Employing Proteolytic Hypomorphs to identify Tiny Particle System regarding Action.
 TRAVIS ("Trajectory Analyzer and Visualizer") is a program package for post-processing and analyzing trajectories from molecular dynamics and Monte Carlo simulations, mostly focused on molecular condensed phase systems. It is an open source free software licensed under the GNU GPL, is platform independent, and does not require any external libraries. Nine years after the original publication of TRAVIS, we highlight some of the recent new functions and features in this article. At the same time, we shortly present some of the underlying algorithms in TRAVIS, which contribute to make trajectory analysis more efficient. Some modern visualization techniques such as Sankey diagrams are also demonstrated. Many analysis functions are implemented, covering structural analyses, dynamical analyses, and functions for predicting vibrational spectra from molecular dynamics simulations. While some of the analyses are known since several decades, others are very recent. For example, TRAVIS has been used to compute the first ab initio predictions in the literature of bulk phase vibrational circular dichroism spectra, bulk phase Raman optical activity spectra, and bulk phase resonance Raman spectra within the last few years.We present a numerical solution of the dynamical mean field theory of infinite-dimensional equilibrium liquids established by Maimbourg et al. [Phys. Rev. Lett. 116, 015902 (2016)]. For soft sphere interactions, we obtain the numerical solution by an iterative algorithm and a straightforward discretization of time. We also discuss the case of hard spheres for which we first derive analytically the dynamical mean field theory as a non-trivial limit of that of soft spheres. We present numerical results for the memory function and the mean square displacement. Our results reproduce and extend kinetic theory in the dilute or short-time limit, while they also describe dynamical arrest toward the glass phase in the dense strongly interacting regime.Thick-shell InP/ZnSe III-V/II-VI quantum dots (QDs) were synthesized with two distinct interfaces between the InP core and ZnSe shell alloy and core/shell. Despite sharing similar optical properties in the spectral domain, these two QD systems have differing amounts of indium incorporation in the shell as determined by high-resolution energy-dispersive x-ray spectroscopy scanning transmission electron microscopy. Ultrafast fluorescence upconversion spectroscopy was used to probe the charge carrier dynamics of these two systems and shows substantial charge carrier trapping in both systems that prevents radiative recombination and reduces the photoluminescence quantum yield. The alloy and core/shell QDs show slight differences in the extent of charge carrier localization with more extensive trapping observed in the alloy nanocrystals. Despite the ability to grow a thick shell, structural defects caused by III-V/II-VI charge carrier imbalances still need to be mitigated to further improve InP QDs.In this paper, we present and review the most recent computational advances in the BERTHA code. BERTHA can be regarded as the state of the art in fully relativistic four-component Dirac-Kohn-Sham (DKS) software. Thanks to the implementation of various parallelization and memory open-ended distribution schemes in combination with efficient "density fitting" algorithms, it greatly reduces the computational burden of four-component DKS calculations. We also report the newly developed OpenMP version of the code, that, together with the berthmod Python module, provides a significant leap forward in terms of usability and applicability of the BERTHA software.  https://www.selleckchem.com/products/mz-1.html  Some applications of the recently developed natural orbitals for chemical valence/charge displacement bonding analysis and the real-time time dependent DKS implementation are also reported.Intrinsically Disordered Proteins (IDPs), unlike folded proteins, lack a unique folded structure and rapidly interconvert among ensembles of disordered states. However, they have specific conformational properties when averaged over their ensembles of disordered states. It is critical to develop a theoretical formalism to predict these ensemble average conformational properties that are encoded in the IDP sequence (the specific order in which amino acids/residues are linked). We present a general heteropolymer theory that analytically computes the ensemble average distance profiles (⟨Rij 2⟩) between any two (i, j) monomers (amino acids for IDPs) as a function of the sequence. Information rich distance profiles provide a detailed description of the IDP in contrast to typical metrics such as scaling exponents, radius of gyration, or end-to-end distance. This generalized formalism supersedes homopolymer-like models or models that are built only on the composition of amino acids but ignore sequence details. The prediction of these distance profiles for highly charged polyampholytes and naturally occurring IDPs unmasks salient features that are hidden in the sequence.  https://www.selleckchem.com/products/mz-1.html  Moreover, the model reveals strategies to modulate the entire distance map to achieve local or global swelling/compaction by subtle changes/modifications-such as phosphorylation, a biologically relevant process-in specific hotspots in the sequence. Sequence-specific distance profiles and their modulation have been benchmarked against all-atom simulations. Our new formalism also predicts residue-pair specific coil-globule transitions. The analytical nature of the theory will facilitate design of new sequences to achieve specific target distance profiles with broad applications in synthetic biology and polymer science.In Paper I [P. Pernot and A. Savin, J. Chem. Phys. 152, 164108 (2020)], we introduced the systematic improvement probability as a tool to assess the level of improvement on absolute errors to be expected when switching between two computational chemistry methods. We also developed two indicators based on robust statistics to address the uncertainty of ranking in computational chemistry benchmarks Pinv, the inversion probability between two values of a statistic, and Pr, the ranking probability matrix. In this second part, these indicators are applied to nine data sets extracted from the recent benchmarking literature. We also illustrate how the correlation between the error sets might contain useful information on the benchmark dataset quality, notably when experimental data are used as reference.