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Concept: Computational chemistry


: This paper introduces a subdomain chemistry format for storing computational chemistry data called CompChem. It has been developed based on the design, concepts and methodologies of Chemical Markup Language (CML) by adding computational chemistry semantics on top of the CML Schema. The format allows a wide range of ab initio quantum chemistry calculations of individual molecules to be stored. These calculations include, for example, single point energy calculation, molecular geometry optimization, and vibrational frequency analysis. The paper also describes the supporting infrastructure, such as processing software, dictionaries, validation tools and database repositories. In addition, some of the challenges and difficulties in developing common computational chemistry dictionaries are discussed. The uses of CompChem are illustrated by two practical applications.

Concepts: Mathematics, Quantum mechanics, Molecule, Chemistry, Computational chemistry, Quantum chemistry, Theoretical chemistry, Molecular Hamiltonian


BACKGROUND: Recently, various metallocenes were synthesized and analyzed by biological activity point of view (such as antiproliferative properties): ruthenocenes, cobaltoceniums, titanocenes, zirconocenes, vanadocenes, niobocenes, molibdocenes etc. Two main disadvantages of metallocenes are the poor hydrosolubility and the hydrolytic instability. These problems could be resolved in two ways: synthetically modifying the structure or finding new formulations with enhanced properties. The aqueous solubility of metallocenes with cytostatic activities could be enhanced by molecular encapsulation in cyclodextrins, as well as the hydrolytic instability of these compounds could be reduced. RESULTS: This study presents a theoretical approach on the nanoencapsulation of a series of titanocenes with cytotoxic activity in alpha-, beta-, and gamma-cyclodextrin. The HyperChem 5.11 package was used for building and molecular modelling of titanocene and cyclodextrin structures, as well as for titanocene/cyclodextrin complex optimization. For titanocene/cyclodextrin complex optimization experiments, the titanocene and cyclodextrin structures in minimal energy conformations were set up at various distances and positions between molecules (molecular mechanics functionality, MM+). The best interaction between titanocene structures and cyclodextrins was obtained in the case of beta- and gamma-cyclodextrin, having the hydrophobic moieties oriented to the secondary face of cyclodextrin. The hydrophobicity of titanocenes (logP) correlate with the titanocene-cyclodextrin interaction parameters, especially with the titanocene-cyclodextrin interaction energy; the compatible geometry and the interaction energy denote that the titanocene/beta- and gamma-cyclodextrin complex can be achieved. Valuable quantitative structure-activity relationships (QSARs) were also obtained in the titanocene class by using the same logP as the main parameter for the in vitro cytotoxic activity against HeLa, K562, and Fem-x cell lines. CONCLUSIONS: According to our theoretical study, the titanocene/cyclodextrin inclusion compounds can be obtained (high interaction energy; the encapsulation is energetically favourable). Further, the most hydrophobic compounds are better encapsulated in beta- and gamma-cyclodextrin molecules and are more stable (from energetically point of view) in comparison with alpha-cyclodextrin case. This study suggests that the titanocene / beta- and gamma-cyclodextrin complexes (or synthetically modified cyclodextrins with higher water solubility) could be experimentally synthesized and could have enhanced cytotoxic activity and even lower toxicity.

Concepts: Molecule, Chemistry, Solubility, Computational chemistry, Experiment, Supramolecular chemistry, Molecular modelling, Cyclodextrin


Bcl-X is a member of Bcl-2 family of proteins involved in the regulation of intrinsic pathway of apoptosis. Its overexpression in many human cancers makes it an important target for anti-cancer drugs. Bcl-X interacts with the BH3 domain of several pro-apoptotic Bcl-2 partners. This helical bundle protein has a pronounced hydrophobic groove which acts as a binding region for the BH3 domains. Eight independent molecular dynamics simulations of the apo/holo forms of Bcl-X were carried out to investigate the behavior of solvent-exposed hydrophobic groove. The simulations used either a twin-range cut-off or particle mesh Ewald (PME) scheme to treat long-range interactions. Destabilization of the BH3 domain-containing helix H2 was observed in all four twin-range cut-off simulations. Most of the other major helices remained stable. The unwinding of H2 can be related to the ability of Bcl-X to bind diverse BH3 ligands. The loss of helical character can also be linked to the formation of homo- or hetero-dimers in Bcl-2 proteins. Several experimental studies have suggested that exposure of BH3 domain is a crucial event before they form dimers. Thus unwinding of H2 seems to be functionally very important. The four PME simulations, however, revealed a stable helix H2. It is possible that the H2 unfolding might occur in PME simulations at longer time scales. Hydrophobic residues in the hydrophobic groove are involved in stable interactions among themselves. The solvent accessible surface areas of bulky hydrophobic residues in the groove are significantly buried by the loop LB connecting the helix H2 and subsequent helix. These observations help to understand how the hydrophobic patch in Bcl-X remains stable in the solvent-exposed state. We suggest that both the destabilization of helix H2 and the conformational heterogeneity of loop LB are important factors for binding of diverse ligands in the hydrophobic groove of Bcl-X.

Concepts: Protein structure, Molecular dynamics, Apoptosis, Computational chemistry, Bcl-2, BH3 interacting domain death agonist, Bcl-2-associated X protein, Bcl-xL


Explicit solvent molecular dynamics simulations have been used to complement preceding experimental and computational studies of folding of guanine quadruplexes (G-DNA). We initiate early stages of unfolding of several G-DNAs by simulating them under no-salt conditions and then try to fold them back using standard excess salt simulations. There is a significant difference between G-DNAs with all-anti parallel stranded stems and those with stems containing mixtures of syn and anti guanosines. The most natural rearrangement for all-anti stems is a vertical mutual slippage of the strands. This leads to stems with reduced numbers of tetrads during unfolding and a reduction of strand slippage during refolding. The presence of syn nucleotides prevents mutual strand slippage; therefore, the antiparallel and hybrid quadruplexes initiate unfolding via separation of the individual strands. The simulations confirm the capability of G-DNA molecules to adopt numerous stable locally and globally misfolded structures. The key point for a proper individual folding attempt appears to be correct prior distribution of syn and anti nucleotides in all four G-strands. The results suggest that at the level of individual molecules, G-DNA folding is an extremely multi-pathway process that is slowed by numerous misfolding arrangements stabilized on highly variable timescales.

Concepts: DNA, Molecular dynamics, Molecule, Protein folding, Computational chemistry, G-quadruplex, Monte Carlo method, Computer simulation


The Rosetta Molecular Modeling suite is a command-line-only collection of applications that enable high-resolution modeling and design of proteins and other molecules. Although extremely useful, Rosetta can be difficult to learn for scientists with little computational or programming experience. To that end, we have created a Graphical User Interface (GUI) for Rosetta, called the PyRosetta Toolkit, for creating and running protocols in Rosetta for common molecular modeling and protein design tasks and for analyzing the results of Rosetta calculations. The program is highly extensible so that developers can add new protocols and analysis tools to the PyRosetta Toolkit GUI.

Concepts: Protein, Oxygen, Molecular biology, Molecule, Protein folding, Computational chemistry, User interface, Graphical user interface


Protein-protein interactions play a central role in cellular function. Improving the understanding of complex formation has many practical applications, including the rational design of new therapeutic agents and the mechanisms governing signal transduction networks. The generally large, flat, and relatively featureless binding sites of protein complexes pose many challenges for drug design. Fragment docking and direct coupling analysis are used in an integrated computational method to estimate druggable protein-protein interfaces. (i) This method explores the binding of fragment-sized molecular probes on the protein surface using a molecular docking-based screen. (ii) The energetically favorable binding sites of the probes, called hot spots, are spatially clustered to map out candidate binding sites on the protein surface. (iii) A coevolution-based interface interaction score is used to discriminate between different candidate binding sites, yielding potential interfacial targets for therapeutic drug design. This approach is validated for important, well-studied disease-related proteins with known pharmaceutical targets, and also identifies targets that have yet to be studied. Moreover, therapeutic agents are proposed by chemically connecting the fragments that are strongly bound to the hot spots.

Concepts: Protein, Pharmacology, Bioinformatics, Molecular biology, Signal transduction, Computational chemistry, Java, Object-oriented programming


The Harvard Organic Photovoltaic Dataset (HOPV15) presented in this work is a collation of experimental photovoltaic data from the literature, and corresponding quantum-chemical calculations performed over a range of conformers, each with quantum chemical results using a variety of density functionals and basis sets. It is anticipated that this dataset will be of use in both relating electronic structure calculations to experimental observations through the generation of calibration schemes, as well as for the creation of new semi-empirical methods and the benchmarking of current and future model chemistries for organic electronic applications.

Concepts: Electron, Chemistry, Computational chemistry, Density functional theory, Quantum chemistry, Molecular orbital, Standard Model, Theoretical chemistry


Observing the intricate chemical transformation of an individual molecule as it undergoes a complex reaction is a longstanding challenge in molecular imaging. Advances in scanning probe microscopy now provide the tools to visualize not only the frontier orbitals of chemical reaction partners and products, but their internal covalent bond configurations as well. Here, we demonstrate the use of noncontact atomic force microscopy to investigate reaction-induced changes in the detailed internal bond structure of individual oligo-(phenylene-1,2-ethynylenes) on Ag(100) as they undergo a series of cyclization processes. Our images reveal the complex surface reaction mechanisms underlying thermally induced cyclization cascades of enediynes. Additional evidence for the proposed reaction pathways is obtained using ab initio density functional theory.

Concepts: Chemical reaction, Chemistry, Atom, Chemical substance, Computational chemistry, Scanning probe microscopy


Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve the Kohn-Sham equations. This should yield substantial savings in computer time, allowing larger systems and/or longer time-scales to be tackled, but attempts to machine-learn this functional have been limited by the need to find its derivative. The present work overcomes this difficulty by directly learning the density-potential and energy-density maps for test systems and various molecules. We perform the first molecular dynamics simulation with a machine-learned density functional on malonaldehyde and are able to capture the intramolecular proton transfer process. Learning density models now allows the construction of accurate density functionals for realistic molecular systems.Machine learning allows electronic structure calculations to access larger system sizes and, in dynamical simulations, longer time scales. Here, the authors perform such a simulation using a machine-learned density functional that avoids direct solution of the Kohn-Sham equations.

Concepts: Molecular dynamics, Atom, Science, Computational chemistry, Density functional theory, Quantum chemistry, Computer simulation, Kohn–Sham equations


DNA imaging techniques using optical microscopy have found numerous applications in biology, chemistry and physics and are based on relatively expensive, bulky and complicated set-ups that limit their use to advanced laboratory settings. Here we demonstrate imaging and length quantification of single molecule DNA strands using a compact, light-weight and cost-effective fluorescence microscope installed on a mobile-phone. In addition to an opto-mechanical attachment that creates a high contrast dark-field imaging set-up using an external lens, thin-film interference filters, a miniature dovetail stage and a laser-diode for oblique-angle excitation, we also created a computational framework and a mobile-phone application connected to a server back-end for measurement of the lengths of individual DNA molecules that are labeled and stretched using disposable chips. Using this mobile-phone platform, we imaged single DNA molecules of various lengths to demonstrate a sizing accuracy of <1 kilobase-pairs (kbp) for 10 kbp and longer DNA samples imaged over a field-of-view of ~2 mm^2.

Concepts: DNA, Biology, Optics, Molecule, Microscope, Chemistry, Computational chemistry, Microscopy