QoS based Effective and Efficient Selection of Web Service and Retrieval of Search Information

Web services are integrated software components for the support of interoperable machine to machine interaction over a network. Web services have been widely employed for building service-oriented applications in both industry and academia in recent years. The number of publicly available Web services is steadily increasing on the Internet. However, this proliferation makes it hard for a user to select a proper Web service among a large amount of service candidates. An inappropriate service selection may cause many problems to the resulting applications. In this paper, a novel collaborative filtering-based Web service recommender system is proposed to help the users and select services with optimal QoS performance. Our recommender system employ an effective and efficient selection of web services and relevant retrieval of information and makes personalized service recommendation to users based on the clustering results. Compared with existing service recommendation methods, the proposed approach achieves considerable improvement on the recommendation accuracy and the QoS performance metrics adopted in this paper shows the better accuracy and relevant web services

Applying Machine Learning to Computational Chemistry: Can We Predict Molecular Properties Faster without Compromising Accuracy?

Non-covalent interactions are crucial in analyzing protein folding and structure, function of DNA and RNA, structures of molecular crystals and aggregates, and many other processes in the fields of biology and chemistry. However, it is time and resource consuming to calculate such interactions using quantum-mechanical formulations. Our group has proposed previously that the effective fragment potential (EFP) method could serve as an efficient alternative to solve this problem. However, one of the computational bottlenecks of the EFP method is obtaining parameters for each molecule/fragment in the system, before the actual EFP simulations can be carried out. Here we present a neural network model that is trained by pre-calculated EFP parameters for a set of fragment geometries, to predict the multipole moment parameters for the fragments with arbitrary geometries. We perform Monte Carlo simulation to assess accuracy of the model. The results demonstrate the ability to predict multipole moments within acceptable margin of error given that the training set is closely spaced. These results contribute towards extending the applicability of the EFP method to new types of chemistries and improving the accuracy and computational efficiency of describing non-covalent interactions

Investigation of Noncovalent Interactions in Complex Systems Using Effective Fragment Potential Method

Computational Chemistry has proven to be an effective means of solving chemical problems. The two main tools of Computational Chemistry - quantum mechanics and molecular mechanics, have provided viable avenues to probe such chemical problems at an electronic or molecular level, with varying levels of accuracy and speed. In this work, attempts have been made to combine the speed of molecular mechanics and the accuracy of quantum mechanics to work across multiples scales of time and length, effectively resulting in simulations of large chemical systems without compromising the accuracy. The primary tool utilized for methods development and application in this work is the Effective Fragment Potential (EFP) method. The EFP method is a computational technique for studying non-covalent interactions in complex systems. EFP is an accurate ab initio force field, with accuracy comparable to many Density Functional Theory (DFT) methods, at significantly lower computational cost. EFP decomposes intermolecular interactions into contributions from four terms: electrostatics, polarization, exchange-repulsion and dispersion. In the first chapter, the possibility of applying EFP method to study large radical-water clusters is probed. An approximate theoretical model in which the transition dipole moments of excitations are computed using the information from the ground state orbitals is implemented. A major challenge to broaden the scope of EFP is to overcome its limitation in describing only small and rigid molecules such as water, acetone, etc. In the second chapter, the extension of EFP method to large covalently bound biomolecules and polymers such as proteins, lipids etc., is described. Using this new method, referred to as BioEFP/mEFP, it is shown that the effect of polarization is non-negligible and must be accounted for when modeling photochemical and electron-transfer processes in photoactive proteins. Another area of interest is the development of novel drug-target binding models, in which a chemically active part of the ligand is modified via functional group modification, while the rest of the system remains intact. In the third chapter, the development and application of a drug-target binding model is explained. Lastly, in the fourth and final chapter, we show the derivation for working equations corresponding to the coupling gradient term describing the dispersion interactions between quantum mechanical and effective fragment potential regions. The primary focus of this work is to explore and expand the boundaries of multiscale QM/MM simulations applied to chemical and biomolecular systems. We believe that the work described here leads to exciting pathways in the future in terms of modeling novel systems and processes such as heterogeneous catalysis, QSAR, crystal structure prediction, etc

Simulating Redox Potentials of Biomolecules: the Case of Cryptochrome 1 from Arabidopsis thaliana

Redox reactions play a key role in various biological processes, including photosynthesis and respiration. Quantitative and predictive computational characterization of redox events is therefore highly desirable for enriching our knowledge on mechanistic features of biological redox-active macromolecules. Here, we present the results of computational studies of the redox potential of flavin adenine dinucleotide (FAD) in cryptochrome 1 from Arabidopsis thaliana (Cry1At). The special attention is paid to fundamental aspects of the theoretical description such as the effects of environment polarization and of the long-range electrostatic interactions on the computed energetic parameters. Environment (protein and the solvent) polarization is shown to be crucial for accurate estimates of the redox potential: hybrid quantum-classical results with and without account for environment polarization differ by 1.4 V. Long-range electrostatic interactions are shown to contribute significantly to the computed redox potential value even at the distances far beyond the protein outer surface. The theoretical estimate (0.07 V) of the midpoint reduction potential of FAD in Cry1At is reported for the first time and is in good agreement with available experimental data

Molybdenum Trioxide on Anatase TiO2(101) - Formation of Monodispersed (MoO3)1 Monomers from Oligomeric (MoO3)n Clusters

Complex oxide systems with hierarchical order are of critical importance in material science and catalysis. Despite their immense potential, their design and synthesis are rather difficult. In this study we demonstrate how the deposition of small oligomeric (MoO3)1-6 clusters, which can be formed by the sublimation of MoO3 powders, leads to the formation of locally ordered layers of (MoO3)1 monomers on anatase TiO2(101). Using both high-resolution imaging and theoretical calculations, we show that at room temperature, such oligomers undergo spontaneous dissociation to their monomeric units. In initial stages of the deposition, this is reflected by the observation of one to six neighboring (MoO3)1 monomers that parallel the size distribution of the oligomers. A transient mobility of such oligomers on both bare TiO2(101) and (MoO3)1 covered areas is key to the formation of a complete layer with a saturation coverage of one (MoO3)1 per two undercoordinated surface Ti sites. We further show that such layers are stable to 500 K, making them highly suitable for a broad range of applications. </p

Extension of the Effective Fragment Potential Method to Macromolecules

The effective fragment potential (EFP) approach, which can be described as a nonempirical polarizable force field, affords an accurate first-principles treatment of noncovalent interactions in extended systems. EFP can also describe the effect of the environment on the electronic properties (e.g., electronic excitation energies and ionization and electron-attachment energies) of a subsystem via the QM/EFP (quantum mechanics/EFP) polarizable embedding scheme. The original formulation of the method assumes that the system can be separated, without breaking covalent bonds, into closed-shell fragments, such as solvent and solute molecules. Here, we present an extension of the EFP method to macromolecules (mEFP). Several schemes for breaking a large molecule into small fragments described by EFP are presented and benchmarked. We focus on the electronic properties of molecules embedded into a protein environment and consider ionization, electron-attachment, and excitation energies (single-point calculations only). The model systems include chromophores of green and red fluorescent proteins surrounded by several nearby amino acid residues and phenolate bound to the T4 lysozyme. All mEFP schemes show robust performance and accurately reproduce the reference full QM calculations. For further applications of mEFP, we recommend either the scheme in which the peptide is cut along the C_α–C bond, giving rise to one fragment per amino acid, or the scheme with two cuts per amino acid, along the C_α–C and C_α–N bonds. While using these fragmentation schemes, the errors in solvatochromic shifts in electronic energy differences (excitation, ionization, electron detachment, or electron-attachment) do not exceed 0.1 eV. The largest error of QM/mEFP against QM/EFP (no fragmentation of the EFP part) is 0.06 eV (in most cases, the errors are 0.01–0.02 eV). The errors in the QM/molecular mechanics calculations with standard point charges can be as large as 0.3 eV