molecular information etc.) and determines the efficiency, speed and accuracy by using diverse algorithms which make the tools beneficial.
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Cite this article as:
Mallick Medhavi, Sharan Vidyarthi Ambarish and Shankaracharya , Tools for Predicting Metal Binding Sites in Protein: A Review, Current Bioinformatics 2011; 6 (4) . https://dx.doi.org/10.2174/157489311798072990
DOI https://dx.doi.org/10.2174/157489311798072990 |
Print ISSN 1574-8936 |
Publisher Name Bentham Science Publisher |
Online ISSN 2212-392X |
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AI-Based Prediction of Functional Proteins
Functional proteins are vital molecules involved in a variety of physiological and pathological processes within the body. Understanding the relationship between protein structure and function is key to unraveling the mechanisms of life sciences, disease processes, and drug discovery. However, traditional experimental methods are often time-consuming, costly, and constrained by ...read more
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Computational genomics has emerged since the beginning of the 21st century. Recently, along with the sequencing technology at single-cell level, computational genomics step forward to the level of singe-cell resolution. Several topics are well studied in this area. Several other topics are still challenging tasks for bioinformatics. This thematic issue ...read more
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