Long-Range Structural Regularities and Collectivity of Folded Proteins
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1227-JJ03-06
Long-Range Structural Regularities and Collectivity of Folded Proteins
Canan Atilgan1, Ibrahim Inanc1, and Ali Rana Atilgan1 1
Faculty of Engineering and Natural Sciences, Sabanci University, 34956 Istanbul, Turkey
ABSTRACT Coarse-grained network models of proteins successfully predict equilibrium properties related to collective modes of motion. In this study, the network construction strategies and their systematic application to proteins are used to explain the role of network models in defining the collective properties of the system. The analysis is based on the radial distribution function, a newly defined angular distribution function and the spectral dimensions of a large set of globular proteins. Our analysis shows that after reaching a certain threshold for cut-off distance, network construction has negligible effect on the collective motions and the fluctuation patterns of the residues. INTRODUCTION Globular proteins show diversified structures and sizes, yet, it has been claimed that they display a nearly random packing of amino acids with strong local symmetry on the one hand [1], and that they are regular structures that occupy specific lattice sites, on the other [2]. It was later shown that this classification depends on the property one investigates, and that proteins display “small-world” properties, where highly ordered structures are altered with few additional links [3]. Furthermore, packing density of proteins scales uniformly with their size [4] which causes them to show similar vibrational spectral characteristics to those of solids [5,6]. Coarse grained protein models have shown a great success in the description of the residue fluctuations and the collective behavior of proteins [7]. Using a single parameter harmonic potential [8], the large amplitude motions of proteins in the native state have been predicted successfully using normal mode analysis [9]. This model, with its simplicity, speed of calculation and relying mostly on geometry and mass distribution of the protein, demonstrates that a single-parameter model can reproduce complex vibrational properties of macromolecular systems. Following the uniform harmonic potential introduced by Tirion [10], residue level application of elastic network models paved the way for the Gaussian Network Model, which is based on the energy balance of the system at the energy minimum [9,11]. Elastic models based on the force balance around each node [12] led to the development of the so called Anisotropic Network Model (ANM) [13]. The applications of these models on many proteins show successful results in terms of predicting the collective behavior of proteins. Despite numerous applications comparing the theoretical and experimental findings on a case-by-case basis [14], only a few attempted a statistical assessment of the models. In another study where 170 pairs of structures were systematically analyzed, it was shown that the success of coarse-grained elastic network models may be improved by recognizing the rigidity of some residue
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