A single mutation in a tunnel to the active site changes the mechanism and kinetics of product release in haloalkane dehalogenase LinB

Many enzymes have buried active sites. The properties of the tunnels connecting the active site with bulk solvent affect ligand binding and unbinding and, therefore, also the catalytic properties. Here, we investigate ligand passage in the haloalkane dehalogenase enzyme LinB, and the effect of replacing leucine by a bulky tryptophan at a tunnel-lining position. Transient kinetic experiments show that the mutation significantly slows down the rate of product release. Moreover, the mechanism of bromide ion release is changed from a one-step process in the wild type enzyme to a two-step process in the mutant. The rate constant of bromide ion release corresponds to the overall steady-state turnover rate constant, suggesting that product release became the rate-limiting step of catalysis in the mutant. We explain the experimental findings by investigating the molecular details of the process computationally. Analysis of trajectories from molecular dynamics simulations with the CAVER 3.0 program reveals differences in the tunnels available for ligand egress. Corresponding differences are seen in simulations of product egress using the Random Acceleration Molecular Dynamics technique. The differences in the free energy barriers for egress of a bromide ion calculated using the Adaptive Biasing Force method are in good agreement with the differences in rates obtained from the transient kinetic experiments. Interactions of the bromide ion with the introduced tryptophan are shown to affect the free energy barrier for its passage. The study demonstrates how the mechanism of an enzymatic catalytic cycle and reaction kinetics can be engineered by modification of protein tunnels

HotSpot Wizard: a web server for identification

of hot spots in protein engineerin

CAVER 3.0: A Tool for the Analysis of Transport Pathways in Dynamic Protein Structures

<div><p>Tunnels and channels facilitate the transport of small molecules, ions and water solvent in a large variety of proteins. Characteristics of individual transport pathways, including their geometry, physico-chemical properties and dynamics are instrumental for understanding of structure-function relationships of these proteins, for the design of new inhibitors and construction of improved biocatalysts. CAVER is a software tool widely used for the identification and characterization of transport pathways in static macromolecular structures. Herein we present a new version of CAVER enabling automatic analysis of tunnels and channels in large ensembles of protein conformations. CAVER 3.0 implements new algorithms for the calculation and clustering of pathways. A trajectory from a molecular dynamics simulation serves as the typical input, while detailed characteristics and summary statistics of the time evolution of individual pathways are provided in the outputs. To illustrate the capabilities of CAVER 3.0, the tool was applied for the analysis of molecular dynamics simulation of the microbial enzyme haloalkane dehalogenase DhaA. CAVER 3.0 safely identified and reliably estimated the importance of all previously published DhaA tunnels, including the tunnels closed in DhaA crystal structures. Obtained results clearly demonstrate that analysis of molecular dynamics simulation is essential for the estimation of pathway characteristics and elucidation of the structural basis of the tunnel gating. CAVER 3.0 paves the way for the study of important biochemical phenomena in the area of molecular transport, molecular recognition and enzymatic catalysis. The software is freely available as a multiplatform command-line application at <a href="http://www.caver.cz">http://www.caver.cz</a>.</p> </div

Comparison of the DhaA tunnels identified by CAVER 3.0 with the previously proposed pathways.

<p>(A) The top ranked collective pathways identified throughout the molecular dynamics simulation of DhaA by CAVER 3.0 are all depicted in one frame as pathway centerlines. The p2a and p2b tunnels were initially identified as one collective pathway—p2ab—using the clustering threshold of 4.3. Decreasing the clustering threshold to 3.5 led to the separation of the p2a and p2b tunnels as well as the splitting of the p1 collective pathway into three clusters—p1a, p1a′ and p1b. A random subsample of identified tunnels is shown for clarity. (B) Representative DhaA pathways (surface representation) for the release of products and/or exchange of water solvent as identified previously by RAMD and classical MD simulations <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002708#pcbi.1002708-Klvana1" target="_blank">[15]</a>.</p

Comparison of the p1 tunnel of DhaA calculated by CAVER 3.0 and MOLE 1.2.

<p>(A) Time evolution of the bottleneck radius calculated by CAVER 3.0 (blue) and MOLE 1.2 (red). Only a part of the 10 ns MD simulation is shown for clarity. The sample snapshot (black arrows) was taken at 0.81 ns. (B) Geometry of the p1 tunnel in the sample snapshot calculated by CAVER 3.0 (blue) and MOLE 1.2 (red). Hydrogen atom of the bottleneck residue Ala145 (white ball) is shown together with the sulfur atom of the bottleneck residue Cys176 (yellow ball) and with sphere representation of the tunnels. The underestimation of the bottleneck radius by MOLE 1.2 is visible as an empty space between the tunnel and the hydrogen atom of Ala145. (C) Profile of the p1 tunnel in the sample snapshot calculated by CAVER 3.0 (blue) and MOLE 1.2 (red). The dashed line indicates the profile representing the maximum possible difference between the CAVER-calculated (solid line) and the correct profile of the p1 tunnel.</p

Time evolution of the bottleneck radii of DhaA tunnels identified by CAVER 3.0.

<p>The color map ranges from very narrow (green) to wide (red) bottlenecks. White color indicates that no pathway with bottleneck radius ≥0.9 Å was identified for the given pathway cluster in the given snapshot.</p

Characteristics of the top ranked tunnels of DhaA identified by CAVER 3.0 in molecular dynamics trajectory using the probe radius of 0.9 Å and the clustering threshold of 3.5.

a<p>number of snapshots in which at least one pathway with bottleneck radius ≥0.9 Å was identified;</p>b<p>number of snapshots in which at least one pathway with bottleneck radius ≥1.4 Å was identified;</p>c<p>characteristics averaged over identified pathways (i.e. pathways with bottleneck radius ≥0.9 Å), real values will be lower, especially for p1a′, p1b, p2a, p2c and p3 tunnels, which were identified only in a small portion of snapshots.</p

Time evolution of the DhaA p1 tunnel.

<p>(A) Evolution of the p1 tunnel bottleneck radius over time. The dotted orange lines indicate bottleneck radii of the p1 tunnel in DhaA crystal structures with added hydrogen atoms: PDB-IDs 1BN6 and 1BN7 (bottleneck radius 1.2 Å) and 1CQW (1.3 Å). The four green circles indicate bottleneck radii of the p1 tunnel from the 2.76 ns (bottleneck radius 1.4 Å), 5.85 ns (2.3 Å), 7.57 ns (0.9 Å) and the 7.72 ns (0.9 Å) snapshots from the molecular dynamics simulation of DhaA. (B) The p1 tunnel identified in DhaA crystal structures with added hydrogen atoms (PDB-IDs 1CQW, 1BN6, 1BN7). (C) The p1 tunnel identified in the 2.76 ns, 5.85 ns, 7.57 ns and 7.72 ns snapshots of the MD trajectory of DhaA.</p