Jiri Damborsky

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

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 http://www.caver.cz.

Redesigning dehalogenase access tunnels as a strategy for degrading an anthropogenic substrate

Engineering enzymes to degrade anthropogenic compounds efficiently is challenging. We obtained Rhodococcus rhodochrous haloalkane dehalogenase mutants with up to 32-fold higher activity than wild type toward the toxic, recalcitrant anthropogenic compound 1,2,3-trichloropropane (TCP) using a new strategy. We identified key residues in access tunnels connecting the buried active site with bulk solvent by rational design and randomized them by directed evolution. The most active mutant has large aromatic residues at two out of three randomized positions and two positions modified by site-directed mutagenesis. These changes apparently enhance activity with TCP by decreasing accessibility of the active site for water molecules, thereby promoting activated complex formation. Kinetic analyses confirmed that the mutations improved carbon-halogen bond cleavage and shifted the rate-limiting step to the release of products. Engineering access tunnels by combining computer-assisted protein design with directed evolution may be a valuable strategy for refining catalytic properties of enzymes with buried active sites.

PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations

Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.

HotSpot Wizard: a web server for identification of hot spots in protein engineering

HotSpot Wizard is a web server for automatic identification of ‘hot spots’ for engineering of substrate specificity, activity or enantioselectivity of enzymes and for annotation of protein structures. The web server implements the protein engineering protocol, which targets evolutionarily variable amino acid positions located in the active site or lining the access tunnels. The ‘hot spots’ for mutagenesis are selected through the integration of structural, functional and evolutionary information obtained from: (i) the databases RCSB PDB, UniProt, PDBSWS, Catalytic Site Atlas and nr NCBI and (ii) the tools CASTp, CAVER, BLAST, CD-HIT, MUSCLE and Rate4Site. The protein structure and e-mail address are the only obligatory inputs for the calculation. In the output, HotSpot Wizard lists annotated residues ordered by estimated mutability. The results of the analysis are mapped on the enzyme structure and visualized in the web browser using Jmol. The HotSpot Wizard server should be useful for protein engineers interested in exploring the structure of their favourite protein and for the design of mutations in site-directed mutagenesis and focused directed evolution experiments. HotSpot Wizard is available at http://loschmidt.chemi.muni.cz/hotspotwizard/.

Computational tools for designing and engineering enzymes

Protein engineering strategies aimed at constructing enzymes with novel or improved activities, specificities, and stabilities greatly benefit from in silico methods. Computational methods can be principally grouped into three main categories: bioinformatics; molecular modelling; and de novo design. Particularly de novo protein design is experiencing rapid development, resulting in more robust and reliable predictions. A recent trend in the field is to combine several computational approaches in an interactive manner and to complement them with structural analysis and directed evolution. A detailed investigation of designed catalysts provides valuable information on the structural basis of molecular recognition, biochemical catalysis, and natural protein evolution.

Enantioselectivity of Haloalkane Dehalogenases and its Modulation by Surface Loop Engineering

Engineering of the surface loop in haloalkane dehalogenases affects their enantiodiscrimination behavior. The temperature dependence of the enantioselectivity (lnE versus 1/T) of -bromoalkanes by haloalkane dehalogenases is reversed (red data points) by deletion of the surface loop; the selectivity switches back when an additional single-point mutation is made. This behavior is not observed for -bromoesters.

Computational tools for designing and engineering biocatalysts

Current computational tools to assist experimentalists for the design and engineering of proteins with desired catalytic properties are reviewed. The applications of these tools for de novo design of protein active sites, optimization of substrate access and product exit pathways, redesign of protein-protein interfaces, identification of neutral/advantageous/deleterious mutations in the libraries from directed evolution and stabilization of protein structures are described. Remarkable progress is seen in de novo design of enzymes catalyzing a chemical reaction for which a natural biocatalyst does not exist. Yet, constructed biocatalysts do not match natural enzymes in their efficiency, suggesting that more research is needed to capture all the important features of natural biocatalysts in theoretical designs.

CAVER Analyst 1.0: graphic tool for interactive visualization and analysis of tunnels and channels in protein structures

UNLABELLED The transport of ligands, ions or solvent molecules into proteins with buried binding sites or through the membrane is enabled by protein tunnels and channels. CAVER Analyst is a software tool for calculation, analysis and real-time visualization of access tunnels and channels in static and dynamic protein structures. It provides an intuitive graphic user interface for setting up the calculation and interactive exploration of identified tunnels/channels and their characteristics. AVAILABILITY AND IMPLEMENTATION CAVER Analyst is a multi-platform software written in JAVA. Binaries and documentation are freely available for non-commercial use at http://www.caver.cz.

Gates of Enzymes

This review highlights the importance of gates in enzymes. The gates control substrate access to the active site and product release, restrict solvent access to specific protein regions, and synchronize processes occurring in distinct parts of the enzyme. Survey of 129 gates in 71 enzymes enabled a rigorous definition of gates and establishment of a new scheme for their classification. Gates were assigned to six distinct classes – wings, swinging doors, apertures, drawbridges, double drawbridges and shells. Presented are summary statistics describing the propensity of specific amino acid residues in particular gate classes. The proposed classification scheme provides guidance for the analysis and engineering of gates in biomolecular systems.

Molecular Dissection of Interactions between Rad51 and Members of the Recombination-Repair Group

ABSTRACT Recombination is important for the repair of DNA damage and for chromosome segregation during meiosis; it has also been shown to participate in the regulation of cell proliferation. In the yeastSaccharomyces cerevisiae, recombination requires products of the RAD52 epistasis group. The Rad51 protein associates with the Rad51, Rad52, Rad54, and Rad55 proteins to form a dynamic complex. We describe a new strategy to screen for mutations which cause specific disruption of the interaction between certain proteins in the complex, leaving other interactions intact. This approach defines distinct protein interaction domains and protein relationships within the Rad51 complex. Alignment of the mutations onto the constructed three-dimensional model of the Rad51 protein reveal possible partially overlapping interfaces for the Rad51-Rad52 and the Rad51-Rad54 interactions. Rad51-Rad55 and Rad51-Rad51 interactions are affected by the same spectrum of mutations, indicating similarity between the two modes of binding. Finally, the detection of a subset of mutations within Rad51 which disrupt the interaction with mutant Rad52 protein but activate the interaction with Rad54 suggests that dynamic changes within the Rad51 protein may contribute to an ordered reaction process.