Untitled

Abstract

Insight into the dynamic properties of a-lytic protease (aLP) has been obtained through the use of low-temperature X-ray crystallography and multiple-conformation refinement. Previous studies of a L P have shown that the residues around the active site are able to move significantly to accommodate substrates of different sizes. Here we show a link between the ability to accommodate ligands and the dynamics of the binding pocket. Although the structure of aLP at 120 K has E-factors with a uniformly low value of 4.8 A* for the main chain, four regions stand out as having significantly higher E-factors. Because thermal motion should be suppressed at cryogenic temperatures, the high E-factors are interpreted as the result of trapped conformational substates. The active site residues that are perturbed during accommodation of different substrates are precisely those showing conformational substates, implying that substrate binding selects a subset of conformations from the ensemble of accessible states. To better characterize the precise nature of these substates, a protein model consisting of 16 structures has been refined and evaluated. The model reveals a number of features that could not be well-described by conventional B-factors: for example, 40% of the main-chain residue conformations are distributed asymmetrically or in discrete clusters. Furthermore, these data demonstrate an unexpected correlation between motions on either side of the binding pocket that we suggest is a consequence of "dynamic close packing." These results provide strong evidence for the role of protein dynamics in substrate binding and are consistent with the results of dynamic studies of ligand binding in myoglobin and ribonuclease A.

Statistical potentials for 3D structure evaluation: From proteins to RNAs*

“…Beyond widely used pairwise two-body potentials, multi-body potentials, especially for three-and fourbody potentials, have been proposed for capturing higherorder interactions. [63,[75][76][77][78][79][95][96][97] In addition, several geometrical techniques, such as Voronoi diagram, Delaunay triangulation, [98] and alpha shape [76,96] were employed to define the neighboring bodies and to identify the multi-body interactions.…”

“…Three-body contacts have been introduced in several studies where physical models were developed with explicitly incorporating a three-body interaction. [95,96,[99][100][101] Based on Delaunay triangulation, Li and Liang used the alpha shape to define three-body interactions for protein structures. [96] Here, we briefly introduce this approach as an example of this class of statistical potential.…”

“…To capture more complicated interactions, several fourbody potentials have been developed, [63,75,[77][78][79]95,97] and Delaunay tessellation algorithms were widely used for finding the nearest neighbors in native structures. Krishnamoorthy and Tropsha have verified that their four-body potentials derived with Delaunay tessellation performed better than pairwise two-body potentials in distinguishing correct sequences/structures and generating Z-scores.…”