MonteCarbo is an open-source software to construct simple 5-, 6-, and 7-membered ring multifunctionalized monosaccharides and nucleobases and dock them into the active site of carbohydrate-active enzymes. The core bash script executes simple orders to generate the Z-matrix of the neutral molecule of interest. After that, a Fortran90 code based on a pseudo-random number generator (Monte Carlo method) is executed to assign dihedral angles to the different rotamers present in the structure (ring and rotating functional groups). The program also has a generalized internal coordinates (GIC) implementation of the Cremer and Pople puckering coordinates ring. Once the structures are generated and optimized, a second code is ready to execute in serial the docking of multiple conformers in the active site of a wide family of enzymes.

We explore how ideas and practices common in Bayesian modeling can be applied to help assess the quality of 3D protein structural models. The basic premise of our approach is that the evaluation of a Bayesian statistical model's fit may reveal aspects of the quality of a structure when the fitted data is related to protein structural properties. Therefore, we fit a Bayesian hierarchical linear regression model to experimental and theoretical 13 Cα chemical shifts. Then, we propose two complementary approaches for the evaluation of such fitting: (a) in terms of the expected differences between experimental and posterior predicted values; (b) in terms of the leave-one-out cross-validation point-wise predictive accuracy. Finally, we present visualizations that can help interpret these evaluations. The analyses presented in this article are aimed to aid in detecting problematic residues in protein structures. The code developed for this work is available on: https://github.com/BIOS-IMASL/Hierarchical-Bayes-NMR-Validation.

Density functionals with asymptotic corrections to the long-range potential provide entry-level methods for calculations on molecules that can sustain charge transfer, but similar applications in materials science are rare. We describe an implementation of the CAM-B3LYP range-separated functional within the Vienna Ab-initio Simulation Package (VASP) framework, together with its analytical functional derivatives. Results obtained for eight representative materials: aluminum, diamond, graphene, silicon, NaCl, MgO, 2D h-BN, and 3D h-BN, indicate that CAM-B3LYP predictions embody mean-absolute deviations (MAD) compared to HSE06 that are reduced by a factor of six for lattice parameters, four for quasiparticle band gaps, three for the lowest optical excitation energies, and six for exciton binding energies. Further, CAM-B3LYP appears competitive compared to ab initio G0 W0 and Bethe-Salpeter equation approaches. The CAM-B3LYP implementation in VASP was verified by comparison of optimized geometries and reaction energies for isolated molecules taken from the ACCDB database, evaluated in large periodic unit cells, to analogous results obtained using Gaussian basis sets. Using standard GW pseudopotentials and energy cutoffs for the plane-wave calculations and the aug-cc-pV5Z basis set for the atomic-basis ones, the MAD in energy for 1738 chemical reactions was 0.34 kcal mol-1 , while for 480 unique bond lengths this was 0.0036 Å; these values reduced to 0.28 kcal mol-1 (largest error 0.94 kcal mol-1 ) and 0.0009 Å by increasing the plane-wave cutoff energy to 850 eV.

We present a standalone ΘΦ (ThetaPhi) package capable to read the results of ab initio DFT/PAW quantum-chemical solid-state calculations processed through various tools projecting them to the atomic basis states as an input and to perform on top of this an analysis of so derived electronic structure which includes (among other options) the possibility to obtain a superconducting (Bardeen-Cooper-Schrieffer, BCS), spin-liquid (resonating valence bond, RVB) states/phases as solutions of the electronic structure problem along with the magnetically ordered phases with an arbitrary pitch (magnetic superstructure) vector. Remarkably, different solutions of electronic-structure problems come out as temperature-dependent (exemplified by various superconducting and spin-liquid phases) which feature is as well implemented. All that is exemplified by model calculations on 1D chain, 2D square lattice as well as on more realistic superconducting doped graphene, magnetic phases of iron, and spin-liquid and magnetically ordered states of a simplest nitrogen-based copper pseudo-oxide, CuNCN, resembling socalled metal-oxide framework (MOF) phases by the atomic interlinkage.

In this contribution, we aim at investigating the mechanism of biosensing in graphene-based materials from first principles. Inspired by recent experiments, we construct an atomistic model composed of a pyrene molecule serving as a linker fragment, which is used in experiment to attach certain aptamers, and a defective zigzag graphene nanoribbons (ZGNRs). Density functional theory including dispersive interaction is employed to study the energetics of the linker absorption on the defective ZGNRs. Combining non-equilibrium Green's function and the Landauer formalism, the total current-bias voltage dependence through the device is evaluated. Modifying the distance between the linker molecule and the nanojunction plane reveals a quantitative change in the total current-bias voltage dependence, which correlates to the experimental measurements. In order to illuminate the geometric origin of these variation observed in the considered systems, the local currents through the device are investigated using the method originally introduced by Evers and co-workers. In our new implementation, the numerical efficiency is improved by applying sparse matrix storage and spectral filtering techniques, without compromising the resolution of the local currents. Local current density maps qualitatively demonstrate the local variation of the interference between the linker molecule and the nanojunction plane.

Hydrogen dinitrate anion, HNO3 (NO3- ), is a proton-bound dimer with a very strong hydrogen bond. By employing ab initio molecular dynamics (AIMD) method, we studied the effects of the proton transfer and the rotation of the nitrates on the vibrational profiles of HNO3 (NO3- )(H2 O)n (n = 0-2). The AIMD results indicate that the structure of the n = 0 cluster is very flexible, even though its hydrogen bond is quite strong. Significant rotations around the hydrogen bond and frequent transfers of proton from HNO3 to NO3- are observed in AIMD simulations. Dynamic changes are therefore an important factor in understanding the broadening of vibrational features. For n = 1, the extent of structural fluctuation increases further, as H2 O could move around the anion while the HNO3 (NO3- ) core also goes through structural changes. Its vibrational spectrum can be understood as a mixture of many isomers visited during AIMD simulations. By n = 2, the structure is stabilized around one isomer, with the linker between the two nitrates being H5 O2+ , rather than H+ . Due to strong hydrogen bonds between nitrates and water molecules, this H5 O2+ takes the extraordinary structure with the H+ localized on one H2 O, rather than being shared. While this novel structure is stable during AIMD simulations, the dynamic fluctuations in hydrogen bond distances still produce significant broadening in its vibrational profile.

The Poisson-Boltzmann (PB) implicit solvent model is a popular framework for studying the electrostatics of solvated biomolecules. In this model the dielectric interface between the biomolecule and solvent is often taken to be the molecular surface or solvent-excluded surface (SES), and the quality of the SES triangulation is critical in boundary element simulations of the model. This work compares the performance of the MSMS and NanoShaper surface triangulation codes for a set of 38 biomolecules. While MSMS produces triangles of exceedingly small area and large aspect ratio, the two codes yield comparable values for the SES surface area and electrostatic solvation energy, where the latter calculations were performed using the treecode-accelerated boundary integral (TABI) PB solver. However we found that NanoShaper is computationally more efficient and reliable than MSMS, especially when parameters are set to produce highly resolved triangulations.

A popular first principles simulation code, the Vienna Ab initio Simulation Package (VASP), and a crystal structure prediction (CSP) package, the Universal Structure Predictor: Evolutionary Xtallography (USPEX) have been integrated into the GDIS visualization software. The aim of this integration is to provide users with a unique and simple interface through which most of the steps of a typical crystal optimization or prediction work. This involved, for the latter, not only setting up a CSP calculation with complete support for the latest version of USPEX, but also displaying the many structure results by linking each structure geometry and its energy via interactive graphics. For the optimization part, any structure displayed by GDIS can now be the starting point for VASP calculations, with support for its most commonly used parameters. Atomic and electronic structures can be displayed as well as dynamic properties such as total energy, force, volume, and pressure for each ionic step. It is not only possible to start calculations from the GDIS visualization software, using an in-place task manager, but a running calculation can also be followed, allowing a greater control of the simulation process. The GDIS software is available under the GNU public license in its second version.

Since the beginning of oil exploration, whole ecosystems have been affected by accidents and bad practices involving petroleum compounds. In this sense, bioremediation stands out as the cheapest and most eco-friendly alternatives to reverse the damage done in oil-impacted areas. However, more efforts must be made to engineer enzymes that could be used in the bioremediation process. Interestingly, a recent work described that α-amylase, one of the most evolutionary conserved enzymes, was able to promiscuously degrade n-alkanes, a class of molecules abundant in the petroleum admixture. Considering that α-amylase is expressed in almost all known organisms, and employed in numerous biotechnological processes, using it can be a great leap toward more efficient applications of enzyme or microorganism-consortia bioremediation approaches. In this work, we employed a strict computational approach to design new α-amylase mutants with potentially enhanced catalytic efficiency toward n-alkanes. Using in silico techniques, such as molecular docking, molecular dynamics, metadynamics, and residue-residue interaction networks, we generated mutants potentially more efficient for degrading n-alkanes, L183Y, and N314A. Our results indicate that the new mutants have an increased binding rate for tetradecane, the longest n-alkane previously tested, which can reside in the catalytic center for more extended periods. Additionally, molecular dynamics and network analysis showed that the new mutations have no negative impact on protein structure than the WT. Our results aid in solidifying this enzyme as one more tool in the petroleum bioremediation toolbox.

Infectious diseases of bacteria and fungi have become a major risk to public health because of antibiotic and antifungal resistance. However, the availability of effective antibacterial and antifungal agents is becoming increasingly limited with growing resistance to existing drugs. In response to that, novel agents are critically needed to overcome such resistance. A new series of 6-hydroxyquinolinone 3, 4, 5a, 5b, 6a and 6b bearing different side chains were synthesized and evaluated as antimicrobials against numbers of bacteria and fungi, using inhibition zone technique. As one of these derivatives, compound 3 was identified as a potent antibacterial and antifungal agent against all tested microorganisms with good minimum inhibitory concentration values comparable to reference drugs. Molecular docking studies were performed on antibacterial and antifungal targets; microbial DNA gyrase B of Staphylococcus aureus (PDB ID: 4URO); N-myristoyltransferase of Candida albicans (PDB ID: 1IYK), respectively, to predict the most probable type of interaction at the active site of the target protein in addition to binding affinities and orientations of docked ligands. Additionally, in silico prediction in terms of detailed physicochemical ADME and toxicity profile relating drug-likeness as well as medicinal chemistry friendliness was performed to all synthesized compounds. The results indicated that a novel 4,6-dihydroxyquinolin-2(1H)-one (3) is likely to be a newly synthesized drug candidate, indicating low toxicity in addition to good in silico absorption. In order to pave the way for more logical production of such compounds, structure-activity and toxicity relationships are also discussed.

A method for the estimation of coordinates of atoms in proteins from coarse-grained geometry by simple analytical formulas (ESCASA), for use in nuclear-magnetic-resonance (NMR) data-assisted coarse-grained simulations of proteins is proposed. In this paper, the formulas for the backbone Hα and amide (HN ) protons, and the side-chain Hβ protons, given the Cα -trace, have been derived and parameterized, by using the interproton distances calculated from a set of 140 high-resolution non-homologous protein structures. The mean standard deviation over all types of proton pairs in the set was 0.44 Å after fitting. Validation against a set of 41 proteins with NMR-determined structures, which were not considered in parameterization, resulted in average standard deviation from average proton-proton distances of the NMR-determined structures of 0.25 Å, compared to 0.21 Å obtained with the PULCHRA all-atom-chain reconstruction algorithm and to the 0.12 Å standard deviation of the average-structure proton-proton distance of NMR-determined ensembles. The formulas provide analytical forces and can, therefore, be used in coarse-grained molecular dynamics.

Density functional theory (DFT) has been extensively benchmarked for energetic properties; however, less attention has been given to equilibrium structures and the effect of using a certain DFT geometry on subsequent energetic properties. We evaluate the performance of 52 contemporary DFT methods for obtaining the structures of 122 species in the W4-11-GEOM database. This dataset includes a total of 246 unique bonds: 117 H─X, 65 X─Y, 49 X═Y, and 15 XY bonds (where X and Y are first- and second-row atoms) and 133 key bond angles: 96 X-Y-H, 22 X-Y-Z, and 15 H-X-H angles. The reference geometries are optimized at the CCSD(T)/jul-cc-pV(n+d)Z level of theory (n = 5, 6). The performance of DFT is evaluated in conjunction with the Def2-nZVPP (n = T, Q), cc-pV(T+d)Z, and jul-cc-pV(T+d)Z basis sets. The root-mean-square deviations (RMSDs) over the bond distances of the best performing functionals from each rung of Jacob's Ladder are 0.0086 (SOGGA11), 0.0088 (τ-HCTH), 0.0059 (B3LYP), 0.0054 (TPSSh), and 0.0032 (DSD-PBEP86) Å. We evaluate the effect of the choice of the DFT geometry on subsequent molecular energies calculated with W1-F12 theory. Geometries obtained with GGA and MGGA methods result in large RMSDs in the subsequent W1-F12 energies; however, six hybrid GGA functionals (B3LYP, B3P86, mPW3PBE, B3PW91, mPW1LYP, and X3LYP) result in an excellent performance with RMSDs between 0.25 and 0.30 kJ mol-1 relative to the CCSD(T)/CBS reference geometries. The B2GP-PLYP and mPW2-PLYP DHDFT methods result in near-CCSD(T) accuracy with RMSDs of 0.11 and 0.10 kJ mol-1 , respectively.