Showing papers in "Journal of Computational Chemistry in 2018"

Implementation and performance of the artificial force induced reaction method in the GRRM17 program

Abstract: This article reports implementation and performance of the artificial force induced reaction (AFIR) method in the upcoming 2017 version of GRRM program (GRRM17). The AFIR method, which is one of automated reaction path search methods, induces geometrical deformations in a system by pushing or pulling fragments defined in the system by an artificial force. In GRRM17, three different algorithms, that is, multicomponent algorithm (MC-AFIR), single-component algorithm (SC-AFIR), and double-sphere algorithm (DS-AFIR), are available, where the MC-AFIR was the only algorithm which has been available in the previous 2014 version. The MC-AFIR does automated sampling of reaction pathways between two or more reactant molecules. The SC-AFIR performs automated generation of global or semiglobal reaction path network. The DS-AFIR finds a single path between given two structures. Exploration of minimum energy structures within the hypersurface in which two different electronic states degenerate, and an interface with the quantum mechanics/molecular mechanics method, are also described. A code termed SAFIRE will also be available, as a visualization software for complicated reaction path networks. © 2017 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Consistent gaussian basis sets of double- and triple-zeta valence with polarization quality of the fifth period for solid-state calculations.

Abstract: Consistent basis sets of double- and triple-zeta valence with polarization quality for the fifth period have been derived for periodic quantum-chemical solid-state calculations with the crystalline-orbital program CRYSTAL. They are an extension of the pob-TZVP basis sets, and are based on the full-relativistic effective core potentials (ECPs) of the Stuttgart/Cologne group and on the def2-SVP and def2-TZVP valence basis of the Ahlrichs group. We optimized orbital exponents and contraction coefficients to supply robust and stable self-consistent field (SCF) convergence for a wide range of different compounds. The computed crystal structures are compared to those obtained with standard basis sets available from the CRYSTAL basis set database. For the applied hybrid density functional PW1PW, the average deviations of calculated lattice constants from experimental references are smaller with pob-DZVP and pob-TZVP than with standard basis sets. © 2018 Wiley Periodicals, Inc.

σ‐holes and π‐holes: Similarities and differences

Abstract: σ-Holes and π-holes are regions of molecules with electronic densities lower than their surroundings. There are often positive electrostatic potentials associated with them. Through these potentials, the molecule can interact attractively with negative sites, such as lone pairs, π electrons, and anions. Such noncovalent interactions, "σ-hole bonding" and "π-hole bonding," are increasingly recognized as being important in a number of different areas. In this article, we discuss and compare the natures and characteristics of σ-holes and π-holes, and factors that influence the strengths and locations of the resulting electrostatic potentials. © 2017 Wiley Periodicals, Inc.

GenIce: Hydrogen-Disordered Ice Generator.

Abstract: GenIce is an efficient and user-friendly tool to generate hydrogen-disordered ice structures. It makes ice and clathrate hydrate structures in various file formats. More than 100 kinds of structures are preset. Users can install their own crystal structures, guest molecules, and file formats as plugins. The algorithm certifies that the generated structures are completely randomized hydrogen-disordered networks obeying the ice rule with zero net polarization. © 2017 The Authors Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Molecular enhanced sampling with autoencoders: On-the-fly collective variable discovery and accelerated free energy landscape exploration.

Abstract: Macromolecular and biomolecular folding landscapes typically contain high free energy barriers that impede efficient sampling of configurational space by standard molecular dynamics simulation. Biased sampling can artificially drive the simulation along prespecified collective variables (CVs), but success depends critically on the availability of good CVs associated with the important collective dynamical motions. Nonlinear machine learning techniques can identify such CVs but typically do not furnish an explicit relationship with the atomic coordinates necessary to perform biased sampling. In this work, we employ auto-associative artificial neural networks ("autoencoders") to learn nonlinear CVs that are explicit and differentiable functions of the atomic coordinates. Our approach offers substantial speedups in exploration of configurational space, and is distinguished from existing approaches by its capacity to simultaneously discover and directly accelerate along data-driven CVs. We demonstrate the approach in simulations of alanine dipeptide and Trp-cage, and have developed an open-source and freely available implementation within OpenMM. © 2018 Wiley Periodicals, Inc.

Parallelization of CPPTRAJ enables large scale analysis of molecular dynamics trajectory data.

Abstract: Advances in biomolecular simulation methods and access to large scale computer resources have led to a massive increase in the amount of data generated. The key enablers have been optimization and parallelization of the simulation codes. However, much of the software used to analyze trajectory data from these simulations is still run in serial, or in some cases many threads via shared memory. Here, we describe the addition of multiple levels of parallel trajectory processing to the molecular dynamics simulation analysis software CPPTRAJ. In addition to the existing OpenMP shared-memory parallelism, CPPTRAJ now has two additional levels of message passing (MPI) parallelism involving both across-trajectory processing and across-ensemble processing. All three levels of parallelism can be simultaneously active, leading to significant speed ups in data analysis of large datasets on the NCSA Blue Waters supercomputer by better leveraging the many available nodes and its parallel file system. © 2018 Wiley Periodicals, Inc.

Single-sequence-based prediction of protein secondary structures and solvent accessibility by deep whole-sequence learning

Abstract: Predicting protein structure from sequence alone is challenging. Thus, the majority of methods for protein structure prediction rely on evolutionary information from multiple sequence alignments. In previous work we showed that Long Short-Term Bidirectional Recurrent Neural Networks (LSTM-BRNNs) improved over regular neural networks by better capturing intra-sequence dependencies. Here we show a single-sequence-based prediction method employing LSTM-BRNNs (SPIDER3-Single), that consistently achieves Q3 accuracy of 72.5%, and correlation coefficient of 0.67 between predicted and actual solvent accessible surface area. Moreover, it yields reasonably accurate prediction of eight-state secondary structure, main-chain angles (backbone ϕ and ψ torsion angles and C α-atom-based θ and τ angles), half-sphere exposure, and contact number. The method is more accurate than the corresponding evolutionary-based method for proteins with few sequence homologs, and computationally efficient for large-scale screening of protein-structural properties. It is available as an option in the SPIDER3 server, and a standalone version for download, at http://sparks-lab.org. © 2018 Wiley Periodicals, Inc.

Molecular dynamics simulations using the drude polarizable force field on GPUs with OpenMM: Implementation, validation, and benchmarks.

Abstract: Presented is the implementation of the Drude force field in the open-source OpenMM simulation package allowing for access to graphical processing unit (GPU) hardware. In the Drude model, electronic degrees of freedom are represented by negatively charged particles attached to their parent atoms via harmonic springs, such that extra computational overhead comes from these additional particles and virtual sites representing lone pairs on electronegative atoms, as well as the associated thermostat and integration algorithms. This leads to an approximately fourfold increase in computational demand over additive force fields. However, by making the Drude model accessible to consumer-grade desktop GPU hardware it will be possible to perform simulations of one microsecond or more in less than a month, indicating that the barrier to employ polarizable models has largely been removed such that polarizable simulations with the classical Drude model are readily accessible and practical.

An Fe-Ni-Cr embedded atom method potential for austenitic and ferritic systems.

Abstract: Fe-Ni-Cr stainless-steels are important structural materials because of their superior strength and corrosion resistance. Atomistic studies of mechanical properties of stainless-steels, however, have been limited by the lack of high-fidelity interatomic potentials. Here using density functional theory as a guide, we have developed a new Fe-Ni-Cr embedded atom method potential. We demonstrate that our potential enables stable molecular dynamics simulations of stainless-steel alloys at high temperatures, accurately reproduces the stacking fault energy-known to strongly influence the mode of plastic deformation (e.g., twinning vs. dislocation glide vs. cross-slip)-of these alloys over a range of compositions, and gives reasonable elastic constants, energies, and volumes for various compositions. The latter are pertinent for determining short-range order and solute strengthening effects. Our results suggest that our potential is suitable for studying mechanical properties of austenitic and ferritic stainless-steels which have vast implementation in the scientific and industrial communities. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.

tsscds2018: A code for automated discovery of chemical reaction mechanisms and solving the kinetics

Abstract: A new software, called tsscds2018, has been developed to discover reaction mechanisms and solve the kinetics in a fully automated fashion. The program employs algorithms based on Graph Theory to find transition state (TS) geometries from accelerated semiempirical dynamics simulations carried out with MOPAC2016. Then, the TSs are connected to the corresponding minima and the reaction network is obtained. Kinetic data like populations vs time or the abundancies of each product can also be obtained with our program thanks to a Kinetic Monte Carlo routine. Highly accurate ab initio potential energy diagrams and kinetics can also be obtained using an interface with Gaussian09. The source code is available on the following site: http://forge.cesga.es/wiki/g/tsscds/HomePage © 2018 Wiley Periodicals, Inc.

A Vehicle Detection Method for Aerial Image Based on YOLO

Abstract: With the application of UAVs in intelligent transportation systems, vehicle detection for aerial images has become a key engineering technology and has academic research significance. In this paper, a vehicle detection method for aerial image based on YOLO deep learning algorithm is presented. The method integrates an aerial image dataset suitable for YOLO training by pro-cessing three public aerial image datasets. Experiments show that the training model has a good performance on unknown aerial images, especially for small objects, rotating objects, as well as compact and dense objects, while meeting the real-time requirements.

Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach

Abstract: The regression model-based tool is developed for predicting the Seebeck coefficient of crystalline materials in the temperature range from 300 K to 1000 K. The tool accounts for the single crystal versus polycrystalline nature of the compound, the production method, and properties of the constituent elements in the chemical formula. We introduce new descriptive features of crystalline materials relevant for the prediction the Seebeck coefficient. To address off-stoichiometry in materials, the predictive tool is trained on a mix of stoichiometric and nonstoichiometric materials. The tool is implemented into a web application (http://info.eecs.northwestern.edu/SeebeckCoefficientPredictor) to assist field scientists in the discovery of novel thermoelectric materials. © 2017 Wiley Periodicals, Inc.

Fast and flexible gpu accelerated binding free energy calculations within the amber molecular dynamics package.

Abstract: Alchemical free energy (AFE) calculations based on molecular dynamics (MD) simulations are key tools in both improving our understanding of a wide variety of biological processes and accelerating the design and optimization of therapeutics for numerous diseases. Computing power and theory have, however, long been insufficient to enable AFE calculations to be routinely applied in early stage drug discovery. One of the major difficulties in performing AFE calculations is the length of time required for calculations to converge to an ensemble average. CPU implementations of MD-based free energy algorithms can effectively only reach tens of nanoseconds per day for systems on the order of 50,000 atoms, even running on massively parallel supercomputers. Therefore, converged free energy calculations on large numbers of potential lead compounds are often untenable, preventing researchers from gaining crucial insight into molecular recognition, potential druggability and other crucial areas of interest. Graphics Processing Units (GPUs) can help address this. We present here a seamless GPU implementation, within the PMEMD module of the AMBER molecular dynamics package, of thermodynamic integration (TI) capable of reaching speeds of >140 ns/day for a 44,907-atom system, with accuracy equivalent to the existing CPU implementation in AMBER. The implementation described here is currently part of the AMBER 18 beta code and will be an integral part of the upcoming version 18 release of AMBER. © 2018 Wiley Periodicals, Inc.

Polarizable force field for RNA based on the classical drude oscillator.

Abstract: RNA molecules are highly dynamic and capable of adopting a wide range of complex, folded structures. The factors driving the folding and dynamics of these structures are dependent on a balance of base pairing, hydration, base stacking, ion interactions, and the conformational sampling of the 2'-hydroxyl group in the ribose sugar. The representation of these features is a challenge for empirical force fields used in molecular dynamics simulations. Toward meeting this challenge, the inclusion of explicit electronic polarization is important in accurately modeling RNA structure. In this work, we present a polarizable force field for RNA based on the classical Drude oscillator model, which represents electronic degrees of freedom via negatively charged particles attached to their parent atoms by harmonic springs. Beginning with parametrization against quantum mechanical base stacking interaction energy and conformational energy data, we have extended the Drude-2017 nucleic acid force field to include RNA. The conformational sampling of a range of RNA sequences were used to validate the force field, including canonical A-form RNA duplexes, stem-loops, and complex tertiary folds that bind multiple Mg2+ ions. Overall, the Drude-2017 RNA force field reproduces important properties of these structures, including the conformational sampling of the 2'-hydroxyl and key interactions with Mg2+ ions. © 2018 Wiley Periodicals, Inc.

OSPREY 3.0: Open-source protein redesign for you, with powerful new features.

Abstract: We present osprey 3.0, a new and greatly improved release of the osprey protein design software. Osprey 3.0 features a convenient new Python interface, which greatly improves its ease of use. It is over two orders of magnitude faster than previous versions of osprey when running the same algorithms on the same hardware. Moreover, osprey 3.0 includes several new algorithms, which introduce substantial speedups as well as improved biophysical modeling. It also includes GPU support, which provides an additional speedup of over an order of magnitude. Like previous versions of osprey, osprey 3.0 offers a unique package of advantages over other design software, including provable design algorithms that account for continuous flexibility during design and model conformational entropy. Finally, we show here empirically that osprey 3.0 accurately predicts the effect of mutations on protein-protein binding. Osprey 3.0 is available at http://www.cs.duke.edu/donaldlab/osprey.php as free and open-source software. © 2018 Wiley Periodicals, Inc.

TopP-S: Persistent homology-based multi-task deep neural networks for simultaneous predictions of partition coefficient and aqueous solubility.

Abstract: Aqueous solubility and partition coefficient are important physical properties of small molecules. Accurate theoretical prediction of aqueous solubility and partition coefficient plays an important role in drug design and discovery. The prediction accuracy depends crucially on molecular descriptors which are typically derived from a theoretical understanding of the chemistry and physics of small molecules. This work introduces an algebraic topology-based method, called element-specific persistent homology (ESPH), as a new representation of small molecules that is entirely different from conventional chemical and/or physical representations. ESPH describes molecular properties in terms of multiscale and multicomponent topological invariants. Such topological representation is systematical, comprehensive, and scalable with respect to molecular size and composition variations. However, it cannot be literally translated into a physical interpretation. Fortunately, it is readily suitable for machine learning methods, rendering topological learning algorithms. Due to the inherent correlation between solubility and partition coefficient, a uniform ESPH representation is developed for both properties, which facilitates multi-task deep neural networks for their simultaneous predictions. This strategy leads to a more accurate prediction of relatively small datasets. A total of six datasets is considered in this work to validate the proposed topological and multitask deep learning approaches. It is demonstrated that the proposed approaches achieve some of the most accurate predictions of aqueous solubility and partition coefficient. Our software is available online at http://weilab.math.msu.edu/TopP-S/. © 2018 Wiley Periodicals, Inc.

Serenity: A subsystem quantum chemistry program.

Abstract: We present the new quantum chemistry program Serenity. It implements a wide variety of functionalities with a focus on subsystem methodology. The modular code structure in combination with publicly available external tools and particular design concepts ensures extensibility and robustness with a focus on the needs of a subsystem program. Several important features of the program are exemplified with sample calculations with subsystem density-functional theory, potential reconstruction techniques, a projection-based embedding approach and combinations thereof with geometry optimization, semi-numerical frequency calculations and linear-response time-dependent density-functional theory. © 2018 Wiley Periodicals, Inc.

Pytim: A python package for the interfacial analysis of molecular simulations.

Abstract: Pytim is a versatile python framework for the analysis of interfacial properties in molecular simulations. The code implements several algorithms for the identification of instantaneous interfaces of arbitrary shape, and analysis tools written specifically for the study of interfacial properties, such as intrinsic profiles. The code is written in the python language, and makes use of the numpy and scipy packages to deliver high computational performances. Pytim relies on the MDAnalysis library to analyze the trajectory file formats of popular simulation packages such as gromacs, charmm, namd, lammps or Amber, and can be used to steer OpenMM simulations. Pytim can write information about surfaces and surface atomic layers to vtk, cube, and pdb files for easy visualization. The classes of Pytim can be easily customized and extended to include new interfacial algorithms or analysis tools. The code is available as open source and is free of charge. © 2018 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.

Extension of the QuickFF force field protocol for an improved accuracy of structural, vibrational, mechanical and thermal properties of metal-organic frameworks

Abstract: QuickFF was originally launched in 2015 to derive accurate force fields for isolated and complex molecular systems in a quick and easy way. Apart from the general applicability, the functionality was especially tested for metal-organic frameworks (MOFs), a class of hybrid materials consisting of organic and inorganic building blocks. Herein, we launch a new release of the QuickFF protocol which includes new major features to predict structural, vibrational, mechanical and thermal properties with greater accuracy, without compromising its robustness and transparent workflow. First, the ab initio data necessary for the fitting procedure may now also be derived from periodic models for the molecular system, as opposed to the earlier cluster-based models. This is essential for an accurate description of MOFs with one-dimensional metal-oxide chains. Second, cross terms that couple internal coordinates (ICs) and anharmonic contributions for bond and bend terms are implemented. These features are essential for a proper description of vibrational and thermal properties. Third, the fitting scheme was modified to improve robustness and accuracy. The new features are tested on MIL-53(Al), MOF-5, CAU-13 and NOTT-300. As expected, periodic input data are proven to be essential for a correct description of structural, vibrational and thermodynamic properties of MIL-53(Al). Bulk moduli and thermal expansion coefficients of MOF-5 are very accurately reproduced by static and dynamic simulations using the newly derived force fields which include cross terms and anharmonic corrections. For the flexible materials CAU-13 and NOTT-300, the transition pressure is accurately predicted provided cross terms are taken into account.

TopoMS: Comprehensive topological exploration for molecular and condensed-matter systems.

Abstract: We introduce TopoMS, a computational tool enabling detailed topological analysis of molecular and condensed-matter systems, including the computation of atomic volumes and charges through the quantum theory of atoms in molecules, as well as the complete molecular graph. With roots in techniques from computational topology, and using a shared-memory parallel approach, TopoMS provides scalable, numerically robust, and topologically consistent analysis. TopoMS can be used as a command-line tool or with a GUI (graphical user interface), where the latter also enables an interactive exploration of the molecular graph. This paper presents algorithmic details of TopoMS and compares it with state-of-the-art tools: Bader charge analysis v1.0 (Arnaldsson et al., 01/11/17) and molecular graph extraction using Critic2 (Otero-de-la-Roza et al., Comput. Phys. Commun. 2014, 185, 1007). TopoMS not only combines the functionality of these individual codes but also demonstrates up to 4× performance gain on a standard laptop, faster convergence to fine-grid solution, robustness against lattice bias, and topological consistency. TopoMS is released publicly under BSD License. © 2018 Wiley Periodicals, Inc.

Bond paths between distant atoms do not necessarily indicate dominant interactions.

Abstract: The goal of the article is to revive discussion on the interpretation of bond paths linking distant atoms, particularly tracing weak interactions in dimers. According to the Pendas' concept of privileged exchange channel, a bond path is formed between this pair of competing atoms, which is associated with larger value of the exchange energy. We point out that, due to the short-range nature of the exchange energy, bond paths linking distant atoms clearly become doubtful indicators of dominant intermolecular interactions, particularly if some other characteristics (geometric, spectroscopic, based on electrostatic parameters, etc.) indicate other intermolecular interactions as dominant. Several such cases are thoroughly investigated. We show that electrostatic parameters are much more reliable indicators of dominant intermolecular interactions than bond paths. Then, we pay attention that the presence of ("unexpected", i.e., not necessarily indicating dominant intermolecular interactions) bond paths between pairs of atoms featuring highly expanded charge distributions can be easily explained by visual exploration of isodensity contour plots. As always pointing in the direction of the steepest increase, the gradient vector of the electron density favors areas of its high values gaining higher exchange energy, yet being blind to highly electron deficient areas nearby, which, however, can quite often be involved in dominant intermolecular interactions as strongly suggested by many other bonding analysis. We also suggest that an interatomic component of Hellmann-Feynman force would most likely be the most reliable indicator of attractive or repulsive character of individual interatomic interaction. © 2018 Wiley Periodicals, Inc.

Molecular Acidity: An Accurate Description With Information-Theoretic Approach in Density Functional Reactivity Theory

Abstract: Molecular acidity is one of the important physiochemical properties of a molecular system, yet its accurate calculation and prediction are still an unresolved problem in the literature. In this work, we propose to make use of the quantities from the information-theoretic (IT) approach in density functional reactivity theory and provide an accurate description of molecular acidity from a completely new perspective. To illustrate our point, five different categories of acidic series, singly and doubly substituted benzoic acids, singly substituted benzenesulfinic acids, benzeneseleninic acids, phenols, and alkyl carboxylic acids, have been thoroughly examined. We show that using IT quantities such as Shannon entropy, Fisher information, Ghosh-Berkowitz-Parr entropy, information gain, Onicescu information energy, and relative Renyi entropy, one is able to simultaneously predict experimental pKa values of these different categories of compounds. Because of the universality of the quantities employed in this work, which are all density dependent, our approach should be general and be applicable to other systems as well. © 2017 Wiley Periodicals, Inc.

Characterization of chalcogen bonding interactions via an in-depth conceptual quantum chemical analysis.

Abstract: The chalcogen bond has been acknowledged as an influential noncovalent interaction (NCI) between an electron-deficient chalcogen (donor) and a Lewis base (acceptor). This work explores the main features of chalcogen bonding through a large-scale computational study on a series of donors and acceptors spanning a wide range in strength and character of this type of bond: (benzo)chalcogenadiazoles (with Ch = Te/Se/S) versus halides and neutral Lewis bases with O, N, and C as donor atoms. We start from Pearson's hard and soft acids and bases (HSAB) principle, where the hard nature of the chalcogen bond is quantified through the molecular electrostatic potential and the soft nature through the Fukui function. The σ-holes are more pronounced when going down in the periodic table and their directionality matches the structural orientation of donors and acceptors in the complexes. The Fukui functions point toward an n→σ*-type interaction. The initial conjectures are further scrutinized using quantum mechanical methods, mostly relating to the systems' electron density. A Ziegler-Rauk energy decomposition analysis shows that electrostatics plays a distinctly larger role for the soft halides than for the hard, uncharged acceptors, associated with the softness matching within the HSAB principle. The natural orbital for chemical valence analysis confirms the n→σ* electron donation mechanism. Finally, the electron density and local density energy at the bond critical point in the quantum theory of atoms in molecules study and the position of the spikes in the reduced density gradient versus density plot in the NCI theory situate the chalcogen bond in the same range as strong hydrogen bonds. © 2017 Wiley Periodicals, Inc.

Hydrogen- and Halogen-Bonds between Ions of like Charges: Are They Anti-Electrostatic in Nature?

Abstract: Recent theoretical studies suggested that hydrogen bonds between ions of like charges are of a covalent nature due to the dominating nD →σ*H-A charge-transfer (CT) interaction. In this work, energy profiles of typical hydrogen (H) and halogen (X) bonding systems formed from ions of like charges are explored using the block-localized wavefunction (BLW) method, which can derive optimal geometries and wave functions with the CT interaction "turned off." The results demonstrate that the kinetic stability, albeit reduced, is maintained for most investigated systems even after the intermolecular CT interaction is quenched. Further energy decomposition analyses based on the BLW method reveal that, despite a net repulsive Coulomb repulsion, a stabilizing component exists due to the polarization effect that plays significant role in the kinetic stability of all systems. Moreover, the fingerprints of the augmented electrostatic interaction due to polarization are apparent in the variation patterns of the electron density. All in all, much like in standard H- and X-bonds, the stability of such bonds between ions of like charges is governed by the competition between the stabilizing electrostatic and charge transfer interactions and the destabilizing deformation energy and Pauli exchange repulsion. While in most cases of "anti-electrostatic" bonds the CT interaction is of a secondary importance, we also find cases where CT is decisive. As such, this work validates the existence of anti-electrostatic H- and X-bonds. © 2017 Wiley Periodicals, Inc.

Prediction of the interaction of metallic moieties with proteins: An update for protein-ligand docking techniques.

Abstract: In this article, we present a new approach to expand the range of application of protein-ligand docking methods in the prediction of the interaction of coordination complexes (i.e., metallodrugs, natural and artificial cofactors, etc.) with proteins. To do so, we assume that, from a pure computational point of view, hydrogen bond functions could be an adequate model for the coordination bonds as both share directionality and polarity aspects. In this model, docking of metalloligands can be performed without using any geometrical constraints or energy restraints. The hard work consists in generating the convenient atom types and scoring functions. To test this approach, we applied our model to 39 high-quality X-ray structures with transition and main group metal complexes bound via a unique coordination bond to a protein. This concept was implemented in the protein-ligand docking program GOLD. The results are in very good agreement with the experimental structures: the percentage for which the RMSD of the simulated pose is smaller than the X-ray spectra resolution is 92.3% and the mean value of RMSD is < 1.0 A. Such results also show the viability of the method to predict metal complexes–proteins interactions when the X-ray structure is not available. This work could be the first step for novel applicability of docking techniques in medicinal and bioinorganic chemistry and appears generalizable enough to be implemented in most protein-ligand docking programs nowadays available. © 2017 Wiley Periodicals, Inc.

MDockPeP: An ab-initio protein-peptide docking server

Abstract: Protein-peptide interactions play a crucial role in a variety of cellular processes. The protein-peptide complex structure is a key to understand the mechanisms underlying protein-peptide interactions and is critical for peptide therapeutic development. We present a user-friendly protein-peptide docking server, MDockPeP. Starting from a peptide sequence and a protein receptor structure, the MDockPeP Server globally docks the all-atom, flexible peptide to the protein receptor. The produced modes are then evaluated with a statistical potential-based scoring function, ITScorePeP. This method was systematically validated using the peptiDB benchmarking database. At least one near-native peptide binding mode was ranked among top 10 (or top 500) in 59% (85%) of the bound cases, and in 40.6% (71.9%) of the challenging unbound cases. The server can be used for both protein-peptide complex structure prediction and initial-stage sampling of the protein-peptide binding modes for other docking or simulation methods. MDockPeP Server is freely available at http://zougrouptoolkit.missouri.edu/mdockpep. © 2018 Wiley Periodicals, Inc.

Rapid estimation of activation energy in heterogeneous catalytic reactions via machine learning.

Abstract: Estimation of activation energies within heterogeneous catalytic reactions is performed using machine learning and catalysts dataset. In particular, descriptors for determining activation energy are revealed within the 788 activation energy dataset. With the implementation of machine learning and chosen descriptors, activation energy can be instantly predicted with over 90% accuracy during cross-validation. Thus, rapid estimation of activation energies within heterogeneous catalytic reactions can be made achievable via machine learning, leading toward the acceleration of catalysts design and characterization. © 2018 Wiley Periodicals, Inc.

Two faces of triel bonds in boron trihalide complexes.

Abstract: The N⋅⋅⋅B triel bonds in complexes of boron trihalides, BX3 (X = F, Cl, Br, and I), with species acting as Lewis bases through the nitrogen center, NH3 , N2 , and HCN, are analyzed theoretically (MP2/aug-cc-pVTZ calculations). It is confirmed that stronger Lewis acid properties of the boron center are observed for the BCl3 moiety than for the BF3 one in complexes with the strong Lewis base (NH3 ); while the opposite order is observed for complexes with the weak Lewis base (N2 ). The BX3 NCH complexes (for X = Cl, Br, and I) are characterized by two tautomeric forms and by two corresponding N⋅⋅⋅B distances, the shorter one possesses characteristics of the covalent bond. In a case of the BF3 NCH complex one energetic minimum is observed. Ab initio calculations are supported by an analysis of molecular electrostatic potentials (EPs) and electron density distributions. The quantum theory of 'atoms in molecules' and the decomposition of the energy of interaction are applied. The aforementioned acidity orders as well as the existence of two tautomers for some of complexes result partly from the electrostatic interactions' balance; the EP distribution is different for the BF3 species than for the other BX3 species where X = Cl, Br, and I. © 2017 Wiley Periodicals, Inc.

Fermi‐Löwdin orbital self‐interaction corrected density functional theory: Ionization potentials and enthalpies of formation

Abstract: The Fermi-Lowdin orbital self-interaction correction (FLO-SIC) methodology is applied to atoms and molecules from the standard G2-1 test set. For the first time FLO-SIC results for the GGA-type PBE functional are presented. In addition, examples where FLO-SIC like any proper SIC provides qualitative improvements compared to standard DFT functionals are discussed in detail: the dissociation limit for H2+ , the step-wise linearity behavior for fractional occupation, as well as the significant reduction of the error of static polarizabilities. Further, ionization potentials and enthalpies of formation obtained by means of the FLO-SIC DFT method are compared to other SIC variants and experimental values. The self-interaction correction gives significant improvements if used with the LDA functional but shows worse performance in case of enthalpies of formation if the PBE-GGA functional is used. The errors are analyzed and the importance of the overbinding of hydrogen is discussed. © 2018 Wiley Periodicals, Inc.

B,N‐Codoped graphene as catalyst for the oxygen reduction reaction: Insights from periodic and cluster DFT calculations

Abstract: A comprehensive theoretical study of the oxygen reduction reaction (ORR) over B,N-codoped graphene has been carried out in the framework of DFT using two different approaches based on periodic or cluster models. The comparison and integration of the information provided by the two approaches allow achieving a more complete description of the studied phenomena, combining the advantages of both models. On one hand, the analysis of the structure, stability, and electronic properties of this catalyst permits to identify and characterize the active sites and provides insights into the origin of its high catalytic activity that should be found in the synergistic coupling of the opposite effects of the two B and N heteroatoms used as dopants. On the other hand, the study of the reaction mechanisms evidences that the process is thermodynamically favorable due to the overall high exothermicity, and that the 4e- transfer is the favorite ORR pathway, being the OH hydrogenation the rate-determining step. Overall, all the reported results clearly underline the superior catalytic activity of B,N-codoped graphene toward this reaction. © 2017 Wiley Periodicals, Inc.