Prediction of functional sites in proteins using conserved functional group analysis

Area of Science:

• Computational biology
• Structural bioinformatics
• Protein functional site prediction

Background:

• Understanding protein function requires detailed knowledge of functional sites.
• The rapid accumulation of protein sequence and structural data outpaces experimental determination of biochemical roles.
• Computational methods are essential for processing evolutionary information to identify functionally important sites and residues.

Purpose of the Study:

• To present a computational method, conserved functional group (CFG) analysis, for identifying protein functional sites.
• To demonstrate the efficacy of CFG analysis in predicting functional site locations across diverse protein families.
• To highlight the utility of CFG analysis in structural genomics for proteins with unknown functions.

Main Methods:

• Conserved functional group (CFG) analysis utilizes a simplified representation of amino acid side-chain chemical groups.
• The method identifies functional sites by analyzing a single protein structure and its sequence homologues.
• CFG analysis processes evolutionary information to pinpoint functionally important residues.

Main Results:

• CFG analysis successfully predicted functional sites, either fully or partially, in approximately 96% of 470 tested cases.
• The method demonstrates robustness, tolerating wide variations in sequence identity between protein homologues.
• An analysis of the hypothetical protein Ydde_Ecoli illustrates the practical application of CFG analysis in identifying potential active sites.

Conclusions:

• CFG analysis provides an efficient and automated solution for identifying protein functional sites and key residues.
• The method is well-suited for structural genomics initiatives, enabling rapid processing of large datasets.
• CFG analysis serves as a valuable tool for predicting protein biochemical function, especially when experimental data is limited.