CAVER Analyst 2.0: analysis and visualization of channels and tunnels in protein structures and molecular dynamics trajectories.

Motivation: Studying the transport paths of ligands, solvents, or ions in transmembrane proteins and proteins with buried binding sites is fundamental to the understanding of their biological function. A detailed analysis of the structural features influencing the transport paths is also important for engineering proteins for biomedical and biotechnological applications. Results: CAVER Analyst 2.0 is a software tool for quantitative analysis and real-time visualization of tunnels and channels in static and dynamic structures. This version provides the users with many new functions, including advanced techniques for intuitive visual inspection of the spatiotemporal behavior of tunnels and channels. Novel integrated algorithms allow an efficient analysis and data reduction in large protein structures and molecular dynamic simulations. Availability and implementation: CAVER Analyst 2.0 is a multi-platform standalone Java-based application. Binaries and documentation are freely available at www.caver.cz. Supplementary information: Supplementary data are available at Bioinformatics online.

MMTF-An efficient file format for the transmission, visualization, and analysis of macromolecular structures.

Recent advances in experimental techniques have led to a rapid growth in complexity, size, and number of macromolecular structures that are made available through the Protein Data Bank. This creates a challenge for macromolecular visualization and analysis. Macromolecular structure files, such as PDB or PDBx/mmCIF files can be slow to transfer, parse, and hard to incorporate into third-party software tools. Here, we present a new binary and compressed data representation, the MacroMolecular Transmission Format, MMTF, as well as software implementations in several languages that have been developed around it, which address these issues. We describe the new format and its APIs and demonstrate that it is several times faster to parse, and about a quarter of the file size of the current standard format, PDBx/mmCIF. As a consequence of the new data representation, it is now possible to visualize structures with millions of atoms in a web browser, keep the whole PDB archive in memory or parse it within few minutes on average computers, which opens up a new way of thinking how to design and implement efficient algorithms in structural bioinformatics. The PDB archive is available in MMTF file format through web services and data that are updated on a weekly basis.

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.

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.

Expansion of access tunnels and active-site cavities influence activity of haloalkane dehalogenases in organic cosolvents.

The use of enzymes for biocatalysis can be significantly enhanced by using organic cosolvents in the reaction mixtures. Selection of the cosolvent type and concentration range for an enzymatic reaction is challenging and requires extensive empirical testing. An understanding of protein-solvent interaction could provide a theoretical framework for rationalising the selection process. Here, the behaviour of three model enzymes (haloalkane dehalogenases) was investigated in the presence of three representative organic cosolvents (acetone, formamide, and isopropanol). Steady-state kinetics assays, molecular dynamics simulations, and time-resolved fluorescence spectroscopy were used to elucidate the molecular mechanisms of enzyme-solvent interactions. Cosolvent molecules entered the enzymes' access tunnels and active sites, enlarged their volumes with no change in overall protein structure, but surprisingly did not act as competitive inhibitors. At low concentrations, the cosolvents either enhanced catalysis by lowering K(0.5) and increasing k(cat), or caused enzyme inactivation by promoting substrate inhibition and decreasing k(cat). The induced activation and inhibition of the enzymes correlated with expansion of the active-site pockets and their occupancy by cosolvent molecules. The study demonstrates that quantitative analysis of the proportions of the access tunnels and active-sites occupied by organic solvent molecules provides the valuable information for rational selection of appropriate protein-solvent pair and effective cosolvent concentration.

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.

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/.

CAVER: Algorithms for Analyzing Dynamics of Tunnels in Macromolecules.

The biological function of a macromolecule often requires that a small molecule or ion is transported through its structure. The transport pathway often leads through void spaces in the structure. The properties of transport pathways change significantly in time; therefore, the analysis of a trajectory from molecular dynamics rather than of a single static structure is needed for understanding the function of pathways. The identification and analysis of transport pathways are challenging because of the high complexity and diversity of macromolecular shapes, the thermal motion of their atoms, and the large amount of conformations needed to properly describe conformational space of protein structure. In this paper, we describe the principles of the CAVER 3.0 algorithms for the identification and analysis of properties of transport pathways both in static and dynamic structures. Moreover, we introduce the improved clustering solution for finding tunnels in macromolecules, which is included in the latest CAVER 3.02 version. Voronoi diagrams are used to identify potential pathways in each snapshot of a molecular dynamics trajectory and clustering is then used to find the correspondence between tunnels from different snapshots. Furthermore, the geometrical properties of pathways and their evolution in time are computed and visualized.

Software tools for identification, visualization and analysis of protein tunnels and channels.

Protein structures contain highly complex systems of voids, making up specific features such as surface clefts or grooves, pockets, protrusions, cavities, pores or channels, and tunnels. Many of them are essential for the migration of solvents, ions and small molecules through proteins, and their binding to the functional sites. Analysis of these structural features is very important for understanding of structure-function relationships, for the design of potential inhibitors or proteins with improved functional properties. Here we critically review existing software tools specialized in rapid identification, visualization, analysis and design of protein tunnels and channels. The strengths and weaknesses of individual tools are reported together with examples of their applications for the analysis and engineering of various biological systems. This review can assist users with selecting a proper software tool for study of their biological problem as well as highlighting possible avenues for further development of existing tools. Development of novel descriptors representing not only geometry, but also electrostatics, hydrophobicity or dynamics, is needed for reliable identification of biologically relevant tunnels and channels.

Haloalkane dehalogenases: biotechnological applications.

Haloalkane dehalogenases (EC 3.8.1.5, HLDs) are α/ß-hydrolases which act to cleave carbon-halogen bonds. Due to their unique catalytic mechanism, broad substrate specificity and high robustness, the members of this enzyme family have been employed in several practical applications: (i) biocatalytic preparation of optically pure building-blocks for organic synthesis; (ii) recycling of by-products from chemical processes; (iii) bioremediation of toxic environmental pollutants; (iv) decontamination of warfare agents; (v) biosensing of environmental pollutants; and (vi) protein tagging for cell imaging and protein analysis. This review discusses the application of HLDs in the context of the biochemical properties of individual enzymes. Further extension of HLD uses within the field of biotechnology will require currently limiting factors - such as low expression, product inhibition, insufficient enzyme selectivity, low affinity and catalytic efficiency towards selected substrates, and instability in the presence of organic co-solvents - to be overcome. We propose that strategies based on protein engineering and isolation of novel HLDs from extremophilic microorganisms may offer solutions.