Domain motions involved in the function of proteins can often be well described as a combination of motions along a handfull of low-frequency modes, that is, with the values of a few normal coordinates. This means that, when the functional motion of a protein is unknown, it should prove possible to predict it, since it amounts to guess a few values. However, without the help of additional experimental data, using normal coordinates for generating accurate conformers far away from the initial one is not so straightforward. To do so, a new approach is proposed: instead of building conformers directly with the values of a subset of normal coordinates, they are built in two steps, the conformer built with normal coordinates being just used for defining a set of distance constraints, the final conformer being built so as to match them. Note that this approach amounts to transform the problem of generating accurate protein conformers using normal coordinates into a better known one: the distance-geometry problem, which is herein solved with the help of the ROSETTA software. In the present study, this approach allowed to rebuild accurately six large amplitude conformational changes, using at most six low-frequency normal coordinates. As a consequence of the low-dimensionality of the corresponding subspace, random exploration also proved enough for generating low-energy conformers close to the known end-point of the conformational change of the LAO binding protein, lysozyme T4 and adenylate kinase.

A new method to extract the free energy of aggregation versus aggregate size from molecular simulation data is proposed and applied to a united atom model of the zwitterionic surfactant dodecyl phosphocholine in water. This system's slow dissociation rate and low critical micelle concentration (CMC of approximately 1-2 mM) make extraction of cluster free energies directly from simulation results using the "partition-enabled analysis of cluster histogram" (PEACH) method impractical. The new approach applies PEACH to a model with weakened attractions between aggregants, which allows sampling of a continuous range of cluster sizes, then recovers the free energy of aggregation under the original fully-attractive force field using the BAR free energy difference method. PEACH-BAR results are compared with free energy differences calculated via umbrella sampling, and are used to make predictions of CMC, average cluster size, and SAXS scattering profiles that are in fair agreement with experiment.

Electric field can initiate decomposition or detonation of explosives, but underlying mechanism is unclear. Here, we performed ReaxFF molecular dynamics simulation for decomposition of a cocrystal, formed by 2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (CL-20) and 1,3,5,7-tetranitro-1,3,5,7-tetrazacyclooctane (HMX), solely induced by electric field. A new analytical method was proposed to obtain detailed decomposition mechanism. Results show that electric fields play important roles in decomposition of CL-20/HMX cocrystal, such as heating the system and causing the explosive to decompose. Strong constant field makes CL-20 molecules in the cocrystal decompose at significantly lower temperature, which greatly increases sensitivity. This is ascribed to the distinct decomposition mechanism that CN bond rupture dominates the initial step of CL-20's decomposition. Contrarily, oscillating field has a stronger heating effect but weaker influence on sensitivity. Moreover, HMX exhibits desensitizing effect in CL-20/HMX cocrystal under electric field. These results enhance our understanding of sensitivity mechanism beyond mechanical stimuli in explosives.

Methods that combine collective variable (CV) based enhanced sampling and global tempering approaches are used in speeding-up the conformational sampling and free energy calculation of large and soft systems with a plethora of energy minima. In this paper, a new method of this kind is proposed in which the well-sliced metadynamics approach (WSMTD) is united with replica exchange with solute tempering (REST2) method. WSMTD employs a divide-and-conquer strategy wherein high-dimensional slices of a free energy surface are independently sampled and combined. The method enables one to accomplish a controlled exploration of the CV-space with a restraining bias as in umbrella sampling, and enhance-sampling of one or more orthogonal CVs using a metadynamics like bias. The new hybrid method proposed here enables boosting the sampling of more slow degrees of freedom in WSMTD simulations, without the need to specify associated CVs, through a replica exchange scheme within the framework of REST2. The high-dimensional slices of the probability distributions of CVs computed from the united WSMTD and REST2 simulations are subsequently combined using the weighted histogram analysis method to obtain the free energy surface. We show that the new method proposed here is accurate, improves the conformational sampling, and achieves quick convergence in free energy estimates. We demonstrate this by computing the conformational free energy landscapes of solvated alanine tripeptide and Trp-cage mini protein in explicit water.

The recently discovered hybrid organic-inorganic perovskites have been suggested for high-performance optoelectronic applications. Owing to the mechanical flexibility of these compounds, they demonstrate structural fluctuation at finite temperatures that have been widely discussed with respect to their optical properties. However, the effect of temperature-induced structural fluctuation is not clear until now, with respect to the equally important charge transport properties. In the present study, through ab initio molecular dynamics simulations of cubic-phase CH3 NH3 PbI3 at different temperatures, the temperature-dependent electronic structure and charge carrier transport properties are examined. Compared with the significant structural fluctuation of organic cations, the structural change of the inorganic framework is minor. In addition, because the band edge states at R point are mainly influenced by the anti-bonding character of the Pb-I bond, CH3 NH3 PbI3 demonstrates relatively small deformation potentials as well as low temperature dependence of band gaps (ΔEg  ≈ 50 meV from 330 K to 400 K) and electron-phonon coupling strengths, despite the large structural fluctuation of organic cations. Furthermore, the effective mass of the valence band increases with the increase of temperature. The predicted mobilities of CH3 NH3 PbI3 can reach above 75 cm2 V-1  s-1 near room temperature, exhibiting an appropriate optoelectronic potential, while the temperature dependence is steeper than T-1.5 of the traditional semiconductors because of the enhanced effective masses.

Although singlet fission presents deep advantages or the generation of solar energy, the list of efficient singlet fission sensitizers is still very short, encouraging the theoreticians to focus their efforts on selecting and designing new candidates. Here, it is presented a computational protocol for the efficient screening of databases to select those species matching the energy requirements for singlet fission. Hence, out of the initial 29,123 species, 254 molecules (0.87%) were found to match singlet fission energy conditions. The consideration of practical concerns such as availability or stability reduced the number to just 24 (0.08%), among which the aminoanthraquinone derivatives are found particularly promising. The proposed protocol correct the deficiencies of the preceding ones, reaching DFT accuracy and minimizing the risk of getting false negatives especially at the early stages of the screening. In addition, this protocol can be used in future high-throughput investigations using datasets composed of millions of species.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is continuously evolving. Although several vaccines were approved, this pandemic is still a major threat to public life. Till date, no established therapies are available against SARS-CoV-2. Peptide inhibitors hold great promise for this viral pathogen due to their efficacy, safety, and specificity. In this study, seventeen antiviral peptides which were known to inhibit SARS-CoV-1 are collected and computationally screened against heptad repeat 1 (HR1) of the SARS-CoV-2 spike protein (S2). Out of 17 peptides, Fp13 and Fp14 showed better binding affinity toward HR1 compared to a control peptide EK1 (a modified pan-coronavirus fusion inhibitor) in molecular docking. To explore the time-dependent interactions of the fusion peptide with HR1, molecular dynamics simulation was performed incorporating lipid membrane. During 100 ns MD simulation, structural and energy parameters of Fp13-HR1 and Fp14-HR1 complexes demonstrated lower fluctuations compared to the control EK1-HR1 complex. Furthermore, principal component analysis and free energy landscape study revealed that these two peptides (Fp13 and Fp14) strongly bind to the HR1 with higher affinity than that of control EK1. Tyr917, Asn919, Gln926, lys933, and Gln949 residues in HR1 protein were found to be crucial residues for peptide interaction. Notably, Fp13, Fp14 showed reasonably better binding free energy and hydrogen bond contribution than that of EK1. Taken together, Fp13 and Fp14 peptides may be highly specific for HR1 which can potentially prevent the formation of the fusion core and could be further developed as therapeutics for treatment or prophylaxis of SARS-CoV-2 infection.

A fast and multipurpose energy decomposition analysis (EDA) program, called XEDA, is introduced for quantitative analysis of intermolecular interactions. This program contains a series of variational EDA methods, including LMO-EDA, GKS-EDA and their extensions, to analyze non-covalent interactions and strong chemical bonds in various environments. XEDA is highly efficient with a similar computational scaling of single point energy calculations. Its efficiency and universality are validated by a series of test examples including van der Waals interactions, hydrogen bonds, radical-radical interactions and strong covalent bonds.

Feshbach resonances in D + HD(v = 1, j = 0) reaction are studied by using the time-independent quantum method. The integral cross section (ICS) results present three Feshbach resonance peaks, which are different from H + HD(v = 1, j = 0) reaction dominated by only one peak. These resonances are attributed to coupling with adiabatic effective potentials of D + HD(v = 1, j = 1) reaction, and the most obvious peak is contributed by J = 1 at 83.16 cm-1 collision energy. For J = 0 and 2, the resonances are related with the same L partial wave and present a double-peak structure in total ICS. The characteristics of product angular distribution show that the resonance of J = 1 is long-lived, while the lifetimes are relatively shorter for the resonance of J = 0 and 2.

We develop and implement a Gaussian approach to calculate partial cross-sections and asymmetry parameters for molecular photoionization. Optimal sets of complex Gaussian-type orbitals (cGTOs) are first obtained by nonlinear optimization, to best fit sets of Coulomb or distorted continuum wave functions for relevant orbital quantum numbers. This allows us to represent the radial wavefunction for the outgoing electron with accurate cGTO expansions. Within a time-independent partial wave approach, we show that all the necessary transition integrals become analytical, in both length and velocity gauges, thus facilitating the numerical evaluation of photoionization observables. Illustrative results, presented for NH3 and H2 O within a one-active-electron monocentric model, validate numerically the proposed strategy based on a complex Gaussian representation of continuum states.

Pyrazole derivatives correspond to a family of heterocycle molecules with important pharmacological and physiological applications. At present, we perform a density functional theory (DFT) calculations and a quantitative structure-activity relationship (QSAR) evaluation on a series of 1-(4,5-dihydro-1H-pyrazol-1-yl) ethan-1-one and 4,5-dihydro-1H-pyrazole-1-carbothioamide derivatives as an epidermal growth factor receptor (EGFR) inhibitory activity. We thus propose a virtual screening protocol based on a machine-learning study. This theoretical model relates the studied compounds' biological activity to their calculated physicochemical descriptors. Moreover, the linear regression function is used to validate the model via the evaluation of Q2ext and Q2cv parameters for external and internal validations, respectively. Our QSAR model shows a good correlation between observed activities IC50 and predicted ones. Our model allows us to mitigate time-consuming problems and waste chemical and biological products in the preclinical phases.

We present an automatized workflow which, starting from molecular dynamics simulations, identifies reaction events, filters them, and prepares them for accurate quantum chemical calculations using, for example, Density Functional Theory (DFT) or Coupled Cluster methods. The capabilities of the automatized workflow are demonstrated by the example of simulations for the combustion of some polycyclic aromatic hydrocarbons (PAHs). It is shown how key elementary reaction candidates are filtered out of a much larger set of redundant reactions and refined further. The molecular species in question are optimized using DFT and reaction energies, barrier heights, and reaction rates are calculated. The setup is general enough to include at this stage configurational sampling, which can be exploited in the future. Using the introduced machinery, we investigate how the observed reaction types depend on the gas atmosphere used in the molecular dynamics simulation. For the re-optimization on the DFT level, we show how the additional information needed to switch from reactive force-field to electronic structure calculations can be filled in and study how well ReaxFF and DFT agree with each other and shine light on the perspective of using more accurate semi-empirical methods in the MD simulation.

We present a software package GoGreenGo-an overlay aimed to model local perturbations of periodic systems due to either chemisorption or point defects. The electronic structure of an ideal crystal is obtained by worldwide-distributed standard quantum physics/chemistry codes, and then processed by various tools performing projection to atomic orbital basis sets. Starting from this, the perturbation is addressed by GoGreenGo with use of the Green's functions formalism, which allows evaluating its effect on the electronic structure, density matrix, and energy of the system. In the present contribution, the main accent is made on processes of chemical nature, such as chemisorption or doping. We address a general theory and its computational implementation supported by a series of test calculations of the electronic structure perturbations for benchmark model solids: simple, face-centered, and body-centered cubium systems. In addition, more realistic problems of local perturbations in graphene lattice, such as lattice substitution, vacancy, and "on-top" chemisorption, are considered.

Finite basis vibrational configuration interaction theory (VCI) is a highly accurate method for the variational calculation of state energies and related properties, but suffers from fast growing computational costs in dependence of the size of the correlation space. In this series of papers, concepts and techniques will be presented, which diminish the computational demands and thus broaden the applicability of this method to larger molecules or more complex situations. This first part focuses on a highly efficient implementation of the vibrational angular momentum (VAM) terms as occurring in the Watson Hamiltonian and the prediagonalization of initial subspaces within an iterative configuration selective VCI implementation. Working equations and benchmark calculations are provided, the latter demonstrating the increased performance of the new algorithm.