Substrate tunnels in enzymes: structure-function relationships and computational methodology

Abstract

In enzymes, the active site is the location where incoming substrates are chemically converted to products. In some enzymes, this site is deeply buried within the core of the protein, and, in order to access the active site, substrates must pass through the body of the protein via a tunnel. In many systems, these tunnels act as filters and have been found to influence both substrate specificity and catalytic mechanism. Identifying and understanding how these tunnels exert such control has been of growing interest over the past several years because of implications in fields such as protein engineering and drug design. This growing interest has spurred the development of several computational methods to identify and analyze tunnels and how ligands migrate through these tunnels. The goal of this review is to outline how tunnels influence substrate specificity and catalytic efficiency in enzymes with buried active sites and to provide a brief summary of the computational tools used to identify and evaluate these tunnels.

Keywords: CAVER; IterTunnel; MOLE; REMD; buried active site; protein tunnels; structure-function relationship.

Figure 1

Comparison of HPL and CRL binding sites. A) In HPL (PDB-code: 1LBP) the binding site is a surface exposed trough and the inhibitor, undecane phosphonate methyl ester (green sticks), binds along the face of the protein. B) Binding tunnel of CRL (1LPO). The inhibitor hexadecane sulfonate is shown in green sticks and binds into a tunnel in the CRL enzyme. Only a small portion of the ligand remains at the surface of the enzyme. It is thought that the different sized binding pockets play a role in substrate selectivity, especially in terms of the length of the chain that each lipase can accommodate.

Figure 2

U-shaped binding tunnel of polyamine oxidase (1B37) colored by electrostatic potential, A) A cutaway of the protein is shown to expose the binding tunnel and the adjacent pocket where the catalytic flavin moiety sits (shown in cyan sticks). B) A surface representation of POA shows the tunnel openings at the surface of the protein.

Figure 3

Out-of-register binding mode in POA. Binding of the inhibitor guazatine (orange sticks, 1H82) in comparison to the natural substrate spermidine (green sticks, 3L1R). The C-N (C shown with a green sphere) bond of spermidine comes into close contact with the reactive nitrogen of the flavin moiety, shown in a blue sphere. However, guazatine binds in a slightly different mode placing the reactive carbon bond (indicated with an orange sphere) slightly shifted away from the reactive flavin. Residues E62 and E170 located on either side of the binding tunnel are thought to play a critical role in substrate differentiation.

Figure 4

Electrostatic potentials in the binding tunnels of ALDH enzymes; the entrances to each tunnel have been circled in black and the substrates have been shown in the upper right corner of each panel. A) ALDH11 (1EUH) has a positive potential and binds a negative substrate, glyceraldehyde-3-phosphate. B) ALDH2 (4KWF) has a relatively neutral binding tunnel and binds the neutral substrate, acetaldehyde. C) ALDH9 (2WME) has a negatively charged binding tunnel and binds the substrate, betaine aldehyde, which is positively charged.

Figure 5

Implementation of Voronoi diagrams in tunnel prediction. A) First atoms of protein residues are converted into spheres based on the van der Walls radii of the atoms. B) Voronoi edges are plotted as to maximize the distance between adjacent spheres. Series of edges represent potential tunnels in the structure. Methods such as Dijkstra's algorithm can then be used to identify connected edges that result in the shortest paths from binding site to bulk solvent.