Nucleotide Binding Affects Intrinsic Dynamics and Structural Communication in Ras GTPases

Francesca      Fanelli; Francesco      Raimondi

doi:10.2174/1381612811319230006

Abstract

The Ras superfamily comprises many guanine nucleotide-binding proteins (G proteins) that are essential to intracellular signal transduction. These proteins act biologically as molecular switches, which, cycling between OFF and ON states, play fundamental role in cell biology.

This review article summarizes the inferences from the widest computational analyses done so far on Ras GTPases aimed at providing a comprehensive structural/dynamic view of the trans-family and family-specific functioning mechanisms. These variegated comparative analyses could infer the evolutionary and intrinsic flexibilities as well as the structural communication features in the most representative G protein families in different functional states.

In spite of the low sequence similarities, the members of the Ras superfamily share the topology of the Ras-like domain, including the nucleotide binding site. GDP and GTP make very similar interactions in all GTPases and differences in their binding modes are localized around the γ-phosphate of GTP. Remarkably, such subtle local differences result in significant differences in the functional dynamics and structural communication features of the protein. In Ras GTPases, the nucleotide plays a central and active role in dictating functional dynamics, establishing the major structure network, and mediating the communication paths instrumental in function retention and specialization.

Collectively, the results of these studies support the speculation that an “extended conformational selection model” that embraces a repertoire of selection and adjustment processes is likely more suitable to describe the nucleotide behavior in these important molecular switches.

Keywords: G proteins, principal component analysis, elastic network model, normal mode analysis, molecular dynamics simulations, functional mode analysis, protein structure networks.

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