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We evaluate the applicability of automated molecular docking techniques and quantum mechanical calculations to the construction of a set of structures of enzyme-substrate complexes for use in Comparative binding energy (COMBINE) analysis to obtain 3D structure-activity relationships. The data set studied consists of the complexes of eighteen substrates docked within the active site of haloalkane dehalogenase (DhlA) from Xanthobacter autotrophicus GJ10. The results of the COMBINE analysis are compared with previously reported data obtained for the same dataset from modelled complexes that were based on an experimentally determined structure of the DhlA-dichloroethane complex. The quality of fit and the internal predictive power of the two COMBINE models are comparable, but better external predictions are obtained with the new approach. Both models show a similar composition of the principal components. Small differences in the relative contributions that are assigned to important residues for explaining binding affinity differences can be directly linked to structural differences in the modelled enzyme-substrate complexes: (i) rotation of all substrates in the active site about their longitudinal axis, (ii) repositioning of the ring of epihalohydrines and the halogen substituents of 1,2-dihalopropanes, and (iii) altered conformation of the long-chain molecules (halobutanes and halohexanes). For external validation, both a novel substrate not included in the training series and two different mutant proteins were used. The results obtained can be useful in the future to guide the rational engineering of substrate specificity in DhlA and other related enzymes.

Free energy calculations tightly bind together experimental observations and computer-aided design through corresponding simulations at atomic level. Nowadays, free energy calculations represent a cornerstone for obtaining deeper insight into any biomolecular system, its molecular structure and especially dynamic behaviour. Despite this fact, several challenges have to be considered before routine utilization of this type of calculations. At first one has to deal properly with so-called sampling problem. Secondly, the appropriate way how to treat their extreme time demands has to be applied for obtaining converged and reliable data comparable with the experimental ones. Here we present the experience obtained through design, implementation and subsequent application of Multiple Walkers Approach (MWA) connected with Adaptive Biasing Force (ABF) and corresponding advantages and added values of MWA ABF run within distributed, heterogeneous grid environment under the EUAsia virtual organization (VO).

The EUAsiaGrid project has developed a Roadmap towards the establishment of a persistent and sustainable e-Infrastructure for research in the Asia- Pacific region. This paper describes its aims and context, the decisions make in its creation and outlines the proposal for an Asia-Pacific Grid Initiative that will allow partners from different countries in the region to coordinate their national initiatives and align themselves with the emerging infrastructure in the region as well as developments elsewhere in the world.

The virtual organizations form a key concept for seamless utilization of all advanced features of available worldwide grid environments, the EGEE Grid in particular. However, apart from large, well organized user communities there is a substantial, non-trivial activation barrier in adopting grid concept and creating a new VO for small research groups. To minimize this barrier, a so-called “catch-all” virtual organization approach has been implemented. Here we present two successful use cases of catch-all virtual organizations (Virtual Organization for Central Europe and for EUAsia), demonstrating the strengths of this approach, discussing their setups, encountered challenges and suggesting corresponding solutions.

Haloalkane dehalogenases are microbial enzymes that cleave a carbon-halogen bond in halogenated compounds. The haloalkane dehalogenase LinB, isolated from Sphingomonas paucimobilis UT26, is a broad-specificity enzyme. Fifty-five halogenated aliphatic and cyclic hydrocarbons were tested for dehalogenation with the LinB enzyme. The compounds for testing were systematically selected using a statistical experimental design. Steady-state kinetic constants K(m) and k(cat) were determined for 25 substrates that showed detectable cleavage by the enzyme and low abiotic hydrolysis. Classical quantitative structure-activity relationships (QSARs) were used to correlate the kinetic constants with molecular descriptors and resulted in a model that explained 94% of the experimental data variability. The binding affinity of the tested substrates for this haloalkane dehalogenase correlated with hydrophobicity, molecular surface, dipole moment, and volume:surface ratio. Binding of the substrate molecules in the active site pocket of LinB depends nonlinearly on the size of the molecules. Binding affinity increases with increasing substrate size up to a chain length of six carbon atoms and then decreases. Comparative binding energy (COMBINE) analysis was then used to identify amino acid residues in LinB that modulate its substrate specificity. A model with three statistically significant principal components explained 95% of the experimental data variability. van der Waals interactions between substrate molecules and the enzyme dominated the COMBINE model, in agreement with the importance of substrate size in the classical QSAR model. Only a limited number of protein residues (6-8%) contribute significantly to the explanation of variability in binding affinities. The amino acid residues important for explaining variability in binding affinities are as follows: (i) first-shell residues Asn38, Asp108, Trp109, Glu132, Ile134, Phe143, Phe151, Phe169, Val173, Trp207, Pro208, Ile211, Leu248, and His272, (ii) tunnel residues Pro144, Asp147, Leu177, and Ala247, and (iii) second-shell residues Pro39 and Phe273. The tunnel and the second-shell residues represent the best targets for modulating specificity since their replacement does not lead to loss of functionality by disruption of the active site architecture. The mechanism of molecular adaptation toward a different specificity is discussed on the basis of quantitative comparison of models derived for two protein family members.

The hydrolysis of haloalkanes to their corresponding alcohols and inorganic halides is catalyzed by alpha/beta-hydrolases called haloalkane dehalogenases. The study of haloalkane dehalogenases is vital for the development of these enzymes if they are to be utilized for bioremediation of organohalide-contaminated industrial waste. We report the kinetic and structural analysis of the haloalkane dehalogenase from Sphingomonas paucimobilis UT26 (LinB) in complex with each of 1,2-dichloroethane and 1,2-dichloropropane and the reaction product of 1-chlorobutane turnover. Activity studies showed very weak but detectable activity of LinB with 1,2-dichloroethane [0.012 nmol s(-1) (mg of enzyme)(-1)] and 1,2-dichloropropane [0.027 nmol s(-1) (mg of enzyme)(-1)]. These activities are much weaker compared, for example, to the activity of LinB with 1-chlorobutane [68.2 nmol s(-1) (mg of enzyme)(-1)]. Inhibition analysis reveals that both 1,2-dichloroethane and 1,2-dichloropropane act as simple competitive inhibitors of the substrate 1-chlorobutane and that 1,2-dichloroethane binds to LinB with lower affinity than 1,2-dichloropropane. Docking calculations on the enzyme in the absence of active site water molecules and halide ions confirm that these compounds could bind productively. However, when these moieties were included in the calculations, they bound in a manner similar to that observed in the crystal structure. These data provide an explanation for the low activity of LinB with small, chlorinated alkanes and show the importance of active site water molecules and reaction products in molecular docking.

Grid middleware stacks, including gLite, matured into the state of being able to process up to millions of jobs per day. Logging and Bookkeeping, the gLite job-tracking service, keeps pace with this rate; however, it is not designed to provide a long-term archive of information on executed jobs. ATLAS — representative of a large user community — addresses this issue with its own job catalogue (ProdDB). Development of such a customized service, not easily reusable, took considerable effort which is not affordable by smaller communities. On the contrary, Job Provenance (JP), a generic gLite service designed for long-term archiving of information on executed jobs focusing on scalability, extensibility, uniform data view, and configurability, allows more specialized catalogues to be easily built. We present the first results of an experimental JP deployment for the ATLAS production infrastructure where a JP installation was fed with a part of ATLAS jobs, and also stress tested with real production data. The main outcome of this work is a demonstration that JP can complement large-scale application-specific job catalogue services, while serving a similar purpose where there are none available.

Comparative binding energy (COMBINE) analysis was conducted for 18 substrates of the haloalkane dehalogenase from Xanthobacter autotrophicusGJ10 (DhlA): 1-chlorobutane, 1-chlorohexane, dichloromethane, 1,2-dichloroethane, 1,2-dichloropropane, 2-chloroethanol, epichlorohydrine, 2-chloro- acetonitrile, 2-chloroacetamide, and their brominated analogues. The purpose of the COMBINE analysis was to identify the amino acid residues determining the substrate specificity of the haloalkane dehalogenase. This knowledge is essential for the tailoring of this enzyme for biotechnological applications. Complexes of the enzyme with these substrates were modeled and then refined by molecular mechanics energy minimization. The intermolecular enzyme-substrate energy was decomposed into residue-wise van der Waals and electrostatic contributions and complemented by surface area dependent and electrostatic desolvation terms. Partial least-squares projection to latent structures analysis was then used to establish relationships between the energy contributions and the experimental apparent dissociation constants. A model containing van der Waals and electrostatic intermolecular interaction energy contributions calculated using the AMBER force field explained 91% (73% cross-validated) of the quantitative variance in the apparent dissociation constants. A model based on van der Waals intermolecular contributions from AMBER and electrostatic interactions derived from the Poisson -Boltzmann equation explained 93% (74% cross- validated) of the quantitative variance. COMBINE models predicted correctly the change in apparent dissociation constants upon single-point mutation of DhlA for six enzyme-substrate complexes. The amino acid residues contributing most significantly to the substrate specificity of DhlA were identified; they include Asp124, Trp125, Phe164, Phe172, Trp175, Phe222, Pro223, and Leu263. These residues are suitable targets for modification by site-directed mutagenesis.