iGNM: a database of protein functional motions based on Gaussian Network Model

Area of Science:

• Biophysics
• Computational Biology
• Structural Biology

Background:

• Protein structure alone is insufficient for understanding protein function, which is inherently dynamic.
• Systematic characterization of protein dynamics has lagged behind structural data accumulation.
• Elastic network models, like the Gaussian Network Model (GNM), enable high-throughput analysis of protein collective dynamics.

Purpose of the Study:

• To systematically analyze and provide access to protein dynamics data using the Gaussian Network Model (GNM).
• To develop a web-based resource (iGNM) for visualizing and downloading protein dynamics information.
• To illustrate the utility of the database in understanding protein function and catalysis.

Main Methods:

• Computed GNM dynamics for 20,058 Protein Data Bank structures.
• Developed the iGNM web-based system for data access and visualization.
• Utilized normal mode analysis to describe protein conformational mobility.

Main Results:

• Generated residue-level equilibrium dynamics information for a large protein dataset.
• Created the iGNM database, offering visualization, download options, and an online calculation engine.
• Demonstrated the connection between collective dynamics, key residues, and catalytic activity in hydrolases.

Conclusions:

• The iGNM database provides a valuable resource for exploring protein dynamics.
• Understanding protein dynamics is essential for elucidating protein function and catalytic mechanisms.
• The GNM approach facilitates high-throughput analysis of protein collective motions.